2026/05/13 - Amazon SageMaker Service - 27 updated api methods
Changes Adds execution role session name mode to reflect user identity in Studio. Adds Flexible Training Plans on Studio apps. Adds restricted model packages to control access to proprietary model artifacts via IAM. Fixed instance type parity between inference endpoints and managed shadow tests.
{'ResourceSpec': {'TrainingPlanArn': 'string'}}
Creates a running app for the specified UserProfile. This operation is automatically invoked by Amazon SageMaker AI upon access to the associated Domain, and when new kernel configurations are selected by the user. A user may have multiple Apps active simultaneously.
See also: AWS API Documentation
Request Syntax
client.create_app(
DomainId='string',
UserProfileName='string',
SpaceName='string',
AppType='JupyterServer'|'KernelGateway'|'DetailedProfiler'|'TensorBoard'|'CodeEditor'|'JupyterLab'|'RStudioServerPro'|'RSessionGateway'|'Canvas',
AppName='string',
Tags=[
{
'Key': 'string',
'Value': 'string'
},
],
ResourceSpec={
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': 'string',
'SageMakerImageVersionAlias': 'string',
'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.g6.xlarge'|'ml.g6.2xlarge'|'ml.g6.4xlarge'|'ml.g6.8xlarge'|'ml.g6.12xlarge'|'ml.g6.16xlarge'|'ml.g6.24xlarge'|'ml.g6.48xlarge'|'ml.g6e.xlarge'|'ml.g6e.2xlarge'|'ml.g6e.4xlarge'|'ml.g6e.8xlarge'|'ml.g6e.12xlarge'|'ml.g6e.16xlarge'|'ml.g6e.24xlarge'|'ml.g6e.48xlarge'|'ml.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.p5.48xlarge'|'ml.p5en.48xlarge'|'ml.p6-b200.48xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.r6i.large'|'ml.r6i.xlarge'|'ml.r6i.2xlarge'|'ml.r6i.4xlarge'|'ml.r6i.8xlarge'|'ml.r6i.12xlarge'|'ml.r6i.16xlarge'|'ml.r6i.24xlarge'|'ml.r6i.32xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge'|'ml.m6id.large'|'ml.m6id.xlarge'|'ml.m6id.2xlarge'|'ml.m6id.4xlarge'|'ml.m6id.8xlarge'|'ml.m6id.12xlarge'|'ml.m6id.16xlarge'|'ml.m6id.24xlarge'|'ml.m6id.32xlarge'|'ml.c6id.large'|'ml.c6id.xlarge'|'ml.c6id.2xlarge'|'ml.c6id.4xlarge'|'ml.c6id.8xlarge'|'ml.c6id.12xlarge'|'ml.c6id.16xlarge'|'ml.c6id.24xlarge'|'ml.c6id.32xlarge'|'ml.r6id.large'|'ml.r6id.xlarge'|'ml.r6id.2xlarge'|'ml.r6id.4xlarge'|'ml.r6id.8xlarge'|'ml.r6id.12xlarge'|'ml.r6id.16xlarge'|'ml.r6id.24xlarge'|'ml.r6id.32xlarge'|'ml.p5.4xlarge',
'LifecycleConfigArn': 'string',
'TrainingPlanArn': 'string'
},
RecoveryMode=True|False
)
string
[REQUIRED]
The domain ID.
string
The user profile name. If this value is not set, then SpaceName must be set.
string
The name of the space. If this value is not set, then UserProfileName must be set.
string
[REQUIRED]
The type of app.
string
[REQUIRED]
The name of the app.
list
Each tag consists of a key and an optional value. Tag keys must be unique per resource.
(dict) --
A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags.
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources. For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy.
Key (string) -- [REQUIRED]
The tag key. Tag keys must be unique per resource.
Value (string) -- [REQUIRED]
The tag value.
dict
The instance type and the Amazon Resource Name (ARN) of the SageMaker AI image created on the instance.
SageMakerImageArn (string) --
The ARN of the SageMaker AI image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance. To clear the value set for SageMakerImageVersionArn, pass None as the value.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
TrainingPlanArn (string) --
The ARN of the SageMaker AI Training Plan to use for this app. When you specify a training plan, the app launches on reserved GPU capacity. This field is supported for JupyterLab and CodeEditor app types.
For more information about how to reserve GPU capacity with SageMaker AI Training Plans, see Using training plans in Studio applications.
boolean
Indicates whether the application is launched in recovery mode.
dict
Response Syntax
{
'AppArn': 'string'
}
Response Structure
(dict) --
AppArn (string) --
The Amazon Resource Name (ARN) of the app.
{'DefaultSpaceSettings': {'JupyterLabAppSettings': {'DefaultResourceSpec': {'TrainingPlanArn': 'string'}},
'JupyterServerAppSettings': {'DefaultResourceSpec': {'TrainingPlanArn': 'string'}},
'KernelGatewayAppSettings': {'DefaultResourceSpec': {'TrainingPlanArn': 'string'}}},
'DefaultUserSettings': {'CodeEditorAppSettings': {'DefaultResourceSpec': {'TrainingPlanArn': 'string'}},
'JupyterLabAppSettings': {'DefaultResourceSpec': {'TrainingPlanArn': 'string'}},
'JupyterServerAppSettings': {'DefaultResourceSpec': {'TrainingPlanArn': 'string'}},
'KernelGatewayAppSettings': {'DefaultResourceSpec': {'TrainingPlanArn': 'string'}},
'RSessionAppSettings': {'DefaultResourceSpec': {'TrainingPlanArn': 'string'}},
'StudioWebPortalSettings': {'ExecutionRoleSessionNameMode': 'STATIC '
'| '
'USER_IDENTITY'},
'TensorBoardAppSettings': {'DefaultResourceSpec': {'TrainingPlanArn': 'string'}}},
'DomainSettings': {'RStudioServerProDomainSettings': {'DefaultResourceSpec': {'TrainingPlanArn': 'string'}}}}
Creates a Domain. A domain consists of an associated Amazon Elastic File System volume, a list of authorized users, and a variety of security, application, policy, and Amazon Virtual Private Cloud (VPC) configurations. Users within a domain can share notebook files and other artifacts with each other.
EFS storage
When a domain is created, an EFS volume is created for use by all of the users within the domain. Each user receives a private home directory within the EFS volume for notebooks, Git repositories, and data files.
SageMaker AI uses the Amazon Web Services Key Management Service (Amazon Web Services KMS) to encrypt the EFS volume attached to the domain with an Amazon Web Services managed key by default. For more control, you can specify a customer managed key. For more information, see Protect Data at Rest Using Encryption.
VPC configuration
All traffic between the domain and the Amazon EFS volume is through the specified VPC and subnets. For other traffic, you can specify the AppNetworkAccessType parameter. AppNetworkAccessType corresponds to the network access type that you choose when you onboard to the domain. The following options are available:
PublicInternetOnly - Non-EFS traffic goes through a VPC managed by Amazon SageMaker AI, which allows internet access. This is the default value.
VpcOnly - All traffic is through the specified VPC and subnets. Internet access is disabled by default. To allow internet access, you must specify a NAT gateway. When internet access is disabled, you won't be able to run a Amazon SageMaker AI Studio notebook or to train or host models unless your VPC has an interface endpoint to the SageMaker AI API and runtime or a NAT gateway and your security groups allow outbound connections.
For more information, see Connect Amazon SageMaker AI Studio Notebooks to Resources in a VPC.
See also: AWS API Documentation
Request Syntax
client.create_domain(
DomainName='string',
AuthMode='SSO'|'IAM',
DefaultUserSettings={
'ExecutionRole': 'string',
'SecurityGroups': [
'string',
],
'SharingSettings': {
'NotebookOutputOption': 'Allowed'|'Disabled',
'S3OutputPath': 'string',
'S3KmsKeyId': 'string'
},
'JupyterServerAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': 'string',
'SageMakerImageVersionAlias': 'string',
'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.g6.xlarge'|'ml.g6.2xlarge'|'ml.g6.4xlarge'|'ml.g6.8xlarge'|'ml.g6.12xlarge'|'ml.g6.16xlarge'|'ml.g6.24xlarge'|'ml.g6.48xlarge'|'ml.g6e.xlarge'|'ml.g6e.2xlarge'|'ml.g6e.4xlarge'|'ml.g6e.8xlarge'|'ml.g6e.12xlarge'|'ml.g6e.16xlarge'|'ml.g6e.24xlarge'|'ml.g6e.48xlarge'|'ml.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.p5.48xlarge'|'ml.p5en.48xlarge'|'ml.p6-b200.48xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.r6i.large'|'ml.r6i.xlarge'|'ml.r6i.2xlarge'|'ml.r6i.4xlarge'|'ml.r6i.8xlarge'|'ml.r6i.12xlarge'|'ml.r6i.16xlarge'|'ml.r6i.24xlarge'|'ml.r6i.32xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge'|'ml.m6id.large'|'ml.m6id.xlarge'|'ml.m6id.2xlarge'|'ml.m6id.4xlarge'|'ml.m6id.8xlarge'|'ml.m6id.12xlarge'|'ml.m6id.16xlarge'|'ml.m6id.24xlarge'|'ml.m6id.32xlarge'|'ml.c6id.large'|'ml.c6id.xlarge'|'ml.c6id.2xlarge'|'ml.c6id.4xlarge'|'ml.c6id.8xlarge'|'ml.c6id.12xlarge'|'ml.c6id.16xlarge'|'ml.c6id.24xlarge'|'ml.c6id.32xlarge'|'ml.r6id.large'|'ml.r6id.xlarge'|'ml.r6id.2xlarge'|'ml.r6id.4xlarge'|'ml.r6id.8xlarge'|'ml.r6id.12xlarge'|'ml.r6id.16xlarge'|'ml.r6id.24xlarge'|'ml.r6id.32xlarge'|'ml.p5.4xlarge',
'LifecycleConfigArn': 'string',
'TrainingPlanArn': 'string'
},
'LifecycleConfigArns': [
'string',
],
'CodeRepositories': [
{
'RepositoryUrl': 'string'
},
]
},
'KernelGatewayAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': 'string',
'SageMakerImageVersionAlias': 'string',
'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.g6.xlarge'|'ml.g6.2xlarge'|'ml.g6.4xlarge'|'ml.g6.8xlarge'|'ml.g6.12xlarge'|'ml.g6.16xlarge'|'ml.g6.24xlarge'|'ml.g6.48xlarge'|'ml.g6e.xlarge'|'ml.g6e.2xlarge'|'ml.g6e.4xlarge'|'ml.g6e.8xlarge'|'ml.g6e.12xlarge'|'ml.g6e.16xlarge'|'ml.g6e.24xlarge'|'ml.g6e.48xlarge'|'ml.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.p5.48xlarge'|'ml.p5en.48xlarge'|'ml.p6-b200.48xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.r6i.large'|'ml.r6i.xlarge'|'ml.r6i.2xlarge'|'ml.r6i.4xlarge'|'ml.r6i.8xlarge'|'ml.r6i.12xlarge'|'ml.r6i.16xlarge'|'ml.r6i.24xlarge'|'ml.r6i.32xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge'|'ml.m6id.large'|'ml.m6id.xlarge'|'ml.m6id.2xlarge'|'ml.m6id.4xlarge'|'ml.m6id.8xlarge'|'ml.m6id.12xlarge'|'ml.m6id.16xlarge'|'ml.m6id.24xlarge'|'ml.m6id.32xlarge'|'ml.c6id.large'|'ml.c6id.xlarge'|'ml.c6id.2xlarge'|'ml.c6id.4xlarge'|'ml.c6id.8xlarge'|'ml.c6id.12xlarge'|'ml.c6id.16xlarge'|'ml.c6id.24xlarge'|'ml.c6id.32xlarge'|'ml.r6id.large'|'ml.r6id.xlarge'|'ml.r6id.2xlarge'|'ml.r6id.4xlarge'|'ml.r6id.8xlarge'|'ml.r6id.12xlarge'|'ml.r6id.16xlarge'|'ml.r6id.24xlarge'|'ml.r6id.32xlarge'|'ml.p5.4xlarge',
'LifecycleConfigArn': 'string',
'TrainingPlanArn': 'string'
},
'CustomImages': [
{
'ImageName': 'string',
'ImageVersionNumber': 123,
'AppImageConfigName': 'string'
},
],
'LifecycleConfigArns': [
'string',
]
},
'TensorBoardAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': 'string',
'SageMakerImageVersionAlias': 'string',
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'SageMakerImageVersionArn': 'string',
'SageMakerImageVersionAlias': 'string',
'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.g6.xlarge'|'ml.g6.2xlarge'|'ml.g6.4xlarge'|'ml.g6.8xlarge'|'ml.g6.12xlarge'|'ml.g6.16xlarge'|'ml.g6.24xlarge'|'ml.g6.48xlarge'|'ml.g6e.xlarge'|'ml.g6e.2xlarge'|'ml.g6e.4xlarge'|'ml.g6e.8xlarge'|'ml.g6e.12xlarge'|'ml.g6e.16xlarge'|'ml.g6e.24xlarge'|'ml.g6e.48xlarge'|'ml.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.p5.48xlarge'|'ml.p5en.48xlarge'|'ml.p6-b200.48xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.r6i.large'|'ml.r6i.xlarge'|'ml.r6i.2xlarge'|'ml.r6i.4xlarge'|'ml.r6i.8xlarge'|'ml.r6i.12xlarge'|'ml.r6i.16xlarge'|'ml.r6i.24xlarge'|'ml.r6i.32xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge'|'ml.m6id.large'|'ml.m6id.xlarge'|'ml.m6id.2xlarge'|'ml.m6id.4xlarge'|'ml.m6id.8xlarge'|'ml.m6id.12xlarge'|'ml.m6id.16xlarge'|'ml.m6id.24xlarge'|'ml.m6id.32xlarge'|'ml.c6id.large'|'ml.c6id.xlarge'|'ml.c6id.2xlarge'|'ml.c6id.4xlarge'|'ml.c6id.8xlarge'|'ml.c6id.12xlarge'|'ml.c6id.16xlarge'|'ml.c6id.24xlarge'|'ml.c6id.32xlarge'|'ml.r6id.large'|'ml.r6id.xlarge'|'ml.r6id.2xlarge'|'ml.r6id.4xlarge'|'ml.r6id.8xlarge'|'ml.r6id.12xlarge'|'ml.r6id.16xlarge'|'ml.r6id.24xlarge'|'ml.r6id.32xlarge'|'ml.p5.4xlarge',
'LifecycleConfigArn': 'string',
'TrainingPlanArn': 'string'
},
'CustomImages': [
{
'ImageName': 'string',
'ImageVersionNumber': 123,
'AppImageConfigName': 'string'
},
],
'LifecycleConfigArns': [
'string',
],
'CodeRepositories': [
{
'RepositoryUrl': 'string'
},
],
'AppLifecycleManagement': {
'IdleSettings': {
'LifecycleManagement': 'ENABLED'|'DISABLED',
'IdleTimeoutInMinutes': 123,
'MinIdleTimeoutInMinutes': 123,
'MaxIdleTimeoutInMinutes': 123
}
},
'EmrSettings': {
'AssumableRoleArns': [
'string',
],
'ExecutionRoleArns': [
'string',
]
},
'BuiltInLifecycleConfigArn': 'string'
},
'SpaceStorageSettings': {
'DefaultEbsStorageSettings': {
'DefaultEbsVolumeSizeInGb': 123,
'MaximumEbsVolumeSizeInGb': 123
}
},
'DefaultLandingUri': 'string',
'StudioWebPortal': 'ENABLED'|'DISABLED',
'CustomPosixUserConfig': {
'Uid': 123,
'Gid': 123
},
'CustomFileSystemConfigs': [
{
'EFSFileSystemConfig': {
'FileSystemId': 'string',
'FileSystemPath': 'string'
},
'FSxLustreFileSystemConfig': {
'FileSystemId': 'string',
'FileSystemPath': 'string'
},
'S3FileSystemConfig': {
'MountPath': 'string',
'S3Uri': 'string'
}
},
],
'StudioWebPortalSettings': {
'HiddenMlTools': [
'DataWrangler'|'FeatureStore'|'EmrClusters'|'AutoMl'|'Experiments'|'Training'|'ModelEvaluation'|'Pipelines'|'Models'|'JumpStart'|'InferenceRecommender'|'Endpoints'|'Projects'|'InferenceOptimization'|'PerformanceEvaluation'|'LakeraGuard'|'Comet'|'DeepchecksLLMEvaluation'|'Fiddler'|'HyperPodClusters'|'RunningInstances'|'Datasets'|'Evaluators',
],
'HiddenAppTypes': [
'JupyterServer'|'KernelGateway'|'DetailedProfiler'|'TensorBoard'|'CodeEditor'|'JupyterLab'|'RStudioServerPro'|'RSessionGateway'|'Canvas',
],
'HiddenInstanceTypes': [
'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.g6.xlarge'|'ml.g6.2xlarge'|'ml.g6.4xlarge'|'ml.g6.8xlarge'|'ml.g6.12xlarge'|'ml.g6.16xlarge'|'ml.g6.24xlarge'|'ml.g6.48xlarge'|'ml.g6e.xlarge'|'ml.g6e.2xlarge'|'ml.g6e.4xlarge'|'ml.g6e.8xlarge'|'ml.g6e.12xlarge'|'ml.g6e.16xlarge'|'ml.g6e.24xlarge'|'ml.g6e.48xlarge'|'ml.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.p5.48xlarge'|'ml.p5en.48xlarge'|'ml.p6-b200.48xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.r6i.large'|'ml.r6i.xlarge'|'ml.r6i.2xlarge'|'ml.r6i.4xlarge'|'ml.r6i.8xlarge'|'ml.r6i.12xlarge'|'ml.r6i.16xlarge'|'ml.r6i.24xlarge'|'ml.r6i.32xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge'|'ml.m6id.large'|'ml.m6id.xlarge'|'ml.m6id.2xlarge'|'ml.m6id.4xlarge'|'ml.m6id.8xlarge'|'ml.m6id.12xlarge'|'ml.m6id.16xlarge'|'ml.m6id.24xlarge'|'ml.m6id.32xlarge'|'ml.c6id.large'|'ml.c6id.xlarge'|'ml.c6id.2xlarge'|'ml.c6id.4xlarge'|'ml.c6id.8xlarge'|'ml.c6id.12xlarge'|'ml.c6id.16xlarge'|'ml.c6id.24xlarge'|'ml.c6id.32xlarge'|'ml.r6id.large'|'ml.r6id.xlarge'|'ml.r6id.2xlarge'|'ml.r6id.4xlarge'|'ml.r6id.8xlarge'|'ml.r6id.12xlarge'|'ml.r6id.16xlarge'|'ml.r6id.24xlarge'|'ml.r6id.32xlarge'|'ml.p5.4xlarge',
],
'HiddenSageMakerImageVersionAliases': [
{
'SageMakerImageName': 'sagemaker_distribution',
'VersionAliases': [
'string',
]
},
],
'ExecutionRoleSessionNameMode': 'STATIC'|'USER_IDENTITY'
},
'AutoMountHomeEFS': 'Enabled'|'Disabled'|'DefaultAsDomain'
},
DomainSettings={
'SecurityGroupIds': [
'string',
],
'RStudioServerProDomainSettings': {
'DomainExecutionRoleArn': 'string',
'RStudioConnectUrl': 'string',
'RStudioPackageManagerUrl': 'string',
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': 'string',
'SageMakerImageVersionAlias': 'string',
'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.g6.xlarge'|'ml.g6.2xlarge'|'ml.g6.4xlarge'|'ml.g6.8xlarge'|'ml.g6.12xlarge'|'ml.g6.16xlarge'|'ml.g6.24xlarge'|'ml.g6.48xlarge'|'ml.g6e.xlarge'|'ml.g6e.2xlarge'|'ml.g6e.4xlarge'|'ml.g6e.8xlarge'|'ml.g6e.12xlarge'|'ml.g6e.16xlarge'|'ml.g6e.24xlarge'|'ml.g6e.48xlarge'|'ml.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.p5.48xlarge'|'ml.p5en.48xlarge'|'ml.p6-b200.48xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.r6i.large'|'ml.r6i.xlarge'|'ml.r6i.2xlarge'|'ml.r6i.4xlarge'|'ml.r6i.8xlarge'|'ml.r6i.12xlarge'|'ml.r6i.16xlarge'|'ml.r6i.24xlarge'|'ml.r6i.32xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge'|'ml.m6id.large'|'ml.m6id.xlarge'|'ml.m6id.2xlarge'|'ml.m6id.4xlarge'|'ml.m6id.8xlarge'|'ml.m6id.12xlarge'|'ml.m6id.16xlarge'|'ml.m6id.24xlarge'|'ml.m6id.32xlarge'|'ml.c6id.large'|'ml.c6id.xlarge'|'ml.c6id.2xlarge'|'ml.c6id.4xlarge'|'ml.c6id.8xlarge'|'ml.c6id.12xlarge'|'ml.c6id.16xlarge'|'ml.c6id.24xlarge'|'ml.c6id.32xlarge'|'ml.r6id.large'|'ml.r6id.xlarge'|'ml.r6id.2xlarge'|'ml.r6id.4xlarge'|'ml.r6id.8xlarge'|'ml.r6id.12xlarge'|'ml.r6id.16xlarge'|'ml.r6id.24xlarge'|'ml.r6id.32xlarge'|'ml.p5.4xlarge',
'LifecycleConfigArn': 'string',
'TrainingPlanArn': 'string'
}
},
'ExecutionRoleIdentityConfig': 'USER_PROFILE_NAME'|'DISABLED',
'TrustedIdentityPropagationSettings': {
'Status': 'ENABLED'|'DISABLED'
},
'DockerSettings': {
'EnableDockerAccess': 'ENABLED'|'DISABLED',
'VpcOnlyTrustedAccounts': [
'string',
],
'RootlessDocker': 'ENABLED'|'DISABLED'
},
'AmazonQSettings': {
'Status': 'ENABLED'|'DISABLED',
'QProfileArn': 'string'
},
'UnifiedStudioSettings': {
'StudioWebPortalAccess': 'ENABLED'|'DISABLED',
'DomainAccountId': 'string',
'DomainRegion': 'string',
'DomainId': 'string',
'ProjectId': 'string',
'EnvironmentId': 'string',
'ProjectS3Path': 'string',
'SingleSignOnApplicationArn': 'string'
},
'IpAddressType': 'ipv4'|'dualstack'
},
SubnetIds=[
'string',
],
VpcId='string',
Tags=[
{
'Key': 'string',
'Value': 'string'
},
],
AppNetworkAccessType='PublicInternetOnly'|'VpcOnly',
HomeEfsFileSystemKmsKeyId='string',
KmsKeyId='string',
AppSecurityGroupManagement='Service'|'Customer',
TagPropagation='ENABLED'|'DISABLED',
DefaultSpaceSettings={
'ExecutionRole': 'string',
'SecurityGroups': [
'string',
],
'JupyterServerAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': 'string',
'SageMakerImageVersionAlias': 'string',
'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.g6.xlarge'|'ml.g6.2xlarge'|'ml.g6.4xlarge'|'ml.g6.8xlarge'|'ml.g6.12xlarge'|'ml.g6.16xlarge'|'ml.g6.24xlarge'|'ml.g6.48xlarge'|'ml.g6e.xlarge'|'ml.g6e.2xlarge'|'ml.g6e.4xlarge'|'ml.g6e.8xlarge'|'ml.g6e.12xlarge'|'ml.g6e.16xlarge'|'ml.g6e.24xlarge'|'ml.g6e.48xlarge'|'ml.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.p5.48xlarge'|'ml.p5en.48xlarge'|'ml.p6-b200.48xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.r6i.large'|'ml.r6i.xlarge'|'ml.r6i.2xlarge'|'ml.r6i.4xlarge'|'ml.r6i.8xlarge'|'ml.r6i.12xlarge'|'ml.r6i.16xlarge'|'ml.r6i.24xlarge'|'ml.r6i.32xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge'|'ml.m6id.large'|'ml.m6id.xlarge'|'ml.m6id.2xlarge'|'ml.m6id.4xlarge'|'ml.m6id.8xlarge'|'ml.m6id.12xlarge'|'ml.m6id.16xlarge'|'ml.m6id.24xlarge'|'ml.m6id.32xlarge'|'ml.c6id.large'|'ml.c6id.xlarge'|'ml.c6id.2xlarge'|'ml.c6id.4xlarge'|'ml.c6id.8xlarge'|'ml.c6id.12xlarge'|'ml.c6id.16xlarge'|'ml.c6id.24xlarge'|'ml.c6id.32xlarge'|'ml.r6id.large'|'ml.r6id.xlarge'|'ml.r6id.2xlarge'|'ml.r6id.4xlarge'|'ml.r6id.8xlarge'|'ml.r6id.12xlarge'|'ml.r6id.16xlarge'|'ml.r6id.24xlarge'|'ml.r6id.32xlarge'|'ml.p5.4xlarge',
'LifecycleConfigArn': 'string',
'TrainingPlanArn': 'string'
},
'LifecycleConfigArns': [
'string',
],
'CodeRepositories': [
{
'RepositoryUrl': 'string'
},
]
},
'KernelGatewayAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': 'string',
'SageMakerImageVersionAlias': 'string',
'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.g6.xlarge'|'ml.g6.2xlarge'|'ml.g6.4xlarge'|'ml.g6.8xlarge'|'ml.g6.12xlarge'|'ml.g6.16xlarge'|'ml.g6.24xlarge'|'ml.g6.48xlarge'|'ml.g6e.xlarge'|'ml.g6e.2xlarge'|'ml.g6e.4xlarge'|'ml.g6e.8xlarge'|'ml.g6e.12xlarge'|'ml.g6e.16xlarge'|'ml.g6e.24xlarge'|'ml.g6e.48xlarge'|'ml.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.p5.48xlarge'|'ml.p5en.48xlarge'|'ml.p6-b200.48xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.r6i.large'|'ml.r6i.xlarge'|'ml.r6i.2xlarge'|'ml.r6i.4xlarge'|'ml.r6i.8xlarge'|'ml.r6i.12xlarge'|'ml.r6i.16xlarge'|'ml.r6i.24xlarge'|'ml.r6i.32xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge'|'ml.m6id.large'|'ml.m6id.xlarge'|'ml.m6id.2xlarge'|'ml.m6id.4xlarge'|'ml.m6id.8xlarge'|'ml.m6id.12xlarge'|'ml.m6id.16xlarge'|'ml.m6id.24xlarge'|'ml.m6id.32xlarge'|'ml.c6id.large'|'ml.c6id.xlarge'|'ml.c6id.2xlarge'|'ml.c6id.4xlarge'|'ml.c6id.8xlarge'|'ml.c6id.12xlarge'|'ml.c6id.16xlarge'|'ml.c6id.24xlarge'|'ml.c6id.32xlarge'|'ml.r6id.large'|'ml.r6id.xlarge'|'ml.r6id.2xlarge'|'ml.r6id.4xlarge'|'ml.r6id.8xlarge'|'ml.r6id.12xlarge'|'ml.r6id.16xlarge'|'ml.r6id.24xlarge'|'ml.r6id.32xlarge'|'ml.p5.4xlarge',
'LifecycleConfigArn': 'string',
'TrainingPlanArn': 'string'
},
'CustomImages': [
{
'ImageName': 'string',
'ImageVersionNumber': 123,
'AppImageConfigName': 'string'
},
],
'LifecycleConfigArns': [
'string',
]
},
'JupyterLabAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': 'string',
'SageMakerImageVersionAlias': 'string',
'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.g6.xlarge'|'ml.g6.2xlarge'|'ml.g6.4xlarge'|'ml.g6.8xlarge'|'ml.g6.12xlarge'|'ml.g6.16xlarge'|'ml.g6.24xlarge'|'ml.g6.48xlarge'|'ml.g6e.xlarge'|'ml.g6e.2xlarge'|'ml.g6e.4xlarge'|'ml.g6e.8xlarge'|'ml.g6e.12xlarge'|'ml.g6e.16xlarge'|'ml.g6e.24xlarge'|'ml.g6e.48xlarge'|'ml.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.p5.48xlarge'|'ml.p5en.48xlarge'|'ml.p6-b200.48xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.r6i.large'|'ml.r6i.xlarge'|'ml.r6i.2xlarge'|'ml.r6i.4xlarge'|'ml.r6i.8xlarge'|'ml.r6i.12xlarge'|'ml.r6i.16xlarge'|'ml.r6i.24xlarge'|'ml.r6i.32xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge'|'ml.m6id.large'|'ml.m6id.xlarge'|'ml.m6id.2xlarge'|'ml.m6id.4xlarge'|'ml.m6id.8xlarge'|'ml.m6id.12xlarge'|'ml.m6id.16xlarge'|'ml.m6id.24xlarge'|'ml.m6id.32xlarge'|'ml.c6id.large'|'ml.c6id.xlarge'|'ml.c6id.2xlarge'|'ml.c6id.4xlarge'|'ml.c6id.8xlarge'|'ml.c6id.12xlarge'|'ml.c6id.16xlarge'|'ml.c6id.24xlarge'|'ml.c6id.32xlarge'|'ml.r6id.large'|'ml.r6id.xlarge'|'ml.r6id.2xlarge'|'ml.r6id.4xlarge'|'ml.r6id.8xlarge'|'ml.r6id.12xlarge'|'ml.r6id.16xlarge'|'ml.r6id.24xlarge'|'ml.r6id.32xlarge'|'ml.p5.4xlarge',
'LifecycleConfigArn': 'string',
'TrainingPlanArn': 'string'
},
'CustomImages': [
{
'ImageName': 'string',
'ImageVersionNumber': 123,
'AppImageConfigName': 'string'
},
],
'LifecycleConfigArns': [
'string',
],
'CodeRepositories': [
{
'RepositoryUrl': 'string'
},
],
'AppLifecycleManagement': {
'IdleSettings': {
'LifecycleManagement': 'ENABLED'|'DISABLED',
'IdleTimeoutInMinutes': 123,
'MinIdleTimeoutInMinutes': 123,
'MaxIdleTimeoutInMinutes': 123
}
},
'EmrSettings': {
'AssumableRoleArns': [
'string',
],
'ExecutionRoleArns': [
'string',
]
},
'BuiltInLifecycleConfigArn': 'string'
},
'SpaceStorageSettings': {
'DefaultEbsStorageSettings': {
'DefaultEbsVolumeSizeInGb': 123,
'MaximumEbsVolumeSizeInGb': 123
}
},
'CustomPosixUserConfig': {
'Uid': 123,
'Gid': 123
},
'CustomFileSystemConfigs': [
{
'EFSFileSystemConfig': {
'FileSystemId': 'string',
'FileSystemPath': 'string'
},
'FSxLustreFileSystemConfig': {
'FileSystemId': 'string',
'FileSystemPath': 'string'
},
'S3FileSystemConfig': {
'MountPath': 'string',
'S3Uri': 'string'
}
},
]
}
)
string
[REQUIRED]
A name for the domain.
string
[REQUIRED]
The mode of authentication that members use to access the domain.
dict
[REQUIRED]
The default settings to use to create a user profile when UserSettings isn't specified in the call to the CreateUserProfile API.
SecurityGroups is aggregated when specified in both calls. For all other settings in UserSettings, the values specified in CreateUserProfile take precedence over those specified in CreateDomain.
ExecutionRole (string) --
The execution role for the user.
SageMaker applies this setting only to private spaces that the user creates in the domain. SageMaker doesn't apply this setting to shared spaces.
SecurityGroups (list) --
The security groups for the Amazon Virtual Private Cloud (VPC) that the domain uses for communication.
Optional when the CreateDomain.AppNetworkAccessType parameter is set to PublicInternetOnly.
Required when the CreateDomain.AppNetworkAccessType parameter is set to VpcOnly, unless specified as part of the DefaultUserSettings for the domain.
Amazon SageMaker AI adds a security group to allow NFS traffic from Amazon SageMaker AI Studio. Therefore, the number of security groups that you can specify is one less than the maximum number shown.
SageMaker applies these settings only to private spaces that the user creates in the domain. SageMaker doesn't apply these settings to shared spaces.
(string) --
SharingSettings (dict) --
Specifies options for sharing Amazon SageMaker AI Studio notebooks.
NotebookOutputOption (string) --
Whether to include the notebook cell output when sharing the notebook. The default is Disabled.
S3OutputPath (string) --
When NotebookOutputOption is Allowed, the Amazon S3 bucket used to store the shared notebook snapshots.
S3KmsKeyId (string) --
When NotebookOutputOption is Allowed, the Amazon Web Services Key Management Service (KMS) encryption key ID used to encrypt the notebook cell output in the Amazon S3 bucket.
JupyterServerAppSettings (dict) --
The Jupyter server's app settings.
DefaultResourceSpec (dict) --
The default instance type and the Amazon Resource Name (ARN) of the default SageMaker AI image used by the JupyterServer app. If you use the LifecycleConfigArns parameter, then this parameter is also required.
SageMakerImageArn (string) --
The ARN of the SageMaker AI image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance. To clear the value set for SageMakerImageVersionArn, pass None as the value.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
TrainingPlanArn (string) --
The ARN of the SageMaker AI Training Plan to use for this app. When you specify a training plan, the app launches on reserved GPU capacity. This field is supported for JupyterLab and CodeEditor app types.
For more information about how to reserve GPU capacity with SageMaker AI Training Plans, see Using training plans in Studio applications.
LifecycleConfigArns (list) --
The Amazon Resource Name (ARN) of the Lifecycle Configurations attached to the JupyterServerApp. If you use this parameter, the DefaultResourceSpec parameter is also required.
(string) --
CodeRepositories (list) --
A list of Git repositories that SageMaker AI automatically displays to users for cloning in the JupyterServer application.
(dict) --
A Git repository that SageMaker AI automatically displays to users for cloning in the JupyterServer application.
RepositoryUrl (string) -- [REQUIRED]
The URL of the Git repository.
KernelGatewayAppSettings (dict) --
The kernel gateway app settings.
DefaultResourceSpec (dict) --
The default instance type and the Amazon Resource Name (ARN) of the default SageMaker AI image used by the KernelGateway app.
SageMakerImageArn (string) --
The ARN of the SageMaker AI image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance. To clear the value set for SageMakerImageVersionArn, pass None as the value.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
TrainingPlanArn (string) --
The ARN of the SageMaker AI Training Plan to use for this app. When you specify a training plan, the app launches on reserved GPU capacity. This field is supported for JupyterLab and CodeEditor app types.
For more information about how to reserve GPU capacity with SageMaker AI Training Plans, see Using training plans in Studio applications.
CustomImages (list) --
A list of custom SageMaker AI images that are configured to run as a KernelGateway app.
The maximum number of custom images are as follows.
On a domain level: 200
On a space level: 5
On a user profile level: 5
(dict) --
A custom SageMaker AI image. For more information, see Bring your own SageMaker AI image.
ImageName (string) -- [REQUIRED]
The name of the CustomImage. Must be unique to your account.
ImageVersionNumber (integer) --
The version number of the CustomImage.
AppImageConfigName (string) -- [REQUIRED]
The name of the AppImageConfig.
LifecycleConfigArns (list) --
The Amazon Resource Name (ARN) of the Lifecycle Configurations attached to the the user profile or domain.
(string) --
TensorBoardAppSettings (dict) --
The TensorBoard app settings.
DefaultResourceSpec (dict) --
The default instance type and the Amazon Resource Name (ARN) of the SageMaker AI image created on the instance.
SageMakerImageArn (string) --
The ARN of the SageMaker AI image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance. To clear the value set for SageMakerImageVersionArn, pass None as the value.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
TrainingPlanArn (string) --
The ARN of the SageMaker AI Training Plan to use for this app. When you specify a training plan, the app launches on reserved GPU capacity. This field is supported for JupyterLab and CodeEditor app types.
For more information about how to reserve GPU capacity with SageMaker AI Training Plans, see Using training plans in Studio applications.
RStudioServerProAppSettings (dict) --
A collection of settings that configure user interaction with the RStudioServerPro app.
AccessStatus (string) --
Indicates whether the current user has access to the RStudioServerPro app.
UserGroup (string) --
The level of permissions that the user has within the RStudioServerPro app. This value defaults to User. The Admin value allows the user access to the RStudio Administrative Dashboard.
RSessionAppSettings (dict) --
A collection of settings that configure the RSessionGateway app.
DefaultResourceSpec (dict) --
Specifies the ARN's of a SageMaker AI image and SageMaker AI image version, and the instance type that the version runs on.
SageMakerImageArn (string) --
The ARN of the SageMaker AI image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance. To clear the value set for SageMakerImageVersionArn, pass None as the value.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
TrainingPlanArn (string) --
The ARN of the SageMaker AI Training Plan to use for this app. When you specify a training plan, the app launches on reserved GPU capacity. This field is supported for JupyterLab and CodeEditor app types.
For more information about how to reserve GPU capacity with SageMaker AI Training Plans, see Using training plans in Studio applications.
CustomImages (list) --
A list of custom SageMaker AI images that are configured to run as a RSession app.
(dict) --
A custom SageMaker AI image. For more information, see Bring your own SageMaker AI image.
ImageName (string) -- [REQUIRED]
The name of the CustomImage. Must be unique to your account.
ImageVersionNumber (integer) --
The version number of the CustomImage.
AppImageConfigName (string) -- [REQUIRED]
The name of the AppImageConfig.
CanvasAppSettings (dict) --
The Canvas app settings.
SageMaker applies these settings only to private spaces that SageMaker creates for the Canvas app.
TimeSeriesForecastingSettings (dict) --
Time series forecast settings for the SageMaker Canvas application.
Status (string) --
Describes whether time series forecasting is enabled or disabled in the Canvas application.
AmazonForecastRoleArn (string) --
The IAM role that Canvas passes to Amazon Forecast for time series forecasting. By default, Canvas uses the execution role specified in the UserProfile that launches the Canvas application. If an execution role is not specified in the UserProfile, Canvas uses the execution role specified in the Domain that owns the UserProfile. To allow time series forecasting, this IAM role should have the AmazonSageMakerCanvasForecastAccess policy attached and forecast.amazonaws.com added in the trust relationship as a service principal.
ModelRegisterSettings (dict) --
The model registry settings for the SageMaker Canvas application.
Status (string) --
Describes whether the integration to the model registry is enabled or disabled in the Canvas application.
CrossAccountModelRegisterRoleArn (string) --
The Amazon Resource Name (ARN) of the SageMaker model registry account. Required only to register model versions created by a different SageMaker Canvas Amazon Web Services account than the Amazon Web Services account in which SageMaker model registry is set up.
WorkspaceSettings (dict) --
The workspace settings for the SageMaker Canvas application.
S3ArtifactPath (string) --
The Amazon S3 bucket used to store artifacts generated by Canvas. Updating the Amazon S3 location impacts existing configuration settings, and Canvas users no longer have access to their artifacts. Canvas users must log out and log back in to apply the new location.
S3KmsKeyId (string) --
The Amazon Web Services Key Management Service (KMS) encryption key ID that is used to encrypt artifacts generated by Canvas in the Amazon S3 bucket.
IdentityProviderOAuthSettings (list) --
The settings for connecting to an external data source with OAuth.
(dict) --
The Amazon SageMaker Canvas application setting where you configure OAuth for connecting to an external data source, such as Snowflake.
DataSourceName (string) --
The name of the data source that you're connecting to. Canvas currently supports OAuth for Snowflake and Salesforce Data Cloud.
Status (string) --
Describes whether OAuth for a data source is enabled or disabled in the Canvas application.
SecretArn (string) --
The ARN of an Amazon Web Services Secrets Manager secret that stores the credentials from your identity provider, such as the client ID and secret, authorization URL, and token URL.
DirectDeploySettings (dict) --
The model deployment settings for the SageMaker Canvas application.
Status (string) --
Describes whether model deployment permissions are enabled or disabled in the Canvas application.
KendraSettings (dict) --
The settings for document querying.
Status (string) --
Describes whether the document querying feature is enabled or disabled in the Canvas application.
GenerativeAiSettings (dict) --
The generative AI settings for the SageMaker Canvas application.
AmazonBedrockRoleArn (string) --
The ARN of an Amazon Web Services IAM role that allows fine-tuning of large language models (LLMs) in Amazon Bedrock. The IAM role should have Amazon S3 read and write permissions, as well as a trust relationship that establishes bedrock.amazonaws.com as a service principal.
EmrServerlessSettings (dict) --
The settings for running Amazon EMR Serverless data processing jobs in SageMaker Canvas.
ExecutionRoleArn (string) --
The Amazon Resource Name (ARN) of the Amazon Web Services IAM role that is assumed for running Amazon EMR Serverless jobs in SageMaker Canvas. This role should have the necessary permissions to read and write data attached and a trust relationship with EMR Serverless.
Status (string) --
Describes whether Amazon EMR Serverless job capabilities are enabled or disabled in the SageMaker Canvas application.
CodeEditorAppSettings (dict) --
The Code Editor application settings.
SageMaker applies these settings only to private spaces that the user creates in the domain. SageMaker doesn't apply these settings to shared spaces.
DefaultResourceSpec (dict) --
Specifies the ARN's of a SageMaker AI image and SageMaker AI image version, and the instance type that the version runs on.
SageMakerImageArn (string) --
The ARN of the SageMaker AI image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance. To clear the value set for SageMakerImageVersionArn, pass None as the value.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
TrainingPlanArn (string) --
The ARN of the SageMaker AI Training Plan to use for this app. When you specify a training plan, the app launches on reserved GPU capacity. This field is supported for JupyterLab and CodeEditor app types.
For more information about how to reserve GPU capacity with SageMaker AI Training Plans, see Using training plans in Studio applications.
CustomImages (list) --
A list of custom SageMaker images that are configured to run as a Code Editor app.
(dict) --
A custom SageMaker AI image. For more information, see Bring your own SageMaker AI image.
ImageName (string) -- [REQUIRED]
The name of the CustomImage. Must be unique to your account.
ImageVersionNumber (integer) --
The version number of the CustomImage.
AppImageConfigName (string) -- [REQUIRED]
The name of the AppImageConfig.
LifecycleConfigArns (list) --
The Amazon Resource Name (ARN) of the Code Editor application lifecycle configuration.
(string) --
AppLifecycleManagement (dict) --
Settings that are used to configure and manage the lifecycle of CodeEditor applications.
IdleSettings (dict) --
Settings related to idle shutdown of Studio applications.
LifecycleManagement (string) --
Indicates whether idle shutdown is activated for the application type.
IdleTimeoutInMinutes (integer) --
The time that SageMaker waits after the application becomes idle before shutting it down.
MinIdleTimeoutInMinutes (integer) --
The minimum value in minutes that custom idle shutdown can be set to by the user.
MaxIdleTimeoutInMinutes (integer) --
The maximum value in minutes that custom idle shutdown can be set to by the user.
BuiltInLifecycleConfigArn (string) --
The lifecycle configuration that runs before the default lifecycle configuration. It can override changes made in the default lifecycle configuration.
JupyterLabAppSettings (dict) --
The settings for the JupyterLab application.
SageMaker applies these settings only to private spaces that the user creates in the domain. SageMaker doesn't apply these settings to shared spaces.
DefaultResourceSpec (dict) --
Specifies the ARN's of a SageMaker AI image and SageMaker AI image version, and the instance type that the version runs on.
SageMakerImageArn (string) --
The ARN of the SageMaker AI image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance. To clear the value set for SageMakerImageVersionArn, pass None as the value.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
TrainingPlanArn (string) --
The ARN of the SageMaker AI Training Plan to use for this app. When you specify a training plan, the app launches on reserved GPU capacity. This field is supported for JupyterLab and CodeEditor app types.
For more information about how to reserve GPU capacity with SageMaker AI Training Plans, see Using training plans in Studio applications.
CustomImages (list) --
A list of custom SageMaker images that are configured to run as a JupyterLab app.
(dict) --
A custom SageMaker AI image. For more information, see Bring your own SageMaker AI image.
ImageName (string) -- [REQUIRED]
The name of the CustomImage. Must be unique to your account.
ImageVersionNumber (integer) --
The version number of the CustomImage.
AppImageConfigName (string) -- [REQUIRED]
The name of the AppImageConfig.
LifecycleConfigArns (list) --
The Amazon Resource Name (ARN) of the lifecycle configurations attached to the user profile or domain. To remove a lifecycle config, you must set LifecycleConfigArns to an empty list.
(string) --
CodeRepositories (list) --
A list of Git repositories that SageMaker automatically displays to users for cloning in the JupyterLab application.
(dict) --
A Git repository that SageMaker AI automatically displays to users for cloning in the JupyterServer application.
RepositoryUrl (string) -- [REQUIRED]
The URL of the Git repository.
AppLifecycleManagement (dict) --
Indicates whether idle shutdown is activated for JupyterLab applications.
IdleSettings (dict) --
Settings related to idle shutdown of Studio applications.
LifecycleManagement (string) --
Indicates whether idle shutdown is activated for the application type.
IdleTimeoutInMinutes (integer) --
The time that SageMaker waits after the application becomes idle before shutting it down.
MinIdleTimeoutInMinutes (integer) --
The minimum value in minutes that custom idle shutdown can be set to by the user.
MaxIdleTimeoutInMinutes (integer) --
The maximum value in minutes that custom idle shutdown can be set to by the user.
EmrSettings (dict) --
The configuration parameters that specify the IAM roles assumed by the execution role of SageMaker (assumable roles) and the cluster instances or job execution environments (execution roles or runtime roles) to manage and access resources required for running Amazon EMR clusters or Amazon EMR Serverless applications.
AssumableRoleArns (list) --
An array of Amazon Resource Names (ARNs) of the IAM roles that the execution role of SageMaker can assume for performing operations or tasks related to Amazon EMR clusters or Amazon EMR Serverless applications. These roles define the permissions and access policies required when performing Amazon EMR-related operations, such as listing, connecting to, or terminating Amazon EMR clusters or Amazon EMR Serverless applications. They are typically used in cross-account access scenarios, where the Amazon EMR resources (clusters or serverless applications) are located in a different Amazon Web Services account than the SageMaker domain.
(string) --
ExecutionRoleArns (list) --
An array of Amazon Resource Names (ARNs) of the IAM roles used by the Amazon EMR cluster instances or job execution environments to access other Amazon Web Services services and resources needed during the runtime of your Amazon EMR or Amazon EMR Serverless workloads, such as Amazon S3 for data access, Amazon CloudWatch for logging, or other Amazon Web Services services based on the particular workload requirements.
(string) --
BuiltInLifecycleConfigArn (string) --
The lifecycle configuration that runs before the default lifecycle configuration. It can override changes made in the default lifecycle configuration.
SpaceStorageSettings (dict) --
The storage settings for a space.
SageMaker applies these settings only to private spaces that the user creates in the domain. SageMaker doesn't apply these settings to shared spaces.
DefaultEbsStorageSettings (dict) --
The default EBS storage settings for a space.
DefaultEbsVolumeSizeInGb (integer) -- [REQUIRED]
The default size of the EBS storage volume for a space.
MaximumEbsVolumeSizeInGb (integer) -- [REQUIRED]
The maximum size of the EBS storage volume for a space.
DefaultLandingUri (string) --
The default experience that the user is directed to when accessing the domain. The supported values are:
studio::: Indicates that Studio is the default experience. This value can only be passed if StudioWebPortal is set to ENABLED.
app:JupyterServer:: Indicates that Studio Classic is the default experience.
StudioWebPortal (string) --
Whether the user can access Studio. If this value is set to DISABLED, the user cannot access Studio, even if that is the default experience for the domain.
CustomPosixUserConfig (dict) --
Details about the POSIX identity that is used for file system operations.
SageMaker applies these settings only to private spaces that the user creates in the domain. SageMaker doesn't apply these settings to shared spaces.
Uid (integer) -- [REQUIRED]
The POSIX user ID.
Gid (integer) -- [REQUIRED]
The POSIX group ID.
CustomFileSystemConfigs (list) --
The settings for assigning a custom file system to a user profile. Permitted users can access this file system in Amazon SageMaker AI Studio.
SageMaker applies these settings only to private spaces that the user creates in the domain. SageMaker doesn't apply these settings to shared spaces.
(dict) --
The settings for assigning a custom file system to a user profile or space for an Amazon SageMaker AI Domain. Permitted users can access this file system in Amazon SageMaker AI Studio.
EFSFileSystemConfig (dict) --
The settings for a custom Amazon EFS file system.
FileSystemId (string) -- [REQUIRED]
The ID of your Amazon EFS file system.
FileSystemPath (string) --
The path to the file system directory that is accessible in Amazon SageMaker AI Studio. Permitted users can access only this directory and below.
FSxLustreFileSystemConfig (dict) --
The settings for a custom Amazon FSx for Lustre file system.
FileSystemId (string) -- [REQUIRED]
The globally unique, 17-digit, ID of the file system, assigned by Amazon FSx for Lustre.
FileSystemPath (string) --
The path to the file system directory that is accessible in Amazon SageMaker Studio. Permitted users can access only this directory and below.
S3FileSystemConfig (dict) --
Configuration settings for a custom Amazon S3 file system.
MountPath (string) --
The file system path where the Amazon S3 storage location will be mounted within the Amazon SageMaker Studio environment.
S3Uri (string) -- [REQUIRED]
The Amazon S3 URI of the S3 file system configuration.
StudioWebPortalSettings (dict) --
Studio settings. If these settings are applied on a user level, they take priority over the settings applied on a domain level.
HiddenMlTools (list) --
The machine learning tools that are hidden from the Studio left navigation pane.
(string) --
HiddenAppTypes (list) --
The Applications supported in Studio that are hidden from the Studio left navigation pane.
(string) --
HiddenInstanceTypes (list) --
The instance types you are hiding from the Studio user interface.
(string) --
HiddenSageMakerImageVersionAliases (list) --
The version aliases you are hiding from the Studio user interface.
(dict) --
The SageMaker images that are hidden from the Studio user interface. You must specify the SageMaker image name and version aliases.
SageMakerImageName (string) --
The SageMaker image name that you are hiding from the Studio user interface.
VersionAliases (list) --
The version aliases you are hiding from the Studio user interface.
(string) --
ExecutionRoleSessionNameMode (string) --
The execution role session name mode. If this value is set to USER_IDENTITY, the session name of the execution role corresponds to the user's identity. For IAM domains, the session name is the IAM session name used to generate the presigned URL. For IAM Identity Center domains, the session name is the username of the associated IAM Identity Center user. If this value is set to STATIC or is not set, the session name defaults to SageMaker.
AutoMountHomeEFS (string) --
Indicates whether auto-mounting of an EFS volume is supported for the user profile. The DefaultAsDomain value is only supported for user profiles. Do not use the DefaultAsDomain value when setting this parameter for a domain.
SageMaker applies this setting only to private spaces that the user creates in the domain. SageMaker doesn't apply this setting to shared spaces.
dict
A collection of Domain settings.
SecurityGroupIds (list) --
The security groups for the Amazon Virtual Private Cloud that the Domain uses for communication between Domain-level apps and user apps.
(string) --
RStudioServerProDomainSettings (dict) --
A collection of settings that configure the RStudioServerPro Domain-level app.
DomainExecutionRoleArn (string) -- [REQUIRED]
The ARN of the execution role for the RStudioServerPro Domain-level app.
RStudioConnectUrl (string) --
A URL pointing to an RStudio Connect server.
RStudioPackageManagerUrl (string) --
A URL pointing to an RStudio Package Manager server.
DefaultResourceSpec (dict) --
Specifies the ARN's of a SageMaker AI image and SageMaker AI image version, and the instance type that the version runs on.
SageMakerImageArn (string) --
The ARN of the SageMaker AI image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance. To clear the value set for SageMakerImageVersionArn, pass None as the value.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
TrainingPlanArn (string) --
The ARN of the SageMaker AI Training Plan to use for this app. When you specify a training plan, the app launches on reserved GPU capacity. This field is supported for JupyterLab and CodeEditor app types.
For more information about how to reserve GPU capacity with SageMaker AI Training Plans, see Using training plans in Studio applications.
ExecutionRoleIdentityConfig (string) --
The configuration for attaching a SageMaker AI user profile name to the execution role as a sts:SourceIdentity key.
TrustedIdentityPropagationSettings (dict) --
The Trusted Identity Propagation (TIP) settings for the SageMaker domain. These settings determine how user identities from IAM Identity Center are propagated through the domain to TIP enabled Amazon Web Services services.
Status (string) -- [REQUIRED]
The status of Trusted Identity Propagation (TIP) at the SageMaker domain level.
When disabled, standard IAM role-based access is used.
When enabled:
User identities from IAM Identity Center are propagated through the application to TIP enabled Amazon Web Services services.
New applications or existing applications that are automatically patched, will use the domain level configuration.
DockerSettings (dict) --
A collection of settings that configure the domain's Docker interaction.
EnableDockerAccess (string) --
Indicates whether the domain can access Docker.
VpcOnlyTrustedAccounts (list) --
The list of Amazon Web Services accounts that are trusted when the domain is created in VPC-only mode.
(string) --
RootlessDocker (string) --
Indicates whether to use rootless Docker.
AmazonQSettings (dict) --
A collection of settings that configure the Amazon Q experience within the domain. The AuthMode that you use to create the domain must be SSO.
Status (string) --
Whether Amazon Q has been enabled within the domain.
QProfileArn (string) --
The ARN of the Amazon Q profile used within the domain.
UnifiedStudioSettings (dict) --
The settings that apply to an SageMaker AI domain when you use it in Amazon SageMaker Unified Studio.
StudioWebPortalAccess (string) --
Sets whether you can access the domain in Amazon SageMaker Studio:
ENABLED
You can access the domain in Amazon SageMaker Studio. If you migrate the domain to Amazon SageMaker Unified Studio, you can access it in both studio interfaces.
DISABLED
You can't access the domain in Amazon SageMaker Studio. If you migrate the domain to Amazon SageMaker Unified Studio, you can access it only in that studio interface.
To migrate a domain to Amazon SageMaker Unified Studio, you specify the UnifiedStudioSettings data type when you use the UpdateDomain action.
DomainAccountId (string) --
The ID of the Amazon Web Services account that has the Amazon SageMaker Unified Studio domain. The default value, if you don't specify an ID, is the ID of the account that has the Amazon SageMaker AI domain.
DomainRegion (string) --
The Amazon Web Services Region where the domain is located in Amazon SageMaker Unified Studio. The default value, if you don't specify a Region, is the Region where the Amazon SageMaker AI domain is located.
DomainId (string) --
The ID of the Amazon SageMaker Unified Studio domain associated with this domain.
ProjectId (string) --
The ID of the Amazon SageMaker Unified Studio project that corresponds to the domain.
EnvironmentId (string) --
The ID of the environment that Amazon SageMaker Unified Studio associates with the domain.
ProjectS3Path (string) --
The location where Amazon S3 stores temporary execution data and other artifacts for the project that corresponds to the domain.
SingleSignOnApplicationArn (string) --
The ARN of the Amazon DataZone application managed by Amazon SageMaker Unified Studio in the Amazon Web Services IAM Identity Center.
IpAddressType (string) --
The IP address type for the domain. Specify ipv4 for IPv4-only connectivity or dualstack for both IPv4 and IPv6 connectivity. When you specify dualstack, the subnet must support IPv6 CIDR blocks. If not specified, defaults to ipv4.
list
The VPC subnets that the domain uses for communication.
The field is optional when the AppNetworkAccessType parameter is set to PublicInternetOnly for domains created from Amazon SageMaker Unified Studio.
(string) --
string
The ID of the Amazon Virtual Private Cloud (VPC) that the domain uses for communication.
The field is optional when the AppNetworkAccessType parameter is set to PublicInternetOnly for domains created from Amazon SageMaker Unified Studio.
list
Tags to associated with the Domain. Each tag consists of a key and an optional value. Tag keys must be unique per resource. Tags are searchable using the Search API.
Tags that you specify for the Domain are also added to all Apps that the Domain launches.
(dict) --
A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags.
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources. For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy.
Key (string) -- [REQUIRED]
The tag key. Tag keys must be unique per resource.
Value (string) -- [REQUIRED]
The tag value.
string
Specifies the VPC used for non-EFS traffic. The default value is PublicInternetOnly.
PublicInternetOnly - Non-EFS traffic is through a VPC managed by Amazon SageMaker AI, which allows direct internet access
VpcOnly - All traffic is through the specified VPC and subnets
string
Use KmsKeyId.
string
SageMaker AI uses Amazon Web Services KMS to encrypt EFS and EBS volumes attached to the domain with an Amazon Web Services managed key by default. For more control, specify a customer managed key.
string
The entity that creates and manages the required security groups for inter-app communication in VPCOnly mode. Required when CreateDomain.AppNetworkAccessType is VPCOnly and DomainSettings.RStudioServerProDomainSettings.DomainExecutionRoleArn is provided. If setting up the domain for use with RStudio, this value must be set to Service.
string
Indicates whether custom tag propagation is supported for the domain. Defaults to DISABLED.
dict
The default settings for shared spaces that users create in the domain.
ExecutionRole (string) --
The ARN of the execution role for the space.
SecurityGroups (list) --
The security group IDs for the Amazon VPC that the space uses for communication.
(string) --
JupyterServerAppSettings (dict) --
The JupyterServer app settings.
DefaultResourceSpec (dict) --
The default instance type and the Amazon Resource Name (ARN) of the default SageMaker AI image used by the JupyterServer app. If you use the LifecycleConfigArns parameter, then this parameter is also required.
SageMakerImageArn (string) --
The ARN of the SageMaker AI image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance. To clear the value set for SageMakerImageVersionArn, pass None as the value.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
TrainingPlanArn (string) --
The ARN of the SageMaker AI Training Plan to use for this app. When you specify a training plan, the app launches on reserved GPU capacity. This field is supported for JupyterLab and CodeEditor app types.
For more information about how to reserve GPU capacity with SageMaker AI Training Plans, see Using training plans in Studio applications.
LifecycleConfigArns (list) --
The Amazon Resource Name (ARN) of the Lifecycle Configurations attached to the JupyterServerApp. If you use this parameter, the DefaultResourceSpec parameter is also required.
(string) --
CodeRepositories (list) --
A list of Git repositories that SageMaker AI automatically displays to users for cloning in the JupyterServer application.
(dict) --
A Git repository that SageMaker AI automatically displays to users for cloning in the JupyterServer application.
RepositoryUrl (string) -- [REQUIRED]
The URL of the Git repository.
KernelGatewayAppSettings (dict) --
The KernelGateway app settings.
DefaultResourceSpec (dict) --
The default instance type and the Amazon Resource Name (ARN) of the default SageMaker AI image used by the KernelGateway app.
SageMakerImageArn (string) --
The ARN of the SageMaker AI image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance. To clear the value set for SageMakerImageVersionArn, pass None as the value.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
TrainingPlanArn (string) --
The ARN of the SageMaker AI Training Plan to use for this app. When you specify a training plan, the app launches on reserved GPU capacity. This field is supported for JupyterLab and CodeEditor app types.
For more information about how to reserve GPU capacity with SageMaker AI Training Plans, see Using training plans in Studio applications.
CustomImages (list) --
A list of custom SageMaker AI images that are configured to run as a KernelGateway app.
The maximum number of custom images are as follows.
On a domain level: 200
On a space level: 5
On a user profile level: 5
(dict) --
A custom SageMaker AI image. For more information, see Bring your own SageMaker AI image.
ImageName (string) -- [REQUIRED]
The name of the CustomImage. Must be unique to your account.
ImageVersionNumber (integer) --
The version number of the CustomImage.
AppImageConfigName (string) -- [REQUIRED]
The name of the AppImageConfig.
LifecycleConfigArns (list) --
The Amazon Resource Name (ARN) of the Lifecycle Configurations attached to the the user profile or domain.
(string) --
JupyterLabAppSettings (dict) --
The settings for the JupyterLab application.
DefaultResourceSpec (dict) --
Specifies the ARN's of a SageMaker AI image and SageMaker AI image version, and the instance type that the version runs on.
SageMakerImageArn (string) --
The ARN of the SageMaker AI image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance. To clear the value set for SageMakerImageVersionArn, pass None as the value.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
TrainingPlanArn (string) --
The ARN of the SageMaker AI Training Plan to use for this app. When you specify a training plan, the app launches on reserved GPU capacity. This field is supported for JupyterLab and CodeEditor app types.
For more information about how to reserve GPU capacity with SageMaker AI Training Plans, see Using training plans in Studio applications.
CustomImages (list) --
A list of custom SageMaker images that are configured to run as a JupyterLab app.
(dict) --
A custom SageMaker AI image. For more information, see Bring your own SageMaker AI image.
ImageName (string) -- [REQUIRED]
The name of the CustomImage. Must be unique to your account.
ImageVersionNumber (integer) --
The version number of the CustomImage.
AppImageConfigName (string) -- [REQUIRED]
The name of the AppImageConfig.
LifecycleConfigArns (list) --
The Amazon Resource Name (ARN) of the lifecycle configurations attached to the user profile or domain. To remove a lifecycle config, you must set LifecycleConfigArns to an empty list.
(string) --
CodeRepositories (list) --
A list of Git repositories that SageMaker automatically displays to users for cloning in the JupyterLab application.
(dict) --
A Git repository that SageMaker AI automatically displays to users for cloning in the JupyterServer application.
RepositoryUrl (string) -- [REQUIRED]
The URL of the Git repository.
AppLifecycleManagement (dict) --
Indicates whether idle shutdown is activated for JupyterLab applications.
IdleSettings (dict) --
Settings related to idle shutdown of Studio applications.
LifecycleManagement (string) --
Indicates whether idle shutdown is activated for the application type.
IdleTimeoutInMinutes (integer) --
The time that SageMaker waits after the application becomes idle before shutting it down.
MinIdleTimeoutInMinutes (integer) --
The minimum value in minutes that custom idle shutdown can be set to by the user.
MaxIdleTimeoutInMinutes (integer) --
The maximum value in minutes that custom idle shutdown can be set to by the user.
EmrSettings (dict) --
The configuration parameters that specify the IAM roles assumed by the execution role of SageMaker (assumable roles) and the cluster instances or job execution environments (execution roles or runtime roles) to manage and access resources required for running Amazon EMR clusters or Amazon EMR Serverless applications.
AssumableRoleArns (list) --
An array of Amazon Resource Names (ARNs) of the IAM roles that the execution role of SageMaker can assume for performing operations or tasks related to Amazon EMR clusters or Amazon EMR Serverless applications. These roles define the permissions and access policies required when performing Amazon EMR-related operations, such as listing, connecting to, or terminating Amazon EMR clusters or Amazon EMR Serverless applications. They are typically used in cross-account access scenarios, where the Amazon EMR resources (clusters or serverless applications) are located in a different Amazon Web Services account than the SageMaker domain.
(string) --
ExecutionRoleArns (list) --
An array of Amazon Resource Names (ARNs) of the IAM roles used by the Amazon EMR cluster instances or job execution environments to access other Amazon Web Services services and resources needed during the runtime of your Amazon EMR or Amazon EMR Serverless workloads, such as Amazon S3 for data access, Amazon CloudWatch for logging, or other Amazon Web Services services based on the particular workload requirements.
(string) --
BuiltInLifecycleConfigArn (string) --
The lifecycle configuration that runs before the default lifecycle configuration. It can override changes made in the default lifecycle configuration.
SpaceStorageSettings (dict) --
The default storage settings for a space.
DefaultEbsStorageSettings (dict) --
The default EBS storage settings for a space.
DefaultEbsVolumeSizeInGb (integer) -- [REQUIRED]
The default size of the EBS storage volume for a space.
MaximumEbsVolumeSizeInGb (integer) -- [REQUIRED]
The maximum size of the EBS storage volume for a space.
CustomPosixUserConfig (dict) --
Details about the POSIX identity that is used for file system operations.
Uid (integer) -- [REQUIRED]
The POSIX user ID.
Gid (integer) -- [REQUIRED]
The POSIX group ID.
CustomFileSystemConfigs (list) --
The settings for assigning a custom file system to a domain. Permitted users can access this file system in Amazon SageMaker AI Studio.
(dict) --
The settings for assigning a custom file system to a user profile or space for an Amazon SageMaker AI Domain. Permitted users can access this file system in Amazon SageMaker AI Studio.
EFSFileSystemConfig (dict) --
The settings for a custom Amazon EFS file system.
FileSystemId (string) -- [REQUIRED]
The ID of your Amazon EFS file system.
FileSystemPath (string) --
The path to the file system directory that is accessible in Amazon SageMaker AI Studio. Permitted users can access only this directory and below.
FSxLustreFileSystemConfig (dict) --
The settings for a custom Amazon FSx for Lustre file system.
FileSystemId (string) -- [REQUIRED]
The globally unique, 17-digit, ID of the file system, assigned by Amazon FSx for Lustre.
FileSystemPath (string) --
The path to the file system directory that is accessible in Amazon SageMaker Studio. Permitted users can access only this directory and below.
S3FileSystemConfig (dict) --
Configuration settings for a custom Amazon S3 file system.
MountPath (string) --
The file system path where the Amazon S3 storage location will be mounted within the Amazon SageMaker Studio environment.
S3Uri (string) -- [REQUIRED]
The Amazon S3 URI of the S3 file system configuration.
dict
Response Syntax
{
'DomainArn': 'string',
'DomainId': 'string',
'Url': 'string'
}
Response Structure
(dict) --
DomainArn (string) --
The Amazon Resource Name (ARN) of the created domain.
DomainId (string) --
The ID of the created domain.
Url (string) --
The URL to the created domain.
{'ModelVariants': {'InfrastructureConfig': {'RealTimeInferenceConfig': {'InstanceType': {'ml.c4.large',
'ml.c5.large',
'ml.c5d.large',
'ml.c6g.12xlarge',
'ml.c6g.16xlarge',
'ml.c6g.2xlarge',
'ml.c6g.4xlarge',
'ml.c6g.8xlarge',
'ml.c6g.large',
'ml.c6g.xlarge',
'ml.c6gd.12xlarge',
'ml.c6gd.16xlarge',
'ml.c6gd.2xlarge',
'ml.c6gd.4xlarge',
'ml.c6gd.8xlarge',
'ml.c6gd.large',
'ml.c6gd.xlarge',
'ml.c6gn.12xlarge',
'ml.c6gn.16xlarge',
'ml.c6gn.2xlarge',
'ml.c6gn.4xlarge',
'ml.c6gn.8xlarge',
'ml.c6gn.large',
'ml.c6gn.xlarge',
'ml.c6in.12xlarge',
'ml.c6in.16xlarge',
'ml.c6in.24xlarge',
'ml.c6in.2xlarge',
'ml.c6in.32xlarge',
'ml.c6in.4xlarge',
'ml.c6in.8xlarge',
'ml.c6in.large',
'ml.c6in.xlarge',
'ml.c7g.12xlarge',
'ml.c7g.16xlarge',
'ml.c7g.2xlarge',
'ml.c7g.4xlarge',
'ml.c7g.8xlarge',
'ml.c7g.large',
'ml.c7g.xlarge',
'ml.c8g.12xlarge',
'ml.c8g.16xlarge',
'ml.c8g.24xlarge',
'ml.c8g.2xlarge',
'ml.c8g.48xlarge',
'ml.c8g.4xlarge',
'ml.c8g.8xlarge',
'ml.c8g.large',
'ml.c8g.medium',
'ml.c8g.xlarge',
'ml.dl1.24xlarge',
'ml.g6e.12xlarge',
'ml.g6e.16xlarge',
'ml.g6e.24xlarge',
'ml.g6e.2xlarge',
'ml.g6e.48xlarge',
'ml.g6e.4xlarge',
'ml.g6e.8xlarge',
'ml.g6e.xlarge',
'ml.g7e.12xlarge',
'ml.g7e.24xlarge',
'ml.g7e.2xlarge',
'ml.g7e.48xlarge',
'ml.g7e.4xlarge',
'ml.g7e.8xlarge',
'ml.m5.large',
'ml.m6g.12xlarge',
'ml.m6g.16xlarge',
'ml.m6g.2xlarge',
'ml.m6g.4xlarge',
'ml.m6g.8xlarge',
'ml.m6g.large',
'ml.m6g.xlarge',
'ml.m6gd.12xlarge',
'ml.m6gd.16xlarge',
'ml.m6gd.2xlarge',
'ml.m6gd.4xlarge',
'ml.m6gd.8xlarge',
'ml.m6gd.large',
'ml.m6gd.xlarge',
'ml.m8g.12xlarge',
'ml.m8g.16xlarge',
'ml.m8g.24xlarge',
'ml.m8g.2xlarge',
'ml.m8g.48xlarge',
'ml.m8g.4xlarge',
'ml.m8g.8xlarge',
'ml.m8g.large',
'ml.m8g.medium',
'ml.m8g.xlarge',
'ml.p5.4xlarge',
'ml.p5e.48xlarge',
'ml.p5en.48xlarge',
'ml.p6-b300.48xlarge',
'ml.p6e-gb200.36xlarge',
'ml.r5d.12xlarge',
'ml.r5d.24xlarge',
'ml.r5d.2xlarge',
'ml.r5d.4xlarge',
'ml.r5d.large',
'ml.r5d.xlarge',
'ml.r6g.12xlarge',
'ml.r6g.16xlarge',
'ml.r6g.2xlarge',
'ml.r6g.4xlarge',
'ml.r6g.8xlarge',
'ml.r6g.large',
'ml.r6g.xlarge',
'ml.r6gd.12xlarge',
'ml.r6gd.16xlarge',
'ml.r6gd.2xlarge',
'ml.r6gd.4xlarge',
'ml.r6gd.8xlarge',
'ml.r6gd.large',
'ml.r6gd.xlarge',
'ml.r7gd.12xlarge',
'ml.r7gd.16xlarge',
'ml.r7gd.2xlarge',
'ml.r7gd.4xlarge',
'ml.r7gd.8xlarge',
'ml.r7gd.large',
'ml.r7gd.medium',
'ml.r7gd.xlarge',
'ml.r8g.12xlarge',
'ml.r8g.16xlarge',
'ml.r8g.24xlarge',
'ml.r8g.2xlarge',
'ml.r8g.48xlarge',
'ml.r8g.4xlarge',
'ml.r8g.8xlarge',
'ml.r8g.large',
'ml.r8g.medium',
'ml.r8g.xlarge',
'ml.trn2.48xlarge'}}}}}
Creates an inference experiment using the configurations specified in the request.
Use this API to setup and schedule an experiment to compare model variants on a Amazon SageMaker inference endpoint. For more information about inference experiments, see Shadow tests.
Amazon SageMaker begins your experiment at the scheduled time and routes traffic to your endpoint's model variants based on your specified configuration.
While the experiment is in progress or after it has concluded, you can view metrics that compare your model variants. For more information, see View, monitor, and edit shadow tests.
See also: AWS API Documentation
Request Syntax
client.create_inference_experiment(
Name='string',
Type='ShadowMode',
Schedule={
'StartTime': datetime(2015, 1, 1),
'EndTime': datetime(2015, 1, 1)
},
Description='string',
RoleArn='string',
EndpointName='string',
ModelVariants=[
{
'ModelName': 'string',
'VariantName': 'string',
'InfrastructureConfig': {
'InfrastructureType': 'RealTimeInference',
'RealTimeInferenceConfig': {
'InstanceType': 'ml.t2.medium'|'ml.t2.large'|'ml.t2.xlarge'|'ml.t2.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.12xlarge'|'ml.m5d.24xlarge'|'ml.c4.large'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5d.large'|'ml.c5d.xlarge'|'ml.c5d.2xlarge'|'ml.c5d.4xlarge'|'ml.c5d.9xlarge'|'ml.c5d.18xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.12xlarge'|'ml.r5.24xlarge'|'ml.r5d.large'|'ml.r5d.xlarge'|'ml.r5d.2xlarge'|'ml.r5d.4xlarge'|'ml.r5d.12xlarge'|'ml.r5d.24xlarge'|'ml.inf1.xlarge'|'ml.inf1.2xlarge'|'ml.inf1.6xlarge'|'ml.inf1.24xlarge'|'ml.dl1.24xlarge'|'ml.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.r6i.large'|'ml.r6i.xlarge'|'ml.r6i.2xlarge'|'ml.r6i.4xlarge'|'ml.r6i.8xlarge'|'ml.r6i.12xlarge'|'ml.r6i.16xlarge'|'ml.r6i.24xlarge'|'ml.r6i.32xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.12xlarge'|'ml.g5.16xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.g6.xlarge'|'ml.g6.2xlarge'|'ml.g6.4xlarge'|'ml.g6.8xlarge'|'ml.g6.12xlarge'|'ml.g6.16xlarge'|'ml.g6.24xlarge'|'ml.g6.48xlarge'|'ml.r8g.medium'|'ml.r8g.large'|'ml.r8g.xlarge'|'ml.r8g.2xlarge'|'ml.r8g.4xlarge'|'ml.r8g.8xlarge'|'ml.r8g.12xlarge'|'ml.r8g.16xlarge'|'ml.r8g.24xlarge'|'ml.r8g.48xlarge'|'ml.g6e.xlarge'|'ml.g6e.2xlarge'|'ml.g6e.4xlarge'|'ml.g6e.8xlarge'|'ml.g6e.12xlarge'|'ml.g6e.16xlarge'|'ml.g6e.24xlarge'|'ml.g6e.48xlarge'|'ml.g7e.2xlarge'|'ml.g7e.4xlarge'|'ml.g7e.8xlarge'|'ml.g7e.12xlarge'|'ml.g7e.24xlarge'|'ml.g7e.48xlarge'|'ml.p4d.24xlarge'|'ml.c7g.large'|'ml.c7g.xlarge'|'ml.c7g.2xlarge'|'ml.c7g.4xlarge'|'ml.c7g.8xlarge'|'ml.c7g.12xlarge'|'ml.c7g.16xlarge'|'ml.m6g.large'|'ml.m6g.xlarge'|'ml.m6g.2xlarge'|'ml.m6g.4xlarge'|'ml.m6g.8xlarge'|'ml.m6g.12xlarge'|'ml.m6g.16xlarge'|'ml.m6gd.large'|'ml.m6gd.xlarge'|'ml.m6gd.2xlarge'|'ml.m6gd.4xlarge'|'ml.m6gd.8xlarge'|'ml.m6gd.12xlarge'|'ml.m6gd.16xlarge'|'ml.c6g.large'|'ml.c6g.xlarge'|'ml.c6g.2xlarge'|'ml.c6g.4xlarge'|'ml.c6g.8xlarge'|'ml.c6g.12xlarge'|'ml.c6g.16xlarge'|'ml.c6gd.large'|'ml.c6gd.xlarge'|'ml.c6gd.2xlarge'|'ml.c6gd.4xlarge'|'ml.c6gd.8xlarge'|'ml.c6gd.12xlarge'|'ml.c6gd.16xlarge'|'ml.c6gn.large'|'ml.c6gn.xlarge'|'ml.c6gn.2xlarge'|'ml.c6gn.4xlarge'|'ml.c6gn.8xlarge'|'ml.c6gn.12xlarge'|'ml.c6gn.16xlarge'|'ml.r6g.large'|'ml.r6g.xlarge'|'ml.r6g.2xlarge'|'ml.r6g.4xlarge'|'ml.r6g.8xlarge'|'ml.r6g.12xlarge'|'ml.r6g.16xlarge'|'ml.r6gd.large'|'ml.r6gd.xlarge'|'ml.r6gd.2xlarge'|'ml.r6gd.4xlarge'|'ml.r6gd.8xlarge'|'ml.r6gd.12xlarge'|'ml.r6gd.16xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.trn2.48xlarge'|'ml.inf2.xlarge'|'ml.inf2.8xlarge'|'ml.inf2.24xlarge'|'ml.inf2.48xlarge'|'ml.p5.48xlarge'|'ml.p5e.48xlarge'|'ml.p5en.48xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge'|'ml.c8g.medium'|'ml.c8g.large'|'ml.c8g.xlarge'|'ml.c8g.2xlarge'|'ml.c8g.4xlarge'|'ml.c8g.8xlarge'|'ml.c8g.12xlarge'|'ml.c8g.16xlarge'|'ml.c8g.24xlarge'|'ml.c8g.48xlarge'|'ml.r7gd.medium'|'ml.r7gd.large'|'ml.r7gd.xlarge'|'ml.r7gd.2xlarge'|'ml.r7gd.4xlarge'|'ml.r7gd.8xlarge'|'ml.r7gd.12xlarge'|'ml.r7gd.16xlarge'|'ml.m8g.medium'|'ml.m8g.large'|'ml.m8g.xlarge'|'ml.m8g.2xlarge'|'ml.m8g.4xlarge'|'ml.m8g.8xlarge'|'ml.m8g.12xlarge'|'ml.m8g.16xlarge'|'ml.m8g.24xlarge'|'ml.m8g.48xlarge'|'ml.c6in.large'|'ml.c6in.xlarge'|'ml.c6in.2xlarge'|'ml.c6in.4xlarge'|'ml.c6in.8xlarge'|'ml.c6in.12xlarge'|'ml.c6in.16xlarge'|'ml.c6in.24xlarge'|'ml.c6in.32xlarge'|'ml.p6-b200.48xlarge'|'ml.p6-b300.48xlarge'|'ml.p6e-gb200.36xlarge'|'ml.p5.4xlarge',
'InstanceCount': 123
}
}
},
],
DataStorageConfig={
'Destination': 'string',
'KmsKey': 'string',
'ContentType': {
'CsvContentTypes': [
'string',
],
'JsonContentTypes': [
'string',
]
}
},
ShadowModeConfig={
'SourceModelVariantName': 'string',
'ShadowModelVariants': [
{
'ShadowModelVariantName': 'string',
'SamplingPercentage': 123
},
]
},
KmsKey='string',
Tags=[
{
'Key': 'string',
'Value': 'string'
},
]
)
string
[REQUIRED]
The name for the inference experiment.
string
[REQUIRED]
The type of the inference experiment that you want to run. The following types of experiments are possible:
ShadowMode: You can use this type to validate a shadow variant. For more information, see Shadow tests.
dict
The duration for which you want the inference experiment to run. If you don't specify this field, the experiment automatically starts immediately upon creation and concludes after 7 days.
StartTime (datetime) --
The timestamp at which the inference experiment started or will start.
EndTime (datetime) --
The timestamp at which the inference experiment ended or will end.
string
A description for the inference experiment.
string
[REQUIRED]
The ARN of the IAM role that Amazon SageMaker can assume to access model artifacts and container images, and manage Amazon SageMaker Inference endpoints for model deployment.
string
[REQUIRED]
The name of the Amazon SageMaker endpoint on which you want to run the inference experiment.
list
[REQUIRED]
An array of ModelVariantConfig objects. There is one for each variant in the inference experiment. Each ModelVariantConfig object in the array describes the infrastructure configuration for the corresponding variant.
(dict) --
Contains information about the deployment options of a model.
ModelName (string) -- [REQUIRED]
The name of the Amazon SageMaker Model entity.
VariantName (string) -- [REQUIRED]
The name of the variant.
InfrastructureConfig (dict) -- [REQUIRED]
The configuration for the infrastructure that the model will be deployed to.
InfrastructureType (string) -- [REQUIRED]
The inference option to which to deploy your model. Possible values are the following:
RealTime: Deploy to real-time inference.
RealTimeInferenceConfig (dict) -- [REQUIRED]
The infrastructure configuration for deploying the model to real-time inference.
InstanceType (string) -- [REQUIRED]
The instance type the model is deployed to.
InstanceCount (integer) -- [REQUIRED]
The number of instances of the type specified by InstanceType.
dict
The Amazon S3 location and configuration for storing inference request and response data.
This is an optional parameter that you can use for data capture. For more information, see Capture data.
Destination (string) -- [REQUIRED]
The Amazon S3 bucket where the inference request and response data is stored.
KmsKey (string) --
The Amazon Web Services Key Management Service key that Amazon SageMaker uses to encrypt captured data at rest using Amazon S3 server-side encryption.
ContentType (dict) --
Configuration specifying how to treat different headers. If no headers are specified Amazon SageMaker AI will by default base64 encode when capturing the data.
CsvContentTypes (list) --
The list of all content type headers that Amazon SageMaker AI will treat as CSV and capture accordingly.
(string) --
JsonContentTypes (list) --
The list of all content type headers that SageMaker AI will treat as JSON and capture accordingly.
(string) --
dict
[REQUIRED]
The configuration of ShadowMode inference experiment type. Use this field to specify a production variant which takes all the inference requests, and a shadow variant to which Amazon SageMaker replicates a percentage of the inference requests. For the shadow variant also specify the percentage of requests that Amazon SageMaker replicates.
SourceModelVariantName (string) -- [REQUIRED]
The name of the production variant, which takes all the inference requests.
ShadowModelVariants (list) -- [REQUIRED]
List of shadow variant configurations.
(dict) --
The name and sampling percentage of a shadow variant.
ShadowModelVariantName (string) -- [REQUIRED]
The name of the shadow variant.
SamplingPercentage (integer) -- [REQUIRED]
The percentage of inference requests that Amazon SageMaker replicates from the production variant to the shadow variant.
string
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance that hosts the endpoint. The KmsKey can be any of the following formats:
KMS key ID "1234abcd-12ab-34cd-56ef-1234567890ab"
Amazon Resource Name (ARN) of a KMS key "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
KMS key Alias "alias/ExampleAlias"
Amazon Resource Name (ARN) of a KMS key Alias "arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias"
If you use a KMS key ID or an alias of your KMS key, the Amazon SageMaker execution role must include permissions to call kms:Encrypt. If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account. Amazon SageMaker uses server-side encryption with KMS managed keys for OutputDataConfig. If you use a bucket policy with an s3:PutObject permission that only allows objects with server-side encryption, set the condition key of s3:x-amz-server-side-encryption to "aws:kms". For more information, see KMS managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.
The KMS key policy must grant permission to the IAM role that you specify in your CreateEndpoint and UpdateEndpoint requests. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide.
list
Array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging your Amazon Web Services Resources.
(dict) --
A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags.
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources. For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy.
Key (string) -- [REQUIRED]
The tag key. Tag keys must be unique per resource.
Value (string) -- [REQUIRED]
The tag value.
dict
Response Syntax
{
'InferenceExperimentArn': 'string'
}
Response Structure
(dict) --
InferenceExperimentArn (string) --
The ARN for your inference experiment.
{'ManagedStorageType': 'Restricted'}
Creates a model package that you can use to create SageMaker models or list on Amazon Web Services Marketplace, or a versioned model that is part of a model group. Buyers can subscribe to model packages listed on Amazon Web Services Marketplace to create models in SageMaker.
To create a model package by specifying a Docker container that contains your inference code and the Amazon S3 location of your model artifacts, provide values for InferenceSpecification. To create a model from an algorithm resource that you created or subscribed to in Amazon Web Services Marketplace, provide a value for SourceAlgorithmSpecification.
See also: AWS API Documentation
Request Syntax
client.create_model_package(
ModelPackageName='string',
ModelPackageGroupName='string',
ModelPackageDescription='string',
ModelPackageRegistrationType='Logged'|'Registered',
InferenceSpecification={
'Containers': [
{
'ContainerHostname': 'string',
'Image': 'string',
'ImageDigest': 'string',
'ModelDataUrl': 'string',
'ModelDataSource': {
'S3DataSource': {
'S3Uri': 'string',
'S3DataType': 'S3Prefix'|'S3Object',
'CompressionType': 'None'|'Gzip',
'ModelAccessConfig': {
'AcceptEula': True|False
},
'HubAccessConfig': {
'HubContentArn': 'string'
},
'ManifestS3Uri': 'string',
'ETag': 'string',
'ManifestEtag': 'string'
}
},
'ProductId': 'string',
'Environment': {
'string': 'string'
},
'ModelInput': {
'DataInputConfig': 'string'
},
'Framework': 'string',
'FrameworkVersion': 'string',
'NearestModelName': 'string',
'AdditionalModelDataSources': [
{
'ChannelName': 'string',
'S3DataSource': {
'S3Uri': 'string',
'S3DataType': 'S3Prefix'|'S3Object',
'CompressionType': 'None'|'Gzip',
'ModelAccessConfig': {
'AcceptEula': True|False
},
'HubAccessConfig': {
'HubContentArn': 'string'
},
'ManifestS3Uri': 'string',
'ETag': 'string',
'ManifestEtag': 'string'
}
},
],
'AdditionalS3DataSource': {
'S3DataType': 'S3Object'|'S3Prefix',
'S3Uri': 'string',
'CompressionType': 'None'|'Gzip',
'ETag': 'string'
},
'ModelDataETag': 'string',
'IsCheckpoint': True|False,
'BaseModel': {
'HubContentName': 'string',
'HubContentVersion': 'string',
'RecipeName': 'string'
}
},
],
'SupportedTransformInstanceTypes': [
'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.r6i.large'|'ml.r6i.xlarge'|'ml.r6i.2xlarge'|'ml.r6i.4xlarge'|'ml.r6i.8xlarge'|'ml.r6i.12xlarge'|'ml.r6i.16xlarge'|'ml.r6i.24xlarge'|'ml.r6i.32xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.12xlarge'|'ml.g5.16xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.inf2.xlarge'|'ml.inf2.8xlarge'|'ml.inf2.24xlarge'|'ml.inf2.48xlarge'|'ml.g6.xlarge'|'ml.g6.2xlarge'|'ml.g6.4xlarge'|'ml.g6.8xlarge'|'ml.g6.12xlarge'|'ml.g6.16xlarge'|'ml.g6.24xlarge'|'ml.g6.48xlarge',
],
'SupportedRealtimeInferenceInstanceTypes': [
'ml.t2.medium'|'ml.t2.large'|'ml.t2.xlarge'|'ml.t2.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.12xlarge'|'ml.m5d.24xlarge'|'ml.c4.large'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5d.large'|'ml.c5d.xlarge'|'ml.c5d.2xlarge'|'ml.c5d.4xlarge'|'ml.c5d.9xlarge'|'ml.c5d.18xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.12xlarge'|'ml.r5.24xlarge'|'ml.r5d.large'|'ml.r5d.xlarge'|'ml.r5d.2xlarge'|'ml.r5d.4xlarge'|'ml.r5d.12xlarge'|'ml.r5d.24xlarge'|'ml.inf1.xlarge'|'ml.inf1.2xlarge'|'ml.inf1.6xlarge'|'ml.inf1.24xlarge'|'ml.dl1.24xlarge'|'ml.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.r6i.large'|'ml.r6i.xlarge'|'ml.r6i.2xlarge'|'ml.r6i.4xlarge'|'ml.r6i.8xlarge'|'ml.r6i.12xlarge'|'ml.r6i.16xlarge'|'ml.r6i.24xlarge'|'ml.r6i.32xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.12xlarge'|'ml.g5.16xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.g6.xlarge'|'ml.g6.2xlarge'|'ml.g6.4xlarge'|'ml.g6.8xlarge'|'ml.g6.12xlarge'|'ml.g6.16xlarge'|'ml.g6.24xlarge'|'ml.g6.48xlarge'|'ml.r8g.medium'|'ml.r8g.large'|'ml.r8g.xlarge'|'ml.r8g.2xlarge'|'ml.r8g.4xlarge'|'ml.r8g.8xlarge'|'ml.r8g.12xlarge'|'ml.r8g.16xlarge'|'ml.r8g.24xlarge'|'ml.r8g.48xlarge'|'ml.g6e.xlarge'|'ml.g6e.2xlarge'|'ml.g6e.4xlarge'|'ml.g6e.8xlarge'|'ml.g6e.12xlarge'|'ml.g6e.16xlarge'|'ml.g6e.24xlarge'|'ml.g6e.48xlarge'|'ml.g7e.2xlarge'|'ml.g7e.4xlarge'|'ml.g7e.8xlarge'|'ml.g7e.12xlarge'|'ml.g7e.24xlarge'|'ml.g7e.48xlarge'|'ml.p4d.24xlarge'|'ml.c7g.large'|'ml.c7g.xlarge'|'ml.c7g.2xlarge'|'ml.c7g.4xlarge'|'ml.c7g.8xlarge'|'ml.c7g.12xlarge'|'ml.c7g.16xlarge'|'ml.m6g.large'|'ml.m6g.xlarge'|'ml.m6g.2xlarge'|'ml.m6g.4xlarge'|'ml.m6g.8xlarge'|'ml.m6g.12xlarge'|'ml.m6g.16xlarge'|'ml.m6gd.large'|'ml.m6gd.xlarge'|'ml.m6gd.2xlarge'|'ml.m6gd.4xlarge'|'ml.m6gd.8xlarge'|'ml.m6gd.12xlarge'|'ml.m6gd.16xlarge'|'ml.c6g.large'|'ml.c6g.xlarge'|'ml.c6g.2xlarge'|'ml.c6g.4xlarge'|'ml.c6g.8xlarge'|'ml.c6g.12xlarge'|'ml.c6g.16xlarge'|'ml.c6gd.large'|'ml.c6gd.xlarge'|'ml.c6gd.2xlarge'|'ml.c6gd.4xlarge'|'ml.c6gd.8xlarge'|'ml.c6gd.12xlarge'|'ml.c6gd.16xlarge'|'ml.c6gn.large'|'ml.c6gn.xlarge'|'ml.c6gn.2xlarge'|'ml.c6gn.4xlarge'|'ml.c6gn.8xlarge'|'ml.c6gn.12xlarge'|'ml.c6gn.16xlarge'|'ml.r6g.large'|'ml.r6g.xlarge'|'ml.r6g.2xlarge'|'ml.r6g.4xlarge'|'ml.r6g.8xlarge'|'ml.r6g.12xlarge'|'ml.r6g.16xlarge'|'ml.r6gd.large'|'ml.r6gd.xlarge'|'ml.r6gd.2xlarge'|'ml.r6gd.4xlarge'|'ml.r6gd.8xlarge'|'ml.r6gd.12xlarge'|'ml.r6gd.16xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.trn2.48xlarge'|'ml.inf2.xlarge'|'ml.inf2.8xlarge'|'ml.inf2.24xlarge'|'ml.inf2.48xlarge'|'ml.p5.48xlarge'|'ml.p5e.48xlarge'|'ml.p5en.48xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge'|'ml.c8g.medium'|'ml.c8g.large'|'ml.c8g.xlarge'|'ml.c8g.2xlarge'|'ml.c8g.4xlarge'|'ml.c8g.8xlarge'|'ml.c8g.12xlarge'|'ml.c8g.16xlarge'|'ml.c8g.24xlarge'|'ml.c8g.48xlarge'|'ml.r7gd.medium'|'ml.r7gd.large'|'ml.r7gd.xlarge'|'ml.r7gd.2xlarge'|'ml.r7gd.4xlarge'|'ml.r7gd.8xlarge'|'ml.r7gd.12xlarge'|'ml.r7gd.16xlarge'|'ml.m8g.medium'|'ml.m8g.large'|'ml.m8g.xlarge'|'ml.m8g.2xlarge'|'ml.m8g.4xlarge'|'ml.m8g.8xlarge'|'ml.m8g.12xlarge'|'ml.m8g.16xlarge'|'ml.m8g.24xlarge'|'ml.m8g.48xlarge'|'ml.c6in.large'|'ml.c6in.xlarge'|'ml.c6in.2xlarge'|'ml.c6in.4xlarge'|'ml.c6in.8xlarge'|'ml.c6in.12xlarge'|'ml.c6in.16xlarge'|'ml.c6in.24xlarge'|'ml.c6in.32xlarge'|'ml.p6-b200.48xlarge'|'ml.p6-b300.48xlarge'|'ml.p6e-gb200.36xlarge'|'ml.p5.4xlarge',
],
'SupportedContentTypes': [
'string',
],
'SupportedResponseMIMETypes': [
'string',
]
},
ValidationSpecification={
'ValidationRole': 'string',
'ValidationProfiles': [
{
'ProfileName': 'string',
'TransformJobDefinition': {
'MaxConcurrentTransforms': 123,
'MaxPayloadInMB': 123,
'BatchStrategy': 'MultiRecord'|'SingleRecord',
'Environment': {
'string': 'string'
},
'TransformInput': {
'DataSource': {
'S3DataSource': {
'S3DataType': 'ManifestFile'|'S3Prefix'|'AugmentedManifestFile'|'Converse',
'S3Uri': 'string'
}
},
'ContentType': 'string',
'CompressionType': 'None'|'Gzip',
'SplitType': 'None'|'Line'|'RecordIO'|'TFRecord'
},
'TransformOutput': {
'S3OutputPath': 'string',
'Accept': 'string',
'AssembleWith': 'None'|'Line',
'KmsKeyId': 'string'
},
'TransformResources': {
'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.r6i.large'|'ml.r6i.xlarge'|'ml.r6i.2xlarge'|'ml.r6i.4xlarge'|'ml.r6i.8xlarge'|'ml.r6i.12xlarge'|'ml.r6i.16xlarge'|'ml.r6i.24xlarge'|'ml.r6i.32xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.12xlarge'|'ml.g5.16xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.inf2.xlarge'|'ml.inf2.8xlarge'|'ml.inf2.24xlarge'|'ml.inf2.48xlarge'|'ml.g6.xlarge'|'ml.g6.2xlarge'|'ml.g6.4xlarge'|'ml.g6.8xlarge'|'ml.g6.12xlarge'|'ml.g6.16xlarge'|'ml.g6.24xlarge'|'ml.g6.48xlarge',
'InstanceCount': 123,
'VolumeKmsKeyId': 'string',
'TransformAmiVersion': 'string'
}
}
},
]
},
SourceAlgorithmSpecification={
'SourceAlgorithms': [
{
'ModelDataUrl': 'string',
'ModelDataSource': {
'S3DataSource': {
'S3Uri': 'string',
'S3DataType': 'S3Prefix'|'S3Object',
'CompressionType': 'None'|'Gzip',
'ModelAccessConfig': {
'AcceptEula': True|False
},
'HubAccessConfig': {
'HubContentArn': 'string'
},
'ManifestS3Uri': 'string',
'ETag': 'string',
'ManifestEtag': 'string'
}
},
'ModelDataETag': 'string',
'AlgorithmName': 'string'
},
]
},
CertifyForMarketplace=True|False,
Tags=[
{
'Key': 'string',
'Value': 'string'
},
],
ModelApprovalStatus='Approved'|'Rejected'|'PendingManualApproval',
MetadataProperties={
'CommitId': 'string',
'Repository': 'string',
'GeneratedBy': 'string',
'ProjectId': 'string'
},
ModelMetrics={
'ModelQuality': {
'Statistics': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
},
'Constraints': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
}
},
'ModelDataQuality': {
'Statistics': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
},
'Constraints': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
}
},
'Bias': {
'Report': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
},
'PreTrainingReport': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
},
'PostTrainingReport': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
}
},
'Explainability': {
'Report': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
}
}
},
ClientToken='string',
Domain='string',
Task='string',
SamplePayloadUrl='string',
CustomerMetadataProperties={
'string': 'string'
},
DriftCheckBaselines={
'Bias': {
'ConfigFile': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
},
'PreTrainingConstraints': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
},
'PostTrainingConstraints': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
}
},
'Explainability': {
'Constraints': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
},
'ConfigFile': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
}
},
'ModelQuality': {
'Statistics': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
},
'Constraints': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
}
},
'ModelDataQuality': {
'Statistics': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
},
'Constraints': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
}
}
},
AdditionalInferenceSpecifications=[
{
'Name': 'string',
'Description': 'string',
'Containers': [
{
'ContainerHostname': 'string',
'Image': 'string',
'ImageDigest': 'string',
'ModelDataUrl': 'string',
'ModelDataSource': {
'S3DataSource': {
'S3Uri': 'string',
'S3DataType': 'S3Prefix'|'S3Object',
'CompressionType': 'None'|'Gzip',
'ModelAccessConfig': {
'AcceptEula': True|False
},
'HubAccessConfig': {
'HubContentArn': 'string'
},
'ManifestS3Uri': 'string',
'ETag': 'string',
'ManifestEtag': 'string'
}
},
'ProductId': 'string',
'Environment': {
'string': 'string'
},
'ModelInput': {
'DataInputConfig': 'string'
},
'Framework': 'string',
'FrameworkVersion': 'string',
'NearestModelName': 'string',
'AdditionalModelDataSources': [
{
'ChannelName': 'string',
'S3DataSource': {
'S3Uri': 'string',
'S3DataType': 'S3Prefix'|'S3Object',
'CompressionType': 'None'|'Gzip',
'ModelAccessConfig': {
'AcceptEula': True|False
},
'HubAccessConfig': {
'HubContentArn': 'string'
},
'ManifestS3Uri': 'string',
'ETag': 'string',
'ManifestEtag': 'string'
}
},
],
'AdditionalS3DataSource': {
'S3DataType': 'S3Object'|'S3Prefix',
'S3Uri': 'string',
'CompressionType': 'None'|'Gzip',
'ETag': 'string'
},
'ModelDataETag': 'string',
'IsCheckpoint': True|False,
'BaseModel': {
'HubContentName': 'string',
'HubContentVersion': 'string',
'RecipeName': 'string'
}
},
],
'SupportedTransformInstanceTypes': [
'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.r6i.large'|'ml.r6i.xlarge'|'ml.r6i.2xlarge'|'ml.r6i.4xlarge'|'ml.r6i.8xlarge'|'ml.r6i.12xlarge'|'ml.r6i.16xlarge'|'ml.r6i.24xlarge'|'ml.r6i.32xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.12xlarge'|'ml.g5.16xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.inf2.xlarge'|'ml.inf2.8xlarge'|'ml.inf2.24xlarge'|'ml.inf2.48xlarge'|'ml.g6.xlarge'|'ml.g6.2xlarge'|'ml.g6.4xlarge'|'ml.g6.8xlarge'|'ml.g6.12xlarge'|'ml.g6.16xlarge'|'ml.g6.24xlarge'|'ml.g6.48xlarge',
],
'SupportedRealtimeInferenceInstanceTypes': [
'ml.t2.medium'|'ml.t2.large'|'ml.t2.xlarge'|'ml.t2.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.12xlarge'|'ml.m5d.24xlarge'|'ml.c4.large'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5d.large'|'ml.c5d.xlarge'|'ml.c5d.2xlarge'|'ml.c5d.4xlarge'|'ml.c5d.9xlarge'|'ml.c5d.18xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.12xlarge'|'ml.r5.24xlarge'|'ml.r5d.large'|'ml.r5d.xlarge'|'ml.r5d.2xlarge'|'ml.r5d.4xlarge'|'ml.r5d.12xlarge'|'ml.r5d.24xlarge'|'ml.inf1.xlarge'|'ml.inf1.2xlarge'|'ml.inf1.6xlarge'|'ml.inf1.24xlarge'|'ml.dl1.24xlarge'|'ml.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.r6i.large'|'ml.r6i.xlarge'|'ml.r6i.2xlarge'|'ml.r6i.4xlarge'|'ml.r6i.8xlarge'|'ml.r6i.12xlarge'|'ml.r6i.16xlarge'|'ml.r6i.24xlarge'|'ml.r6i.32xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.12xlarge'|'ml.g5.16xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.g6.xlarge'|'ml.g6.2xlarge'|'ml.g6.4xlarge'|'ml.g6.8xlarge'|'ml.g6.12xlarge'|'ml.g6.16xlarge'|'ml.g6.24xlarge'|'ml.g6.48xlarge'|'ml.r8g.medium'|'ml.r8g.large'|'ml.r8g.xlarge'|'ml.r8g.2xlarge'|'ml.r8g.4xlarge'|'ml.r8g.8xlarge'|'ml.r8g.12xlarge'|'ml.r8g.16xlarge'|'ml.r8g.24xlarge'|'ml.r8g.48xlarge'|'ml.g6e.xlarge'|'ml.g6e.2xlarge'|'ml.g6e.4xlarge'|'ml.g6e.8xlarge'|'ml.g6e.12xlarge'|'ml.g6e.16xlarge'|'ml.g6e.24xlarge'|'ml.g6e.48xlarge'|'ml.g7e.2xlarge'|'ml.g7e.4xlarge'|'ml.g7e.8xlarge'|'ml.g7e.12xlarge'|'ml.g7e.24xlarge'|'ml.g7e.48xlarge'|'ml.p4d.24xlarge'|'ml.c7g.large'|'ml.c7g.xlarge'|'ml.c7g.2xlarge'|'ml.c7g.4xlarge'|'ml.c7g.8xlarge'|'ml.c7g.12xlarge'|'ml.c7g.16xlarge'|'ml.m6g.large'|'ml.m6g.xlarge'|'ml.m6g.2xlarge'|'ml.m6g.4xlarge'|'ml.m6g.8xlarge'|'ml.m6g.12xlarge'|'ml.m6g.16xlarge'|'ml.m6gd.large'|'ml.m6gd.xlarge'|'ml.m6gd.2xlarge'|'ml.m6gd.4xlarge'|'ml.m6gd.8xlarge'|'ml.m6gd.12xlarge'|'ml.m6gd.16xlarge'|'ml.c6g.large'|'ml.c6g.xlarge'|'ml.c6g.2xlarge'|'ml.c6g.4xlarge'|'ml.c6g.8xlarge'|'ml.c6g.12xlarge'|'ml.c6g.16xlarge'|'ml.c6gd.large'|'ml.c6gd.xlarge'|'ml.c6gd.2xlarge'|'ml.c6gd.4xlarge'|'ml.c6gd.8xlarge'|'ml.c6gd.12xlarge'|'ml.c6gd.16xlarge'|'ml.c6gn.large'|'ml.c6gn.xlarge'|'ml.c6gn.2xlarge'|'ml.c6gn.4xlarge'|'ml.c6gn.8xlarge'|'ml.c6gn.12xlarge'|'ml.c6gn.16xlarge'|'ml.r6g.large'|'ml.r6g.xlarge'|'ml.r6g.2xlarge'|'ml.r6g.4xlarge'|'ml.r6g.8xlarge'|'ml.r6g.12xlarge'|'ml.r6g.16xlarge'|'ml.r6gd.large'|'ml.r6gd.xlarge'|'ml.r6gd.2xlarge'|'ml.r6gd.4xlarge'|'ml.r6gd.8xlarge'|'ml.r6gd.12xlarge'|'ml.r6gd.16xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.trn2.48xlarge'|'ml.inf2.xlarge'|'ml.inf2.8xlarge'|'ml.inf2.24xlarge'|'ml.inf2.48xlarge'|'ml.p5.48xlarge'|'ml.p5e.48xlarge'|'ml.p5en.48xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge'|'ml.c8g.medium'|'ml.c8g.large'|'ml.c8g.xlarge'|'ml.c8g.2xlarge'|'ml.c8g.4xlarge'|'ml.c8g.8xlarge'|'ml.c8g.12xlarge'|'ml.c8g.16xlarge'|'ml.c8g.24xlarge'|'ml.c8g.48xlarge'|'ml.r7gd.medium'|'ml.r7gd.large'|'ml.r7gd.xlarge'|'ml.r7gd.2xlarge'|'ml.r7gd.4xlarge'|'ml.r7gd.8xlarge'|'ml.r7gd.12xlarge'|'ml.r7gd.16xlarge'|'ml.m8g.medium'|'ml.m8g.large'|'ml.m8g.xlarge'|'ml.m8g.2xlarge'|'ml.m8g.4xlarge'|'ml.m8g.8xlarge'|'ml.m8g.12xlarge'|'ml.m8g.16xlarge'|'ml.m8g.24xlarge'|'ml.m8g.48xlarge'|'ml.c6in.large'|'ml.c6in.xlarge'|'ml.c6in.2xlarge'|'ml.c6in.4xlarge'|'ml.c6in.8xlarge'|'ml.c6in.12xlarge'|'ml.c6in.16xlarge'|'ml.c6in.24xlarge'|'ml.c6in.32xlarge'|'ml.p6-b200.48xlarge'|'ml.p6-b300.48xlarge'|'ml.p6e-gb200.36xlarge'|'ml.p5.4xlarge',
],
'SupportedContentTypes': [
'string',
],
'SupportedResponseMIMETypes': [
'string',
]
},
],
SkipModelValidation='All'|'None',
SourceUri='string',
SecurityConfig={
'KmsKeyId': 'string'
},
ModelCard={
'ModelCardContent': 'string',
'ModelCardStatus': 'Draft'|'PendingReview'|'Approved'|'Archived'
},
ModelLifeCycle={
'Stage': 'string',
'StageStatus': 'string',
'StageDescription': 'string'
},
ManagedStorageType='Restricted'
)
string
The name of the model package. The name must have 1 to 63 characters. Valid characters are a-z, A-Z, 0-9, and - (hyphen).
This parameter is required for unversioned models. It is not applicable to versioned models.
string
The name or Amazon Resource Name (ARN) of the model package group that this model version belongs to.
This parameter is required for versioned models, and does not apply to unversioned models.
string
A description of the model package.
string
The package registration type of the model package input.
dict
Specifies details about inference jobs that you can run with models based on this model package, including the following information:
The Amazon ECR paths of containers that contain the inference code and model artifacts.
The instance types that the model package supports for transform jobs and real-time endpoints used for inference.
The input and output content formats that the model package supports for inference.
Containers (list) -- [REQUIRED]
The Amazon ECR registry path of the Docker image that contains the inference code.
(dict) --
Describes the Docker container for the model package.
ContainerHostname (string) --
The DNS host name for the Docker container.
Image (string) --
The Amazon Elastic Container Registry (Amazon ECR) path where inference code is stored.
If you are using your own custom algorithm instead of an algorithm provided by SageMaker, the inference code must meet SageMaker requirements. SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker.
ImageDigest (string) --
An MD5 hash of the training algorithm that identifies the Docker image used for training.
ModelDataUrl (string) --
The Amazon S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive ( .tar.gz suffix).
ModelDataSource (dict) --
Specifies the location of ML model data to deploy during endpoint creation.
S3DataSource (dict) --
Specifies the S3 location of ML model data to deploy.
S3Uri (string) -- [REQUIRED]
Specifies the S3 path of ML model data to deploy.
S3DataType (string) -- [REQUIRED]
Specifies the type of ML model data to deploy.
If you choose S3Prefix, S3Uri identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix as part of the ML model data to deploy. A valid key name prefix identified by S3Uri always ends with a forward slash (/).
If you choose S3Object, S3Uri identifies an object that is the ML model data to deploy.
CompressionType (string) -- [REQUIRED]
Specifies how the ML model data is prepared.
If you choose Gzip and choose S3Object as the value of S3DataType, S3Uri identifies an object that is a gzip-compressed TAR archive. SageMaker will attempt to decompress and untar the object during model deployment.
If you choose None and chooose S3Object as the value of S3DataType, S3Uri identifies an object that represents an uncompressed ML model to deploy.
If you choose None and choose S3Prefix as the value of S3DataType, S3Uri identifies a key name prefix, under which all objects represents the uncompressed ML model to deploy.
If you choose None, then SageMaker will follow rules below when creating model data files under /opt/ml/model directory for use by your inference code:
If you choose S3Object as the value of S3DataType, then SageMaker will split the key of the S3 object referenced by S3Uri by slash (/), and use the last part as the filename of the file holding the content of the S3 object.
If you choose S3Prefix as the value of S3DataType, then for each S3 object under the key name pefix referenced by S3Uri, SageMaker will trim its key by the prefix, and use the remainder as the path (relative to /opt/ml/model) of the file holding the content of the S3 object. SageMaker will split the remainder by slash (/), using intermediate parts as directory names and the last part as filename of the file holding the content of the S3 object.
Do not use any of the following as file names or directory names:
An empty or blank string
A string which contains null bytes
A string longer than 255 bytes
A single dot ( .)
A double dot ( ..)
Ambiguous file names will result in model deployment failure. For example, if your uncompressed ML model consists of two S3 objects s3://mybucket/model/weights and s3://mybucket/model/weights/part1 and you specify s3://mybucket/model/ as the value of S3Uri and S3Prefix as the value of S3DataType, then it will result in name clash between /opt/ml/model/weights (a regular file) and /opt/ml/model/weights/ (a directory).
Do not organize the model artifacts in S3 console using folders. When you create a folder in S3 console, S3 creates a 0-byte object with a key set to the folder name you provide. They key of the 0-byte object ends with a slash (/) which violates SageMaker restrictions on model artifact file names, leading to model deployment failure.
ModelAccessConfig (dict) --
Specifies the access configuration file for the ML model. You can explicitly accept the model end-user license agreement (EULA) within the ModelAccessConfig. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.
AcceptEula (boolean) -- [REQUIRED]
Specifies agreement to the model end-user license agreement (EULA). The AcceptEula value must be explicitly defined as True in order to accept the EULA that this model requires. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.
HubAccessConfig (dict) --
Configuration information for hub access.
HubContentArn (string) -- [REQUIRED]
The ARN of the hub content for which deployment access is allowed.
ManifestS3Uri (string) --
The Amazon S3 URI of the manifest file. The manifest file is a CSV file that stores the artifact locations.
ETag (string) --
The ETag associated with S3 URI.
ManifestEtag (string) --
The ETag associated with Manifest S3 URI.
ProductId (string) --
The Amazon Web Services Marketplace product ID of the model package.
Environment (dict) --
The environment variables to set in the Docker container. Each key and value in the Environment string to string map can have length of up to 1024. We support up to 16 entries in the map.
(string) --
(string) --
ModelInput (dict) --
A structure with Model Input details.
DataInputConfig (string) -- [REQUIRED]
The input configuration object for the model.
Framework (string) --
The machine learning framework of the model package container image.
FrameworkVersion (string) --
The framework version of the Model Package Container Image.
NearestModelName (string) --
The name of a pre-trained machine learning benchmarked by Amazon SageMaker Inference Recommender model that matches your model. You can find a list of benchmarked models by calling ListModelMetadata.
AdditionalModelDataSources (list) --
Data sources that are available to your model in addition to the one that you specify for ModelDataSource when you use the CreateModelPackage action.
(dict) --
Data sources that are available to your model in addition to the one that you specify for ModelDataSource when you use the CreateModel action.
ChannelName (string) -- [REQUIRED]
A custom name for this AdditionalModelDataSource object.
S3DataSource (dict) -- [REQUIRED]
Specifies the S3 location of ML model data to deploy.
S3Uri (string) -- [REQUIRED]
Specifies the S3 path of ML model data to deploy.
S3DataType (string) -- [REQUIRED]
Specifies the type of ML model data to deploy.
If you choose S3Prefix, S3Uri identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix as part of the ML model data to deploy. A valid key name prefix identified by S3Uri always ends with a forward slash (/).
If you choose S3Object, S3Uri identifies an object that is the ML model data to deploy.
CompressionType (string) -- [REQUIRED]
Specifies how the ML model data is prepared.
If you choose Gzip and choose S3Object as the value of S3DataType, S3Uri identifies an object that is a gzip-compressed TAR archive. SageMaker will attempt to decompress and untar the object during model deployment.
If you choose None and chooose S3Object as the value of S3DataType, S3Uri identifies an object that represents an uncompressed ML model to deploy.
If you choose None and choose S3Prefix as the value of S3DataType, S3Uri identifies a key name prefix, under which all objects represents the uncompressed ML model to deploy.
If you choose None, then SageMaker will follow rules below when creating model data files under /opt/ml/model directory for use by your inference code:
If you choose S3Object as the value of S3DataType, then SageMaker will split the key of the S3 object referenced by S3Uri by slash (/), and use the last part as the filename of the file holding the content of the S3 object.
If you choose S3Prefix as the value of S3DataType, then for each S3 object under the key name pefix referenced by S3Uri, SageMaker will trim its key by the prefix, and use the remainder as the path (relative to /opt/ml/model) of the file holding the content of the S3 object. SageMaker will split the remainder by slash (/), using intermediate parts as directory names and the last part as filename of the file holding the content of the S3 object.
Do not use any of the following as file names or directory names:
An empty or blank string
A string which contains null bytes
A string longer than 255 bytes
A single dot ( .)
A double dot ( ..)
Ambiguous file names will result in model deployment failure. For example, if your uncompressed ML model consists of two S3 objects s3://mybucket/model/weights and s3://mybucket/model/weights/part1 and you specify s3://mybucket/model/ as the value of S3Uri and S3Prefix as the value of S3DataType, then it will result in name clash between /opt/ml/model/weights (a regular file) and /opt/ml/model/weights/ (a directory).
Do not organize the model artifacts in S3 console using folders. When you create a folder in S3 console, S3 creates a 0-byte object with a key set to the folder name you provide. They key of the 0-byte object ends with a slash (/) which violates SageMaker restrictions on model artifact file names, leading to model deployment failure.
ModelAccessConfig (dict) --
Specifies the access configuration file for the ML model. You can explicitly accept the model end-user license agreement (EULA) within the ModelAccessConfig. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.
AcceptEula (boolean) -- [REQUIRED]
Specifies agreement to the model end-user license agreement (EULA). The AcceptEula value must be explicitly defined as True in order to accept the EULA that this model requires. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.
HubAccessConfig (dict) --
Configuration information for hub access.
HubContentArn (string) -- [REQUIRED]
The ARN of the hub content for which deployment access is allowed.
ManifestS3Uri (string) --
The Amazon S3 URI of the manifest file. The manifest file is a CSV file that stores the artifact locations.
ETag (string) --
The ETag associated with S3 URI.
ManifestEtag (string) --
The ETag associated with Manifest S3 URI.
AdditionalS3DataSource (dict) --
The additional data source that is used during inference in the Docker container for your model package.
S3DataType (string) -- [REQUIRED]
The data type of the additional data source that you specify for use in inference or training.
S3Uri (string) -- [REQUIRED]
The uniform resource identifier (URI) used to identify an additional data source used in inference or training.
CompressionType (string) --
The type of compression used for an additional data source used in inference or training. Specify None if your additional data source is not compressed.
ETag (string) --
The ETag associated with S3 URI.
ModelDataETag (string) --
The ETag associated with Model Data URL.
IsCheckpoint (boolean) --
Specifies whether the model data is a training checkpoint.
BaseModel (dict) --
Identifies the foundation model that was used as the starting point for model customization.
HubContentName (string) --
The hub content name of the base model.
HubContentVersion (string) --
The hub content version of the base model.
RecipeName (string) --
The recipe name of the base model.
SupportedTransformInstanceTypes (list) --
A list of the instance types on which a transformation job can be run or on which an endpoint can be deployed.
This parameter is required for unversioned models, and optional for versioned models.
(string) --
SupportedRealtimeInferenceInstanceTypes (list) --
A list of the instance types that are used to generate inferences in real-time.
This parameter is required for unversioned models, and optional for versioned models.
(string) --
SupportedContentTypes (list) --
The supported MIME types for the input data.
(string) --
SupportedResponseMIMETypes (list) --
The supported MIME types for the output data.
(string) --
dict
Specifies configurations for one or more transform jobs that SageMaker runs to test the model package.
ValidationRole (string) -- [REQUIRED]
The IAM roles to be used for the validation of the model package.
ValidationProfiles (list) -- [REQUIRED]
An array of ModelPackageValidationProfile objects, each of which specifies a batch transform job that SageMaker runs to validate your model package.
(dict) --
Contains data, such as the inputs and targeted instance types that are used in the process of validating the model package.
The data provided in the validation profile is made available to your buyers on Amazon Web Services Marketplace.
ProfileName (string) -- [REQUIRED]
The name of the profile for the model package.
TransformJobDefinition (dict) -- [REQUIRED]
The TransformJobDefinition object that describes the transform job used for the validation of the model package.
MaxConcurrentTransforms (integer) --
The maximum number of parallel requests that can be sent to each instance in a transform job. The default value is 1.
MaxPayloadInMB (integer) --
The maximum payload size allowed, in MB. A payload is the data portion of a record (without metadata).
BatchStrategy (string) --
A string that determines the number of records included in a single mini-batch.
SingleRecord means only one record is used per mini-batch. MultiRecord means a mini-batch is set to contain as many records that can fit within the MaxPayloadInMB limit.
Environment (dict) --
The environment variables to set in the Docker container. We support up to 16 key and values entries in the map.
(string) --
(string) --
TransformInput (dict) -- [REQUIRED]
A description of the input source and the way the transform job consumes it.
DataSource (dict) -- [REQUIRED]
Describes the location of the channel data, which is, the S3 location of the input data that the model can consume.
S3DataSource (dict) -- [REQUIRED]
The S3 location of the data source that is associated with a channel.
S3DataType (string) -- [REQUIRED]
If you choose S3Prefix, S3Uri identifies a key name prefix. Amazon SageMaker uses all objects with the specified key name prefix for batch transform.
If you choose ManifestFile, S3Uri identifies an object that is a manifest file containing a list of object keys that you want Amazon SageMaker to use for batch transform.
The following values are compatible: ManifestFile, S3Prefix
The following value is not compatible: AugmentedManifestFile
S3Uri (string) -- [REQUIRED]
Depending on the value specified for the S3DataType, identifies either a key name prefix or a manifest. For example:
A key name prefix might look like this: s3://bucketname/exampleprefix/.
A manifest might look like this: s3://bucketname/example.manifest The manifest is an S3 object which is a JSON file with the following format: [ {"prefix": "s3://customer_bucket/some/prefix/"}, "relative/path/to/custdata-1", "relative/path/custdata-2", ... "relative/path/custdata-N" ] The preceding JSON matches the following S3Uris: s3://customer_bucket/some/prefix/relative/path/to/custdata-1 s3://customer_bucket/some/prefix/relative/path/custdata-2 ... s3://customer_bucket/some/prefix/relative/path/custdata-N The complete set of S3Uris in this manifest constitutes the input data for the channel for this datasource. The object that each S3Uris points to must be readable by the IAM role that Amazon SageMaker uses to perform tasks on your behalf.
ContentType (string) --
The multipurpose internet mail extension (MIME) type of the data. Amazon SageMaker uses the MIME type with each http call to transfer data to the transform job.
CompressionType (string) --
If your transform data is compressed, specify the compression type. Amazon SageMaker automatically decompresses the data for the transform job accordingly. The default value is None.
SplitType (string) --
The method to use to split the transform job's data files into smaller batches. Splitting is necessary when the total size of each object is too large to fit in a single request. You can also use data splitting to improve performance by processing multiple concurrent mini-batches. The default value for SplitType is None, which indicates that input data files are not split, and request payloads contain the entire contents of an input object. Set the value of this parameter to Line to split records on a newline character boundary. SplitType also supports a number of record-oriented binary data formats. Currently, the supported record formats are:
RecordIO
TFRecord
When splitting is enabled, the size of a mini-batch depends on the values of the BatchStrategy and MaxPayloadInMB parameters. When the value of BatchStrategy is MultiRecord, Amazon SageMaker sends the maximum number of records in each request, up to the MaxPayloadInMB limit. If the value of BatchStrategy is SingleRecord, Amazon SageMaker sends individual records in each request.
TransformOutput (dict) -- [REQUIRED]
Identifies the Amazon S3 location where you want Amazon SageMaker to save the results from the transform job.
S3OutputPath (string) -- [REQUIRED]
The Amazon S3 path where you want Amazon SageMaker to store the results of the transform job. For example, s3://bucket-name/key-name-prefix.
For every S3 object used as input for the transform job, batch transform stores the transformed data with an . out suffix in a corresponding subfolder in the location in the output prefix. For example, for the input data stored at s3://bucket-name/input-name-prefix/dataset01/data.csv, batch transform stores the transformed data at s3://bucket-name/output-name-prefix/input-name-prefix/data.csv.out. Batch transform doesn't upload partially processed objects. For an input S3 object that contains multiple records, it creates an . out file only if the transform job succeeds on the entire file. When the input contains multiple S3 objects, the batch transform job processes the listed S3 objects and uploads only the output for successfully processed objects. If any object fails in the transform job batch transform marks the job as failed to prompt investigation.
Accept (string) --
The MIME type used to specify the output data. Amazon SageMaker uses the MIME type with each http call to transfer data from the transform job.
AssembleWith (string) --
Defines how to assemble the results of the transform job as a single S3 object. Choose a format that is most convenient to you. To concatenate the results in binary format, specify None. To add a newline character at the end of every transformed record, specify Line.
KmsKeyId (string) --
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The KmsKeyId can be any of the following formats:
Key ID: 1234abcd-12ab-34cd-56ef-1234567890ab
Key ARN: arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab
Alias name: alias/ExampleAlias
Alias name ARN: arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias
If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.
The KMS key policy must grant permission to the IAM role that you specify in your CreateModel request. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide.
TransformResources (dict) -- [REQUIRED]
Identifies the ML compute instances for the transform job.
InstanceType (string) -- [REQUIRED]
The ML compute instance type for the transform job. If you are using built-in algorithms to transform moderately sized datasets, we recommend using ml.m4.xlarge or ``ml.m5.large``instance types.
InstanceCount (integer) -- [REQUIRED]
The number of ML compute instances to use in the transform job. The default value is 1, and the maximum is 100. For distributed transform jobs, specify a value greater than 1.
VolumeKmsKeyId (string) --
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt model data on the storage volume attached to the ML compute instance(s) that run the batch transform job.
The VolumeKmsKeyId can be any of the following formats:
Key ID: 1234abcd-12ab-34cd-56ef-1234567890ab
Key ARN: arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab
Alias name: alias/ExampleAlias
Alias name ARN: arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias
TransformAmiVersion (string) --
Specifies an option from a collection of preconfigured Amazon Machine Image (AMI) images. Each image is configured by Amazon Web Services with a set of software and driver versions.
al2-ami-sagemaker-batch-gpu-470
Accelerator: GPU
NVIDIA driver version: 470
al2-ami-sagemaker-batch-gpu-535
Accelerator: GPU
NVIDIA driver version: 535
dict
Details about the algorithm that was used to create the model package.
SourceAlgorithms (list) -- [REQUIRED]
A list of the algorithms that were used to create a model package.
(dict) --
Specifies an algorithm that was used to create the model package. The algorithm must be either an algorithm resource in your SageMaker account or an algorithm in Amazon Web Services Marketplace that you are subscribed to.
ModelDataUrl (string) --
The Amazon S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive ( .tar.gz suffix).
ModelDataSource (dict) --
Specifies the location of ML model data to deploy during endpoint creation.
S3DataSource (dict) --
Specifies the S3 location of ML model data to deploy.
S3Uri (string) -- [REQUIRED]
Specifies the S3 path of ML model data to deploy.
S3DataType (string) -- [REQUIRED]
Specifies the type of ML model data to deploy.
If you choose S3Prefix, S3Uri identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix as part of the ML model data to deploy. A valid key name prefix identified by S3Uri always ends with a forward slash (/).
If you choose S3Object, S3Uri identifies an object that is the ML model data to deploy.
CompressionType (string) -- [REQUIRED]
Specifies how the ML model data is prepared.
If you choose Gzip and choose S3Object as the value of S3DataType, S3Uri identifies an object that is a gzip-compressed TAR archive. SageMaker will attempt to decompress and untar the object during model deployment.
If you choose None and chooose S3Object as the value of S3DataType, S3Uri identifies an object that represents an uncompressed ML model to deploy.
If you choose None and choose S3Prefix as the value of S3DataType, S3Uri identifies a key name prefix, under which all objects represents the uncompressed ML model to deploy.
If you choose None, then SageMaker will follow rules below when creating model data files under /opt/ml/model directory for use by your inference code:
If you choose S3Object as the value of S3DataType, then SageMaker will split the key of the S3 object referenced by S3Uri by slash (/), and use the last part as the filename of the file holding the content of the S3 object.
If you choose S3Prefix as the value of S3DataType, then for each S3 object under the key name pefix referenced by S3Uri, SageMaker will trim its key by the prefix, and use the remainder as the path (relative to /opt/ml/model) of the file holding the content of the S3 object. SageMaker will split the remainder by slash (/), using intermediate parts as directory names and the last part as filename of the file holding the content of the S3 object.
Do not use any of the following as file names or directory names:
An empty or blank string
A string which contains null bytes
A string longer than 255 bytes
A single dot ( .)
A double dot ( ..)
Ambiguous file names will result in model deployment failure. For example, if your uncompressed ML model consists of two S3 objects s3://mybucket/model/weights and s3://mybucket/model/weights/part1 and you specify s3://mybucket/model/ as the value of S3Uri and S3Prefix as the value of S3DataType, then it will result in name clash between /opt/ml/model/weights (a regular file) and /opt/ml/model/weights/ (a directory).
Do not organize the model artifacts in S3 console using folders. When you create a folder in S3 console, S3 creates a 0-byte object with a key set to the folder name you provide. They key of the 0-byte object ends with a slash (/) which violates SageMaker restrictions on model artifact file names, leading to model deployment failure.
ModelAccessConfig (dict) --
Specifies the access configuration file for the ML model. You can explicitly accept the model end-user license agreement (EULA) within the ModelAccessConfig. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.
AcceptEula (boolean) -- [REQUIRED]
Specifies agreement to the model end-user license agreement (EULA). The AcceptEula value must be explicitly defined as True in order to accept the EULA that this model requires. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.
HubAccessConfig (dict) --
Configuration information for hub access.
HubContentArn (string) -- [REQUIRED]
The ARN of the hub content for which deployment access is allowed.
ManifestS3Uri (string) --
The Amazon S3 URI of the manifest file. The manifest file is a CSV file that stores the artifact locations.
ETag (string) --
The ETag associated with S3 URI.
ManifestEtag (string) --
The ETag associated with Manifest S3 URI.
ModelDataETag (string) --
The ETag associated with Model Data URL.
AlgorithmName (string) -- [REQUIRED]
The name of an algorithm that was used to create the model package. The algorithm must be either an algorithm resource in your SageMaker account or an algorithm in Amazon Web Services Marketplace that you are subscribed to.
boolean
Whether to certify the model package for listing on Amazon Web Services Marketplace.
This parameter is optional for unversioned models, and does not apply to versioned models.
list
A list of key value pairs associated with the model. For more information, see Tagging Amazon Web Services resources in the Amazon Web Services General Reference Guide.
If you supply ModelPackageGroupName, your model package belongs to the model group you specify and uses the tags associated with the model group. In this case, you cannot supply a tag argument.
(dict) --
A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags.
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources. For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy.
Key (string) -- [REQUIRED]
The tag key. Tag keys must be unique per resource.
Value (string) -- [REQUIRED]
The tag value.
string
Whether the model is approved for deployment.
This parameter is optional for versioned models, and does not apply to unversioned models.
For versioned models, the value of this parameter must be set to Approved to deploy the model.
dict
Metadata properties of the tracking entity, trial, or trial component.
CommitId (string) --
The commit ID.
Repository (string) --
The repository.
GeneratedBy (string) --
The entity this entity was generated by.
ProjectId (string) --
The project ID.
dict
A structure that contains model metrics reports.
ModelQuality (dict) --
Metrics that measure the quality of a model.
Statistics (dict) --
Model quality statistics.
ContentType (string) -- [REQUIRED]
The metric source content type.
ContentDigest (string) --
The hash key used for the metrics source.
S3Uri (string) -- [REQUIRED]
The S3 URI for the metrics source.
Constraints (dict) --
Model quality constraints.
ContentType (string) -- [REQUIRED]
The metric source content type.
ContentDigest (string) --
The hash key used for the metrics source.
S3Uri (string) -- [REQUIRED]
The S3 URI for the metrics source.
ModelDataQuality (dict) --
Metrics that measure the quality of the input data for a model.
Statistics (dict) --
Data quality statistics for a model.
ContentType (string) -- [REQUIRED]
The metric source content type.
ContentDigest (string) --
The hash key used for the metrics source.
S3Uri (string) -- [REQUIRED]
The S3 URI for the metrics source.
Constraints (dict) --
Data quality constraints for a model.
ContentType (string) -- [REQUIRED]
The metric source content type.
ContentDigest (string) --
The hash key used for the metrics source.
S3Uri (string) -- [REQUIRED]
The S3 URI for the metrics source.
Bias (dict) --
Metrics that measure bias in a model.
Report (dict) --
The bias report for a model
ContentType (string) -- [REQUIRED]
The metric source content type.
ContentDigest (string) --
The hash key used for the metrics source.
S3Uri (string) -- [REQUIRED]
The S3 URI for the metrics source.
PreTrainingReport (dict) --
The pre-training bias report for a model.
ContentType (string) -- [REQUIRED]
The metric source content type.
ContentDigest (string) --
The hash key used for the metrics source.
S3Uri (string) -- [REQUIRED]
The S3 URI for the metrics source.
PostTrainingReport (dict) --
The post-training bias report for a model.
ContentType (string) -- [REQUIRED]
The metric source content type.
ContentDigest (string) --
The hash key used for the metrics source.
S3Uri (string) -- [REQUIRED]
The S3 URI for the metrics source.
Explainability (dict) --
Metrics that help explain a model.
Report (dict) --
The explainability report for a model.
ContentType (string) -- [REQUIRED]
The metric source content type.
ContentDigest (string) --
The hash key used for the metrics source.
S3Uri (string) -- [REQUIRED]
The S3 URI for the metrics source.
string
A unique token that guarantees that the call to this API is idempotent.
This field is autopopulated if not provided.
string
The machine learning domain of your model package and its components. Common machine learning domains include computer vision and natural language processing.
string
The machine learning task your model package accomplishes. Common machine learning tasks include object detection and image classification. The following tasks are supported by Inference Recommender: "IMAGE_CLASSIFICATION" | "OBJECT_DETECTION" | "TEXT_GENERATION" | "IMAGE_SEGMENTATION" | "FILL_MASK" | "CLASSIFICATION" | "REGRESSION" | "OTHER".
Specify "OTHER" if none of the tasks listed fit your use case.
string
The Amazon Simple Storage Service (Amazon S3) path where the sample payload is stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix). This archive can hold multiple files that are all equally used in the load test. Each file in the archive must satisfy the size constraints of the InvokeEndpoint call.
dict
The metadata properties associated with the model package versions.
(string) --
(string) --
dict
Represents the drift check baselines that can be used when the model monitor is set using the model package. For more information, see the topic on Drift Detection against Previous Baselines in SageMaker Pipelines in the Amazon SageMaker Developer Guide.
Bias (dict) --
Represents the drift check bias baselines that can be used when the model monitor is set using the model package.
ConfigFile (dict) --
The bias config file for a model.
ContentType (string) --
The type of content stored in the file source.
ContentDigest (string) --
The digest of the file source.
S3Uri (string) -- [REQUIRED]
The Amazon S3 URI for the file source.
PreTrainingConstraints (dict) --
The pre-training constraints.
ContentType (string) -- [REQUIRED]
The metric source content type.
ContentDigest (string) --
The hash key used for the metrics source.
S3Uri (string) -- [REQUIRED]
The S3 URI for the metrics source.
PostTrainingConstraints (dict) --
The post-training constraints.
ContentType (string) -- [REQUIRED]
The metric source content type.
ContentDigest (string) --
The hash key used for the metrics source.
S3Uri (string) -- [REQUIRED]
The S3 URI for the metrics source.
Explainability (dict) --
Represents the drift check explainability baselines that can be used when the model monitor is set using the model package.
Constraints (dict) --
The drift check explainability constraints.
ContentType (string) -- [REQUIRED]
The metric source content type.
ContentDigest (string) --
The hash key used for the metrics source.
S3Uri (string) -- [REQUIRED]
The S3 URI for the metrics source.
ConfigFile (dict) --
The explainability config file for the model.
ContentType (string) --
The type of content stored in the file source.
ContentDigest (string) --
The digest of the file source.
S3Uri (string) -- [REQUIRED]
The Amazon S3 URI for the file source.
ModelQuality (dict) --
Represents the drift check model quality baselines that can be used when the model monitor is set using the model package.
Statistics (dict) --
The drift check model quality statistics.
ContentType (string) -- [REQUIRED]
The metric source content type.
ContentDigest (string) --
The hash key used for the metrics source.
S3Uri (string) -- [REQUIRED]
The S3 URI for the metrics source.
Constraints (dict) --
The drift check model quality constraints.
ContentType (string) -- [REQUIRED]
The metric source content type.
ContentDigest (string) --
The hash key used for the metrics source.
S3Uri (string) -- [REQUIRED]
The S3 URI for the metrics source.
ModelDataQuality (dict) --
Represents the drift check model data quality baselines that can be used when the model monitor is set using the model package.
Statistics (dict) --
The drift check model data quality statistics.
ContentType (string) -- [REQUIRED]
The metric source content type.
ContentDigest (string) --
The hash key used for the metrics source.
S3Uri (string) -- [REQUIRED]
The S3 URI for the metrics source.
Constraints (dict) --
The drift check model data quality constraints.
ContentType (string) -- [REQUIRED]
The metric source content type.
ContentDigest (string) --
The hash key used for the metrics source.
S3Uri (string) -- [REQUIRED]
The S3 URI for the metrics source.
list
An array of additional Inference Specification objects. Each additional Inference Specification specifies artifacts based on this model package that can be used on inference endpoints. Generally used with SageMaker Neo to store the compiled artifacts.
(dict) --
A structure of additional Inference Specification. Additional Inference Specification specifies details about inference jobs that can be run with models based on this model package
Name (string) -- [REQUIRED]
A unique name to identify the additional inference specification. The name must be unique within the list of your additional inference specifications for a particular model package.
Description (string) --
A description of the additional Inference specification
Containers (list) -- [REQUIRED]
The Amazon ECR registry path of the Docker image that contains the inference code.
(dict) --
Describes the Docker container for the model package.
ContainerHostname (string) --
The DNS host name for the Docker container.
Image (string) --
The Amazon Elastic Container Registry (Amazon ECR) path where inference code is stored.
If you are using your own custom algorithm instead of an algorithm provided by SageMaker, the inference code must meet SageMaker requirements. SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker.
ImageDigest (string) --
An MD5 hash of the training algorithm that identifies the Docker image used for training.
ModelDataUrl (string) --
The Amazon S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive ( .tar.gz suffix).
ModelDataSource (dict) --
Specifies the location of ML model data to deploy during endpoint creation.
S3DataSource (dict) --
Specifies the S3 location of ML model data to deploy.
S3Uri (string) -- [REQUIRED]
Specifies the S3 path of ML model data to deploy.
S3DataType (string) -- [REQUIRED]
Specifies the type of ML model data to deploy.
If you choose S3Prefix, S3Uri identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix as part of the ML model data to deploy. A valid key name prefix identified by S3Uri always ends with a forward slash (/).
If you choose S3Object, S3Uri identifies an object that is the ML model data to deploy.
CompressionType (string) -- [REQUIRED]
Specifies how the ML model data is prepared.
If you choose Gzip and choose S3Object as the value of S3DataType, S3Uri identifies an object that is a gzip-compressed TAR archive. SageMaker will attempt to decompress and untar the object during model deployment.
If you choose None and chooose S3Object as the value of S3DataType, S3Uri identifies an object that represents an uncompressed ML model to deploy.
If you choose None and choose S3Prefix as the value of S3DataType, S3Uri identifies a key name prefix, under which all objects represents the uncompressed ML model to deploy.
If you choose None, then SageMaker will follow rules below when creating model data files under /opt/ml/model directory for use by your inference code:
If you choose S3Object as the value of S3DataType, then SageMaker will split the key of the S3 object referenced by S3Uri by slash (/), and use the last part as the filename of the file holding the content of the S3 object.
If you choose S3Prefix as the value of S3DataType, then for each S3 object under the key name pefix referenced by S3Uri, SageMaker will trim its key by the prefix, and use the remainder as the path (relative to /opt/ml/model) of the file holding the content of the S3 object. SageMaker will split the remainder by slash (/), using intermediate parts as directory names and the last part as filename of the file holding the content of the S3 object.
Do not use any of the following as file names or directory names:
An empty or blank string
A string which contains null bytes
A string longer than 255 bytes
A single dot ( .)
A double dot ( ..)
Ambiguous file names will result in model deployment failure. For example, if your uncompressed ML model consists of two S3 objects s3://mybucket/model/weights and s3://mybucket/model/weights/part1 and you specify s3://mybucket/model/ as the value of S3Uri and S3Prefix as the value of S3DataType, then it will result in name clash between /opt/ml/model/weights (a regular file) and /opt/ml/model/weights/ (a directory).
Do not organize the model artifacts in S3 console using folders. When you create a folder in S3 console, S3 creates a 0-byte object with a key set to the folder name you provide. They key of the 0-byte object ends with a slash (/) which violates SageMaker restrictions on model artifact file names, leading to model deployment failure.
ModelAccessConfig (dict) --
Specifies the access configuration file for the ML model. You can explicitly accept the model end-user license agreement (EULA) within the ModelAccessConfig. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.
AcceptEula (boolean) -- [REQUIRED]
Specifies agreement to the model end-user license agreement (EULA). The AcceptEula value must be explicitly defined as True in order to accept the EULA that this model requires. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.
HubAccessConfig (dict) --
Configuration information for hub access.
HubContentArn (string) -- [REQUIRED]
The ARN of the hub content for which deployment access is allowed.
ManifestS3Uri (string) --
The Amazon S3 URI of the manifest file. The manifest file is a CSV file that stores the artifact locations.
ETag (string) --
The ETag associated with S3 URI.
ManifestEtag (string) --
The ETag associated with Manifest S3 URI.
ProductId (string) --
The Amazon Web Services Marketplace product ID of the model package.
Environment (dict) --
The environment variables to set in the Docker container. Each key and value in the Environment string to string map can have length of up to 1024. We support up to 16 entries in the map.
(string) --
(string) --
ModelInput (dict) --
A structure with Model Input details.
DataInputConfig (string) -- [REQUIRED]
The input configuration object for the model.
Framework (string) --
The machine learning framework of the model package container image.
FrameworkVersion (string) --
The framework version of the Model Package Container Image.
NearestModelName (string) --
The name of a pre-trained machine learning benchmarked by Amazon SageMaker Inference Recommender model that matches your model. You can find a list of benchmarked models by calling ListModelMetadata.
AdditionalModelDataSources (list) --
Data sources that are available to your model in addition to the one that you specify for ModelDataSource when you use the CreateModelPackage action.
(dict) --
Data sources that are available to your model in addition to the one that you specify for ModelDataSource when you use the CreateModel action.
ChannelName (string) -- [REQUIRED]
A custom name for this AdditionalModelDataSource object.
S3DataSource (dict) -- [REQUIRED]
Specifies the S3 location of ML model data to deploy.
S3Uri (string) -- [REQUIRED]
Specifies the S3 path of ML model data to deploy.
S3DataType (string) -- [REQUIRED]
Specifies the type of ML model data to deploy.
If you choose S3Prefix, S3Uri identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix as part of the ML model data to deploy. A valid key name prefix identified by S3Uri always ends with a forward slash (/).
If you choose S3Object, S3Uri identifies an object that is the ML model data to deploy.
CompressionType (string) -- [REQUIRED]
Specifies how the ML model data is prepared.
If you choose Gzip and choose S3Object as the value of S3DataType, S3Uri identifies an object that is a gzip-compressed TAR archive. SageMaker will attempt to decompress and untar the object during model deployment.
If you choose None and chooose S3Object as the value of S3DataType, S3Uri identifies an object that represents an uncompressed ML model to deploy.
If you choose None and choose S3Prefix as the value of S3DataType, S3Uri identifies a key name prefix, under which all objects represents the uncompressed ML model to deploy.
If you choose None, then SageMaker will follow rules below when creating model data files under /opt/ml/model directory for use by your inference code:
If you choose S3Object as the value of S3DataType, then SageMaker will split the key of the S3 object referenced by S3Uri by slash (/), and use the last part as the filename of the file holding the content of the S3 object.
If you choose S3Prefix as the value of S3DataType, then for each S3 object under the key name pefix referenced by S3Uri, SageMaker will trim its key by the prefix, and use the remainder as the path (relative to /opt/ml/model) of the file holding the content of the S3 object. SageMaker will split the remainder by slash (/), using intermediate parts as directory names and the last part as filename of the file holding the content of the S3 object.
Do not use any of the following as file names or directory names:
An empty or blank string
A string which contains null bytes
A string longer than 255 bytes
A single dot ( .)
A double dot ( ..)
Ambiguous file names will result in model deployment failure. For example, if your uncompressed ML model consists of two S3 objects s3://mybucket/model/weights and s3://mybucket/model/weights/part1 and you specify s3://mybucket/model/ as the value of S3Uri and S3Prefix as the value of S3DataType, then it will result in name clash between /opt/ml/model/weights (a regular file) and /opt/ml/model/weights/ (a directory).
Do not organize the model artifacts in S3 console using folders. When you create a folder in S3 console, S3 creates a 0-byte object with a key set to the folder name you provide. They key of the 0-byte object ends with a slash (/) which violates SageMaker restrictions on model artifact file names, leading to model deployment failure.
ModelAccessConfig (dict) --
Specifies the access configuration file for the ML model. You can explicitly accept the model end-user license agreement (EULA) within the ModelAccessConfig. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.
AcceptEula (boolean) -- [REQUIRED]
Specifies agreement to the model end-user license agreement (EULA). The AcceptEula value must be explicitly defined as True in order to accept the EULA that this model requires. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.
HubAccessConfig (dict) --
Configuration information for hub access.
HubContentArn (string) -- [REQUIRED]
The ARN of the hub content for which deployment access is allowed.
ManifestS3Uri (string) --
The Amazon S3 URI of the manifest file. The manifest file is a CSV file that stores the artifact locations.
ETag (string) --
The ETag associated with S3 URI.
ManifestEtag (string) --
The ETag associated with Manifest S3 URI.
AdditionalS3DataSource (dict) --
The additional data source that is used during inference in the Docker container for your model package.
S3DataType (string) -- [REQUIRED]
The data type of the additional data source that you specify for use in inference or training.
S3Uri (string) -- [REQUIRED]
The uniform resource identifier (URI) used to identify an additional data source used in inference or training.
CompressionType (string) --
The type of compression used for an additional data source used in inference or training. Specify None if your additional data source is not compressed.
ETag (string) --
The ETag associated with S3 URI.
ModelDataETag (string) --
The ETag associated with Model Data URL.
IsCheckpoint (boolean) --
Specifies whether the model data is a training checkpoint.
BaseModel (dict) --
Identifies the foundation model that was used as the starting point for model customization.
HubContentName (string) --
The hub content name of the base model.
HubContentVersion (string) --
The hub content version of the base model.
RecipeName (string) --
The recipe name of the base model.
SupportedTransformInstanceTypes (list) --
A list of the instance types on which a transformation job can be run or on which an endpoint can be deployed.
(string) --
SupportedRealtimeInferenceInstanceTypes (list) --
A list of the instance types that are used to generate inferences in real-time.
(string) --
SupportedContentTypes (list) --
The supported MIME types for the input data.
(string) --
SupportedResponseMIMETypes (list) --
The supported MIME types for the output data.
(string) --
string
Indicates if you want to skip model validation.
string
The URI of the source for the model package. If you want to clone a model package, set it to the model package Amazon Resource Name (ARN). If you want to register a model, set it to the model ARN.
dict
The KMS Key ID ( KMSKeyId) used for encryption of model package information.
KmsKeyId (string) -- [REQUIRED]
The KMS Key ID ( KMSKeyId) used for encryption of model package information.
dict
The model card associated with the model package. Since ModelPackageModelCard is tied to a model package, it is a specific usage of a model card and its schema is simplified compared to the schema of ModelCard. The ModelPackageModelCard schema does not include model_package_details, and model_overview is composed of the model_creator and model_artifact properties. For more information about the model package model card schema, see Model package model card schema. For more information about the model card associated with the model package, see View the Details of a Model Version.
ModelCardContent (string) --
The content of the model card. The content must follow the schema described in Model Package Model Card Schema.
ModelCardStatus (string) --
The approval status of the model card within your organization. Different organizations might have different criteria for model card review and approval.
Draft: The model card is a work in progress.
PendingReview: The model card is pending review.
Approved: The model card is approved.
Archived: The model card is archived. No more updates can be made to the model card content. If you try to update the model card content, you will receive the message Model Card is in Archived state.
dict
A structure describing the current state of the model in its life cycle.
Stage (string) -- [REQUIRED]
The current stage in the model life cycle.
StageStatus (string) -- [REQUIRED]
The current status of a stage in model life cycle.
StageDescription (string) --
Describes the stage related details.
string
The storage type of the model package.
dict
Response Syntax
{
'ModelPackageArn': 'string'
}
Response Structure
(dict) --
ModelPackageArn (string) --
The Amazon Resource Name (ARN) of the new model package.
{'ManagedConfiguration': {'ManagedStorageType': 'Restricted'}}
Creates a model group. A model group contains a group of model versions.
See also: AWS API Documentation
Request Syntax
client.create_model_package_group(
ModelPackageGroupName='string',
ModelPackageGroupDescription='string',
Tags=[
{
'Key': 'string',
'Value': 'string'
},
],
ManagedConfiguration={
'ManagedStorageType': 'Restricted'
}
)
string
[REQUIRED]
The name of the model group.
string
A description for the model group.
list
A list of key value pairs associated with the model group. For more information, see Tagging Amazon Web Services resources in the Amazon Web Services General Reference Guide.
(dict) --
A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags.
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources. For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy.
Key (string) -- [REQUIRED]
The tag key. Tag keys must be unique per resource.
Value (string) -- [REQUIRED]
The tag value.
dict
The managed configuration of the model package group.
ManagedStorageType (string) --
The storage type of the model package.
dict
Response Syntax
{
'ModelPackageGroupArn': 'string'
}
Response Structure
(dict) --
ModelPackageGroupArn (string) --
The Amazon Resource Name (ARN) of the model group.
{'SpaceSettings': {'CodeEditorAppSettings': {'DefaultResourceSpec': {'TrainingPlanArn': 'string'}},
'JupyterLabAppSettings': {'DefaultResourceSpec': {'TrainingPlanArn': 'string'}},
'JupyterServerAppSettings': {'DefaultResourceSpec': {'TrainingPlanArn': 'string'}},
'KernelGatewayAppSettings': {'DefaultResourceSpec': {'TrainingPlanArn': 'string'}}}}
Creates a private space or a space used for real time collaboration in a domain.
See also: AWS API Documentation
Request Syntax
client.create_space(
DomainId='string',
SpaceName='string',
Tags=[
{
'Key': 'string',
'Value': 'string'
},
],
SpaceSettings={
'JupyterServerAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': 'string',
'SageMakerImageVersionAlias': 'string',
'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.g6.xlarge'|'ml.g6.2xlarge'|'ml.g6.4xlarge'|'ml.g6.8xlarge'|'ml.g6.12xlarge'|'ml.g6.16xlarge'|'ml.g6.24xlarge'|'ml.g6.48xlarge'|'ml.g6e.xlarge'|'ml.g6e.2xlarge'|'ml.g6e.4xlarge'|'ml.g6e.8xlarge'|'ml.g6e.12xlarge'|'ml.g6e.16xlarge'|'ml.g6e.24xlarge'|'ml.g6e.48xlarge'|'ml.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.p5.48xlarge'|'ml.p5en.48xlarge'|'ml.p6-b200.48xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.r6i.large'|'ml.r6i.xlarge'|'ml.r6i.2xlarge'|'ml.r6i.4xlarge'|'ml.r6i.8xlarge'|'ml.r6i.12xlarge'|'ml.r6i.16xlarge'|'ml.r6i.24xlarge'|'ml.r6i.32xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge'|'ml.m6id.large'|'ml.m6id.xlarge'|'ml.m6id.2xlarge'|'ml.m6id.4xlarge'|'ml.m6id.8xlarge'|'ml.m6id.12xlarge'|'ml.m6id.16xlarge'|'ml.m6id.24xlarge'|'ml.m6id.32xlarge'|'ml.c6id.large'|'ml.c6id.xlarge'|'ml.c6id.2xlarge'|'ml.c6id.4xlarge'|'ml.c6id.8xlarge'|'ml.c6id.12xlarge'|'ml.c6id.16xlarge'|'ml.c6id.24xlarge'|'ml.c6id.32xlarge'|'ml.r6id.large'|'ml.r6id.xlarge'|'ml.r6id.2xlarge'|'ml.r6id.4xlarge'|'ml.r6id.8xlarge'|'ml.r6id.12xlarge'|'ml.r6id.16xlarge'|'ml.r6id.24xlarge'|'ml.r6id.32xlarge'|'ml.p5.4xlarge',
'LifecycleConfigArn': 'string',
'TrainingPlanArn': 'string'
},
'LifecycleConfigArns': [
'string',
],
'CodeRepositories': [
{
'RepositoryUrl': 'string'
},
]
},
'KernelGatewayAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': 'string',
'SageMakerImageVersionAlias': 'string',
'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.g6.xlarge'|'ml.g6.2xlarge'|'ml.g6.4xlarge'|'ml.g6.8xlarge'|'ml.g6.12xlarge'|'ml.g6.16xlarge'|'ml.g6.24xlarge'|'ml.g6.48xlarge'|'ml.g6e.xlarge'|'ml.g6e.2xlarge'|'ml.g6e.4xlarge'|'ml.g6e.8xlarge'|'ml.g6e.12xlarge'|'ml.g6e.16xlarge'|'ml.g6e.24xlarge'|'ml.g6e.48xlarge'|'ml.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.p5.48xlarge'|'ml.p5en.48xlarge'|'ml.p6-b200.48xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.r6i.large'|'ml.r6i.xlarge'|'ml.r6i.2xlarge'|'ml.r6i.4xlarge'|'ml.r6i.8xlarge'|'ml.r6i.12xlarge'|'ml.r6i.16xlarge'|'ml.r6i.24xlarge'|'ml.r6i.32xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge'|'ml.m6id.large'|'ml.m6id.xlarge'|'ml.m6id.2xlarge'|'ml.m6id.4xlarge'|'ml.m6id.8xlarge'|'ml.m6id.12xlarge'|'ml.m6id.16xlarge'|'ml.m6id.24xlarge'|'ml.m6id.32xlarge'|'ml.c6id.large'|'ml.c6id.xlarge'|'ml.c6id.2xlarge'|'ml.c6id.4xlarge'|'ml.c6id.8xlarge'|'ml.c6id.12xlarge'|'ml.c6id.16xlarge'|'ml.c6id.24xlarge'|'ml.c6id.32xlarge'|'ml.r6id.large'|'ml.r6id.xlarge'|'ml.r6id.2xlarge'|'ml.r6id.4xlarge'|'ml.r6id.8xlarge'|'ml.r6id.12xlarge'|'ml.r6id.16xlarge'|'ml.r6id.24xlarge'|'ml.r6id.32xlarge'|'ml.p5.4xlarge',
'LifecycleConfigArn': 'string',
'TrainingPlanArn': 'string'
},
'CustomImages': [
{
'ImageName': 'string',
'ImageVersionNumber': 123,
'AppImageConfigName': 'string'
},
],
'LifecycleConfigArns': [
'string',
]
},
'CodeEditorAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': 'string',
'SageMakerImageVersionAlias': 'string',
'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.g6.xlarge'|'ml.g6.2xlarge'|'ml.g6.4xlarge'|'ml.g6.8xlarge'|'ml.g6.12xlarge'|'ml.g6.16xlarge'|'ml.g6.24xlarge'|'ml.g6.48xlarge'|'ml.g6e.xlarge'|'ml.g6e.2xlarge'|'ml.g6e.4xlarge'|'ml.g6e.8xlarge'|'ml.g6e.12xlarge'|'ml.g6e.16xlarge'|'ml.g6e.24xlarge'|'ml.g6e.48xlarge'|'ml.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.p5.48xlarge'|'ml.p5en.48xlarge'|'ml.p6-b200.48xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.r6i.large'|'ml.r6i.xlarge'|'ml.r6i.2xlarge'|'ml.r6i.4xlarge'|'ml.r6i.8xlarge'|'ml.r6i.12xlarge'|'ml.r6i.16xlarge'|'ml.r6i.24xlarge'|'ml.r6i.32xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge'|'ml.m6id.large'|'ml.m6id.xlarge'|'ml.m6id.2xlarge'|'ml.m6id.4xlarge'|'ml.m6id.8xlarge'|'ml.m6id.12xlarge'|'ml.m6id.16xlarge'|'ml.m6id.24xlarge'|'ml.m6id.32xlarge'|'ml.c6id.large'|'ml.c6id.xlarge'|'ml.c6id.2xlarge'|'ml.c6id.4xlarge'|'ml.c6id.8xlarge'|'ml.c6id.12xlarge'|'ml.c6id.16xlarge'|'ml.c6id.24xlarge'|'ml.c6id.32xlarge'|'ml.r6id.large'|'ml.r6id.xlarge'|'ml.r6id.2xlarge'|'ml.r6id.4xlarge'|'ml.r6id.8xlarge'|'ml.r6id.12xlarge'|'ml.r6id.16xlarge'|'ml.r6id.24xlarge'|'ml.r6id.32xlarge'|'ml.p5.4xlarge',
'LifecycleConfigArn': 'string',
'TrainingPlanArn': 'string'
},
'AppLifecycleManagement': {
'IdleSettings': {
'IdleTimeoutInMinutes': 123
}
}
},
'JupyterLabAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': 'string',
'SageMakerImageVersionAlias': 'string',
'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.g6.xlarge'|'ml.g6.2xlarge'|'ml.g6.4xlarge'|'ml.g6.8xlarge'|'ml.g6.12xlarge'|'ml.g6.16xlarge'|'ml.g6.24xlarge'|'ml.g6.48xlarge'|'ml.g6e.xlarge'|'ml.g6e.2xlarge'|'ml.g6e.4xlarge'|'ml.g6e.8xlarge'|'ml.g6e.12xlarge'|'ml.g6e.16xlarge'|'ml.g6e.24xlarge'|'ml.g6e.48xlarge'|'ml.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.p5.48xlarge'|'ml.p5en.48xlarge'|'ml.p6-b200.48xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.r6i.large'|'ml.r6i.xlarge'|'ml.r6i.2xlarge'|'ml.r6i.4xlarge'|'ml.r6i.8xlarge'|'ml.r6i.12xlarge'|'ml.r6i.16xlarge'|'ml.r6i.24xlarge'|'ml.r6i.32xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge'|'ml.m6id.large'|'ml.m6id.xlarge'|'ml.m6id.2xlarge'|'ml.m6id.4xlarge'|'ml.m6id.8xlarge'|'ml.m6id.12xlarge'|'ml.m6id.16xlarge'|'ml.m6id.24xlarge'|'ml.m6id.32xlarge'|'ml.c6id.large'|'ml.c6id.xlarge'|'ml.c6id.2xlarge'|'ml.c6id.4xlarge'|'ml.c6id.8xlarge'|'ml.c6id.12xlarge'|'ml.c6id.16xlarge'|'ml.c6id.24xlarge'|'ml.c6id.32xlarge'|'ml.r6id.large'|'ml.r6id.xlarge'|'ml.r6id.2xlarge'|'ml.r6id.4xlarge'|'ml.r6id.8xlarge'|'ml.r6id.12xlarge'|'ml.r6id.16xlarge'|'ml.r6id.24xlarge'|'ml.r6id.32xlarge'|'ml.p5.4xlarge',
'LifecycleConfigArn': 'string',
'TrainingPlanArn': 'string'
},
'CodeRepositories': [
{
'RepositoryUrl': 'string'
},
],
'AppLifecycleManagement': {
'IdleSettings': {
'IdleTimeoutInMinutes': 123
}
}
},
'AppType': 'JupyterServer'|'KernelGateway'|'DetailedProfiler'|'TensorBoard'|'CodeEditor'|'JupyterLab'|'RStudioServerPro'|'RSessionGateway'|'Canvas',
'SpaceStorageSettings': {
'EbsStorageSettings': {
'EbsVolumeSizeInGb': 123
}
},
'SpaceManagedResources': 'ENABLED'|'DISABLED',
'CustomFileSystems': [
{
'EFSFileSystem': {
'FileSystemId': 'string'
},
'FSxLustreFileSystem': {
'FileSystemId': 'string'
},
'S3FileSystem': {
'S3Uri': 'string'
}
},
],
'RemoteAccess': 'ENABLED'|'DISABLED'
},
OwnershipSettings={
'OwnerUserProfileName': 'string'
},
SpaceSharingSettings={
'SharingType': 'Private'|'Shared'
},
SpaceDisplayName='string'
)
string
[REQUIRED]
The ID of the associated domain.
string
[REQUIRED]
The name of the space.
list
Tags to associated with the space. Each tag consists of a key and an optional value. Tag keys must be unique for each resource. Tags are searchable using the Search API.
(dict) --
A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags.
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources. For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy.
Key (string) -- [REQUIRED]
The tag key. Tag keys must be unique per resource.
Value (string) -- [REQUIRED]
The tag value.
dict
A collection of space settings.
JupyterServerAppSettings (dict) --
The JupyterServer app settings.
DefaultResourceSpec (dict) --
The default instance type and the Amazon Resource Name (ARN) of the default SageMaker AI image used by the JupyterServer app. If you use the LifecycleConfigArns parameter, then this parameter is also required.
SageMakerImageArn (string) --
The ARN of the SageMaker AI image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance. To clear the value set for SageMakerImageVersionArn, pass None as the value.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
TrainingPlanArn (string) --
The ARN of the SageMaker AI Training Plan to use for this app. When you specify a training plan, the app launches on reserved GPU capacity. This field is supported for JupyterLab and CodeEditor app types.
For more information about how to reserve GPU capacity with SageMaker AI Training Plans, see Using training plans in Studio applications.
LifecycleConfigArns (list) --
The Amazon Resource Name (ARN) of the Lifecycle Configurations attached to the JupyterServerApp. If you use this parameter, the DefaultResourceSpec parameter is also required.
(string) --
CodeRepositories (list) --
A list of Git repositories that SageMaker AI automatically displays to users for cloning in the JupyterServer application.
(dict) --
A Git repository that SageMaker AI automatically displays to users for cloning in the JupyterServer application.
RepositoryUrl (string) -- [REQUIRED]
The URL of the Git repository.
KernelGatewayAppSettings (dict) --
The KernelGateway app settings.
DefaultResourceSpec (dict) --
The default instance type and the Amazon Resource Name (ARN) of the default SageMaker AI image used by the KernelGateway app.
SageMakerImageArn (string) --
The ARN of the SageMaker AI image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance. To clear the value set for SageMakerImageVersionArn, pass None as the value.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
TrainingPlanArn (string) --
The ARN of the SageMaker AI Training Plan to use for this app. When you specify a training plan, the app launches on reserved GPU capacity. This field is supported for JupyterLab and CodeEditor app types.
For more information about how to reserve GPU capacity with SageMaker AI Training Plans, see Using training plans in Studio applications.
CustomImages (list) --
A list of custom SageMaker AI images that are configured to run as a KernelGateway app.
The maximum number of custom images are as follows.
On a domain level: 200
On a space level: 5
On a user profile level: 5
(dict) --
A custom SageMaker AI image. For more information, see Bring your own SageMaker AI image.
ImageName (string) -- [REQUIRED]
The name of the CustomImage. Must be unique to your account.
ImageVersionNumber (integer) --
The version number of the CustomImage.
AppImageConfigName (string) -- [REQUIRED]
The name of the AppImageConfig.
LifecycleConfigArns (list) --
The Amazon Resource Name (ARN) of the Lifecycle Configurations attached to the the user profile or domain.
(string) --
CodeEditorAppSettings (dict) --
The Code Editor application settings.
DefaultResourceSpec (dict) --
Specifies the ARN's of a SageMaker AI image and SageMaker AI image version, and the instance type that the version runs on.
SageMakerImageArn (string) --
The ARN of the SageMaker AI image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance. To clear the value set for SageMakerImageVersionArn, pass None as the value.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
TrainingPlanArn (string) --
The ARN of the SageMaker AI Training Plan to use for this app. When you specify a training plan, the app launches on reserved GPU capacity. This field is supported for JupyterLab and CodeEditor app types.
For more information about how to reserve GPU capacity with SageMaker AI Training Plans, see Using training plans in Studio applications.
AppLifecycleManagement (dict) --
Settings that are used to configure and manage the lifecycle of CodeEditor applications in a space.
IdleSettings (dict) --
Settings related to idle shutdown of Studio applications.
IdleTimeoutInMinutes (integer) --
The time that SageMaker waits after the application becomes idle before shutting it down.
JupyterLabAppSettings (dict) --
The settings for the JupyterLab application.
DefaultResourceSpec (dict) --
Specifies the ARN's of a SageMaker AI image and SageMaker AI image version, and the instance type that the version runs on.
SageMakerImageArn (string) --
The ARN of the SageMaker AI image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance. To clear the value set for SageMakerImageVersionArn, pass None as the value.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
TrainingPlanArn (string) --
The ARN of the SageMaker AI Training Plan to use for this app. When you specify a training plan, the app launches on reserved GPU capacity. This field is supported for JupyterLab and CodeEditor app types.
For more information about how to reserve GPU capacity with SageMaker AI Training Plans, see Using training plans in Studio applications.
CodeRepositories (list) --
A list of Git repositories that SageMaker automatically displays to users for cloning in the JupyterLab application.
(dict) --
A Git repository that SageMaker AI automatically displays to users for cloning in the JupyterServer application.
RepositoryUrl (string) -- [REQUIRED]
The URL of the Git repository.
AppLifecycleManagement (dict) --
Settings that are used to configure and manage the lifecycle of JupyterLab applications in a space.
IdleSettings (dict) --
Settings related to idle shutdown of Studio applications.
IdleTimeoutInMinutes (integer) --
The time that SageMaker waits after the application becomes idle before shutting it down.
AppType (string) --
The type of app created within the space.
If using the UpdateSpace API, you can't change the app type of your space by specifying a different value for this field.
SpaceStorageSettings (dict) --
The storage settings for a space.
EbsStorageSettings (dict) --
A collection of EBS storage settings for a space.
EbsVolumeSizeInGb (integer) -- [REQUIRED]
The size of an EBS storage volume for a space.
SpaceManagedResources (string) --
If you enable this option, SageMaker AI creates the following resources on your behalf when you create the space:
The user profile that possesses the space.
The app that the space contains.
CustomFileSystems (list) --
A file system, created by you, that you assign to a space for an Amazon SageMaker AI Domain. Permitted users can access this file system in Amazon SageMaker AI Studio.
(dict) --
A file system, created by you, that you assign to a user profile or space for an Amazon SageMaker AI Domain. Permitted users can access this file system in Amazon SageMaker AI Studio.
EFSFileSystem (dict) --
A custom file system in Amazon EFS.
FileSystemId (string) -- [REQUIRED]
The ID of your Amazon EFS file system.
FSxLustreFileSystem (dict) --
A custom file system in Amazon FSx for Lustre.
FileSystemId (string) -- [REQUIRED]
Amazon FSx for Lustre file system ID.
S3FileSystem (dict) --
A custom file system in Amazon S3. This is only supported in Amazon SageMaker Unified Studio.
S3Uri (string) -- [REQUIRED]
The Amazon S3 URI that specifies the location in S3 where files are stored, which is mounted within the Studio environment. For example: s3://<bucket-name>/<prefix>/.
RemoteAccess (string) --
A setting that enables or disables remote access for a SageMaker space. When enabled, this allows you to connect to the remote space from your local IDE.
dict
A collection of ownership settings.
OwnerUserProfileName (string) -- [REQUIRED]
The user profile who is the owner of the space.
dict
A collection of space sharing settings.
SharingType (string) -- [REQUIRED]
Specifies the sharing type of the space.
string
The name of the space that appears in the SageMaker Studio UI.
dict
Response Syntax
{
'SpaceArn': 'string'
}
Response Structure
(dict) --
SpaceArn (string) --
The space's Amazon Resource Name (ARN).
{'UserSettings': {'CodeEditorAppSettings': {'DefaultResourceSpec': {'TrainingPlanArn': 'string'}},
'JupyterLabAppSettings': {'DefaultResourceSpec': {'TrainingPlanArn': 'string'}},
'JupyterServerAppSettings': {'DefaultResourceSpec': {'TrainingPlanArn': 'string'}},
'KernelGatewayAppSettings': {'DefaultResourceSpec': {'TrainingPlanArn': 'string'}},
'RSessionAppSettings': {'DefaultResourceSpec': {'TrainingPlanArn': 'string'}},
'StudioWebPortalSettings': {'ExecutionRoleSessionNameMode': 'STATIC '
'| '
'USER_IDENTITY'},
'TensorBoardAppSettings': {'DefaultResourceSpec': {'TrainingPlanArn': 'string'}}}}
Creates a user profile. A user profile represents a single user within a domain, and is the main way to reference a "person" for the purposes of sharing, reporting, and other user-oriented features. This entity is created when a user onboards to a domain. If an administrator invites a person by email or imports them from IAM Identity Center, a user profile is automatically created. A user profile is the primary holder of settings for an individual user and has a reference to the user's private Amazon Elastic File System home directory.
See also: AWS API Documentation
Request Syntax
client.create_user_profile(
DomainId='string',
UserProfileName='string',
SingleSignOnUserIdentifier='string',
SingleSignOnUserValue='string',
Tags=[
{
'Key': 'string',
'Value': 'string'
},
],
UserSettings={
'ExecutionRole': 'string',
'SecurityGroups': [
'string',
],
'SharingSettings': {
'NotebookOutputOption': 'Allowed'|'Disabled',
'S3OutputPath': 'string',
'S3KmsKeyId': 'string'
},
'JupyterServerAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': 'string',
'SageMakerImageVersionAlias': 'string',
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],
'DirectDeploySettings': {
'Status': 'ENABLED'|'DISABLED'
},
'KendraSettings': {
'Status': 'ENABLED'|'DISABLED'
},
'GenerativeAiSettings': {
'AmazonBedrockRoleArn': 'string'
},
'EmrServerlessSettings': {
'ExecutionRoleArn': 'string',
'Status': 'ENABLED'|'DISABLED'
}
},
'CodeEditorAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': 'string',
'SageMakerImageVersionAlias': 'string',
'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.g6.xlarge'|'ml.g6.2xlarge'|'ml.g6.4xlarge'|'ml.g6.8xlarge'|'ml.g6.12xlarge'|'ml.g6.16xlarge'|'ml.g6.24xlarge'|'ml.g6.48xlarge'|'ml.g6e.xlarge'|'ml.g6e.2xlarge'|'ml.g6e.4xlarge'|'ml.g6e.8xlarge'|'ml.g6e.12xlarge'|'ml.g6e.16xlarge'|'ml.g6e.24xlarge'|'ml.g6e.48xlarge'|'ml.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.p5.48xlarge'|'ml.p5en.48xlarge'|'ml.p6-b200.48xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.r6i.large'|'ml.r6i.xlarge'|'ml.r6i.2xlarge'|'ml.r6i.4xlarge'|'ml.r6i.8xlarge'|'ml.r6i.12xlarge'|'ml.r6i.16xlarge'|'ml.r6i.24xlarge'|'ml.r6i.32xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge'|'ml.m6id.large'|'ml.m6id.xlarge'|'ml.m6id.2xlarge'|'ml.m6id.4xlarge'|'ml.m6id.8xlarge'|'ml.m6id.12xlarge'|'ml.m6id.16xlarge'|'ml.m6id.24xlarge'|'ml.m6id.32xlarge'|'ml.c6id.large'|'ml.c6id.xlarge'|'ml.c6id.2xlarge'|'ml.c6id.4xlarge'|'ml.c6id.8xlarge'|'ml.c6id.12xlarge'|'ml.c6id.16xlarge'|'ml.c6id.24xlarge'|'ml.c6id.32xlarge'|'ml.r6id.large'|'ml.r6id.xlarge'|'ml.r6id.2xlarge'|'ml.r6id.4xlarge'|'ml.r6id.8xlarge'|'ml.r6id.12xlarge'|'ml.r6id.16xlarge'|'ml.r6id.24xlarge'|'ml.r6id.32xlarge'|'ml.p5.4xlarge',
'LifecycleConfigArn': 'string',
'TrainingPlanArn': 'string'
},
'CustomImages': [
{
'ImageName': 'string',
'ImageVersionNumber': 123,
'AppImageConfigName': 'string'
},
],
'LifecycleConfigArns': [
'string',
],
'AppLifecycleManagement': {
'IdleSettings': {
'LifecycleManagement': 'ENABLED'|'DISABLED',
'IdleTimeoutInMinutes': 123,
'MinIdleTimeoutInMinutes': 123,
'MaxIdleTimeoutInMinutes': 123
}
},
'BuiltInLifecycleConfigArn': 'string'
},
'JupyterLabAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': 'string',
'SageMakerImageVersionAlias': 'string',
'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.g6.xlarge'|'ml.g6.2xlarge'|'ml.g6.4xlarge'|'ml.g6.8xlarge'|'ml.g6.12xlarge'|'ml.g6.16xlarge'|'ml.g6.24xlarge'|'ml.g6.48xlarge'|'ml.g6e.xlarge'|'ml.g6e.2xlarge'|'ml.g6e.4xlarge'|'ml.g6e.8xlarge'|'ml.g6e.12xlarge'|'ml.g6e.16xlarge'|'ml.g6e.24xlarge'|'ml.g6e.48xlarge'|'ml.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.p5.48xlarge'|'ml.p5en.48xlarge'|'ml.p6-b200.48xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.r6i.large'|'ml.r6i.xlarge'|'ml.r6i.2xlarge'|'ml.r6i.4xlarge'|'ml.r6i.8xlarge'|'ml.r6i.12xlarge'|'ml.r6i.16xlarge'|'ml.r6i.24xlarge'|'ml.r6i.32xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge'|'ml.m6id.large'|'ml.m6id.xlarge'|'ml.m6id.2xlarge'|'ml.m6id.4xlarge'|'ml.m6id.8xlarge'|'ml.m6id.12xlarge'|'ml.m6id.16xlarge'|'ml.m6id.24xlarge'|'ml.m6id.32xlarge'|'ml.c6id.large'|'ml.c6id.xlarge'|'ml.c6id.2xlarge'|'ml.c6id.4xlarge'|'ml.c6id.8xlarge'|'ml.c6id.12xlarge'|'ml.c6id.16xlarge'|'ml.c6id.24xlarge'|'ml.c6id.32xlarge'|'ml.r6id.large'|'ml.r6id.xlarge'|'ml.r6id.2xlarge'|'ml.r6id.4xlarge'|'ml.r6id.8xlarge'|'ml.r6id.12xlarge'|'ml.r6id.16xlarge'|'ml.r6id.24xlarge'|'ml.r6id.32xlarge'|'ml.p5.4xlarge',
'LifecycleConfigArn': 'string',
'TrainingPlanArn': 'string'
},
'CustomImages': [
{
'ImageName': 'string',
'ImageVersionNumber': 123,
'AppImageConfigName': 'string'
},
],
'LifecycleConfigArns': [
'string',
],
'CodeRepositories': [
{
'RepositoryUrl': 'string'
},
],
'AppLifecycleManagement': {
'IdleSettings': {
'LifecycleManagement': 'ENABLED'|'DISABLED',
'IdleTimeoutInMinutes': 123,
'MinIdleTimeoutInMinutes': 123,
'MaxIdleTimeoutInMinutes': 123
}
},
'EmrSettings': {
'AssumableRoleArns': [
'string',
],
'ExecutionRoleArns': [
'string',
]
},
'BuiltInLifecycleConfigArn': 'string'
},
'SpaceStorageSettings': {
'DefaultEbsStorageSettings': {
'DefaultEbsVolumeSizeInGb': 123,
'MaximumEbsVolumeSizeInGb': 123
}
},
'DefaultLandingUri': 'string',
'StudioWebPortal': 'ENABLED'|'DISABLED',
'CustomPosixUserConfig': {
'Uid': 123,
'Gid': 123
},
'CustomFileSystemConfigs': [
{
'EFSFileSystemConfig': {
'FileSystemId': 'string',
'FileSystemPath': 'string'
},
'FSxLustreFileSystemConfig': {
'FileSystemId': 'string',
'FileSystemPath': 'string'
},
'S3FileSystemConfig': {
'MountPath': 'string',
'S3Uri': 'string'
}
},
],
'StudioWebPortalSettings': {
'HiddenMlTools': [
'DataWrangler'|'FeatureStore'|'EmrClusters'|'AutoMl'|'Experiments'|'Training'|'ModelEvaluation'|'Pipelines'|'Models'|'JumpStart'|'InferenceRecommender'|'Endpoints'|'Projects'|'InferenceOptimization'|'PerformanceEvaluation'|'LakeraGuard'|'Comet'|'DeepchecksLLMEvaluation'|'Fiddler'|'HyperPodClusters'|'RunningInstances'|'Datasets'|'Evaluators',
],
'HiddenAppTypes': [
'JupyterServer'|'KernelGateway'|'DetailedProfiler'|'TensorBoard'|'CodeEditor'|'JupyterLab'|'RStudioServerPro'|'RSessionGateway'|'Canvas',
],
'HiddenInstanceTypes': [
'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.g6.xlarge'|'ml.g6.2xlarge'|'ml.g6.4xlarge'|'ml.g6.8xlarge'|'ml.g6.12xlarge'|'ml.g6.16xlarge'|'ml.g6.24xlarge'|'ml.g6.48xlarge'|'ml.g6e.xlarge'|'ml.g6e.2xlarge'|'ml.g6e.4xlarge'|'ml.g6e.8xlarge'|'ml.g6e.12xlarge'|'ml.g6e.16xlarge'|'ml.g6e.24xlarge'|'ml.g6e.48xlarge'|'ml.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.p5.48xlarge'|'ml.p5en.48xlarge'|'ml.p6-b200.48xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.r6i.large'|'ml.r6i.xlarge'|'ml.r6i.2xlarge'|'ml.r6i.4xlarge'|'ml.r6i.8xlarge'|'ml.r6i.12xlarge'|'ml.r6i.16xlarge'|'ml.r6i.24xlarge'|'ml.r6i.32xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge'|'ml.m6id.large'|'ml.m6id.xlarge'|'ml.m6id.2xlarge'|'ml.m6id.4xlarge'|'ml.m6id.8xlarge'|'ml.m6id.12xlarge'|'ml.m6id.16xlarge'|'ml.m6id.24xlarge'|'ml.m6id.32xlarge'|'ml.c6id.large'|'ml.c6id.xlarge'|'ml.c6id.2xlarge'|'ml.c6id.4xlarge'|'ml.c6id.8xlarge'|'ml.c6id.12xlarge'|'ml.c6id.16xlarge'|'ml.c6id.24xlarge'|'ml.c6id.32xlarge'|'ml.r6id.large'|'ml.r6id.xlarge'|'ml.r6id.2xlarge'|'ml.r6id.4xlarge'|'ml.r6id.8xlarge'|'ml.r6id.12xlarge'|'ml.r6id.16xlarge'|'ml.r6id.24xlarge'|'ml.r6id.32xlarge'|'ml.p5.4xlarge',
],
'HiddenSageMakerImageVersionAliases': [
{
'SageMakerImageName': 'sagemaker_distribution',
'VersionAliases': [
'string',
]
},
],
'ExecutionRoleSessionNameMode': 'STATIC'|'USER_IDENTITY'
},
'AutoMountHomeEFS': 'Enabled'|'Disabled'|'DefaultAsDomain'
}
)
string
[REQUIRED]
The ID of the associated Domain.
string
[REQUIRED]
A name for the UserProfile. This value is not case sensitive.
string
A specifier for the type of value specified in SingleSignOnUserValue. Currently, the only supported value is "UserName". If the Domain's AuthMode is IAM Identity Center, this field is required. If the Domain's AuthMode is not IAM Identity Center, this field cannot be specified.
string
The username of the associated Amazon Web Services Single Sign-On User for this UserProfile. If the Domain's AuthMode is IAM Identity Center, this field is required, and must match a valid username of a user in your directory. If the Domain's AuthMode is not IAM Identity Center, this field cannot be specified.
list
Each tag consists of a key and an optional value. Tag keys must be unique per resource.
Tags that you specify for the User Profile are also added to all Apps that the User Profile launches.
(dict) --
A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags.
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources. For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy.
Key (string) -- [REQUIRED]
The tag key. Tag keys must be unique per resource.
Value (string) -- [REQUIRED]
The tag value.
dict
A collection of settings.
ExecutionRole (string) --
The execution role for the user.
SageMaker applies this setting only to private spaces that the user creates in the domain. SageMaker doesn't apply this setting to shared spaces.
SecurityGroups (list) --
The security groups for the Amazon Virtual Private Cloud (VPC) that the domain uses for communication.
Optional when the CreateDomain.AppNetworkAccessType parameter is set to PublicInternetOnly.
Required when the CreateDomain.AppNetworkAccessType parameter is set to VpcOnly, unless specified as part of the DefaultUserSettings for the domain.
Amazon SageMaker AI adds a security group to allow NFS traffic from Amazon SageMaker AI Studio. Therefore, the number of security groups that you can specify is one less than the maximum number shown.
SageMaker applies these settings only to private spaces that the user creates in the domain. SageMaker doesn't apply these settings to shared spaces.
(string) --
SharingSettings (dict) --
Specifies options for sharing Amazon SageMaker AI Studio notebooks.
NotebookOutputOption (string) --
Whether to include the notebook cell output when sharing the notebook. The default is Disabled.
S3OutputPath (string) --
When NotebookOutputOption is Allowed, the Amazon S3 bucket used to store the shared notebook snapshots.
S3KmsKeyId (string) --
When NotebookOutputOption is Allowed, the Amazon Web Services Key Management Service (KMS) encryption key ID used to encrypt the notebook cell output in the Amazon S3 bucket.
JupyterServerAppSettings (dict) --
The Jupyter server's app settings.
DefaultResourceSpec (dict) --
The default instance type and the Amazon Resource Name (ARN) of the default SageMaker AI image used by the JupyterServer app. If you use the LifecycleConfigArns parameter, then this parameter is also required.
SageMakerImageArn (string) --
The ARN of the SageMaker AI image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance. To clear the value set for SageMakerImageVersionArn, pass None as the value.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
TrainingPlanArn (string) --
The ARN of the SageMaker AI Training Plan to use for this app. When you specify a training plan, the app launches on reserved GPU capacity. This field is supported for JupyterLab and CodeEditor app types.
For more information about how to reserve GPU capacity with SageMaker AI Training Plans, see Using training plans in Studio applications.
LifecycleConfigArns (list) --
The Amazon Resource Name (ARN) of the Lifecycle Configurations attached to the JupyterServerApp. If you use this parameter, the DefaultResourceSpec parameter is also required.
(string) --
CodeRepositories (list) --
A list of Git repositories that SageMaker AI automatically displays to users for cloning in the JupyterServer application.
(dict) --
A Git repository that SageMaker AI automatically displays to users for cloning in the JupyterServer application.
RepositoryUrl (string) -- [REQUIRED]
The URL of the Git repository.
KernelGatewayAppSettings (dict) --
The kernel gateway app settings.
DefaultResourceSpec (dict) --
The default instance type and the Amazon Resource Name (ARN) of the default SageMaker AI image used by the KernelGateway app.
SageMakerImageArn (string) --
The ARN of the SageMaker AI image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance. To clear the value set for SageMakerImageVersionArn, pass None as the value.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
TrainingPlanArn (string) --
The ARN of the SageMaker AI Training Plan to use for this app. When you specify a training plan, the app launches on reserved GPU capacity. This field is supported for JupyterLab and CodeEditor app types.
For more information about how to reserve GPU capacity with SageMaker AI Training Plans, see Using training plans in Studio applications.
CustomImages (list) --
A list of custom SageMaker AI images that are configured to run as a KernelGateway app.
The maximum number of custom images are as follows.
On a domain level: 200
On a space level: 5
On a user profile level: 5
(dict) --
A custom SageMaker AI image. For more information, see Bring your own SageMaker AI image.
ImageName (string) -- [REQUIRED]
The name of the CustomImage. Must be unique to your account.
ImageVersionNumber (integer) --
The version number of the CustomImage.
AppImageConfigName (string) -- [REQUIRED]
The name of the AppImageConfig.
LifecycleConfigArns (list) --
The Amazon Resource Name (ARN) of the Lifecycle Configurations attached to the the user profile or domain.
(string) --
TensorBoardAppSettings (dict) --
The TensorBoard app settings.
DefaultResourceSpec (dict) --
The default instance type and the Amazon Resource Name (ARN) of the SageMaker AI image created on the instance.
SageMakerImageArn (string) --
The ARN of the SageMaker AI image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance. To clear the value set for SageMakerImageVersionArn, pass None as the value.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
TrainingPlanArn (string) --
The ARN of the SageMaker AI Training Plan to use for this app. When you specify a training plan, the app launches on reserved GPU capacity. This field is supported for JupyterLab and CodeEditor app types.
For more information about how to reserve GPU capacity with SageMaker AI Training Plans, see Using training plans in Studio applications.
RStudioServerProAppSettings (dict) --
A collection of settings that configure user interaction with the RStudioServerPro app.
AccessStatus (string) --
Indicates whether the current user has access to the RStudioServerPro app.
UserGroup (string) --
The level of permissions that the user has within the RStudioServerPro app. This value defaults to User. The Admin value allows the user access to the RStudio Administrative Dashboard.
RSessionAppSettings (dict) --
A collection of settings that configure the RSessionGateway app.
DefaultResourceSpec (dict) --
Specifies the ARN's of a SageMaker AI image and SageMaker AI image version, and the instance type that the version runs on.
SageMakerImageArn (string) --
The ARN of the SageMaker AI image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance. To clear the value set for SageMakerImageVersionArn, pass None as the value.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
TrainingPlanArn (string) --
The ARN of the SageMaker AI Training Plan to use for this app. When you specify a training plan, the app launches on reserved GPU capacity. This field is supported for JupyterLab and CodeEditor app types.
For more information about how to reserve GPU capacity with SageMaker AI Training Plans, see Using training plans in Studio applications.
CustomImages (list) --
A list of custom SageMaker AI images that are configured to run as a RSession app.
(dict) --
A custom SageMaker AI image. For more information, see Bring your own SageMaker AI image.
ImageName (string) -- [REQUIRED]
The name of the CustomImage. Must be unique to your account.
ImageVersionNumber (integer) --
The version number of the CustomImage.
AppImageConfigName (string) -- [REQUIRED]
The name of the AppImageConfig.
CanvasAppSettings (dict) --
The Canvas app settings.
SageMaker applies these settings only to private spaces that SageMaker creates for the Canvas app.
TimeSeriesForecastingSettings (dict) --
Time series forecast settings for the SageMaker Canvas application.
Status (string) --
Describes whether time series forecasting is enabled or disabled in the Canvas application.
AmazonForecastRoleArn (string) --
The IAM role that Canvas passes to Amazon Forecast for time series forecasting. By default, Canvas uses the execution role specified in the UserProfile that launches the Canvas application. If an execution role is not specified in the UserProfile, Canvas uses the execution role specified in the Domain that owns the UserProfile. To allow time series forecasting, this IAM role should have the AmazonSageMakerCanvasForecastAccess policy attached and forecast.amazonaws.com added in the trust relationship as a service principal.
ModelRegisterSettings (dict) --
The model registry settings for the SageMaker Canvas application.
Status (string) --
Describes whether the integration to the model registry is enabled or disabled in the Canvas application.
CrossAccountModelRegisterRoleArn (string) --
The Amazon Resource Name (ARN) of the SageMaker model registry account. Required only to register model versions created by a different SageMaker Canvas Amazon Web Services account than the Amazon Web Services account in which SageMaker model registry is set up.
WorkspaceSettings (dict) --
The workspace settings for the SageMaker Canvas application.
S3ArtifactPath (string) --
The Amazon S3 bucket used to store artifacts generated by Canvas. Updating the Amazon S3 location impacts existing configuration settings, and Canvas users no longer have access to their artifacts. Canvas users must log out and log back in to apply the new location.
S3KmsKeyId (string) --
The Amazon Web Services Key Management Service (KMS) encryption key ID that is used to encrypt artifacts generated by Canvas in the Amazon S3 bucket.
IdentityProviderOAuthSettings (list) --
The settings for connecting to an external data source with OAuth.
(dict) --
The Amazon SageMaker Canvas application setting where you configure OAuth for connecting to an external data source, such as Snowflake.
DataSourceName (string) --
The name of the data source that you're connecting to. Canvas currently supports OAuth for Snowflake and Salesforce Data Cloud.
Status (string) --
Describes whether OAuth for a data source is enabled or disabled in the Canvas application.
SecretArn (string) --
The ARN of an Amazon Web Services Secrets Manager secret that stores the credentials from your identity provider, such as the client ID and secret, authorization URL, and token URL.
DirectDeploySettings (dict) --
The model deployment settings for the SageMaker Canvas application.
Status (string) --
Describes whether model deployment permissions are enabled or disabled in the Canvas application.
KendraSettings (dict) --
The settings for document querying.
Status (string) --
Describes whether the document querying feature is enabled or disabled in the Canvas application.
GenerativeAiSettings (dict) --
The generative AI settings for the SageMaker Canvas application.
AmazonBedrockRoleArn (string) --
The ARN of an Amazon Web Services IAM role that allows fine-tuning of large language models (LLMs) in Amazon Bedrock. The IAM role should have Amazon S3 read and write permissions, as well as a trust relationship that establishes bedrock.amazonaws.com as a service principal.
EmrServerlessSettings (dict) --
The settings for running Amazon EMR Serverless data processing jobs in SageMaker Canvas.
ExecutionRoleArn (string) --
The Amazon Resource Name (ARN) of the Amazon Web Services IAM role that is assumed for running Amazon EMR Serverless jobs in SageMaker Canvas. This role should have the necessary permissions to read and write data attached and a trust relationship with EMR Serverless.
Status (string) --
Describes whether Amazon EMR Serverless job capabilities are enabled or disabled in the SageMaker Canvas application.
CodeEditorAppSettings (dict) --
The Code Editor application settings.
SageMaker applies these settings only to private spaces that the user creates in the domain. SageMaker doesn't apply these settings to shared spaces.
DefaultResourceSpec (dict) --
Specifies the ARN's of a SageMaker AI image and SageMaker AI image version, and the instance type that the version runs on.
SageMakerImageArn (string) --
The ARN of the SageMaker AI image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance. To clear the value set for SageMakerImageVersionArn, pass None as the value.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
TrainingPlanArn (string) --
The ARN of the SageMaker AI Training Plan to use for this app. When you specify a training plan, the app launches on reserved GPU capacity. This field is supported for JupyterLab and CodeEditor app types.
For more information about how to reserve GPU capacity with SageMaker AI Training Plans, see Using training plans in Studio applications.
CustomImages (list) --
A list of custom SageMaker images that are configured to run as a Code Editor app.
(dict) --
A custom SageMaker AI image. For more information, see Bring your own SageMaker AI image.
ImageName (string) -- [REQUIRED]
The name of the CustomImage. Must be unique to your account.
ImageVersionNumber (integer) --
The version number of the CustomImage.
AppImageConfigName (string) -- [REQUIRED]
The name of the AppImageConfig.
LifecycleConfigArns (list) --
The Amazon Resource Name (ARN) of the Code Editor application lifecycle configuration.
(string) --
AppLifecycleManagement (dict) --
Settings that are used to configure and manage the lifecycle of CodeEditor applications.
IdleSettings (dict) --
Settings related to idle shutdown of Studio applications.
LifecycleManagement (string) --
Indicates whether idle shutdown is activated for the application type.
IdleTimeoutInMinutes (integer) --
The time that SageMaker waits after the application becomes idle before shutting it down.
MinIdleTimeoutInMinutes (integer) --
The minimum value in minutes that custom idle shutdown can be set to by the user.
MaxIdleTimeoutInMinutes (integer) --
The maximum value in minutes that custom idle shutdown can be set to by the user.
BuiltInLifecycleConfigArn (string) --
The lifecycle configuration that runs before the default lifecycle configuration. It can override changes made in the default lifecycle configuration.
JupyterLabAppSettings (dict) --
The settings for the JupyterLab application.
SageMaker applies these settings only to private spaces that the user creates in the domain. SageMaker doesn't apply these settings to shared spaces.
DefaultResourceSpec (dict) --
Specifies the ARN's of a SageMaker AI image and SageMaker AI image version, and the instance type that the version runs on.
SageMakerImageArn (string) --
The ARN of the SageMaker AI image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance. To clear the value set for SageMakerImageVersionArn, pass None as the value.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
TrainingPlanArn (string) --
The ARN of the SageMaker AI Training Plan to use for this app. When you specify a training plan, the app launches on reserved GPU capacity. This field is supported for JupyterLab and CodeEditor app types.
For more information about how to reserve GPU capacity with SageMaker AI Training Plans, see Using training plans in Studio applications.
CustomImages (list) --
A list of custom SageMaker images that are configured to run as a JupyterLab app.
(dict) --
A custom SageMaker AI image. For more information, see Bring your own SageMaker AI image.
ImageName (string) -- [REQUIRED]
The name of the CustomImage. Must be unique to your account.
ImageVersionNumber (integer) --
The version number of the CustomImage.
AppImageConfigName (string) -- [REQUIRED]
The name of the AppImageConfig.
LifecycleConfigArns (list) --
The Amazon Resource Name (ARN) of the lifecycle configurations attached to the user profile or domain. To remove a lifecycle config, you must set LifecycleConfigArns to an empty list.
(string) --
CodeRepositories (list) --
A list of Git repositories that SageMaker automatically displays to users for cloning in the JupyterLab application.
(dict) --
A Git repository that SageMaker AI automatically displays to users for cloning in the JupyterServer application.
RepositoryUrl (string) -- [REQUIRED]
The URL of the Git repository.
AppLifecycleManagement (dict) --
Indicates whether idle shutdown is activated for JupyterLab applications.
IdleSettings (dict) --
Settings related to idle shutdown of Studio applications.
LifecycleManagement (string) --
Indicates whether idle shutdown is activated for the application type.
IdleTimeoutInMinutes (integer) --
The time that SageMaker waits after the application becomes idle before shutting it down.
MinIdleTimeoutInMinutes (integer) --
The minimum value in minutes that custom idle shutdown can be set to by the user.
MaxIdleTimeoutInMinutes (integer) --
The maximum value in minutes that custom idle shutdown can be set to by the user.
EmrSettings (dict) --
The configuration parameters that specify the IAM roles assumed by the execution role of SageMaker (assumable roles) and the cluster instances or job execution environments (execution roles or runtime roles) to manage and access resources required for running Amazon EMR clusters or Amazon EMR Serverless applications.
AssumableRoleArns (list) --
An array of Amazon Resource Names (ARNs) of the IAM roles that the execution role of SageMaker can assume for performing operations or tasks related to Amazon EMR clusters or Amazon EMR Serverless applications. These roles define the permissions and access policies required when performing Amazon EMR-related operations, such as listing, connecting to, or terminating Amazon EMR clusters or Amazon EMR Serverless applications. They are typically used in cross-account access scenarios, where the Amazon EMR resources (clusters or serverless applications) are located in a different Amazon Web Services account than the SageMaker domain.
(string) --
ExecutionRoleArns (list) --
An array of Amazon Resource Names (ARNs) of the IAM roles used by the Amazon EMR cluster instances or job execution environments to access other Amazon Web Services services and resources needed during the runtime of your Amazon EMR or Amazon EMR Serverless workloads, such as Amazon S3 for data access, Amazon CloudWatch for logging, or other Amazon Web Services services based on the particular workload requirements.
(string) --
BuiltInLifecycleConfigArn (string) --
The lifecycle configuration that runs before the default lifecycle configuration. It can override changes made in the default lifecycle configuration.
SpaceStorageSettings (dict) --
The storage settings for a space.
SageMaker applies these settings only to private spaces that the user creates in the domain. SageMaker doesn't apply these settings to shared spaces.
DefaultEbsStorageSettings (dict) --
The default EBS storage settings for a space.
DefaultEbsVolumeSizeInGb (integer) -- [REQUIRED]
The default size of the EBS storage volume for a space.
MaximumEbsVolumeSizeInGb (integer) -- [REQUIRED]
The maximum size of the EBS storage volume for a space.
DefaultLandingUri (string) --
The default experience that the user is directed to when accessing the domain. The supported values are:
studio::: Indicates that Studio is the default experience. This value can only be passed if StudioWebPortal is set to ENABLED.
app:JupyterServer:: Indicates that Studio Classic is the default experience.
StudioWebPortal (string) --
Whether the user can access Studio. If this value is set to DISABLED, the user cannot access Studio, even if that is the default experience for the domain.
CustomPosixUserConfig (dict) --
Details about the POSIX identity that is used for file system operations.
SageMaker applies these settings only to private spaces that the user creates in the domain. SageMaker doesn't apply these settings to shared spaces.
Uid (integer) -- [REQUIRED]
The POSIX user ID.
Gid (integer) -- [REQUIRED]
The POSIX group ID.
CustomFileSystemConfigs (list) --
The settings for assigning a custom file system to a user profile. Permitted users can access this file system in Amazon SageMaker AI Studio.
SageMaker applies these settings only to private spaces that the user creates in the domain. SageMaker doesn't apply these settings to shared spaces.
(dict) --
The settings for assigning a custom file system to a user profile or space for an Amazon SageMaker AI Domain. Permitted users can access this file system in Amazon SageMaker AI Studio.
EFSFileSystemConfig (dict) --
The settings for a custom Amazon EFS file system.
FileSystemId (string) -- [REQUIRED]
The ID of your Amazon EFS file system.
FileSystemPath (string) --
The path to the file system directory that is accessible in Amazon SageMaker AI Studio. Permitted users can access only this directory and below.
FSxLustreFileSystemConfig (dict) --
The settings for a custom Amazon FSx for Lustre file system.
FileSystemId (string) -- [REQUIRED]
The globally unique, 17-digit, ID of the file system, assigned by Amazon FSx for Lustre.
FileSystemPath (string) --
The path to the file system directory that is accessible in Amazon SageMaker Studio. Permitted users can access only this directory and below.
S3FileSystemConfig (dict) --
Configuration settings for a custom Amazon S3 file system.
MountPath (string) --
The file system path where the Amazon S3 storage location will be mounted within the Amazon SageMaker Studio environment.
S3Uri (string) -- [REQUIRED]
The Amazon S3 URI of the S3 file system configuration.
StudioWebPortalSettings (dict) --
Studio settings. If these settings are applied on a user level, they take priority over the settings applied on a domain level.
HiddenMlTools (list) --
The machine learning tools that are hidden from the Studio left navigation pane.
(string) --
HiddenAppTypes (list) --
The Applications supported in Studio that are hidden from the Studio left navigation pane.
(string) --
HiddenInstanceTypes (list) --
The instance types you are hiding from the Studio user interface.
(string) --
HiddenSageMakerImageVersionAliases (list) --
The version aliases you are hiding from the Studio user interface.
(dict) --
The SageMaker images that are hidden from the Studio user interface. You must specify the SageMaker image name and version aliases.
SageMakerImageName (string) --
The SageMaker image name that you are hiding from the Studio user interface.
VersionAliases (list) --
The version aliases you are hiding from the Studio user interface.
(string) --
ExecutionRoleSessionNameMode (string) --
The execution role session name mode. If this value is set to USER_IDENTITY, the session name of the execution role corresponds to the user's identity. For IAM domains, the session name is the IAM session name used to generate the presigned URL. For IAM Identity Center domains, the session name is the username of the associated IAM Identity Center user. If this value is set to STATIC or is not set, the session name defaults to SageMaker.
AutoMountHomeEFS (string) --
Indicates whether auto-mounting of an EFS volume is supported for the user profile. The DefaultAsDomain value is only supported for user profiles. Do not use the DefaultAsDomain value when setting this parameter for a domain.
SageMaker applies this setting only to private spaces that the user creates in the domain. SageMaker doesn't apply this setting to shared spaces.
dict
Response Syntax
{
'UserProfileArn': 'string'
}
Response Structure
(dict) --
UserProfileArn (string) --
The user profile Amazon Resource Name (ARN).
{'ResourceSpec': {'TrainingPlanArn': 'string'}}
Describes the app.
See also: AWS API Documentation
Request Syntax
client.describe_app(
DomainId='string',
UserProfileName='string',
SpaceName='string',
AppType='JupyterServer'|'KernelGateway'|'DetailedProfiler'|'TensorBoard'|'CodeEditor'|'JupyterLab'|'RStudioServerPro'|'RSessionGateway'|'Canvas',
AppName='string'
)
string
[REQUIRED]
The domain ID.
string
The user profile name. If this value is not set, then SpaceName must be set.
string
The name of the space.
string
[REQUIRED]
The type of app.
string
[REQUIRED]
The name of the app.
dict
Response Syntax
{
'AppArn': 'string',
'AppType': 'JupyterServer'|'KernelGateway'|'DetailedProfiler'|'TensorBoard'|'CodeEditor'|'JupyterLab'|'RStudioServerPro'|'RSessionGateway'|'Canvas',
'AppName': 'string',
'DomainId': 'string',
'UserProfileName': 'string',
'SpaceName': 'string',
'Status': 'Deleted'|'Deleting'|'Failed'|'InService'|'Pending',
'EffectiveTrustedIdentityPropagationStatus': 'ENABLED'|'DISABLED',
'RecoveryMode': True|False,
'LastHealthCheckTimestamp': datetime(2015, 1, 1),
'LastUserActivityTimestamp': datetime(2015, 1, 1),
'CreationTime': datetime(2015, 1, 1),
'FailureReason': 'string',
'ResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': 'string',
'SageMakerImageVersionAlias': 'string',
'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.g6.xlarge'|'ml.g6.2xlarge'|'ml.g6.4xlarge'|'ml.g6.8xlarge'|'ml.g6.12xlarge'|'ml.g6.16xlarge'|'ml.g6.24xlarge'|'ml.g6.48xlarge'|'ml.g6e.xlarge'|'ml.g6e.2xlarge'|'ml.g6e.4xlarge'|'ml.g6e.8xlarge'|'ml.g6e.12xlarge'|'ml.g6e.16xlarge'|'ml.g6e.24xlarge'|'ml.g6e.48xlarge'|'ml.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.p5.48xlarge'|'ml.p5en.48xlarge'|'ml.p6-b200.48xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.r6i.large'|'ml.r6i.xlarge'|'ml.r6i.2xlarge'|'ml.r6i.4xlarge'|'ml.r6i.8xlarge'|'ml.r6i.12xlarge'|'ml.r6i.16xlarge'|'ml.r6i.24xlarge'|'ml.r6i.32xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge'|'ml.m6id.large'|'ml.m6id.xlarge'|'ml.m6id.2xlarge'|'ml.m6id.4xlarge'|'ml.m6id.8xlarge'|'ml.m6id.12xlarge'|'ml.m6id.16xlarge'|'ml.m6id.24xlarge'|'ml.m6id.32xlarge'|'ml.c6id.large'|'ml.c6id.xlarge'|'ml.c6id.2xlarge'|'ml.c6id.4xlarge'|'ml.c6id.8xlarge'|'ml.c6id.12xlarge'|'ml.c6id.16xlarge'|'ml.c6id.24xlarge'|'ml.c6id.32xlarge'|'ml.r6id.large'|'ml.r6id.xlarge'|'ml.r6id.2xlarge'|'ml.r6id.4xlarge'|'ml.r6id.8xlarge'|'ml.r6id.12xlarge'|'ml.r6id.16xlarge'|'ml.r6id.24xlarge'|'ml.r6id.32xlarge'|'ml.p5.4xlarge',
'LifecycleConfigArn': 'string',
'TrainingPlanArn': 'string'
},
'BuiltInLifecycleConfigArn': 'string'
}
Response Structure
(dict) --
AppArn (string) --
The Amazon Resource Name (ARN) of the app.
AppType (string) --
The type of app.
AppName (string) --
The name of the app.
DomainId (string) --
The domain ID.
UserProfileName (string) --
The user profile name.
SpaceName (string) --
The name of the space. If this value is not set, then UserProfileName must be set.
Status (string) --
The status.
EffectiveTrustedIdentityPropagationStatus (string) --
The effective status of Trusted Identity Propagation (TIP) for this application. When enabled, user identities from IAM Identity Center are being propagated through the application to TIP enabled Amazon Web Services services. When disabled, standard IAM role-based access is used.
RecoveryMode (boolean) --
Indicates whether the application is launched in recovery mode.
LastHealthCheckTimestamp (datetime) --
The timestamp of the last health check.
LastUserActivityTimestamp (datetime) --
The timestamp of the last user's activity. LastUserActivityTimestamp is also updated when SageMaker AI performs health checks without user activity. As a result, this value is set to the same value as LastHealthCheckTimestamp.
CreationTime (datetime) --
The creation time of the application.
FailureReason (string) --
The failure reason.
ResourceSpec (dict) --
The instance type and the Amazon Resource Name (ARN) of the SageMaker AI image created on the instance.
SageMakerImageArn (string) --
The ARN of the SageMaker AI image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance. To clear the value set for SageMakerImageVersionArn, pass None as the value.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
TrainingPlanArn (string) --
The ARN of the SageMaker AI Training Plan to use for this app. When you specify a training plan, the app launches on reserved GPU capacity. This field is supported for JupyterLab and CodeEditor app types.
For more information about how to reserve GPU capacity with SageMaker AI Training Plans, see Using training plans in Studio applications.
BuiltInLifecycleConfigArn (string) --
The lifecycle configuration that runs before the default lifecycle configuration
{'EventDetails': {'EventDetails': {'EventMetadata': {'Instance': {'InstanceRequirementsEniConfigurations': [{'AdditionalEnis': {'EfaEnis': ['string']},
'CustomerEni': 'string'}]}}},
'EventLevel': 'Info | Warn | Error'}}
Retrieves detailed information about a specific event for a given HyperPod cluster. This functionality is only supported when the NodeProvisioningMode is set to Continuous.
See also: AWS API Documentation
Request Syntax
client.describe_cluster_event(
EventId='string',
ClusterName='string'
)
string
[REQUIRED]
The unique identifier (UUID) of the event to describe. This ID can be obtained from the ListClusterEvents operation.
string
[REQUIRED]
The name or Amazon Resource Name (ARN) of the HyperPod cluster associated with the event.
dict
Response Syntax
{
'EventDetails': {
'EventId': 'string',
'ClusterArn': 'string',
'ClusterName': 'string',
'InstanceGroupName': 'string',
'InstanceId': 'string',
'ResourceType': 'Cluster'|'InstanceGroup'|'Instance',
'EventTime': datetime(2015, 1, 1),
'EventDetails': {
'EventMetadata': {
'Cluster': {
'FailureMessage': 'string',
'EksRoleAccessEntries': [
'string',
],
'SlrAccessEntry': 'string'
},
'InstanceGroup': {
'FailureMessage': 'string',
'AvailabilityZoneId': 'string',
'CapacityReservation': {
'Arn': 'string',
'Type': 'ODCR'|'CRG'
},
'SubnetId': 'string',
'SecurityGroupIds': [
'string',
],
'AmiOverride': 'string'
},
'InstanceGroupScaling': {
'InstanceCount': 123,
'TargetCount': 123,
'MinCount': 123,
'FailureMessage': 'string'
},
'Instance': {
'CustomerEni': 'string',
'AdditionalEnis': {
'EfaEnis': [
'string',
]
},
'InstanceRequirementsEniConfigurations': [
{
'CustomerEni': 'string',
'AdditionalEnis': {
'EfaEnis': [
'string',
]
}
},
],
'CapacityReservation': {
'Arn': 'string',
'Type': 'ODCR'|'CRG'
},
'FailureMessage': 'string',
'LcsExecutionState': 'string',
'NodeLogicalId': 'string'
}
}
},
'Description': 'string',
'EventLevel': 'Info'|'Warn'|'Error'
}
}
Response Structure
(dict) --
EventDetails (dict) --
Detailed information about the requested cluster event, including event metadata for various resource types such as Cluster, InstanceGroup, Instance, and their associated attributes.
EventId (string) --
The unique identifier (UUID) of the event.
ClusterArn (string) --
The Amazon Resource Name (ARN) of the HyperPod cluster associated with the event.
ClusterName (string) --
The name of the HyperPod cluster associated with the event.
InstanceGroupName (string) --
The name of the instance group associated with the event, if applicable.
InstanceId (string) --
The EC2 instance ID associated with the event, if applicable.
ResourceType (string) --
The type of resource associated with the event. Valid values are Cluster, InstanceGroup, or Instance.
EventTime (datetime) --
The timestamp when the event occurred.
EventDetails (dict) --
Additional details about the event, including event-specific metadata.
EventMetadata (dict) --
Metadata specific to the event, which may include information about the cluster, instance group, or instance involved.
Cluster (dict) --
Metadata specific to cluster-level events.
FailureMessage (string) --
An error message describing why the cluster level operation (such as creating, updating, or deleting) failed.
EksRoleAccessEntries (list) --
A list of Amazon EKS IAM role ARNs associated with the cluster. This is created by HyperPod on your behalf and only applies for EKS orchestrated clusters.
(string) --
SlrAccessEntry (string) --
The Service-Linked Role (SLR) associated with the cluster. This is created by HyperPod on your behalf and only applies for EKS orchestrated clusters.
InstanceGroup (dict) --
Metadata specific to instance group-level events.
FailureMessage (string) --
An error message describing why the instance group level operation (such as creating, scaling, or deleting) failed.
AvailabilityZoneId (string) --
The ID of the Availability Zone where the instance group is located.
CapacityReservation (dict) --
Information about the Capacity Reservation used by the instance group.
Arn (string) --
The Amazon Resource Name (ARN) of the Capacity Reservation.
Type (string) --
The type of Capacity Reservation. Valid values are ODCR (On-Demand Capacity Reservation) or CRG (Capacity Reservation Group).
SubnetId (string) --
The ID of the subnet where the instance group is located.
SecurityGroupIds (list) --
A list of security group IDs associated with the instance group.
(string) --
AmiOverride (string) --
If you use a custom Amazon Machine Image (AMI) for the instance group, this field shows the ID of the custom AMI.
InstanceGroupScaling (dict) --
Metadata related to instance group scaling events.
InstanceCount (integer) --
The current number of instances in the group.
TargetCount (integer) --
The desired number of instances for the group after scaling.
MinCount (integer) --
Minimum instance count of the instance group.
FailureMessage (string) --
An error message describing why the scaling operation failed, if applicable.
Instance (dict) --
Metadata specific to instance-level events.
CustomerEni (string) --
The ID of the customer-managed Elastic Network Interface (ENI) associated with the instance.
AdditionalEnis (dict) --
Information about additional Elastic Network Interfaces (ENIs) associated with the instance.
EfaEnis (list) --
A list of Elastic Fabric Adapter (EFA) ENIs associated with the instance.
(string) --
InstanceRequirementsEniConfigurations (list) --
The ENI configurations for the instance types in the instance requirements, grouped by network interface category (for example, ENI-only or EFA with ENIs). At most one configuration per category.
(dict) --
The customer ENI and additional ENIs associated with a network interface category.
CustomerEni (string) --
The ID of the customer-managed Elastic Network Interface (ENI) associated with the instance type category.
AdditionalEnis (dict) --
Information about additional Elastic Network Interfaces (ENIs) associated with the instance type category.
EfaEnis (list) --
A list of Elastic Fabric Adapter (EFA) ENIs associated with the instance.
(string) --
CapacityReservation (dict) --
Information about the Capacity Reservation used by the instance.
Arn (string) --
The Amazon Resource Name (ARN) of the Capacity Reservation.
Type (string) --
The type of Capacity Reservation. Valid values are ODCR (On-Demand Capacity Reservation) or CRG (Capacity Reservation Group).
FailureMessage (string) --
An error message describing why the instance creation or update failed, if applicable.
LcsExecutionState (string) --
The execution state of the Lifecycle Script (LCS) for the instance.
NodeLogicalId (string) --
The unique logical identifier of the node within the cluster. The ID used here is the same object as in the BatchAddClusterNodes API.
Description (string) --
A human-readable description of the event.
EventLevel (string) --
The severity level of the event. Valid values are Info, Warn, and Error.
{'DefaultSpaceSettings': {'JupyterLabAppSettings': {'DefaultResourceSpec': {'TrainingPlanArn': 'string'}},
'JupyterServerAppSettings': {'DefaultResourceSpec': {'TrainingPlanArn': 'string'}},
'KernelGatewayAppSettings': {'DefaultResourceSpec': {'TrainingPlanArn': 'string'}}},
'DefaultUserSettings': {'CodeEditorAppSettings': {'DefaultResourceSpec': {'TrainingPlanArn': 'string'}},
'JupyterLabAppSettings': {'DefaultResourceSpec': {'TrainingPlanArn': 'string'}},
'JupyterServerAppSettings': {'DefaultResourceSpec': {'TrainingPlanArn': 'string'}},
'KernelGatewayAppSettings': {'DefaultResourceSpec': {'TrainingPlanArn': 'string'}},
'RSessionAppSettings': {'DefaultResourceSpec': {'TrainingPlanArn': 'string'}},
'StudioWebPortalSettings': {'ExecutionRoleSessionNameMode': 'STATIC '
'| '
'USER_IDENTITY'},
'TensorBoardAppSettings': {'DefaultResourceSpec': {'TrainingPlanArn': 'string'}}},
'DomainSettings': {'RStudioServerProDomainSettings': {'DefaultResourceSpec': {'TrainingPlanArn': 'string'}}}}
The description of the domain.
See also: AWS API Documentation
Request Syntax
client.describe_domain(
DomainId='string'
)
string
[REQUIRED]
The domain ID.
dict
Response Syntax
{
'DomainArn': 'string',
'DomainId': 'string',
'DomainName': 'string',
'HomeEfsFileSystemId': 'string',
'SingleSignOnManagedApplicationInstanceId': 'string',
'SingleSignOnApplicationArn': 'string',
'Status': 'Deleting'|'Failed'|'InService'|'Pending'|'Updating'|'Update_Failed'|'Delete_Failed',
'CreationTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'FailureReason': 'string',
'SecurityGroupIdForDomainBoundary': 'string',
'AuthMode': 'SSO'|'IAM',
'DefaultUserSettings': {
'ExecutionRole': 'string',
'SecurityGroups': [
'string',
],
'SharingSettings': {
'NotebookOutputOption': 'Allowed'|'Disabled',
'S3OutputPath': 'string',
'S3KmsKeyId': 'string'
},
'JupyterServerAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': 'string',
'SageMakerImageVersionAlias': 'string',
'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.g6.xlarge'|'ml.g6.2xlarge'|'ml.g6.4xlarge'|'ml.g6.8xlarge'|'ml.g6.12xlarge'|'ml.g6.16xlarge'|'ml.g6.24xlarge'|'ml.g6.48xlarge'|'ml.g6e.xlarge'|'ml.g6e.2xlarge'|'ml.g6e.4xlarge'|'ml.g6e.8xlarge'|'ml.g6e.12xlarge'|'ml.g6e.16xlarge'|'ml.g6e.24xlarge'|'ml.g6e.48xlarge'|'ml.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.p5.48xlarge'|'ml.p5en.48xlarge'|'ml.p6-b200.48xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.r6i.large'|'ml.r6i.xlarge'|'ml.r6i.2xlarge'|'ml.r6i.4xlarge'|'ml.r6i.8xlarge'|'ml.r6i.12xlarge'|'ml.r6i.16xlarge'|'ml.r6i.24xlarge'|'ml.r6i.32xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge'|'ml.m6id.large'|'ml.m6id.xlarge'|'ml.m6id.2xlarge'|'ml.m6id.4xlarge'|'ml.m6id.8xlarge'|'ml.m6id.12xlarge'|'ml.m6id.16xlarge'|'ml.m6id.24xlarge'|'ml.m6id.32xlarge'|'ml.c6id.large'|'ml.c6id.xlarge'|'ml.c6id.2xlarge'|'ml.c6id.4xlarge'|'ml.c6id.8xlarge'|'ml.c6id.12xlarge'|'ml.c6id.16xlarge'|'ml.c6id.24xlarge'|'ml.c6id.32xlarge'|'ml.r6id.large'|'ml.r6id.xlarge'|'ml.r6id.2xlarge'|'ml.r6id.4xlarge'|'ml.r6id.8xlarge'|'ml.r6id.12xlarge'|'ml.r6id.16xlarge'|'ml.r6id.24xlarge'|'ml.r6id.32xlarge'|'ml.p5.4xlarge',
'LifecycleConfigArn': 'string',
'TrainingPlanArn': 'string'
},
'LifecycleConfigArns': [
'string',
],
'CodeRepositories': [
{
'RepositoryUrl': 'string'
},
]
},
'KernelGatewayAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': 'string',
'SageMakerImageVersionAlias': 'string',
'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.g6.xlarge'|'ml.g6.2xlarge'|'ml.g6.4xlarge'|'ml.g6.8xlarge'|'ml.g6.12xlarge'|'ml.g6.16xlarge'|'ml.g6.24xlarge'|'ml.g6.48xlarge'|'ml.g6e.xlarge'|'ml.g6e.2xlarge'|'ml.g6e.4xlarge'|'ml.g6e.8xlarge'|'ml.g6e.12xlarge'|'ml.g6e.16xlarge'|'ml.g6e.24xlarge'|'ml.g6e.48xlarge'|'ml.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.p5.48xlarge'|'ml.p5en.48xlarge'|'ml.p6-b200.48xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.r6i.large'|'ml.r6i.xlarge'|'ml.r6i.2xlarge'|'ml.r6i.4xlarge'|'ml.r6i.8xlarge'|'ml.r6i.12xlarge'|'ml.r6i.16xlarge'|'ml.r6i.24xlarge'|'ml.r6i.32xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge'|'ml.m6id.large'|'ml.m6id.xlarge'|'ml.m6id.2xlarge'|'ml.m6id.4xlarge'|'ml.m6id.8xlarge'|'ml.m6id.12xlarge'|'ml.m6id.16xlarge'|'ml.m6id.24xlarge'|'ml.m6id.32xlarge'|'ml.c6id.large'|'ml.c6id.xlarge'|'ml.c6id.2xlarge'|'ml.c6id.4xlarge'|'ml.c6id.8xlarge'|'ml.c6id.12xlarge'|'ml.c6id.16xlarge'|'ml.c6id.24xlarge'|'ml.c6id.32xlarge'|'ml.r6id.large'|'ml.r6id.xlarge'|'ml.r6id.2xlarge'|'ml.r6id.4xlarge'|'ml.r6id.8xlarge'|'ml.r6id.12xlarge'|'ml.r6id.16xlarge'|'ml.r6id.24xlarge'|'ml.r6id.32xlarge'|'ml.p5.4xlarge',
'LifecycleConfigArn': 'string',
'TrainingPlanArn': 'string'
},
'CustomImages': [
{
'ImageName': 'string',
'ImageVersionNumber': 123,
'AppImageConfigName': 'string'
},
],
'LifecycleConfigArns': [
'string',
]
},
'TensorBoardAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': 'string',
'SageMakerImageVersionAlias': 'string',
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'SageMakerImageVersionArn': 'string',
'SageMakerImageVersionAlias': 'string',
'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.g6.xlarge'|'ml.g6.2xlarge'|'ml.g6.4xlarge'|'ml.g6.8xlarge'|'ml.g6.12xlarge'|'ml.g6.16xlarge'|'ml.g6.24xlarge'|'ml.g6.48xlarge'|'ml.g6e.xlarge'|'ml.g6e.2xlarge'|'ml.g6e.4xlarge'|'ml.g6e.8xlarge'|'ml.g6e.12xlarge'|'ml.g6e.16xlarge'|'ml.g6e.24xlarge'|'ml.g6e.48xlarge'|'ml.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.p5.48xlarge'|'ml.p5en.48xlarge'|'ml.p6-b200.48xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.r6i.large'|'ml.r6i.xlarge'|'ml.r6i.2xlarge'|'ml.r6i.4xlarge'|'ml.r6i.8xlarge'|'ml.r6i.12xlarge'|'ml.r6i.16xlarge'|'ml.r6i.24xlarge'|'ml.r6i.32xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge'|'ml.m6id.large'|'ml.m6id.xlarge'|'ml.m6id.2xlarge'|'ml.m6id.4xlarge'|'ml.m6id.8xlarge'|'ml.m6id.12xlarge'|'ml.m6id.16xlarge'|'ml.m6id.24xlarge'|'ml.m6id.32xlarge'|'ml.c6id.large'|'ml.c6id.xlarge'|'ml.c6id.2xlarge'|'ml.c6id.4xlarge'|'ml.c6id.8xlarge'|'ml.c6id.12xlarge'|'ml.c6id.16xlarge'|'ml.c6id.24xlarge'|'ml.c6id.32xlarge'|'ml.r6id.large'|'ml.r6id.xlarge'|'ml.r6id.2xlarge'|'ml.r6id.4xlarge'|'ml.r6id.8xlarge'|'ml.r6id.12xlarge'|'ml.r6id.16xlarge'|'ml.r6id.24xlarge'|'ml.r6id.32xlarge'|'ml.p5.4xlarge',
'LifecycleConfigArn': 'string',
'TrainingPlanArn': 'string'
},
'CustomImages': [
{
'ImageName': 'string',
'ImageVersionNumber': 123,
'AppImageConfigName': 'string'
},
],
'LifecycleConfigArns': [
'string',
],
'CodeRepositories': [
{
'RepositoryUrl': 'string'
},
],
'AppLifecycleManagement': {
'IdleSettings': {
'LifecycleManagement': 'ENABLED'|'DISABLED',
'IdleTimeoutInMinutes': 123,
'MinIdleTimeoutInMinutes': 123,
'MaxIdleTimeoutInMinutes': 123
}
},
'EmrSettings': {
'AssumableRoleArns': [
'string',
],
'ExecutionRoleArns': [
'string',
]
},
'BuiltInLifecycleConfigArn': 'string'
},
'SpaceStorageSettings': {
'DefaultEbsStorageSettings': {
'DefaultEbsVolumeSizeInGb': 123,
'MaximumEbsVolumeSizeInGb': 123
}
},
'DefaultLandingUri': 'string',
'StudioWebPortal': 'ENABLED'|'DISABLED',
'CustomPosixUserConfig': {
'Uid': 123,
'Gid': 123
},
'CustomFileSystemConfigs': [
{
'EFSFileSystemConfig': {
'FileSystemId': 'string',
'FileSystemPath': 'string'
},
'FSxLustreFileSystemConfig': {
'FileSystemId': 'string',
'FileSystemPath': 'string'
},
'S3FileSystemConfig': {
'MountPath': 'string',
'S3Uri': 'string'
}
},
],
'StudioWebPortalSettings': {
'HiddenMlTools': [
'DataWrangler'|'FeatureStore'|'EmrClusters'|'AutoMl'|'Experiments'|'Training'|'ModelEvaluation'|'Pipelines'|'Models'|'JumpStart'|'InferenceRecommender'|'Endpoints'|'Projects'|'InferenceOptimization'|'PerformanceEvaluation'|'LakeraGuard'|'Comet'|'DeepchecksLLMEvaluation'|'Fiddler'|'HyperPodClusters'|'RunningInstances'|'Datasets'|'Evaluators',
],
'HiddenAppTypes': [
'JupyterServer'|'KernelGateway'|'DetailedProfiler'|'TensorBoard'|'CodeEditor'|'JupyterLab'|'RStudioServerPro'|'RSessionGateway'|'Canvas',
],
'HiddenInstanceTypes': [
'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.g6.xlarge'|'ml.g6.2xlarge'|'ml.g6.4xlarge'|'ml.g6.8xlarge'|'ml.g6.12xlarge'|'ml.g6.16xlarge'|'ml.g6.24xlarge'|'ml.g6.48xlarge'|'ml.g6e.xlarge'|'ml.g6e.2xlarge'|'ml.g6e.4xlarge'|'ml.g6e.8xlarge'|'ml.g6e.12xlarge'|'ml.g6e.16xlarge'|'ml.g6e.24xlarge'|'ml.g6e.48xlarge'|'ml.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.p5.48xlarge'|'ml.p5en.48xlarge'|'ml.p6-b200.48xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.r6i.large'|'ml.r6i.xlarge'|'ml.r6i.2xlarge'|'ml.r6i.4xlarge'|'ml.r6i.8xlarge'|'ml.r6i.12xlarge'|'ml.r6i.16xlarge'|'ml.r6i.24xlarge'|'ml.r6i.32xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge'|'ml.m6id.large'|'ml.m6id.xlarge'|'ml.m6id.2xlarge'|'ml.m6id.4xlarge'|'ml.m6id.8xlarge'|'ml.m6id.12xlarge'|'ml.m6id.16xlarge'|'ml.m6id.24xlarge'|'ml.m6id.32xlarge'|'ml.c6id.large'|'ml.c6id.xlarge'|'ml.c6id.2xlarge'|'ml.c6id.4xlarge'|'ml.c6id.8xlarge'|'ml.c6id.12xlarge'|'ml.c6id.16xlarge'|'ml.c6id.24xlarge'|'ml.c6id.32xlarge'|'ml.r6id.large'|'ml.r6id.xlarge'|'ml.r6id.2xlarge'|'ml.r6id.4xlarge'|'ml.r6id.8xlarge'|'ml.r6id.12xlarge'|'ml.r6id.16xlarge'|'ml.r6id.24xlarge'|'ml.r6id.32xlarge'|'ml.p5.4xlarge',
],
'HiddenSageMakerImageVersionAliases': [
{
'SageMakerImageName': 'sagemaker_distribution',
'VersionAliases': [
'string',
]
},
],
'ExecutionRoleSessionNameMode': 'STATIC'|'USER_IDENTITY'
},
'AutoMountHomeEFS': 'Enabled'|'Disabled'|'DefaultAsDomain'
},
'DomainSettings': {
'SecurityGroupIds': [
'string',
],
'RStudioServerProDomainSettings': {
'DomainExecutionRoleArn': 'string',
'RStudioConnectUrl': 'string',
'RStudioPackageManagerUrl': 'string',
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': 'string',
'SageMakerImageVersionAlias': 'string',
'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.g6.xlarge'|'ml.g6.2xlarge'|'ml.g6.4xlarge'|'ml.g6.8xlarge'|'ml.g6.12xlarge'|'ml.g6.16xlarge'|'ml.g6.24xlarge'|'ml.g6.48xlarge'|'ml.g6e.xlarge'|'ml.g6e.2xlarge'|'ml.g6e.4xlarge'|'ml.g6e.8xlarge'|'ml.g6e.12xlarge'|'ml.g6e.16xlarge'|'ml.g6e.24xlarge'|'ml.g6e.48xlarge'|'ml.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.p5.48xlarge'|'ml.p5en.48xlarge'|'ml.p6-b200.48xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.r6i.large'|'ml.r6i.xlarge'|'ml.r6i.2xlarge'|'ml.r6i.4xlarge'|'ml.r6i.8xlarge'|'ml.r6i.12xlarge'|'ml.r6i.16xlarge'|'ml.r6i.24xlarge'|'ml.r6i.32xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge'|'ml.m6id.large'|'ml.m6id.xlarge'|'ml.m6id.2xlarge'|'ml.m6id.4xlarge'|'ml.m6id.8xlarge'|'ml.m6id.12xlarge'|'ml.m6id.16xlarge'|'ml.m6id.24xlarge'|'ml.m6id.32xlarge'|'ml.c6id.large'|'ml.c6id.xlarge'|'ml.c6id.2xlarge'|'ml.c6id.4xlarge'|'ml.c6id.8xlarge'|'ml.c6id.12xlarge'|'ml.c6id.16xlarge'|'ml.c6id.24xlarge'|'ml.c6id.32xlarge'|'ml.r6id.large'|'ml.r6id.xlarge'|'ml.r6id.2xlarge'|'ml.r6id.4xlarge'|'ml.r6id.8xlarge'|'ml.r6id.12xlarge'|'ml.r6id.16xlarge'|'ml.r6id.24xlarge'|'ml.r6id.32xlarge'|'ml.p5.4xlarge',
'LifecycleConfigArn': 'string',
'TrainingPlanArn': 'string'
}
},
'ExecutionRoleIdentityConfig': 'USER_PROFILE_NAME'|'DISABLED',
'TrustedIdentityPropagationSettings': {
'Status': 'ENABLED'|'DISABLED'
},
'DockerSettings': {
'EnableDockerAccess': 'ENABLED'|'DISABLED',
'VpcOnlyTrustedAccounts': [
'string',
],
'RootlessDocker': 'ENABLED'|'DISABLED'
},
'AmazonQSettings': {
'Status': 'ENABLED'|'DISABLED',
'QProfileArn': 'string'
},
'UnifiedStudioSettings': {
'StudioWebPortalAccess': 'ENABLED'|'DISABLED',
'DomainAccountId': 'string',
'DomainRegion': 'string',
'DomainId': 'string',
'ProjectId': 'string',
'EnvironmentId': 'string',
'ProjectS3Path': 'string',
'SingleSignOnApplicationArn': 'string'
},
'IpAddressType': 'ipv4'|'dualstack'
},
'AppNetworkAccessType': 'PublicInternetOnly'|'VpcOnly',
'HomeEfsFileSystemKmsKeyId': 'string',
'SubnetIds': [
'string',
],
'Url': 'string',
'VpcId': 'string',
'KmsKeyId': 'string',
'AppSecurityGroupManagement': 'Service'|'Customer',
'TagPropagation': 'ENABLED'|'DISABLED',
'DefaultSpaceSettings': {
'ExecutionRole': 'string',
'SecurityGroups': [
'string',
],
'JupyterServerAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': 'string',
'SageMakerImageVersionAlias': 'string',
'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.g6.xlarge'|'ml.g6.2xlarge'|'ml.g6.4xlarge'|'ml.g6.8xlarge'|'ml.g6.12xlarge'|'ml.g6.16xlarge'|'ml.g6.24xlarge'|'ml.g6.48xlarge'|'ml.g6e.xlarge'|'ml.g6e.2xlarge'|'ml.g6e.4xlarge'|'ml.g6e.8xlarge'|'ml.g6e.12xlarge'|'ml.g6e.16xlarge'|'ml.g6e.24xlarge'|'ml.g6e.48xlarge'|'ml.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.p5.48xlarge'|'ml.p5en.48xlarge'|'ml.p6-b200.48xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.r6i.large'|'ml.r6i.xlarge'|'ml.r6i.2xlarge'|'ml.r6i.4xlarge'|'ml.r6i.8xlarge'|'ml.r6i.12xlarge'|'ml.r6i.16xlarge'|'ml.r6i.24xlarge'|'ml.r6i.32xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge'|'ml.m6id.large'|'ml.m6id.xlarge'|'ml.m6id.2xlarge'|'ml.m6id.4xlarge'|'ml.m6id.8xlarge'|'ml.m6id.12xlarge'|'ml.m6id.16xlarge'|'ml.m6id.24xlarge'|'ml.m6id.32xlarge'|'ml.c6id.large'|'ml.c6id.xlarge'|'ml.c6id.2xlarge'|'ml.c6id.4xlarge'|'ml.c6id.8xlarge'|'ml.c6id.12xlarge'|'ml.c6id.16xlarge'|'ml.c6id.24xlarge'|'ml.c6id.32xlarge'|'ml.r6id.large'|'ml.r6id.xlarge'|'ml.r6id.2xlarge'|'ml.r6id.4xlarge'|'ml.r6id.8xlarge'|'ml.r6id.12xlarge'|'ml.r6id.16xlarge'|'ml.r6id.24xlarge'|'ml.r6id.32xlarge'|'ml.p5.4xlarge',
'LifecycleConfigArn': 'string',
'TrainingPlanArn': 'string'
},
'LifecycleConfigArns': [
'string',
],
'CodeRepositories': [
{
'RepositoryUrl': 'string'
},
]
},
'KernelGatewayAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': 'string',
'SageMakerImageVersionAlias': 'string',
'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.g6.xlarge'|'ml.g6.2xlarge'|'ml.g6.4xlarge'|'ml.g6.8xlarge'|'ml.g6.12xlarge'|'ml.g6.16xlarge'|'ml.g6.24xlarge'|'ml.g6.48xlarge'|'ml.g6e.xlarge'|'ml.g6e.2xlarge'|'ml.g6e.4xlarge'|'ml.g6e.8xlarge'|'ml.g6e.12xlarge'|'ml.g6e.16xlarge'|'ml.g6e.24xlarge'|'ml.g6e.48xlarge'|'ml.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.p5.48xlarge'|'ml.p5en.48xlarge'|'ml.p6-b200.48xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.r6i.large'|'ml.r6i.xlarge'|'ml.r6i.2xlarge'|'ml.r6i.4xlarge'|'ml.r6i.8xlarge'|'ml.r6i.12xlarge'|'ml.r6i.16xlarge'|'ml.r6i.24xlarge'|'ml.r6i.32xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge'|'ml.m6id.large'|'ml.m6id.xlarge'|'ml.m6id.2xlarge'|'ml.m6id.4xlarge'|'ml.m6id.8xlarge'|'ml.m6id.12xlarge'|'ml.m6id.16xlarge'|'ml.m6id.24xlarge'|'ml.m6id.32xlarge'|'ml.c6id.large'|'ml.c6id.xlarge'|'ml.c6id.2xlarge'|'ml.c6id.4xlarge'|'ml.c6id.8xlarge'|'ml.c6id.12xlarge'|'ml.c6id.16xlarge'|'ml.c6id.24xlarge'|'ml.c6id.32xlarge'|'ml.r6id.large'|'ml.r6id.xlarge'|'ml.r6id.2xlarge'|'ml.r6id.4xlarge'|'ml.r6id.8xlarge'|'ml.r6id.12xlarge'|'ml.r6id.16xlarge'|'ml.r6id.24xlarge'|'ml.r6id.32xlarge'|'ml.p5.4xlarge',
'LifecycleConfigArn': 'string',
'TrainingPlanArn': 'string'
},
'CustomImages': [
{
'ImageName': 'string',
'ImageVersionNumber': 123,
'AppImageConfigName': 'string'
},
],
'LifecycleConfigArns': [
'string',
]
},
'JupyterLabAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': 'string',
'SageMakerImageVersionAlias': 'string',
'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.g6.xlarge'|'ml.g6.2xlarge'|'ml.g6.4xlarge'|'ml.g6.8xlarge'|'ml.g6.12xlarge'|'ml.g6.16xlarge'|'ml.g6.24xlarge'|'ml.g6.48xlarge'|'ml.g6e.xlarge'|'ml.g6e.2xlarge'|'ml.g6e.4xlarge'|'ml.g6e.8xlarge'|'ml.g6e.12xlarge'|'ml.g6e.16xlarge'|'ml.g6e.24xlarge'|'ml.g6e.48xlarge'|'ml.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.p5.48xlarge'|'ml.p5en.48xlarge'|'ml.p6-b200.48xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.r6i.large'|'ml.r6i.xlarge'|'ml.r6i.2xlarge'|'ml.r6i.4xlarge'|'ml.r6i.8xlarge'|'ml.r6i.12xlarge'|'ml.r6i.16xlarge'|'ml.r6i.24xlarge'|'ml.r6i.32xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge'|'ml.m6id.large'|'ml.m6id.xlarge'|'ml.m6id.2xlarge'|'ml.m6id.4xlarge'|'ml.m6id.8xlarge'|'ml.m6id.12xlarge'|'ml.m6id.16xlarge'|'ml.m6id.24xlarge'|'ml.m6id.32xlarge'|'ml.c6id.large'|'ml.c6id.xlarge'|'ml.c6id.2xlarge'|'ml.c6id.4xlarge'|'ml.c6id.8xlarge'|'ml.c6id.12xlarge'|'ml.c6id.16xlarge'|'ml.c6id.24xlarge'|'ml.c6id.32xlarge'|'ml.r6id.large'|'ml.r6id.xlarge'|'ml.r6id.2xlarge'|'ml.r6id.4xlarge'|'ml.r6id.8xlarge'|'ml.r6id.12xlarge'|'ml.r6id.16xlarge'|'ml.r6id.24xlarge'|'ml.r6id.32xlarge'|'ml.p5.4xlarge',
'LifecycleConfigArn': 'string',
'TrainingPlanArn': 'string'
},
'CustomImages': [
{
'ImageName': 'string',
'ImageVersionNumber': 123,
'AppImageConfigName': 'string'
},
],
'LifecycleConfigArns': [
'string',
],
'CodeRepositories': [
{
'RepositoryUrl': 'string'
},
],
'AppLifecycleManagement': {
'IdleSettings': {
'LifecycleManagement': 'ENABLED'|'DISABLED',
'IdleTimeoutInMinutes': 123,
'MinIdleTimeoutInMinutes': 123,
'MaxIdleTimeoutInMinutes': 123
}
},
'EmrSettings': {
'AssumableRoleArns': [
'string',
],
'ExecutionRoleArns': [
'string',
]
},
'BuiltInLifecycleConfigArn': 'string'
},
'SpaceStorageSettings': {
'DefaultEbsStorageSettings': {
'DefaultEbsVolumeSizeInGb': 123,
'MaximumEbsVolumeSizeInGb': 123
}
},
'CustomPosixUserConfig': {
'Uid': 123,
'Gid': 123
},
'CustomFileSystemConfigs': [
{
'EFSFileSystemConfig': {
'FileSystemId': 'string',
'FileSystemPath': 'string'
},
'FSxLustreFileSystemConfig': {
'FileSystemId': 'string',
'FileSystemPath': 'string'
},
'S3FileSystemConfig': {
'MountPath': 'string',
'S3Uri': 'string'
}
},
]
}
}
Response Structure
(dict) --
DomainArn (string) --
The domain's Amazon Resource Name (ARN).
DomainId (string) --
The domain ID.
DomainName (string) --
The domain name.
HomeEfsFileSystemId (string) --
The ID of the Amazon Elastic File System managed by this Domain.
SingleSignOnManagedApplicationInstanceId (string) --
The IAM Identity Center managed application instance ID.
SingleSignOnApplicationArn (string) --
The ARN of the application managed by SageMaker AI in IAM Identity Center. This value is only returned for domains created after October 1, 2023.
Status (string) --
The status.
CreationTime (datetime) --
The creation time.
LastModifiedTime (datetime) --
The last modified time.
FailureReason (string) --
The failure reason.
SecurityGroupIdForDomainBoundary (string) --
The ID of the security group that authorizes traffic between the RSessionGateway apps and the RStudioServerPro app.
AuthMode (string) --
The domain's authentication mode.
DefaultUserSettings (dict) --
Settings which are applied to UserProfiles in this domain if settings are not explicitly specified in a given UserProfile.
ExecutionRole (string) --
The execution role for the user.
SageMaker applies this setting only to private spaces that the user creates in the domain. SageMaker doesn't apply this setting to shared spaces.
SecurityGroups (list) --
The security groups for the Amazon Virtual Private Cloud (VPC) that the domain uses for communication.
Optional when the CreateDomain.AppNetworkAccessType parameter is set to PublicInternetOnly.
Required when the CreateDomain.AppNetworkAccessType parameter is set to VpcOnly, unless specified as part of the DefaultUserSettings for the domain.
Amazon SageMaker AI adds a security group to allow NFS traffic from Amazon SageMaker AI Studio. Therefore, the number of security groups that you can specify is one less than the maximum number shown.
SageMaker applies these settings only to private spaces that the user creates in the domain. SageMaker doesn't apply these settings to shared spaces.
(string) --
SharingSettings (dict) --
Specifies options for sharing Amazon SageMaker AI Studio notebooks.
NotebookOutputOption (string) --
Whether to include the notebook cell output when sharing the notebook. The default is Disabled.
S3OutputPath (string) --
When NotebookOutputOption is Allowed, the Amazon S3 bucket used to store the shared notebook snapshots.
S3KmsKeyId (string) --
When NotebookOutputOption is Allowed, the Amazon Web Services Key Management Service (KMS) encryption key ID used to encrypt the notebook cell output in the Amazon S3 bucket.
JupyterServerAppSettings (dict) --
The Jupyter server's app settings.
DefaultResourceSpec (dict) --
The default instance type and the Amazon Resource Name (ARN) of the default SageMaker AI image used by the JupyterServer app. If you use the LifecycleConfigArns parameter, then this parameter is also required.
SageMakerImageArn (string) --
The ARN of the SageMaker AI image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance. To clear the value set for SageMakerImageVersionArn, pass None as the value.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
TrainingPlanArn (string) --
The ARN of the SageMaker AI Training Plan to use for this app. When you specify a training plan, the app launches on reserved GPU capacity. This field is supported for JupyterLab and CodeEditor app types.
For more information about how to reserve GPU capacity with SageMaker AI Training Plans, see Using training plans in Studio applications.
LifecycleConfigArns (list) --
The Amazon Resource Name (ARN) of the Lifecycle Configurations attached to the JupyterServerApp. If you use this parameter, the DefaultResourceSpec parameter is also required.
(string) --
CodeRepositories (list) --
A list of Git repositories that SageMaker AI automatically displays to users for cloning in the JupyterServer application.
(dict) --
A Git repository that SageMaker AI automatically displays to users for cloning in the JupyterServer application.
RepositoryUrl (string) --
The URL of the Git repository.
KernelGatewayAppSettings (dict) --
The kernel gateway app settings.
DefaultResourceSpec (dict) --
The default instance type and the Amazon Resource Name (ARN) of the default SageMaker AI image used by the KernelGateway app.
SageMakerImageArn (string) --
The ARN of the SageMaker AI image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance. To clear the value set for SageMakerImageVersionArn, pass None as the value.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
TrainingPlanArn (string) --
The ARN of the SageMaker AI Training Plan to use for this app. When you specify a training plan, the app launches on reserved GPU capacity. This field is supported for JupyterLab and CodeEditor app types.
For more information about how to reserve GPU capacity with SageMaker AI Training Plans, see Using training plans in Studio applications.
CustomImages (list) --
A list of custom SageMaker AI images that are configured to run as a KernelGateway app.
The maximum number of custom images are as follows.
On a domain level: 200
On a space level: 5
On a user profile level: 5
(dict) --
A custom SageMaker AI image. For more information, see Bring your own SageMaker AI image.
ImageName (string) --
The name of the CustomImage. Must be unique to your account.
ImageVersionNumber (integer) --
The version number of the CustomImage.
AppImageConfigName (string) --
The name of the AppImageConfig.
LifecycleConfigArns (list) --
The Amazon Resource Name (ARN) of the Lifecycle Configurations attached to the the user profile or domain.
(string) --
TensorBoardAppSettings (dict) --
The TensorBoard app settings.
DefaultResourceSpec (dict) --
The default instance type and the Amazon Resource Name (ARN) of the SageMaker AI image created on the instance.
SageMakerImageArn (string) --
The ARN of the SageMaker AI image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance. To clear the value set for SageMakerImageVersionArn, pass None as the value.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
TrainingPlanArn (string) --
The ARN of the SageMaker AI Training Plan to use for this app. When you specify a training plan, the app launches on reserved GPU capacity. This field is supported for JupyterLab and CodeEditor app types.
For more information about how to reserve GPU capacity with SageMaker AI Training Plans, see Using training plans in Studio applications.
RStudioServerProAppSettings (dict) --
A collection of settings that configure user interaction with the RStudioServerPro app.
AccessStatus (string) --
Indicates whether the current user has access to the RStudioServerPro app.
UserGroup (string) --
The level of permissions that the user has within the RStudioServerPro app. This value defaults to User. The Admin value allows the user access to the RStudio Administrative Dashboard.
RSessionAppSettings (dict) --
A collection of settings that configure the RSessionGateway app.
DefaultResourceSpec (dict) --
Specifies the ARN's of a SageMaker AI image and SageMaker AI image version, and the instance type that the version runs on.
SageMakerImageArn (string) --
The ARN of the SageMaker AI image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance. To clear the value set for SageMakerImageVersionArn, pass None as the value.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
TrainingPlanArn (string) --
The ARN of the SageMaker AI Training Plan to use for this app. When you specify a training plan, the app launches on reserved GPU capacity. This field is supported for JupyterLab and CodeEditor app types.
For more information about how to reserve GPU capacity with SageMaker AI Training Plans, see Using training plans in Studio applications.
CustomImages (list) --
A list of custom SageMaker AI images that are configured to run as a RSession app.
(dict) --
A custom SageMaker AI image. For more information, see Bring your own SageMaker AI image.
ImageName (string) --
The name of the CustomImage. Must be unique to your account.
ImageVersionNumber (integer) --
The version number of the CustomImage.
AppImageConfigName (string) --
The name of the AppImageConfig.
CanvasAppSettings (dict) --
The Canvas app settings.
SageMaker applies these settings only to private spaces that SageMaker creates for the Canvas app.
TimeSeriesForecastingSettings (dict) --
Time series forecast settings for the SageMaker Canvas application.
Status (string) --
Describes whether time series forecasting is enabled or disabled in the Canvas application.
AmazonForecastRoleArn (string) --
The IAM role that Canvas passes to Amazon Forecast for time series forecasting. By default, Canvas uses the execution role specified in the UserProfile that launches the Canvas application. If an execution role is not specified in the UserProfile, Canvas uses the execution role specified in the Domain that owns the UserProfile. To allow time series forecasting, this IAM role should have the AmazonSageMakerCanvasForecastAccess policy attached and forecast.amazonaws.com added in the trust relationship as a service principal.
ModelRegisterSettings (dict) --
The model registry settings for the SageMaker Canvas application.
Status (string) --
Describes whether the integration to the model registry is enabled or disabled in the Canvas application.
CrossAccountModelRegisterRoleArn (string) --
The Amazon Resource Name (ARN) of the SageMaker model registry account. Required only to register model versions created by a different SageMaker Canvas Amazon Web Services account than the Amazon Web Services account in which SageMaker model registry is set up.
WorkspaceSettings (dict) --
The workspace settings for the SageMaker Canvas application.
S3ArtifactPath (string) --
The Amazon S3 bucket used to store artifacts generated by Canvas. Updating the Amazon S3 location impacts existing configuration settings, and Canvas users no longer have access to their artifacts. Canvas users must log out and log back in to apply the new location.
S3KmsKeyId (string) --
The Amazon Web Services Key Management Service (KMS) encryption key ID that is used to encrypt artifacts generated by Canvas in the Amazon S3 bucket.
IdentityProviderOAuthSettings (list) --
The settings for connecting to an external data source with OAuth.
(dict) --
The Amazon SageMaker Canvas application setting where you configure OAuth for connecting to an external data source, such as Snowflake.
DataSourceName (string) --
The name of the data source that you're connecting to. Canvas currently supports OAuth for Snowflake and Salesforce Data Cloud.
Status (string) --
Describes whether OAuth for a data source is enabled or disabled in the Canvas application.
SecretArn (string) --
The ARN of an Amazon Web Services Secrets Manager secret that stores the credentials from your identity provider, such as the client ID and secret, authorization URL, and token URL.
DirectDeploySettings (dict) --
The model deployment settings for the SageMaker Canvas application.
Status (string) --
Describes whether model deployment permissions are enabled or disabled in the Canvas application.
KendraSettings (dict) --
The settings for document querying.
Status (string) --
Describes whether the document querying feature is enabled or disabled in the Canvas application.
GenerativeAiSettings (dict) --
The generative AI settings for the SageMaker Canvas application.
AmazonBedrockRoleArn (string) --
The ARN of an Amazon Web Services IAM role that allows fine-tuning of large language models (LLMs) in Amazon Bedrock. The IAM role should have Amazon S3 read and write permissions, as well as a trust relationship that establishes bedrock.amazonaws.com as a service principal.
EmrServerlessSettings (dict) --
The settings for running Amazon EMR Serverless data processing jobs in SageMaker Canvas.
ExecutionRoleArn (string) --
The Amazon Resource Name (ARN) of the Amazon Web Services IAM role that is assumed for running Amazon EMR Serverless jobs in SageMaker Canvas. This role should have the necessary permissions to read and write data attached and a trust relationship with EMR Serverless.
Status (string) --
Describes whether Amazon EMR Serverless job capabilities are enabled or disabled in the SageMaker Canvas application.
CodeEditorAppSettings (dict) --
The Code Editor application settings.
SageMaker applies these settings only to private spaces that the user creates in the domain. SageMaker doesn't apply these settings to shared spaces.
DefaultResourceSpec (dict) --
Specifies the ARN's of a SageMaker AI image and SageMaker AI image version, and the instance type that the version runs on.
SageMakerImageArn (string) --
The ARN of the SageMaker AI image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance. To clear the value set for SageMakerImageVersionArn, pass None as the value.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
TrainingPlanArn (string) --
The ARN of the SageMaker AI Training Plan to use for this app. When you specify a training plan, the app launches on reserved GPU capacity. This field is supported for JupyterLab and CodeEditor app types.
For more information about how to reserve GPU capacity with SageMaker AI Training Plans, see Using training plans in Studio applications.
CustomImages (list) --
A list of custom SageMaker images that are configured to run as a Code Editor app.
(dict) --
A custom SageMaker AI image. For more information, see Bring your own SageMaker AI image.
ImageName (string) --
The name of the CustomImage. Must be unique to your account.
ImageVersionNumber (integer) --
The version number of the CustomImage.
AppImageConfigName (string) --
The name of the AppImageConfig.
LifecycleConfigArns (list) --
The Amazon Resource Name (ARN) of the Code Editor application lifecycle configuration.
(string) --
AppLifecycleManagement (dict) --
Settings that are used to configure and manage the lifecycle of CodeEditor applications.
IdleSettings (dict) --
Settings related to idle shutdown of Studio applications.
LifecycleManagement (string) --
Indicates whether idle shutdown is activated for the application type.
IdleTimeoutInMinutes (integer) --
The time that SageMaker waits after the application becomes idle before shutting it down.
MinIdleTimeoutInMinutes (integer) --
The minimum value in minutes that custom idle shutdown can be set to by the user.
MaxIdleTimeoutInMinutes (integer) --
The maximum value in minutes that custom idle shutdown can be set to by the user.
BuiltInLifecycleConfigArn (string) --
The lifecycle configuration that runs before the default lifecycle configuration. It can override changes made in the default lifecycle configuration.
JupyterLabAppSettings (dict) --
The settings for the JupyterLab application.
SageMaker applies these settings only to private spaces that the user creates in the domain. SageMaker doesn't apply these settings to shared spaces.
DefaultResourceSpec (dict) --
Specifies the ARN's of a SageMaker AI image and SageMaker AI image version, and the instance type that the version runs on.
SageMakerImageArn (string) --
The ARN of the SageMaker AI image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance. To clear the value set for SageMakerImageVersionArn, pass None as the value.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
TrainingPlanArn (string) --
The ARN of the SageMaker AI Training Plan to use for this app. When you specify a training plan, the app launches on reserved GPU capacity. This field is supported for JupyterLab and CodeEditor app types.
For more information about how to reserve GPU capacity with SageMaker AI Training Plans, see Using training plans in Studio applications.
CustomImages (list) --
A list of custom SageMaker images that are configured to run as a JupyterLab app.
(dict) --
A custom SageMaker AI image. For more information, see Bring your own SageMaker AI image.
ImageName (string) --
The name of the CustomImage. Must be unique to your account.
ImageVersionNumber (integer) --
The version number of the CustomImage.
AppImageConfigName (string) --
The name of the AppImageConfig.
LifecycleConfigArns (list) --
The Amazon Resource Name (ARN) of the lifecycle configurations attached to the user profile or domain. To remove a lifecycle config, you must set LifecycleConfigArns to an empty list.
(string) --
CodeRepositories (list) --
A list of Git repositories that SageMaker automatically displays to users for cloning in the JupyterLab application.
(dict) --
A Git repository that SageMaker AI automatically displays to users for cloning in the JupyterServer application.
RepositoryUrl (string) --
The URL of the Git repository.
AppLifecycleManagement (dict) --
Indicates whether idle shutdown is activated for JupyterLab applications.
IdleSettings (dict) --
Settings related to idle shutdown of Studio applications.
LifecycleManagement (string) --
Indicates whether idle shutdown is activated for the application type.
IdleTimeoutInMinutes (integer) --
The time that SageMaker waits after the application becomes idle before shutting it down.
MinIdleTimeoutInMinutes (integer) --
The minimum value in minutes that custom idle shutdown can be set to by the user.
MaxIdleTimeoutInMinutes (integer) --
The maximum value in minutes that custom idle shutdown can be set to by the user.
EmrSettings (dict) --
The configuration parameters that specify the IAM roles assumed by the execution role of SageMaker (assumable roles) and the cluster instances or job execution environments (execution roles or runtime roles) to manage and access resources required for running Amazon EMR clusters or Amazon EMR Serverless applications.
AssumableRoleArns (list) --
An array of Amazon Resource Names (ARNs) of the IAM roles that the execution role of SageMaker can assume for performing operations or tasks related to Amazon EMR clusters or Amazon EMR Serverless applications. These roles define the permissions and access policies required when performing Amazon EMR-related operations, such as listing, connecting to, or terminating Amazon EMR clusters or Amazon EMR Serverless applications. They are typically used in cross-account access scenarios, where the Amazon EMR resources (clusters or serverless applications) are located in a different Amazon Web Services account than the SageMaker domain.
(string) --
ExecutionRoleArns (list) --
An array of Amazon Resource Names (ARNs) of the IAM roles used by the Amazon EMR cluster instances or job execution environments to access other Amazon Web Services services and resources needed during the runtime of your Amazon EMR or Amazon EMR Serverless workloads, such as Amazon S3 for data access, Amazon CloudWatch for logging, or other Amazon Web Services services based on the particular workload requirements.
(string) --
BuiltInLifecycleConfigArn (string) --
The lifecycle configuration that runs before the default lifecycle configuration. It can override changes made in the default lifecycle configuration.
SpaceStorageSettings (dict) --
The storage settings for a space.
SageMaker applies these settings only to private spaces that the user creates in the domain. SageMaker doesn't apply these settings to shared spaces.
DefaultEbsStorageSettings (dict) --
The default EBS storage settings for a space.
DefaultEbsVolumeSizeInGb (integer) --
The default size of the EBS storage volume for a space.
MaximumEbsVolumeSizeInGb (integer) --
The maximum size of the EBS storage volume for a space.
DefaultLandingUri (string) --
The default experience that the user is directed to when accessing the domain. The supported values are:
studio::: Indicates that Studio is the default experience. This value can only be passed if StudioWebPortal is set to ENABLED.
app:JupyterServer:: Indicates that Studio Classic is the default experience.
StudioWebPortal (string) --
Whether the user can access Studio. If this value is set to DISABLED, the user cannot access Studio, even if that is the default experience for the domain.
CustomPosixUserConfig (dict) --
Details about the POSIX identity that is used for file system operations.
SageMaker applies these settings only to private spaces that the user creates in the domain. SageMaker doesn't apply these settings to shared spaces.
Uid (integer) --
The POSIX user ID.
Gid (integer) --
The POSIX group ID.
CustomFileSystemConfigs (list) --
The settings for assigning a custom file system to a user profile. Permitted users can access this file system in Amazon SageMaker AI Studio.
SageMaker applies these settings only to private spaces that the user creates in the domain. SageMaker doesn't apply these settings to shared spaces.
(dict) --
The settings for assigning a custom file system to a user profile or space for an Amazon SageMaker AI Domain. Permitted users can access this file system in Amazon SageMaker AI Studio.
EFSFileSystemConfig (dict) --
The settings for a custom Amazon EFS file system.
FileSystemId (string) --
The ID of your Amazon EFS file system.
FileSystemPath (string) --
The path to the file system directory that is accessible in Amazon SageMaker AI Studio. Permitted users can access only this directory and below.
FSxLustreFileSystemConfig (dict) --
The settings for a custom Amazon FSx for Lustre file system.
FileSystemId (string) --
The globally unique, 17-digit, ID of the file system, assigned by Amazon FSx for Lustre.
FileSystemPath (string) --
The path to the file system directory that is accessible in Amazon SageMaker Studio. Permitted users can access only this directory and below.
S3FileSystemConfig (dict) --
Configuration settings for a custom Amazon S3 file system.
MountPath (string) --
The file system path where the Amazon S3 storage location will be mounted within the Amazon SageMaker Studio environment.
S3Uri (string) --
The Amazon S3 URI of the S3 file system configuration.
StudioWebPortalSettings (dict) --
Studio settings. If these settings are applied on a user level, they take priority over the settings applied on a domain level.
HiddenMlTools (list) --
The machine learning tools that are hidden from the Studio left navigation pane.
(string) --
HiddenAppTypes (list) --
The Applications supported in Studio that are hidden from the Studio left navigation pane.
(string) --
HiddenInstanceTypes (list) --
The instance types you are hiding from the Studio user interface.
(string) --
HiddenSageMakerImageVersionAliases (list) --
The version aliases you are hiding from the Studio user interface.
(dict) --
The SageMaker images that are hidden from the Studio user interface. You must specify the SageMaker image name and version aliases.
SageMakerImageName (string) --
The SageMaker image name that you are hiding from the Studio user interface.
VersionAliases (list) --
The version aliases you are hiding from the Studio user interface.
(string) --
ExecutionRoleSessionNameMode (string) --
The execution role session name mode. If this value is set to USER_IDENTITY, the session name of the execution role corresponds to the user's identity. For IAM domains, the session name is the IAM session name used to generate the presigned URL. For IAM Identity Center domains, the session name is the username of the associated IAM Identity Center user. If this value is set to STATIC or is not set, the session name defaults to SageMaker.
AutoMountHomeEFS (string) --
Indicates whether auto-mounting of an EFS volume is supported for the user profile. The DefaultAsDomain value is only supported for user profiles. Do not use the DefaultAsDomain value when setting this parameter for a domain.
SageMaker applies this setting only to private spaces that the user creates in the domain. SageMaker doesn't apply this setting to shared spaces.
DomainSettings (dict) --
A collection of Domain settings.
SecurityGroupIds (list) --
The security groups for the Amazon Virtual Private Cloud that the Domain uses for communication between Domain-level apps and user apps.
(string) --
RStudioServerProDomainSettings (dict) --
A collection of settings that configure the RStudioServerPro Domain-level app.
DomainExecutionRoleArn (string) --
The ARN of the execution role for the RStudioServerPro Domain-level app.
RStudioConnectUrl (string) --
A URL pointing to an RStudio Connect server.
RStudioPackageManagerUrl (string) --
A URL pointing to an RStudio Package Manager server.
DefaultResourceSpec (dict) --
Specifies the ARN's of a SageMaker AI image and SageMaker AI image version, and the instance type that the version runs on.
SageMakerImageArn (string) --
The ARN of the SageMaker AI image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance. To clear the value set for SageMakerImageVersionArn, pass None as the value.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
TrainingPlanArn (string) --
The ARN of the SageMaker AI Training Plan to use for this app. When you specify a training plan, the app launches on reserved GPU capacity. This field is supported for JupyterLab and CodeEditor app types.
For more information about how to reserve GPU capacity with SageMaker AI Training Plans, see Using training plans in Studio applications.
ExecutionRoleIdentityConfig (string) --
The configuration for attaching a SageMaker AI user profile name to the execution role as a sts:SourceIdentity key.
TrustedIdentityPropagationSettings (dict) --
The Trusted Identity Propagation (TIP) settings for the SageMaker domain. These settings determine how user identities from IAM Identity Center are propagated through the domain to TIP enabled Amazon Web Services services.
Status (string) --
The status of Trusted Identity Propagation (TIP) at the SageMaker domain level.
When disabled, standard IAM role-based access is used.
When enabled:
User identities from IAM Identity Center are propagated through the application to TIP enabled Amazon Web Services services.
New applications or existing applications that are automatically patched, will use the domain level configuration.
DockerSettings (dict) --
A collection of settings that configure the domain's Docker interaction.
EnableDockerAccess (string) --
Indicates whether the domain can access Docker.
VpcOnlyTrustedAccounts (list) --
The list of Amazon Web Services accounts that are trusted when the domain is created in VPC-only mode.
(string) --
RootlessDocker (string) --
Indicates whether to use rootless Docker.
AmazonQSettings (dict) --
A collection of settings that configure the Amazon Q experience within the domain. The AuthMode that you use to create the domain must be SSO.
Status (string) --
Whether Amazon Q has been enabled within the domain.
QProfileArn (string) --
The ARN of the Amazon Q profile used within the domain.
UnifiedStudioSettings (dict) --
The settings that apply to an SageMaker AI domain when you use it in Amazon SageMaker Unified Studio.
StudioWebPortalAccess (string) --
Sets whether you can access the domain in Amazon SageMaker Studio:
ENABLED
You can access the domain in Amazon SageMaker Studio. If you migrate the domain to Amazon SageMaker Unified Studio, you can access it in both studio interfaces.
DISABLED
You can't access the domain in Amazon SageMaker Studio. If you migrate the domain to Amazon SageMaker Unified Studio, you can access it only in that studio interface.
To migrate a domain to Amazon SageMaker Unified Studio, you specify the UnifiedStudioSettings data type when you use the UpdateDomain action.
DomainAccountId (string) --
The ID of the Amazon Web Services account that has the Amazon SageMaker Unified Studio domain. The default value, if you don't specify an ID, is the ID of the account that has the Amazon SageMaker AI domain.
DomainRegion (string) --
The Amazon Web Services Region where the domain is located in Amazon SageMaker Unified Studio. The default value, if you don't specify a Region, is the Region where the Amazon SageMaker AI domain is located.
DomainId (string) --
The ID of the Amazon SageMaker Unified Studio domain associated with this domain.
ProjectId (string) --
The ID of the Amazon SageMaker Unified Studio project that corresponds to the domain.
EnvironmentId (string) --
The ID of the environment that Amazon SageMaker Unified Studio associates with the domain.
ProjectS3Path (string) --
The location where Amazon S3 stores temporary execution data and other artifacts for the project that corresponds to the domain.
SingleSignOnApplicationArn (string) --
The ARN of the Amazon DataZone application managed by Amazon SageMaker Unified Studio in the Amazon Web Services IAM Identity Center.
IpAddressType (string) --
The IP address type for the domain. Specify ipv4 for IPv4-only connectivity or dualstack for both IPv4 and IPv6 connectivity. When you specify dualstack, the subnet must support IPv6 CIDR blocks. If not specified, defaults to ipv4.
AppNetworkAccessType (string) --
Specifies the VPC used for non-EFS traffic. The default value is PublicInternetOnly.
PublicInternetOnly - Non-EFS traffic is through a VPC managed by Amazon SageMaker AI, which allows direct internet access
VpcOnly - All traffic is through the specified VPC and subnets
HomeEfsFileSystemKmsKeyId (string) --
Use KmsKeyId.
SubnetIds (list) --
The VPC subnets that the domain uses for communication.
(string) --
Url (string) --
The domain's URL.
VpcId (string) --
The ID of the Amazon Virtual Private Cloud (VPC) that the domain uses for communication.
KmsKeyId (string) --
The Amazon Web Services KMS customer managed key used to encrypt the EFS volume attached to the domain.
AppSecurityGroupManagement (string) --
The entity that creates and manages the required security groups for inter-app communication in VPCOnly mode. Required when CreateDomain.AppNetworkAccessType is VPCOnly and DomainSettings.RStudioServerProDomainSettings.DomainExecutionRoleArn is provided.
TagPropagation (string) --
Indicates whether custom tag propagation is supported for the domain.
DefaultSpaceSettings (dict) --
The default settings for shared spaces that users create in the domain.
ExecutionRole (string) --
The ARN of the execution role for the space.
SecurityGroups (list) --
The security group IDs for the Amazon VPC that the space uses for communication.
(string) --
JupyterServerAppSettings (dict) --
The JupyterServer app settings.
DefaultResourceSpec (dict) --
The default instance type and the Amazon Resource Name (ARN) of the default SageMaker AI image used by the JupyterServer app. If you use the LifecycleConfigArns parameter, then this parameter is also required.
SageMakerImageArn (string) --
The ARN of the SageMaker AI image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance. To clear the value set for SageMakerImageVersionArn, pass None as the value.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
TrainingPlanArn (string) --
The ARN of the SageMaker AI Training Plan to use for this app. When you specify a training plan, the app launches on reserved GPU capacity. This field is supported for JupyterLab and CodeEditor app types.
For more information about how to reserve GPU capacity with SageMaker AI Training Plans, see Using training plans in Studio applications.
LifecycleConfigArns (list) --
The Amazon Resource Name (ARN) of the Lifecycle Configurations attached to the JupyterServerApp. If you use this parameter, the DefaultResourceSpec parameter is also required.
(string) --
CodeRepositories (list) --
A list of Git repositories that SageMaker AI automatically displays to users for cloning in the JupyterServer application.
(dict) --
A Git repository that SageMaker AI automatically displays to users for cloning in the JupyterServer application.
RepositoryUrl (string) --
The URL of the Git repository.
KernelGatewayAppSettings (dict) --
The KernelGateway app settings.
DefaultResourceSpec (dict) --
The default instance type and the Amazon Resource Name (ARN) of the default SageMaker AI image used by the KernelGateway app.
SageMakerImageArn (string) --
The ARN of the SageMaker AI image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance. To clear the value set for SageMakerImageVersionArn, pass None as the value.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
TrainingPlanArn (string) --
The ARN of the SageMaker AI Training Plan to use for this app. When you specify a training plan, the app launches on reserved GPU capacity. This field is supported for JupyterLab and CodeEditor app types.
For more information about how to reserve GPU capacity with SageMaker AI Training Plans, see Using training plans in Studio applications.
CustomImages (list) --
A list of custom SageMaker AI images that are configured to run as a KernelGateway app.
The maximum number of custom images are as follows.
On a domain level: 200
On a space level: 5
On a user profile level: 5
(dict) --
A custom SageMaker AI image. For more information, see Bring your own SageMaker AI image.
ImageName (string) --
The name of the CustomImage. Must be unique to your account.
ImageVersionNumber (integer) --
The version number of the CustomImage.
AppImageConfigName (string) --
The name of the AppImageConfig.
LifecycleConfigArns (list) --
The Amazon Resource Name (ARN) of the Lifecycle Configurations attached to the the user profile or domain.
(string) --
JupyterLabAppSettings (dict) --
The settings for the JupyterLab application.
DefaultResourceSpec (dict) --
Specifies the ARN's of a SageMaker AI image and SageMaker AI image version, and the instance type that the version runs on.
SageMakerImageArn (string) --
The ARN of the SageMaker AI image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance. To clear the value set for SageMakerImageVersionArn, pass None as the value.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
TrainingPlanArn (string) --
The ARN of the SageMaker AI Training Plan to use for this app. When you specify a training plan, the app launches on reserved GPU capacity. This field is supported for JupyterLab and CodeEditor app types.
For more information about how to reserve GPU capacity with SageMaker AI Training Plans, see Using training plans in Studio applications.
CustomImages (list) --
A list of custom SageMaker images that are configured to run as a JupyterLab app.
(dict) --
A custom SageMaker AI image. For more information, see Bring your own SageMaker AI image.
ImageName (string) --
The name of the CustomImage. Must be unique to your account.
ImageVersionNumber (integer) --
The version number of the CustomImage.
AppImageConfigName (string) --
The name of the AppImageConfig.
LifecycleConfigArns (list) --
The Amazon Resource Name (ARN) of the lifecycle configurations attached to the user profile or domain. To remove a lifecycle config, you must set LifecycleConfigArns to an empty list.
(string) --
CodeRepositories (list) --
A list of Git repositories that SageMaker automatically displays to users for cloning in the JupyterLab application.
(dict) --
A Git repository that SageMaker AI automatically displays to users for cloning in the JupyterServer application.
RepositoryUrl (string) --
The URL of the Git repository.
AppLifecycleManagement (dict) --
Indicates whether idle shutdown is activated for JupyterLab applications.
IdleSettings (dict) --
Settings related to idle shutdown of Studio applications.
LifecycleManagement (string) --
Indicates whether idle shutdown is activated for the application type.
IdleTimeoutInMinutes (integer) --
The time that SageMaker waits after the application becomes idle before shutting it down.
MinIdleTimeoutInMinutes (integer) --
The minimum value in minutes that custom idle shutdown can be set to by the user.
MaxIdleTimeoutInMinutes (integer) --
The maximum value in minutes that custom idle shutdown can be set to by the user.
EmrSettings (dict) --
The configuration parameters that specify the IAM roles assumed by the execution role of SageMaker (assumable roles) and the cluster instances or job execution environments (execution roles or runtime roles) to manage and access resources required for running Amazon EMR clusters or Amazon EMR Serverless applications.
AssumableRoleArns (list) --
An array of Amazon Resource Names (ARNs) of the IAM roles that the execution role of SageMaker can assume for performing operations or tasks related to Amazon EMR clusters or Amazon EMR Serverless applications. These roles define the permissions and access policies required when performing Amazon EMR-related operations, such as listing, connecting to, or terminating Amazon EMR clusters or Amazon EMR Serverless applications. They are typically used in cross-account access scenarios, where the Amazon EMR resources (clusters or serverless applications) are located in a different Amazon Web Services account than the SageMaker domain.
(string) --
ExecutionRoleArns (list) --
An array of Amazon Resource Names (ARNs) of the IAM roles used by the Amazon EMR cluster instances or job execution environments to access other Amazon Web Services services and resources needed during the runtime of your Amazon EMR or Amazon EMR Serverless workloads, such as Amazon S3 for data access, Amazon CloudWatch for logging, or other Amazon Web Services services based on the particular workload requirements.
(string) --
BuiltInLifecycleConfigArn (string) --
The lifecycle configuration that runs before the default lifecycle configuration. It can override changes made in the default lifecycle configuration.
SpaceStorageSettings (dict) --
The default storage settings for a space.
DefaultEbsStorageSettings (dict) --
The default EBS storage settings for a space.
DefaultEbsVolumeSizeInGb (integer) --
The default size of the EBS storage volume for a space.
MaximumEbsVolumeSizeInGb (integer) --
The maximum size of the EBS storage volume for a space.
CustomPosixUserConfig (dict) --
Details about the POSIX identity that is used for file system operations.
Uid (integer) --
The POSIX user ID.
Gid (integer) --
The POSIX group ID.
CustomFileSystemConfigs (list) --
The settings for assigning a custom file system to a domain. Permitted users can access this file system in Amazon SageMaker AI Studio.
(dict) --
The settings for assigning a custom file system to a user profile or space for an Amazon SageMaker AI Domain. Permitted users can access this file system in Amazon SageMaker AI Studio.
EFSFileSystemConfig (dict) --
The settings for a custom Amazon EFS file system.
FileSystemId (string) --
The ID of your Amazon EFS file system.
FileSystemPath (string) --
The path to the file system directory that is accessible in Amazon SageMaker AI Studio. Permitted users can access only this directory and below.
FSxLustreFileSystemConfig (dict) --
The settings for a custom Amazon FSx for Lustre file system.
FileSystemId (string) --
The globally unique, 17-digit, ID of the file system, assigned by Amazon FSx for Lustre.
FileSystemPath (string) --
The path to the file system directory that is accessible in Amazon SageMaker Studio. Permitted users can access only this directory and below.
S3FileSystemConfig (dict) --
Configuration settings for a custom Amazon S3 file system.
MountPath (string) --
The file system path where the Amazon S3 storage location will be mounted within the Amazon SageMaker Studio environment.
S3Uri (string) --
The Amazon S3 URI of the S3 file system configuration.
{'ModelVariants': {'InfrastructureConfig': {'RealTimeInferenceConfig': {'InstanceType': {'ml.c4.large',
'ml.c5.large',
'ml.c5d.large',
'ml.c6g.12xlarge',
'ml.c6g.16xlarge',
'ml.c6g.2xlarge',
'ml.c6g.4xlarge',
'ml.c6g.8xlarge',
'ml.c6g.large',
'ml.c6g.xlarge',
'ml.c6gd.12xlarge',
'ml.c6gd.16xlarge',
'ml.c6gd.2xlarge',
'ml.c6gd.4xlarge',
'ml.c6gd.8xlarge',
'ml.c6gd.large',
'ml.c6gd.xlarge',
'ml.c6gn.12xlarge',
'ml.c6gn.16xlarge',
'ml.c6gn.2xlarge',
'ml.c6gn.4xlarge',
'ml.c6gn.8xlarge',
'ml.c6gn.large',
'ml.c6gn.xlarge',
'ml.c6in.12xlarge',
'ml.c6in.16xlarge',
'ml.c6in.24xlarge',
'ml.c6in.2xlarge',
'ml.c6in.32xlarge',
'ml.c6in.4xlarge',
'ml.c6in.8xlarge',
'ml.c6in.large',
'ml.c6in.xlarge',
'ml.c7g.12xlarge',
'ml.c7g.16xlarge',
'ml.c7g.2xlarge',
'ml.c7g.4xlarge',
'ml.c7g.8xlarge',
'ml.c7g.large',
'ml.c7g.xlarge',
'ml.c8g.12xlarge',
'ml.c8g.16xlarge',
'ml.c8g.24xlarge',
'ml.c8g.2xlarge',
'ml.c8g.48xlarge',
'ml.c8g.4xlarge',
'ml.c8g.8xlarge',
'ml.c8g.large',
'ml.c8g.medium',
'ml.c8g.xlarge',
'ml.dl1.24xlarge',
'ml.g6e.12xlarge',
'ml.g6e.16xlarge',
'ml.g6e.24xlarge',
'ml.g6e.2xlarge',
'ml.g6e.48xlarge',
'ml.g6e.4xlarge',
'ml.g6e.8xlarge',
'ml.g6e.xlarge',
'ml.g7e.12xlarge',
'ml.g7e.24xlarge',
'ml.g7e.2xlarge',
'ml.g7e.48xlarge',
'ml.g7e.4xlarge',
'ml.g7e.8xlarge',
'ml.m5.large',
'ml.m6g.12xlarge',
'ml.m6g.16xlarge',
'ml.m6g.2xlarge',
'ml.m6g.4xlarge',
'ml.m6g.8xlarge',
'ml.m6g.large',
'ml.m6g.xlarge',
'ml.m6gd.12xlarge',
'ml.m6gd.16xlarge',
'ml.m6gd.2xlarge',
'ml.m6gd.4xlarge',
'ml.m6gd.8xlarge',
'ml.m6gd.large',
'ml.m6gd.xlarge',
'ml.m8g.12xlarge',
'ml.m8g.16xlarge',
'ml.m8g.24xlarge',
'ml.m8g.2xlarge',
'ml.m8g.48xlarge',
'ml.m8g.4xlarge',
'ml.m8g.8xlarge',
'ml.m8g.large',
'ml.m8g.medium',
'ml.m8g.xlarge',
'ml.p5.4xlarge',
'ml.p5e.48xlarge',
'ml.p5en.48xlarge',
'ml.p6-b300.48xlarge',
'ml.p6e-gb200.36xlarge',
'ml.r5d.12xlarge',
'ml.r5d.24xlarge',
'ml.r5d.2xlarge',
'ml.r5d.4xlarge',
'ml.r5d.large',
'ml.r5d.xlarge',
'ml.r6g.12xlarge',
'ml.r6g.16xlarge',
'ml.r6g.2xlarge',
'ml.r6g.4xlarge',
'ml.r6g.8xlarge',
'ml.r6g.large',
'ml.r6g.xlarge',
'ml.r6gd.12xlarge',
'ml.r6gd.16xlarge',
'ml.r6gd.2xlarge',
'ml.r6gd.4xlarge',
'ml.r6gd.8xlarge',
'ml.r6gd.large',
'ml.r6gd.xlarge',
'ml.r7gd.12xlarge',
'ml.r7gd.16xlarge',
'ml.r7gd.2xlarge',
'ml.r7gd.4xlarge',
'ml.r7gd.8xlarge',
'ml.r7gd.large',
'ml.r7gd.medium',
'ml.r7gd.xlarge',
'ml.r8g.12xlarge',
'ml.r8g.16xlarge',
'ml.r8g.24xlarge',
'ml.r8g.2xlarge',
'ml.r8g.48xlarge',
'ml.r8g.4xlarge',
'ml.r8g.8xlarge',
'ml.r8g.large',
'ml.r8g.medium',
'ml.r8g.xlarge',
'ml.trn2.48xlarge'}}}}}
Returns details about an inference experiment.
See also: AWS API Documentation
Request Syntax
client.describe_inference_experiment(
Name='string'
)
string
[REQUIRED]
The name of the inference experiment to describe.
dict
Response Syntax
{
'Arn': 'string',
'Name': 'string',
'Type': 'ShadowMode',
'Schedule': {
'StartTime': datetime(2015, 1, 1),
'EndTime': datetime(2015, 1, 1)
},
'Status': 'Creating'|'Created'|'Updating'|'Running'|'Starting'|'Stopping'|'Completed'|'Cancelled',
'StatusReason': 'string',
'Description': 'string',
'CreationTime': datetime(2015, 1, 1),
'CompletionTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'RoleArn': 'string',
'EndpointMetadata': {
'EndpointName': 'string',
'EndpointConfigName': 'string',
'EndpointStatus': 'OutOfService'|'Creating'|'Updating'|'SystemUpdating'|'RollingBack'|'InService'|'Deleting'|'Failed'|'UpdateRollbackFailed',
'FailureReason': 'string'
},
'ModelVariants': [
{
'ModelName': 'string',
'VariantName': 'string',
'InfrastructureConfig': {
'InfrastructureType': 'RealTimeInference',
'RealTimeInferenceConfig': {
'InstanceType': 'ml.t2.medium'|'ml.t2.large'|'ml.t2.xlarge'|'ml.t2.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.12xlarge'|'ml.m5d.24xlarge'|'ml.c4.large'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5d.large'|'ml.c5d.xlarge'|'ml.c5d.2xlarge'|'ml.c5d.4xlarge'|'ml.c5d.9xlarge'|'ml.c5d.18xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.12xlarge'|'ml.r5.24xlarge'|'ml.r5d.large'|'ml.r5d.xlarge'|'ml.r5d.2xlarge'|'ml.r5d.4xlarge'|'ml.r5d.12xlarge'|'ml.r5d.24xlarge'|'ml.inf1.xlarge'|'ml.inf1.2xlarge'|'ml.inf1.6xlarge'|'ml.inf1.24xlarge'|'ml.dl1.24xlarge'|'ml.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.r6i.large'|'ml.r6i.xlarge'|'ml.r6i.2xlarge'|'ml.r6i.4xlarge'|'ml.r6i.8xlarge'|'ml.r6i.12xlarge'|'ml.r6i.16xlarge'|'ml.r6i.24xlarge'|'ml.r6i.32xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.12xlarge'|'ml.g5.16xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.g6.xlarge'|'ml.g6.2xlarge'|'ml.g6.4xlarge'|'ml.g6.8xlarge'|'ml.g6.12xlarge'|'ml.g6.16xlarge'|'ml.g6.24xlarge'|'ml.g6.48xlarge'|'ml.r8g.medium'|'ml.r8g.large'|'ml.r8g.xlarge'|'ml.r8g.2xlarge'|'ml.r8g.4xlarge'|'ml.r8g.8xlarge'|'ml.r8g.12xlarge'|'ml.r8g.16xlarge'|'ml.r8g.24xlarge'|'ml.r8g.48xlarge'|'ml.g6e.xlarge'|'ml.g6e.2xlarge'|'ml.g6e.4xlarge'|'ml.g6e.8xlarge'|'ml.g6e.12xlarge'|'ml.g6e.16xlarge'|'ml.g6e.24xlarge'|'ml.g6e.48xlarge'|'ml.g7e.2xlarge'|'ml.g7e.4xlarge'|'ml.g7e.8xlarge'|'ml.g7e.12xlarge'|'ml.g7e.24xlarge'|'ml.g7e.48xlarge'|'ml.p4d.24xlarge'|'ml.c7g.large'|'ml.c7g.xlarge'|'ml.c7g.2xlarge'|'ml.c7g.4xlarge'|'ml.c7g.8xlarge'|'ml.c7g.12xlarge'|'ml.c7g.16xlarge'|'ml.m6g.large'|'ml.m6g.xlarge'|'ml.m6g.2xlarge'|'ml.m6g.4xlarge'|'ml.m6g.8xlarge'|'ml.m6g.12xlarge'|'ml.m6g.16xlarge'|'ml.m6gd.large'|'ml.m6gd.xlarge'|'ml.m6gd.2xlarge'|'ml.m6gd.4xlarge'|'ml.m6gd.8xlarge'|'ml.m6gd.12xlarge'|'ml.m6gd.16xlarge'|'ml.c6g.large'|'ml.c6g.xlarge'|'ml.c6g.2xlarge'|'ml.c6g.4xlarge'|'ml.c6g.8xlarge'|'ml.c6g.12xlarge'|'ml.c6g.16xlarge'|'ml.c6gd.large'|'ml.c6gd.xlarge'|'ml.c6gd.2xlarge'|'ml.c6gd.4xlarge'|'ml.c6gd.8xlarge'|'ml.c6gd.12xlarge'|'ml.c6gd.16xlarge'|'ml.c6gn.large'|'ml.c6gn.xlarge'|'ml.c6gn.2xlarge'|'ml.c6gn.4xlarge'|'ml.c6gn.8xlarge'|'ml.c6gn.12xlarge'|'ml.c6gn.16xlarge'|'ml.r6g.large'|'ml.r6g.xlarge'|'ml.r6g.2xlarge'|'ml.r6g.4xlarge'|'ml.r6g.8xlarge'|'ml.r6g.12xlarge'|'ml.r6g.16xlarge'|'ml.r6gd.large'|'ml.r6gd.xlarge'|'ml.r6gd.2xlarge'|'ml.r6gd.4xlarge'|'ml.r6gd.8xlarge'|'ml.r6gd.12xlarge'|'ml.r6gd.16xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.trn2.48xlarge'|'ml.inf2.xlarge'|'ml.inf2.8xlarge'|'ml.inf2.24xlarge'|'ml.inf2.48xlarge'|'ml.p5.48xlarge'|'ml.p5e.48xlarge'|'ml.p5en.48xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge'|'ml.c8g.medium'|'ml.c8g.large'|'ml.c8g.xlarge'|'ml.c8g.2xlarge'|'ml.c8g.4xlarge'|'ml.c8g.8xlarge'|'ml.c8g.12xlarge'|'ml.c8g.16xlarge'|'ml.c8g.24xlarge'|'ml.c8g.48xlarge'|'ml.r7gd.medium'|'ml.r7gd.large'|'ml.r7gd.xlarge'|'ml.r7gd.2xlarge'|'ml.r7gd.4xlarge'|'ml.r7gd.8xlarge'|'ml.r7gd.12xlarge'|'ml.r7gd.16xlarge'|'ml.m8g.medium'|'ml.m8g.large'|'ml.m8g.xlarge'|'ml.m8g.2xlarge'|'ml.m8g.4xlarge'|'ml.m8g.8xlarge'|'ml.m8g.12xlarge'|'ml.m8g.16xlarge'|'ml.m8g.24xlarge'|'ml.m8g.48xlarge'|'ml.c6in.large'|'ml.c6in.xlarge'|'ml.c6in.2xlarge'|'ml.c6in.4xlarge'|'ml.c6in.8xlarge'|'ml.c6in.12xlarge'|'ml.c6in.16xlarge'|'ml.c6in.24xlarge'|'ml.c6in.32xlarge'|'ml.p6-b200.48xlarge'|'ml.p6-b300.48xlarge'|'ml.p6e-gb200.36xlarge'|'ml.p5.4xlarge',
'InstanceCount': 123
}
},
'Status': 'Creating'|'Updating'|'InService'|'Deleting'|'Deleted'
},
],
'DataStorageConfig': {
'Destination': 'string',
'KmsKey': 'string',
'ContentType': {
'CsvContentTypes': [
'string',
],
'JsonContentTypes': [
'string',
]
}
},
'ShadowModeConfig': {
'SourceModelVariantName': 'string',
'ShadowModelVariants': [
{
'ShadowModelVariantName': 'string',
'SamplingPercentage': 123
},
]
},
'KmsKey': 'string'
}
Response Structure
(dict) --
Arn (string) --
The ARN of the inference experiment being described.
Name (string) --
The name of the inference experiment.
Type (string) --
The type of the inference experiment.
Schedule (dict) --
The duration for which the inference experiment ran or will run.
StartTime (datetime) --
The timestamp at which the inference experiment started or will start.
EndTime (datetime) --
The timestamp at which the inference experiment ended or will end.
Status (string) --
The status of the inference experiment. The following are the possible statuses for an inference experiment:
Creating - Amazon SageMaker is creating your experiment.
Created - Amazon SageMaker has finished the creation of your experiment and will begin the experiment at the scheduled time.
Updating - When you make changes to your experiment, your experiment shows as updating.
Starting - Amazon SageMaker is beginning your experiment.
Running - Your experiment is in progress.
Stopping - Amazon SageMaker is stopping your experiment.
Completed - Your experiment has completed.
Cancelled - When you conclude your experiment early using the StopInferenceExperiment API, or if any operation fails with an unexpected error, it shows as cancelled.
StatusReason (string) --
The error message or client-specified Reason from the StopInferenceExperiment API, that explains the status of the inference experiment.
Description (string) --
The description of the inference experiment.
CreationTime (datetime) --
The timestamp at which you created the inference experiment.
CompletionTime (datetime) --
The timestamp at which the inference experiment was completed.
LastModifiedTime (datetime) --
The timestamp at which you last modified the inference experiment.
RoleArn (string) --
The ARN of the IAM role that Amazon SageMaker can assume to access model artifacts and container images, and manage Amazon SageMaker Inference endpoints for model deployment.
EndpointMetadata (dict) --
The metadata of the endpoint on which the inference experiment ran.
EndpointName (string) --
The name of the endpoint.
EndpointConfigName (string) --
The name of the endpoint configuration.
EndpointStatus (string) --
The status of the endpoint. For possible values of the status of an endpoint, see EndpointSummary.
FailureReason (string) --
If the status of the endpoint is Failed, or the status is InService but update operation fails, this provides the reason why it failed.
ModelVariants (list) --
An array of ModelVariantConfigSummary objects. There is one for each variant in the inference experiment. Each ModelVariantConfigSummary object in the array describes the infrastructure configuration for deploying the corresponding variant.
(dict) --
Summary of the deployment configuration of a model.
ModelName (string) --
The name of the Amazon SageMaker Model entity.
VariantName (string) --
The name of the variant.
InfrastructureConfig (dict) --
The configuration of the infrastructure that the model has been deployed to.
InfrastructureType (string) --
The inference option to which to deploy your model. Possible values are the following:
RealTime: Deploy to real-time inference.
RealTimeInferenceConfig (dict) --
The infrastructure configuration for deploying the model to real-time inference.
InstanceType (string) --
The instance type the model is deployed to.
InstanceCount (integer) --
The number of instances of the type specified by InstanceType.
Status (string) --
The status of deployment for the model variant on the hosted inference endpoint.
Creating - Amazon SageMaker is preparing the model variant on the hosted inference endpoint.
InService - The model variant is running on the hosted inference endpoint.
Updating - Amazon SageMaker is updating the model variant on the hosted inference endpoint.
Deleting - Amazon SageMaker is deleting the model variant on the hosted inference endpoint.
Deleted - The model variant has been deleted on the hosted inference endpoint. This can only happen after stopping the experiment.
DataStorageConfig (dict) --
The Amazon S3 location and configuration for storing inference request and response data.
Destination (string) --
The Amazon S3 bucket where the inference request and response data is stored.
KmsKey (string) --
The Amazon Web Services Key Management Service key that Amazon SageMaker uses to encrypt captured data at rest using Amazon S3 server-side encryption.
ContentType (dict) --
Configuration specifying how to treat different headers. If no headers are specified Amazon SageMaker AI will by default base64 encode when capturing the data.
CsvContentTypes (list) --
The list of all content type headers that Amazon SageMaker AI will treat as CSV and capture accordingly.
(string) --
JsonContentTypes (list) --
The list of all content type headers that SageMaker AI will treat as JSON and capture accordingly.
(string) --
ShadowModeConfig (dict) --
The configuration of ShadowMode inference experiment type, which shows the production variant that takes all the inference requests, and the shadow variant to which Amazon SageMaker replicates a percentage of the inference requests. For the shadow variant it also shows the percentage of requests that Amazon SageMaker replicates.
SourceModelVariantName (string) --
The name of the production variant, which takes all the inference requests.
ShadowModelVariants (list) --
List of shadow variant configurations.
(dict) --
The name and sampling percentage of a shadow variant.
ShadowModelVariantName (string) --
The name of the shadow variant.
SamplingPercentage (integer) --
The percentage of inference requests that Amazon SageMaker replicates from the production variant to the shadow variant.
KmsKey (string) --
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance that hosts the endpoint. For more information, see CreateInferenceExperiment.
{'ManagedStorageType': 'Restricted'}
Returns a description of the specified model package, which is used to create SageMaker models or list them on Amazon Web Services Marketplace.
To create models in SageMaker, buyers can subscribe to model packages listed on Amazon Web Services Marketplace.
See also: AWS API Documentation
Request Syntax
client.describe_model_package(
ModelPackageName='string'
)
string
[REQUIRED]
The name or Amazon Resource Name (ARN) of the model package to describe.
When you specify a name, the name must have 1 to 63 characters. Valid characters are a-z, A-Z, 0-9, and - (hyphen).
dict
Response Syntax
{
'ModelPackageName': 'string',
'ModelPackageGroupName': 'string',
'ModelPackageVersion': 123,
'ModelPackageRegistrationType': 'Logged'|'Registered',
'ModelPackageArn': 'string',
'ModelPackageDescription': 'string',
'CreationTime': datetime(2015, 1, 1),
'InferenceSpecification': {
'Containers': [
{
'ContainerHostname': 'string',
'Image': 'string',
'ImageDigest': 'string',
'ModelDataUrl': 'string',
'ModelDataSource': {
'S3DataSource': {
'S3Uri': 'string',
'S3DataType': 'S3Prefix'|'S3Object',
'CompressionType': 'None'|'Gzip',
'ModelAccessConfig': {
'AcceptEula': True|False
},
'HubAccessConfig': {
'HubContentArn': 'string'
},
'ManifestS3Uri': 'string',
'ETag': 'string',
'ManifestEtag': 'string'
}
},
'ProductId': 'string',
'Environment': {
'string': 'string'
},
'ModelInput': {
'DataInputConfig': 'string'
},
'Framework': 'string',
'FrameworkVersion': 'string',
'NearestModelName': 'string',
'AdditionalModelDataSources': [
{
'ChannelName': 'string',
'S3DataSource': {
'S3Uri': 'string',
'S3DataType': 'S3Prefix'|'S3Object',
'CompressionType': 'None'|'Gzip',
'ModelAccessConfig': {
'AcceptEula': True|False
},
'HubAccessConfig': {
'HubContentArn': 'string'
},
'ManifestS3Uri': 'string',
'ETag': 'string',
'ManifestEtag': 'string'
}
},
],
'AdditionalS3DataSource': {
'S3DataType': 'S3Object'|'S3Prefix',
'S3Uri': 'string',
'CompressionType': 'None'|'Gzip',
'ETag': 'string'
},
'ModelDataETag': 'string',
'IsCheckpoint': True|False,
'BaseModel': {
'HubContentName': 'string',
'HubContentVersion': 'string',
'RecipeName': 'string'
}
},
],
'SupportedTransformInstanceTypes': [
'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.r6i.large'|'ml.r6i.xlarge'|'ml.r6i.2xlarge'|'ml.r6i.4xlarge'|'ml.r6i.8xlarge'|'ml.r6i.12xlarge'|'ml.r6i.16xlarge'|'ml.r6i.24xlarge'|'ml.r6i.32xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.12xlarge'|'ml.g5.16xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.inf2.xlarge'|'ml.inf2.8xlarge'|'ml.inf2.24xlarge'|'ml.inf2.48xlarge'|'ml.g6.xlarge'|'ml.g6.2xlarge'|'ml.g6.4xlarge'|'ml.g6.8xlarge'|'ml.g6.12xlarge'|'ml.g6.16xlarge'|'ml.g6.24xlarge'|'ml.g6.48xlarge',
],
'SupportedRealtimeInferenceInstanceTypes': [
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],
'SupportedContentTypes': [
'string',
],
'SupportedResponseMIMETypes': [
'string',
]
},
'SourceAlgorithmSpecification': {
'SourceAlgorithms': [
{
'ModelDataUrl': 'string',
'ModelDataSource': {
'S3DataSource': {
'S3Uri': 'string',
'S3DataType': 'S3Prefix'|'S3Object',
'CompressionType': 'None'|'Gzip',
'ModelAccessConfig': {
'AcceptEula': True|False
},
'HubAccessConfig': {
'HubContentArn': 'string'
},
'ManifestS3Uri': 'string',
'ETag': 'string',
'ManifestEtag': 'string'
}
},
'ModelDataETag': 'string',
'AlgorithmName': 'string'
},
]
},
'ValidationSpecification': {
'ValidationRole': 'string',
'ValidationProfiles': [
{
'ProfileName': 'string',
'TransformJobDefinition': {
'MaxConcurrentTransforms': 123,
'MaxPayloadInMB': 123,
'BatchStrategy': 'MultiRecord'|'SingleRecord',
'Environment': {
'string': 'string'
},
'TransformInput': {
'DataSource': {
'S3DataSource': {
'S3DataType': 'ManifestFile'|'S3Prefix'|'AugmentedManifestFile'|'Converse',
'S3Uri': 'string'
}
},
'ContentType': 'string',
'CompressionType': 'None'|'Gzip',
'SplitType': 'None'|'Line'|'RecordIO'|'TFRecord'
},
'TransformOutput': {
'S3OutputPath': 'string',
'Accept': 'string',
'AssembleWith': 'None'|'Line',
'KmsKeyId': 'string'
},
'TransformResources': {
'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.r6i.large'|'ml.r6i.xlarge'|'ml.r6i.2xlarge'|'ml.r6i.4xlarge'|'ml.r6i.8xlarge'|'ml.r6i.12xlarge'|'ml.r6i.16xlarge'|'ml.r6i.24xlarge'|'ml.r6i.32xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.12xlarge'|'ml.g5.16xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.inf2.xlarge'|'ml.inf2.8xlarge'|'ml.inf2.24xlarge'|'ml.inf2.48xlarge'|'ml.g6.xlarge'|'ml.g6.2xlarge'|'ml.g6.4xlarge'|'ml.g6.8xlarge'|'ml.g6.12xlarge'|'ml.g6.16xlarge'|'ml.g6.24xlarge'|'ml.g6.48xlarge',
'InstanceCount': 123,
'VolumeKmsKeyId': 'string',
'TransformAmiVersion': 'string'
}
}
},
]
},
'ModelPackageStatus': 'Pending'|'InProgress'|'Completed'|'Failed'|'Deleting',
'ModelPackageStatusDetails': {
'ValidationStatuses': [
{
'Name': 'string',
'Status': 'NotStarted'|'InProgress'|'Completed'|'Failed',
'FailureReason': 'string'
},
],
'ImageScanStatuses': [
{
'Name': 'string',
'Status': 'NotStarted'|'InProgress'|'Completed'|'Failed',
'FailureReason': 'string'
},
]
},
'CertifyForMarketplace': True|False,
'ModelApprovalStatus': 'Approved'|'Rejected'|'PendingManualApproval',
'CreatedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string',
'IamIdentity': {
'Arn': 'string',
'PrincipalId': 'string',
'SourceIdentity': 'string'
}
},
'MetadataProperties': {
'CommitId': 'string',
'Repository': 'string',
'GeneratedBy': 'string',
'ProjectId': 'string'
},
'ModelMetrics': {
'ModelQuality': {
'Statistics': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
},
'Constraints': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
}
},
'ModelDataQuality': {
'Statistics': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
},
'Constraints': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
}
},
'Bias': {
'Report': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
},
'PreTrainingReport': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
},
'PostTrainingReport': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
}
},
'Explainability': {
'Report': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
}
}
},
'LastModifiedTime': datetime(2015, 1, 1),
'LastModifiedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string',
'IamIdentity': {
'Arn': 'string',
'PrincipalId': 'string',
'SourceIdentity': 'string'
}
},
'ApprovalDescription': 'string',
'Domain': 'string',
'Task': 'string',
'SamplePayloadUrl': 'string',
'CustomerMetadataProperties': {
'string': 'string'
},
'DriftCheckBaselines': {
'Bias': {
'ConfigFile': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
},
'PreTrainingConstraints': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
},
'PostTrainingConstraints': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
}
},
'Explainability': {
'Constraints': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
},
'ConfigFile': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
}
},
'ModelQuality': {
'Statistics': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
},
'Constraints': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
}
},
'ModelDataQuality': {
'Statistics': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
},
'Constraints': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
}
}
},
'AdditionalInferenceSpecifications': [
{
'Name': 'string',
'Description': 'string',
'Containers': [
{
'ContainerHostname': 'string',
'Image': 'string',
'ImageDigest': 'string',
'ModelDataUrl': 'string',
'ModelDataSource': {
'S3DataSource': {
'S3Uri': 'string',
'S3DataType': 'S3Prefix'|'S3Object',
'CompressionType': 'None'|'Gzip',
'ModelAccessConfig': {
'AcceptEula': True|False
},
'HubAccessConfig': {
'HubContentArn': 'string'
},
'ManifestS3Uri': 'string',
'ETag': 'string',
'ManifestEtag': 'string'
}
},
'ProductId': 'string',
'Environment': {
'string': 'string'
},
'ModelInput': {
'DataInputConfig': 'string'
},
'Framework': 'string',
'FrameworkVersion': 'string',
'NearestModelName': 'string',
'AdditionalModelDataSources': [
{
'ChannelName': 'string',
'S3DataSource': {
'S3Uri': 'string',
'S3DataType': 'S3Prefix'|'S3Object',
'CompressionType': 'None'|'Gzip',
'ModelAccessConfig': {
'AcceptEula': True|False
},
'HubAccessConfig': {
'HubContentArn': 'string'
},
'ManifestS3Uri': 'string',
'ETag': 'string',
'ManifestEtag': 'string'
}
},
],
'AdditionalS3DataSource': {
'S3DataType': 'S3Object'|'S3Prefix',
'S3Uri': 'string',
'CompressionType': 'None'|'Gzip',
'ETag': 'string'
},
'ModelDataETag': 'string',
'IsCheckpoint': True|False,
'BaseModel': {
'HubContentName': 'string',
'HubContentVersion': 'string',
'RecipeName': 'string'
}
},
],
'SupportedTransformInstanceTypes': [
'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.r6i.large'|'ml.r6i.xlarge'|'ml.r6i.2xlarge'|'ml.r6i.4xlarge'|'ml.r6i.8xlarge'|'ml.r6i.12xlarge'|'ml.r6i.16xlarge'|'ml.r6i.24xlarge'|'ml.r6i.32xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.12xlarge'|'ml.g5.16xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.inf2.xlarge'|'ml.inf2.8xlarge'|'ml.inf2.24xlarge'|'ml.inf2.48xlarge'|'ml.g6.xlarge'|'ml.g6.2xlarge'|'ml.g6.4xlarge'|'ml.g6.8xlarge'|'ml.g6.12xlarge'|'ml.g6.16xlarge'|'ml.g6.24xlarge'|'ml.g6.48xlarge',
],
'SupportedRealtimeInferenceInstanceTypes': [
'ml.t2.medium'|'ml.t2.large'|'ml.t2.xlarge'|'ml.t2.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.12xlarge'|'ml.m5d.24xlarge'|'ml.c4.large'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5d.large'|'ml.c5d.xlarge'|'ml.c5d.2xlarge'|'ml.c5d.4xlarge'|'ml.c5d.9xlarge'|'ml.c5d.18xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.12xlarge'|'ml.r5.24xlarge'|'ml.r5d.large'|'ml.r5d.xlarge'|'ml.r5d.2xlarge'|'ml.r5d.4xlarge'|'ml.r5d.12xlarge'|'ml.r5d.24xlarge'|'ml.inf1.xlarge'|'ml.inf1.2xlarge'|'ml.inf1.6xlarge'|'ml.inf1.24xlarge'|'ml.dl1.24xlarge'|'ml.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.r6i.large'|'ml.r6i.xlarge'|'ml.r6i.2xlarge'|'ml.r6i.4xlarge'|'ml.r6i.8xlarge'|'ml.r6i.12xlarge'|'ml.r6i.16xlarge'|'ml.r6i.24xlarge'|'ml.r6i.32xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.12xlarge'|'ml.g5.16xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.g6.xlarge'|'ml.g6.2xlarge'|'ml.g6.4xlarge'|'ml.g6.8xlarge'|'ml.g6.12xlarge'|'ml.g6.16xlarge'|'ml.g6.24xlarge'|'ml.g6.48xlarge'|'ml.r8g.medium'|'ml.r8g.large'|'ml.r8g.xlarge'|'ml.r8g.2xlarge'|'ml.r8g.4xlarge'|'ml.r8g.8xlarge'|'ml.r8g.12xlarge'|'ml.r8g.16xlarge'|'ml.r8g.24xlarge'|'ml.r8g.48xlarge'|'ml.g6e.xlarge'|'ml.g6e.2xlarge'|'ml.g6e.4xlarge'|'ml.g6e.8xlarge'|'ml.g6e.12xlarge'|'ml.g6e.16xlarge'|'ml.g6e.24xlarge'|'ml.g6e.48xlarge'|'ml.g7e.2xlarge'|'ml.g7e.4xlarge'|'ml.g7e.8xlarge'|'ml.g7e.12xlarge'|'ml.g7e.24xlarge'|'ml.g7e.48xlarge'|'ml.p4d.24xlarge'|'ml.c7g.large'|'ml.c7g.xlarge'|'ml.c7g.2xlarge'|'ml.c7g.4xlarge'|'ml.c7g.8xlarge'|'ml.c7g.12xlarge'|'ml.c7g.16xlarge'|'ml.m6g.large'|'ml.m6g.xlarge'|'ml.m6g.2xlarge'|'ml.m6g.4xlarge'|'ml.m6g.8xlarge'|'ml.m6g.12xlarge'|'ml.m6g.16xlarge'|'ml.m6gd.large'|'ml.m6gd.xlarge'|'ml.m6gd.2xlarge'|'ml.m6gd.4xlarge'|'ml.m6gd.8xlarge'|'ml.m6gd.12xlarge'|'ml.m6gd.16xlarge'|'ml.c6g.large'|'ml.c6g.xlarge'|'ml.c6g.2xlarge'|'ml.c6g.4xlarge'|'ml.c6g.8xlarge'|'ml.c6g.12xlarge'|'ml.c6g.16xlarge'|'ml.c6gd.large'|'ml.c6gd.xlarge'|'ml.c6gd.2xlarge'|'ml.c6gd.4xlarge'|'ml.c6gd.8xlarge'|'ml.c6gd.12xlarge'|'ml.c6gd.16xlarge'|'ml.c6gn.large'|'ml.c6gn.xlarge'|'ml.c6gn.2xlarge'|'ml.c6gn.4xlarge'|'ml.c6gn.8xlarge'|'ml.c6gn.12xlarge'|'ml.c6gn.16xlarge'|'ml.r6g.large'|'ml.r6g.xlarge'|'ml.r6g.2xlarge'|'ml.r6g.4xlarge'|'ml.r6g.8xlarge'|'ml.r6g.12xlarge'|'ml.r6g.16xlarge'|'ml.r6gd.large'|'ml.r6gd.xlarge'|'ml.r6gd.2xlarge'|'ml.r6gd.4xlarge'|'ml.r6gd.8xlarge'|'ml.r6gd.12xlarge'|'ml.r6gd.16xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.trn2.48xlarge'|'ml.inf2.xlarge'|'ml.inf2.8xlarge'|'ml.inf2.24xlarge'|'ml.inf2.48xlarge'|'ml.p5.48xlarge'|'ml.p5e.48xlarge'|'ml.p5en.48xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge'|'ml.c8g.medium'|'ml.c8g.large'|'ml.c8g.xlarge'|'ml.c8g.2xlarge'|'ml.c8g.4xlarge'|'ml.c8g.8xlarge'|'ml.c8g.12xlarge'|'ml.c8g.16xlarge'|'ml.c8g.24xlarge'|'ml.c8g.48xlarge'|'ml.r7gd.medium'|'ml.r7gd.large'|'ml.r7gd.xlarge'|'ml.r7gd.2xlarge'|'ml.r7gd.4xlarge'|'ml.r7gd.8xlarge'|'ml.r7gd.12xlarge'|'ml.r7gd.16xlarge'|'ml.m8g.medium'|'ml.m8g.large'|'ml.m8g.xlarge'|'ml.m8g.2xlarge'|'ml.m8g.4xlarge'|'ml.m8g.8xlarge'|'ml.m8g.12xlarge'|'ml.m8g.16xlarge'|'ml.m8g.24xlarge'|'ml.m8g.48xlarge'|'ml.c6in.large'|'ml.c6in.xlarge'|'ml.c6in.2xlarge'|'ml.c6in.4xlarge'|'ml.c6in.8xlarge'|'ml.c6in.12xlarge'|'ml.c6in.16xlarge'|'ml.c6in.24xlarge'|'ml.c6in.32xlarge'|'ml.p6-b200.48xlarge'|'ml.p6-b300.48xlarge'|'ml.p6e-gb200.36xlarge'|'ml.p5.4xlarge',
],
'SupportedContentTypes': [
'string',
],
'SupportedResponseMIMETypes': [
'string',
]
},
],
'SkipModelValidation': 'All'|'None',
'SourceUri': 'string',
'SecurityConfig': {
'KmsKeyId': 'string'
},
'ModelCard': {
'ModelCardContent': 'string',
'ModelCardStatus': 'Draft'|'PendingReview'|'Approved'|'Archived'
},
'ModelLifeCycle': {
'Stage': 'string',
'StageStatus': 'string',
'StageDescription': 'string'
},
'ManagedStorageType': 'Restricted'
}
Response Structure
(dict) --
ModelPackageName (string) --
The name of the model package being described.
ModelPackageGroupName (string) --
If the model is a versioned model, the name of the model group that the versioned model belongs to.
ModelPackageVersion (integer) --
The version of the model package.
ModelPackageRegistrationType (string) --
The package registration type of the model package output.
ModelPackageArn (string) --
The Amazon Resource Name (ARN) of the model package.
ModelPackageDescription (string) --
A brief summary of the model package.
CreationTime (datetime) --
A timestamp specifying when the model package was created.
InferenceSpecification (dict) --
Details about inference jobs that you can run with models based on this model package.
Containers (list) --
The Amazon ECR registry path of the Docker image that contains the inference code.
(dict) --
Describes the Docker container for the model package.
ContainerHostname (string) --
The DNS host name for the Docker container.
Image (string) --
The Amazon Elastic Container Registry (Amazon ECR) path where inference code is stored.
If you are using your own custom algorithm instead of an algorithm provided by SageMaker, the inference code must meet SageMaker requirements. SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker.
ImageDigest (string) --
An MD5 hash of the training algorithm that identifies the Docker image used for training.
ModelDataUrl (string) --
The Amazon S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive ( .tar.gz suffix).
ModelDataSource (dict) --
Specifies the location of ML model data to deploy during endpoint creation.
S3DataSource (dict) --
Specifies the S3 location of ML model data to deploy.
S3Uri (string) --
Specifies the S3 path of ML model data to deploy.
S3DataType (string) --
Specifies the type of ML model data to deploy.
If you choose S3Prefix, S3Uri identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix as part of the ML model data to deploy. A valid key name prefix identified by S3Uri always ends with a forward slash (/).
If you choose S3Object, S3Uri identifies an object that is the ML model data to deploy.
CompressionType (string) --
Specifies how the ML model data is prepared.
If you choose Gzip and choose S3Object as the value of S3DataType, S3Uri identifies an object that is a gzip-compressed TAR archive. SageMaker will attempt to decompress and untar the object during model deployment.
If you choose None and chooose S3Object as the value of S3DataType, S3Uri identifies an object that represents an uncompressed ML model to deploy.
If you choose None and choose S3Prefix as the value of S3DataType, S3Uri identifies a key name prefix, under which all objects represents the uncompressed ML model to deploy.
If you choose None, then SageMaker will follow rules below when creating model data files under /opt/ml/model directory for use by your inference code:
If you choose S3Object as the value of S3DataType, then SageMaker will split the key of the S3 object referenced by S3Uri by slash (/), and use the last part as the filename of the file holding the content of the S3 object.
If you choose S3Prefix as the value of S3DataType, then for each S3 object under the key name pefix referenced by S3Uri, SageMaker will trim its key by the prefix, and use the remainder as the path (relative to /opt/ml/model) of the file holding the content of the S3 object. SageMaker will split the remainder by slash (/), using intermediate parts as directory names and the last part as filename of the file holding the content of the S3 object.
Do not use any of the following as file names or directory names:
An empty or blank string
A string which contains null bytes
A string longer than 255 bytes
A single dot ( .)
A double dot ( ..)
Ambiguous file names will result in model deployment failure. For example, if your uncompressed ML model consists of two S3 objects s3://mybucket/model/weights and s3://mybucket/model/weights/part1 and you specify s3://mybucket/model/ as the value of S3Uri and S3Prefix as the value of S3DataType, then it will result in name clash between /opt/ml/model/weights (a regular file) and /opt/ml/model/weights/ (a directory).
Do not organize the model artifacts in S3 console using folders. When you create a folder in S3 console, S3 creates a 0-byte object with a key set to the folder name you provide. They key of the 0-byte object ends with a slash (/) which violates SageMaker restrictions on model artifact file names, leading to model deployment failure.
ModelAccessConfig (dict) --
Specifies the access configuration file for the ML model. You can explicitly accept the model end-user license agreement (EULA) within the ModelAccessConfig. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.
AcceptEula (boolean) --
Specifies agreement to the model end-user license agreement (EULA). The AcceptEula value must be explicitly defined as True in order to accept the EULA that this model requires. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.
HubAccessConfig (dict) --
Configuration information for hub access.
HubContentArn (string) --
The ARN of the hub content for which deployment access is allowed.
ManifestS3Uri (string) --
The Amazon S3 URI of the manifest file. The manifest file is a CSV file that stores the artifact locations.
ETag (string) --
The ETag associated with S3 URI.
ManifestEtag (string) --
The ETag associated with Manifest S3 URI.
ProductId (string) --
The Amazon Web Services Marketplace product ID of the model package.
Environment (dict) --
The environment variables to set in the Docker container. Each key and value in the Environment string to string map can have length of up to 1024. We support up to 16 entries in the map.
(string) --
(string) --
ModelInput (dict) --
A structure with Model Input details.
DataInputConfig (string) --
The input configuration object for the model.
Framework (string) --
The machine learning framework of the model package container image.
FrameworkVersion (string) --
The framework version of the Model Package Container Image.
NearestModelName (string) --
The name of a pre-trained machine learning benchmarked by Amazon SageMaker Inference Recommender model that matches your model. You can find a list of benchmarked models by calling ListModelMetadata.
AdditionalModelDataSources (list) --
Data sources that are available to your model in addition to the one that you specify for ModelDataSource when you use the CreateModelPackage action.
(dict) --
Data sources that are available to your model in addition to the one that you specify for ModelDataSource when you use the CreateModel action.
ChannelName (string) --
A custom name for this AdditionalModelDataSource object.
S3DataSource (dict) --
Specifies the S3 location of ML model data to deploy.
S3Uri (string) --
Specifies the S3 path of ML model data to deploy.
S3DataType (string) --
Specifies the type of ML model data to deploy.
If you choose S3Prefix, S3Uri identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix as part of the ML model data to deploy. A valid key name prefix identified by S3Uri always ends with a forward slash (/).
If you choose S3Object, S3Uri identifies an object that is the ML model data to deploy.
CompressionType (string) --
Specifies how the ML model data is prepared.
If you choose Gzip and choose S3Object as the value of S3DataType, S3Uri identifies an object that is a gzip-compressed TAR archive. SageMaker will attempt to decompress and untar the object during model deployment.
If you choose None and chooose S3Object as the value of S3DataType, S3Uri identifies an object that represents an uncompressed ML model to deploy.
If you choose None and choose S3Prefix as the value of S3DataType, S3Uri identifies a key name prefix, under which all objects represents the uncompressed ML model to deploy.
If you choose None, then SageMaker will follow rules below when creating model data files under /opt/ml/model directory for use by your inference code:
If you choose S3Object as the value of S3DataType, then SageMaker will split the key of the S3 object referenced by S3Uri by slash (/), and use the last part as the filename of the file holding the content of the S3 object.
If you choose S3Prefix as the value of S3DataType, then for each S3 object under the key name pefix referenced by S3Uri, SageMaker will trim its key by the prefix, and use the remainder as the path (relative to /opt/ml/model) of the file holding the content of the S3 object. SageMaker will split the remainder by slash (/), using intermediate parts as directory names and the last part as filename of the file holding the content of the S3 object.
Do not use any of the following as file names or directory names:
An empty or blank string
A string which contains null bytes
A string longer than 255 bytes
A single dot ( .)
A double dot ( ..)
Ambiguous file names will result in model deployment failure. For example, if your uncompressed ML model consists of two S3 objects s3://mybucket/model/weights and s3://mybucket/model/weights/part1 and you specify s3://mybucket/model/ as the value of S3Uri and S3Prefix as the value of S3DataType, then it will result in name clash between /opt/ml/model/weights (a regular file) and /opt/ml/model/weights/ (a directory).
Do not organize the model artifacts in S3 console using folders. When you create a folder in S3 console, S3 creates a 0-byte object with a key set to the folder name you provide. They key of the 0-byte object ends with a slash (/) which violates SageMaker restrictions on model artifact file names, leading to model deployment failure.
ModelAccessConfig (dict) --
Specifies the access configuration file for the ML model. You can explicitly accept the model end-user license agreement (EULA) within the ModelAccessConfig. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.
AcceptEula (boolean) --
Specifies agreement to the model end-user license agreement (EULA). The AcceptEula value must be explicitly defined as True in order to accept the EULA that this model requires. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.
HubAccessConfig (dict) --
Configuration information for hub access.
HubContentArn (string) --
The ARN of the hub content for which deployment access is allowed.
ManifestS3Uri (string) --
The Amazon S3 URI of the manifest file. The manifest file is a CSV file that stores the artifact locations.
ETag (string) --
The ETag associated with S3 URI.
ManifestEtag (string) --
The ETag associated with Manifest S3 URI.
AdditionalS3DataSource (dict) --
The additional data source that is used during inference in the Docker container for your model package.
S3DataType (string) --
The data type of the additional data source that you specify for use in inference or training.
S3Uri (string) --
The uniform resource identifier (URI) used to identify an additional data source used in inference or training.
CompressionType (string) --
The type of compression used for an additional data source used in inference or training. Specify None if your additional data source is not compressed.
ETag (string) --
The ETag associated with S3 URI.
ModelDataETag (string) --
The ETag associated with Model Data URL.
IsCheckpoint (boolean) --
Specifies whether the model data is a training checkpoint.
BaseModel (dict) --
Identifies the foundation model that was used as the starting point for model customization.
HubContentName (string) --
The hub content name of the base model.
HubContentVersion (string) --
The hub content version of the base model.
RecipeName (string) --
The recipe name of the base model.
SupportedTransformInstanceTypes (list) --
A list of the instance types on which a transformation job can be run or on which an endpoint can be deployed.
This parameter is required for unversioned models, and optional for versioned models.
(string) --
SupportedRealtimeInferenceInstanceTypes (list) --
A list of the instance types that are used to generate inferences in real-time.
This parameter is required for unversioned models, and optional for versioned models.
(string) --
SupportedContentTypes (list) --
The supported MIME types for the input data.
(string) --
SupportedResponseMIMETypes (list) --
The supported MIME types for the output data.
(string) --
SourceAlgorithmSpecification (dict) --
Details about the algorithm that was used to create the model package.
SourceAlgorithms (list) --
A list of the algorithms that were used to create a model package.
(dict) --
Specifies an algorithm that was used to create the model package. The algorithm must be either an algorithm resource in your SageMaker account or an algorithm in Amazon Web Services Marketplace that you are subscribed to.
ModelDataUrl (string) --
The Amazon S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive ( .tar.gz suffix).
ModelDataSource (dict) --
Specifies the location of ML model data to deploy during endpoint creation.
S3DataSource (dict) --
Specifies the S3 location of ML model data to deploy.
S3Uri (string) --
Specifies the S3 path of ML model data to deploy.
S3DataType (string) --
Specifies the type of ML model data to deploy.
If you choose S3Prefix, S3Uri identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix as part of the ML model data to deploy. A valid key name prefix identified by S3Uri always ends with a forward slash (/).
If you choose S3Object, S3Uri identifies an object that is the ML model data to deploy.
CompressionType (string) --
Specifies how the ML model data is prepared.
If you choose Gzip and choose S3Object as the value of S3DataType, S3Uri identifies an object that is a gzip-compressed TAR archive. SageMaker will attempt to decompress and untar the object during model deployment.
If you choose None and chooose S3Object as the value of S3DataType, S3Uri identifies an object that represents an uncompressed ML model to deploy.
If you choose None and choose S3Prefix as the value of S3DataType, S3Uri identifies a key name prefix, under which all objects represents the uncompressed ML model to deploy.
If you choose None, then SageMaker will follow rules below when creating model data files under /opt/ml/model directory for use by your inference code:
If you choose S3Object as the value of S3DataType, then SageMaker will split the key of the S3 object referenced by S3Uri by slash (/), and use the last part as the filename of the file holding the content of the S3 object.
If you choose S3Prefix as the value of S3DataType, then for each S3 object under the key name pefix referenced by S3Uri, SageMaker will trim its key by the prefix, and use the remainder as the path (relative to /opt/ml/model) of the file holding the content of the S3 object. SageMaker will split the remainder by slash (/), using intermediate parts as directory names and the last part as filename of the file holding the content of the S3 object.
Do not use any of the following as file names or directory names:
An empty or blank string
A string which contains null bytes
A string longer than 255 bytes
A single dot ( .)
A double dot ( ..)
Ambiguous file names will result in model deployment failure. For example, if your uncompressed ML model consists of two S3 objects s3://mybucket/model/weights and s3://mybucket/model/weights/part1 and you specify s3://mybucket/model/ as the value of S3Uri and S3Prefix as the value of S3DataType, then it will result in name clash between /opt/ml/model/weights (a regular file) and /opt/ml/model/weights/ (a directory).
Do not organize the model artifacts in S3 console using folders. When you create a folder in S3 console, S3 creates a 0-byte object with a key set to the folder name you provide. They key of the 0-byte object ends with a slash (/) which violates SageMaker restrictions on model artifact file names, leading to model deployment failure.
ModelAccessConfig (dict) --
Specifies the access configuration file for the ML model. You can explicitly accept the model end-user license agreement (EULA) within the ModelAccessConfig. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.
AcceptEula (boolean) --
Specifies agreement to the model end-user license agreement (EULA). The AcceptEula value must be explicitly defined as True in order to accept the EULA that this model requires. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.
HubAccessConfig (dict) --
Configuration information for hub access.
HubContentArn (string) --
The ARN of the hub content for which deployment access is allowed.
ManifestS3Uri (string) --
The Amazon S3 URI of the manifest file. The manifest file is a CSV file that stores the artifact locations.
ETag (string) --
The ETag associated with S3 URI.
ManifestEtag (string) --
The ETag associated with Manifest S3 URI.
ModelDataETag (string) --
The ETag associated with Model Data URL.
AlgorithmName (string) --
The name of an algorithm that was used to create the model package. The algorithm must be either an algorithm resource in your SageMaker account or an algorithm in Amazon Web Services Marketplace that you are subscribed to.
ValidationSpecification (dict) --
Configurations for one or more transform jobs that SageMaker runs to test the model package.
ValidationRole (string) --
The IAM roles to be used for the validation of the model package.
ValidationProfiles (list) --
An array of ModelPackageValidationProfile objects, each of which specifies a batch transform job that SageMaker runs to validate your model package.
(dict) --
Contains data, such as the inputs and targeted instance types that are used in the process of validating the model package.
The data provided in the validation profile is made available to your buyers on Amazon Web Services Marketplace.
ProfileName (string) --
The name of the profile for the model package.
TransformJobDefinition (dict) --
The TransformJobDefinition object that describes the transform job used for the validation of the model package.
MaxConcurrentTransforms (integer) --
The maximum number of parallel requests that can be sent to each instance in a transform job. The default value is 1.
MaxPayloadInMB (integer) --
The maximum payload size allowed, in MB. A payload is the data portion of a record (without metadata).
BatchStrategy (string) --
A string that determines the number of records included in a single mini-batch.
SingleRecord means only one record is used per mini-batch. MultiRecord means a mini-batch is set to contain as many records that can fit within the MaxPayloadInMB limit.
Environment (dict) --
The environment variables to set in the Docker container. We support up to 16 key and values entries in the map.
(string) --
(string) --
TransformInput (dict) --
A description of the input source and the way the transform job consumes it.
DataSource (dict) --
Describes the location of the channel data, which is, the S3 location of the input data that the model can consume.
S3DataSource (dict) --
The S3 location of the data source that is associated with a channel.
S3DataType (string) --
If you choose S3Prefix, S3Uri identifies a key name prefix. Amazon SageMaker uses all objects with the specified key name prefix for batch transform.
If you choose ManifestFile, S3Uri identifies an object that is a manifest file containing a list of object keys that you want Amazon SageMaker to use for batch transform.
The following values are compatible: ManifestFile, S3Prefix
The following value is not compatible: AugmentedManifestFile
S3Uri (string) --
Depending on the value specified for the S3DataType, identifies either a key name prefix or a manifest. For example:
A key name prefix might look like this: s3://bucketname/exampleprefix/.
A manifest might look like this: s3://bucketname/example.manifest The manifest is an S3 object which is a JSON file with the following format: [ {"prefix": "s3://customer_bucket/some/prefix/"}, "relative/path/to/custdata-1", "relative/path/custdata-2", ... "relative/path/custdata-N" ] The preceding JSON matches the following S3Uris: s3://customer_bucket/some/prefix/relative/path/to/custdata-1 s3://customer_bucket/some/prefix/relative/path/custdata-2 ... s3://customer_bucket/some/prefix/relative/path/custdata-N The complete set of S3Uris in this manifest constitutes the input data for the channel for this datasource. The object that each S3Uris points to must be readable by the IAM role that Amazon SageMaker uses to perform tasks on your behalf.
ContentType (string) --
The multipurpose internet mail extension (MIME) type of the data. Amazon SageMaker uses the MIME type with each http call to transfer data to the transform job.
CompressionType (string) --
If your transform data is compressed, specify the compression type. Amazon SageMaker automatically decompresses the data for the transform job accordingly. The default value is None.
SplitType (string) --
The method to use to split the transform job's data files into smaller batches. Splitting is necessary when the total size of each object is too large to fit in a single request. You can also use data splitting to improve performance by processing multiple concurrent mini-batches. The default value for SplitType is None, which indicates that input data files are not split, and request payloads contain the entire contents of an input object. Set the value of this parameter to Line to split records on a newline character boundary. SplitType also supports a number of record-oriented binary data formats. Currently, the supported record formats are:
RecordIO
TFRecord
When splitting is enabled, the size of a mini-batch depends on the values of the BatchStrategy and MaxPayloadInMB parameters. When the value of BatchStrategy is MultiRecord, Amazon SageMaker sends the maximum number of records in each request, up to the MaxPayloadInMB limit. If the value of BatchStrategy is SingleRecord, Amazon SageMaker sends individual records in each request.
TransformOutput (dict) --
Identifies the Amazon S3 location where you want Amazon SageMaker to save the results from the transform job.
S3OutputPath (string) --
The Amazon S3 path where you want Amazon SageMaker to store the results of the transform job. For example, s3://bucket-name/key-name-prefix.
For every S3 object used as input for the transform job, batch transform stores the transformed data with an . out suffix in a corresponding subfolder in the location in the output prefix. For example, for the input data stored at s3://bucket-name/input-name-prefix/dataset01/data.csv, batch transform stores the transformed data at s3://bucket-name/output-name-prefix/input-name-prefix/data.csv.out. Batch transform doesn't upload partially processed objects. For an input S3 object that contains multiple records, it creates an . out file only if the transform job succeeds on the entire file. When the input contains multiple S3 objects, the batch transform job processes the listed S3 objects and uploads only the output for successfully processed objects. If any object fails in the transform job batch transform marks the job as failed to prompt investigation.
Accept (string) --
The MIME type used to specify the output data. Amazon SageMaker uses the MIME type with each http call to transfer data from the transform job.
AssembleWith (string) --
Defines how to assemble the results of the transform job as a single S3 object. Choose a format that is most convenient to you. To concatenate the results in binary format, specify None. To add a newline character at the end of every transformed record, specify Line.
KmsKeyId (string) --
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The KmsKeyId can be any of the following formats:
Key ID: 1234abcd-12ab-34cd-56ef-1234567890ab
Key ARN: arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab
Alias name: alias/ExampleAlias
Alias name ARN: arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias
If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.
The KMS key policy must grant permission to the IAM role that you specify in your CreateModel request. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide.
TransformResources (dict) --
Identifies the ML compute instances for the transform job.
InstanceType (string) --
The ML compute instance type for the transform job. If you are using built-in algorithms to transform moderately sized datasets, we recommend using ml.m4.xlarge or ``ml.m5.large``instance types.
InstanceCount (integer) --
The number of ML compute instances to use in the transform job. The default value is 1, and the maximum is 100. For distributed transform jobs, specify a value greater than 1.
VolumeKmsKeyId (string) --
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt model data on the storage volume attached to the ML compute instance(s) that run the batch transform job.
The VolumeKmsKeyId can be any of the following formats:
Key ID: 1234abcd-12ab-34cd-56ef-1234567890ab
Key ARN: arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab
Alias name: alias/ExampleAlias
Alias name ARN: arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias
TransformAmiVersion (string) --
Specifies an option from a collection of preconfigured Amazon Machine Image (AMI) images. Each image is configured by Amazon Web Services with a set of software and driver versions.
al2-ami-sagemaker-batch-gpu-470
Accelerator: GPU
NVIDIA driver version: 470
al2-ami-sagemaker-batch-gpu-535
Accelerator: GPU
NVIDIA driver version: 535
ModelPackageStatus (string) --
The current status of the model package.
ModelPackageStatusDetails (dict) --
Details about the current status of the model package.
ValidationStatuses (list) --
The validation status of the model package.
(dict) --
Represents the overall status of a model package.
Name (string) --
The name of the model package for which the overall status is being reported.
Status (string) --
The current status.
FailureReason (string) --
if the overall status is Failed, the reason for the failure.
ImageScanStatuses (list) --
The status of the scan of the Docker image container for the model package.
(dict) --
Represents the overall status of a model package.
Name (string) --
The name of the model package for which the overall status is being reported.
Status (string) --
The current status.
FailureReason (string) --
if the overall status is Failed, the reason for the failure.
CertifyForMarketplace (boolean) --
Whether the model package is certified for listing on Amazon Web Services Marketplace.
ModelApprovalStatus (string) --
The approval status of the model package.
CreatedBy (dict) --
Information about the user who created or modified a SageMaker resource.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
IamIdentity (dict) --
The IAM Identity details associated with the user. These details are associated with model package groups, model packages, and project entities only.
Arn (string) --
The Amazon Resource Name (ARN) of the IAM identity.
PrincipalId (string) --
The ID of the principal that assumes the IAM identity.
SourceIdentity (string) --
The person or application which assumes the IAM identity.
MetadataProperties (dict) --
Metadata properties of the tracking entity, trial, or trial component.
CommitId (string) --
The commit ID.
Repository (string) --
The repository.
GeneratedBy (string) --
The entity this entity was generated by.
ProjectId (string) --
The project ID.
ModelMetrics (dict) --
Metrics for the model.
ModelQuality (dict) --
Metrics that measure the quality of a model.
Statistics (dict) --
Model quality statistics.
ContentType (string) --
The metric source content type.
ContentDigest (string) --
The hash key used for the metrics source.
S3Uri (string) --
The S3 URI for the metrics source.
Constraints (dict) --
Model quality constraints.
ContentType (string) --
The metric source content type.
ContentDigest (string) --
The hash key used for the metrics source.
S3Uri (string) --
The S3 URI for the metrics source.
ModelDataQuality (dict) --
Metrics that measure the quality of the input data for a model.
Statistics (dict) --
Data quality statistics for a model.
ContentType (string) --
The metric source content type.
ContentDigest (string) --
The hash key used for the metrics source.
S3Uri (string) --
The S3 URI for the metrics source.
Constraints (dict) --
Data quality constraints for a model.
ContentType (string) --
The metric source content type.
ContentDigest (string) --
The hash key used for the metrics source.
S3Uri (string) --
The S3 URI for the metrics source.
Bias (dict) --
Metrics that measure bias in a model.
Report (dict) --
The bias report for a model
ContentType (string) --
The metric source content type.
ContentDigest (string) --
The hash key used for the metrics source.
S3Uri (string) --
The S3 URI for the metrics source.
PreTrainingReport (dict) --
The pre-training bias report for a model.
ContentType (string) --
The metric source content type.
ContentDigest (string) --
The hash key used for the metrics source.
S3Uri (string) --
The S3 URI for the metrics source.
PostTrainingReport (dict) --
The post-training bias report for a model.
ContentType (string) --
The metric source content type.
ContentDigest (string) --
The hash key used for the metrics source.
S3Uri (string) --
The S3 URI for the metrics source.
Explainability (dict) --
Metrics that help explain a model.
Report (dict) --
The explainability report for a model.
ContentType (string) --
The metric source content type.
ContentDigest (string) --
The hash key used for the metrics source.
S3Uri (string) --
The S3 URI for the metrics source.
LastModifiedTime (datetime) --
The last time that the model package was modified.
LastModifiedBy (dict) --
Information about the user who created or modified a SageMaker resource.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
IamIdentity (dict) --
The IAM Identity details associated with the user. These details are associated with model package groups, model packages, and project entities only.
Arn (string) --
The Amazon Resource Name (ARN) of the IAM identity.
PrincipalId (string) --
The ID of the principal that assumes the IAM identity.
SourceIdentity (string) --
The person or application which assumes the IAM identity.
ApprovalDescription (string) --
A description provided for the model approval.
Domain (string) --
The machine learning domain of the model package you specified. Common machine learning domains include computer vision and natural language processing.
Task (string) --
The machine learning task you specified that your model package accomplishes. Common machine learning tasks include object detection and image classification.
SamplePayloadUrl (string) --
The Amazon Simple Storage Service (Amazon S3) path where the sample payload are stored. This path points to a single gzip compressed tar archive (.tar.gz suffix).
CustomerMetadataProperties (dict) --
The metadata properties associated with the model package versions.
(string) --
(string) --
DriftCheckBaselines (dict) --
Represents the drift check baselines that can be used when the model monitor is set using the model package. For more information, see the topic on Drift Detection against Previous Baselines in SageMaker Pipelines in the Amazon SageMaker Developer Guide.
Bias (dict) --
Represents the drift check bias baselines that can be used when the model monitor is set using the model package.
ConfigFile (dict) --
The bias config file for a model.
ContentType (string) --
The type of content stored in the file source.
ContentDigest (string) --
The digest of the file source.
S3Uri (string) --
The Amazon S3 URI for the file source.
PreTrainingConstraints (dict) --
The pre-training constraints.
ContentType (string) --
The metric source content type.
ContentDigest (string) --
The hash key used for the metrics source.
S3Uri (string) --
The S3 URI for the metrics source.
PostTrainingConstraints (dict) --
The post-training constraints.
ContentType (string) --
The metric source content type.
ContentDigest (string) --
The hash key used for the metrics source.
S3Uri (string) --
The S3 URI for the metrics source.
Explainability (dict) --
Represents the drift check explainability baselines that can be used when the model monitor is set using the model package.
Constraints (dict) --
The drift check explainability constraints.
ContentType (string) --
The metric source content type.
ContentDigest (string) --
The hash key used for the metrics source.
S3Uri (string) --
The S3 URI for the metrics source.
ConfigFile (dict) --
The explainability config file for the model.
ContentType (string) --
The type of content stored in the file source.
ContentDigest (string) --
The digest of the file source.
S3Uri (string) --
The Amazon S3 URI for the file source.
ModelQuality (dict) --
Represents the drift check model quality baselines that can be used when the model monitor is set using the model package.
Statistics (dict) --
The drift check model quality statistics.
ContentType (string) --
The metric source content type.
ContentDigest (string) --
The hash key used for the metrics source.
S3Uri (string) --
The S3 URI for the metrics source.
Constraints (dict) --
The drift check model quality constraints.
ContentType (string) --
The metric source content type.
ContentDigest (string) --
The hash key used for the metrics source.
S3Uri (string) --
The S3 URI for the metrics source.
ModelDataQuality (dict) --
Represents the drift check model data quality baselines that can be used when the model monitor is set using the model package.
Statistics (dict) --
The drift check model data quality statistics.
ContentType (string) --
The metric source content type.
ContentDigest (string) --
The hash key used for the metrics source.
S3Uri (string) --
The S3 URI for the metrics source.
Constraints (dict) --
The drift check model data quality constraints.
ContentType (string) --
The metric source content type.
ContentDigest (string) --
The hash key used for the metrics source.
S3Uri (string) --
The S3 URI for the metrics source.
AdditionalInferenceSpecifications (list) --
An array of additional Inference Specification objects. Each additional Inference Specification specifies artifacts based on this model package that can be used on inference endpoints. Generally used with SageMaker Neo to store the compiled artifacts.
(dict) --
A structure of additional Inference Specification. Additional Inference Specification specifies details about inference jobs that can be run with models based on this model package
Name (string) --
A unique name to identify the additional inference specification. The name must be unique within the list of your additional inference specifications for a particular model package.
Description (string) --
A description of the additional Inference specification
Containers (list) --
The Amazon ECR registry path of the Docker image that contains the inference code.
(dict) --
Describes the Docker container for the model package.
ContainerHostname (string) --
The DNS host name for the Docker container.
Image (string) --
The Amazon Elastic Container Registry (Amazon ECR) path where inference code is stored.
If you are using your own custom algorithm instead of an algorithm provided by SageMaker, the inference code must meet SageMaker requirements. SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker.
ImageDigest (string) --
An MD5 hash of the training algorithm that identifies the Docker image used for training.
ModelDataUrl (string) --
The Amazon S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive ( .tar.gz suffix).
ModelDataSource (dict) --
Specifies the location of ML model data to deploy during endpoint creation.
S3DataSource (dict) --
Specifies the S3 location of ML model data to deploy.
S3Uri (string) --
Specifies the S3 path of ML model data to deploy.
S3DataType (string) --
Specifies the type of ML model data to deploy.
If you choose S3Prefix, S3Uri identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix as part of the ML model data to deploy. A valid key name prefix identified by S3Uri always ends with a forward slash (/).
If you choose S3Object, S3Uri identifies an object that is the ML model data to deploy.
CompressionType (string) --
Specifies how the ML model data is prepared.
If you choose Gzip and choose S3Object as the value of S3DataType, S3Uri identifies an object that is a gzip-compressed TAR archive. SageMaker will attempt to decompress and untar the object during model deployment.
If you choose None and chooose S3Object as the value of S3DataType, S3Uri identifies an object that represents an uncompressed ML model to deploy.
If you choose None and choose S3Prefix as the value of S3DataType, S3Uri identifies a key name prefix, under which all objects represents the uncompressed ML model to deploy.
If you choose None, then SageMaker will follow rules below when creating model data files under /opt/ml/model directory for use by your inference code:
If you choose S3Object as the value of S3DataType, then SageMaker will split the key of the S3 object referenced by S3Uri by slash (/), and use the last part as the filename of the file holding the content of the S3 object.
If you choose S3Prefix as the value of S3DataType, then for each S3 object under the key name pefix referenced by S3Uri, SageMaker will trim its key by the prefix, and use the remainder as the path (relative to /opt/ml/model) of the file holding the content of the S3 object. SageMaker will split the remainder by slash (/), using intermediate parts as directory names and the last part as filename of the file holding the content of the S3 object.
Do not use any of the following as file names or directory names:
An empty or blank string
A string which contains null bytes
A string longer than 255 bytes
A single dot ( .)
A double dot ( ..)
Ambiguous file names will result in model deployment failure. For example, if your uncompressed ML model consists of two S3 objects s3://mybucket/model/weights and s3://mybucket/model/weights/part1 and you specify s3://mybucket/model/ as the value of S3Uri and S3Prefix as the value of S3DataType, then it will result in name clash between /opt/ml/model/weights (a regular file) and /opt/ml/model/weights/ (a directory).
Do not organize the model artifacts in S3 console using folders. When you create a folder in S3 console, S3 creates a 0-byte object with a key set to the folder name you provide. They key of the 0-byte object ends with a slash (/) which violates SageMaker restrictions on model artifact file names, leading to model deployment failure.
ModelAccessConfig (dict) --
Specifies the access configuration file for the ML model. You can explicitly accept the model end-user license agreement (EULA) within the ModelAccessConfig. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.
AcceptEula (boolean) --
Specifies agreement to the model end-user license agreement (EULA). The AcceptEula value must be explicitly defined as True in order to accept the EULA that this model requires. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.
HubAccessConfig (dict) --
Configuration information for hub access.
HubContentArn (string) --
The ARN of the hub content for which deployment access is allowed.
ManifestS3Uri (string) --
The Amazon S3 URI of the manifest file. The manifest file is a CSV file that stores the artifact locations.
ETag (string) --
The ETag associated with S3 URI.
ManifestEtag (string) --
The ETag associated with Manifest S3 URI.
ProductId (string) --
The Amazon Web Services Marketplace product ID of the model package.
Environment (dict) --
The environment variables to set in the Docker container. Each key and value in the Environment string to string map can have length of up to 1024. We support up to 16 entries in the map.
(string) --
(string) --
ModelInput (dict) --
A structure with Model Input details.
DataInputConfig (string) --
The input configuration object for the model.
Framework (string) --
The machine learning framework of the model package container image.
FrameworkVersion (string) --
The framework version of the Model Package Container Image.
NearestModelName (string) --
The name of a pre-trained machine learning benchmarked by Amazon SageMaker Inference Recommender model that matches your model. You can find a list of benchmarked models by calling ListModelMetadata.
AdditionalModelDataSources (list) --
Data sources that are available to your model in addition to the one that you specify for ModelDataSource when you use the CreateModelPackage action.
(dict) --
Data sources that are available to your model in addition to the one that you specify for ModelDataSource when you use the CreateModel action.
ChannelName (string) --
A custom name for this AdditionalModelDataSource object.
S3DataSource (dict) --
Specifies the S3 location of ML model data to deploy.
S3Uri (string) --
Specifies the S3 path of ML model data to deploy.
S3DataType (string) --
Specifies the type of ML model data to deploy.
If you choose S3Prefix, S3Uri identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix as part of the ML model data to deploy. A valid key name prefix identified by S3Uri always ends with a forward slash (/).
If you choose S3Object, S3Uri identifies an object that is the ML model data to deploy.
CompressionType (string) --
Specifies how the ML model data is prepared.
If you choose Gzip and choose S3Object as the value of S3DataType, S3Uri identifies an object that is a gzip-compressed TAR archive. SageMaker will attempt to decompress and untar the object during model deployment.
If you choose None and chooose S3Object as the value of S3DataType, S3Uri identifies an object that represents an uncompressed ML model to deploy.
If you choose None and choose S3Prefix as the value of S3DataType, S3Uri identifies a key name prefix, under which all objects represents the uncompressed ML model to deploy.
If you choose None, then SageMaker will follow rules below when creating model data files under /opt/ml/model directory for use by your inference code:
If you choose S3Object as the value of S3DataType, then SageMaker will split the key of the S3 object referenced by S3Uri by slash (/), and use the last part as the filename of the file holding the content of the S3 object.
If you choose S3Prefix as the value of S3DataType, then for each S3 object under the key name pefix referenced by S3Uri, SageMaker will trim its key by the prefix, and use the remainder as the path (relative to /opt/ml/model) of the file holding the content of the S3 object. SageMaker will split the remainder by slash (/), using intermediate parts as directory names and the last part as filename of the file holding the content of the S3 object.
Do not use any of the following as file names or directory names:
An empty or blank string
A string which contains null bytes
A string longer than 255 bytes
A single dot ( .)
A double dot ( ..)
Ambiguous file names will result in model deployment failure. For example, if your uncompressed ML model consists of two S3 objects s3://mybucket/model/weights and s3://mybucket/model/weights/part1 and you specify s3://mybucket/model/ as the value of S3Uri and S3Prefix as the value of S3DataType, then it will result in name clash between /opt/ml/model/weights (a regular file) and /opt/ml/model/weights/ (a directory).
Do not organize the model artifacts in S3 console using folders. When you create a folder in S3 console, S3 creates a 0-byte object with a key set to the folder name you provide. They key of the 0-byte object ends with a slash (/) which violates SageMaker restrictions on model artifact file names, leading to model deployment failure.
ModelAccessConfig (dict) --
Specifies the access configuration file for the ML model. You can explicitly accept the model end-user license agreement (EULA) within the ModelAccessConfig. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.
AcceptEula (boolean) --
Specifies agreement to the model end-user license agreement (EULA). The AcceptEula value must be explicitly defined as True in order to accept the EULA that this model requires. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.
HubAccessConfig (dict) --
Configuration information for hub access.
HubContentArn (string) --
The ARN of the hub content for which deployment access is allowed.
ManifestS3Uri (string) --
The Amazon S3 URI of the manifest file. The manifest file is a CSV file that stores the artifact locations.
ETag (string) --
The ETag associated with S3 URI.
ManifestEtag (string) --
The ETag associated with Manifest S3 URI.
AdditionalS3DataSource (dict) --
The additional data source that is used during inference in the Docker container for your model package.
S3DataType (string) --
The data type of the additional data source that you specify for use in inference or training.
S3Uri (string) --
The uniform resource identifier (URI) used to identify an additional data source used in inference or training.
CompressionType (string) --
The type of compression used for an additional data source used in inference or training. Specify None if your additional data source is not compressed.
ETag (string) --
The ETag associated with S3 URI.
ModelDataETag (string) --
The ETag associated with Model Data URL.
IsCheckpoint (boolean) --
Specifies whether the model data is a training checkpoint.
BaseModel (dict) --
Identifies the foundation model that was used as the starting point for model customization.
HubContentName (string) --
The hub content name of the base model.
HubContentVersion (string) --
The hub content version of the base model.
RecipeName (string) --
The recipe name of the base model.
SupportedTransformInstanceTypes (list) --
A list of the instance types on which a transformation job can be run or on which an endpoint can be deployed.
(string) --
SupportedRealtimeInferenceInstanceTypes (list) --
A list of the instance types that are used to generate inferences in real-time.
(string) --
SupportedContentTypes (list) --
The supported MIME types for the input data.
(string) --
SupportedResponseMIMETypes (list) --
The supported MIME types for the output data.
(string) --
SkipModelValidation (string) --
Indicates if you want to skip model validation.
SourceUri (string) --
The URI of the source for the model package.
SecurityConfig (dict) --
The KMS Key ID ( KMSKeyId) used for encryption of model package information.
KmsKeyId (string) --
The KMS Key ID ( KMSKeyId) used for encryption of model package information.
ModelCard (dict) --
The model card associated with the model package. Since ModelPackageModelCard is tied to a model package, it is a specific usage of a model card and its schema is simplified compared to the schema of ModelCard. The ModelPackageModelCard schema does not include model_package_details, and model_overview is composed of the model_creator and model_artifact properties. For more information about the model package model card schema, see Model package model card schema. For more information about the model card associated with the model package, see View the Details of a Model Version.
ModelCardContent (string) --
The content of the model card. The content must follow the schema described in Model Package Model Card Schema.
ModelCardStatus (string) --
The approval status of the model card within your organization. Different organizations might have different criteria for model card review and approval.
Draft: The model card is a work in progress.
PendingReview: The model card is pending review.
Approved: The model card is approved.
Archived: The model card is archived. No more updates can be made to the model card content. If you try to update the model card content, you will receive the message Model Card is in Archived state.
ModelLifeCycle (dict) --
A structure describing the current state of the model in its life cycle.
Stage (string) --
The current stage in the model life cycle.
StageStatus (string) --
The current status of a stage in model life cycle.
StageDescription (string) --
Describes the stage related details.
ManagedStorageType (string) --
The storage type of the model package.
{'ManagedConfiguration': {'ManagedStorageType': 'Restricted'}}
Gets a description for the specified model group.
See also: AWS API Documentation
Request Syntax
client.describe_model_package_group(
ModelPackageGroupName='string'
)
string
[REQUIRED]
The name of the model group to describe.
dict
Response Syntax
{
'ModelPackageGroupName': 'string',
'ModelPackageGroupArn': 'string',
'ModelPackageGroupDescription': 'string',
'CreationTime': datetime(2015, 1, 1),
'CreatedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string',
'IamIdentity': {
'Arn': 'string',
'PrincipalId': 'string',
'SourceIdentity': 'string'
}
},
'ModelPackageGroupStatus': 'Pending'|'InProgress'|'Completed'|'Failed'|'Deleting'|'DeleteFailed',
'ManagedConfiguration': {
'ManagedStorageType': 'Restricted'
}
}
Response Structure
(dict) --
ModelPackageGroupName (string) --
The name of the model group.
ModelPackageGroupArn (string) --
The Amazon Resource Name (ARN) of the model group.
ModelPackageGroupDescription (string) --
A description of the model group.
CreationTime (datetime) --
The time that the model group was created.
CreatedBy (dict) --
Information about the user who created or modified a SageMaker resource.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
IamIdentity (dict) --
The IAM Identity details associated with the user. These details are associated with model package groups, model packages, and project entities only.
Arn (string) --
The Amazon Resource Name (ARN) of the IAM identity.
PrincipalId (string) --
The ID of the principal that assumes the IAM identity.
SourceIdentity (string) --
The person or application which assumes the IAM identity.
ModelPackageGroupStatus (string) --
The status of the model group.
ManagedConfiguration (dict) --
The managed configuration of the model package group.
ManagedStorageType (string) --
The storage type of the model package.
{'SpaceSettings': {'CodeEditorAppSettings': {'DefaultResourceSpec': {'TrainingPlanArn': 'string'}},
'JupyterLabAppSettings': {'DefaultResourceSpec': {'TrainingPlanArn': 'string'}},
'JupyterServerAppSettings': {'DefaultResourceSpec': {'TrainingPlanArn': 'string'}},
'KernelGatewayAppSettings': {'DefaultResourceSpec': {'TrainingPlanArn': 'string'}}}}
Describes the space.
See also: AWS API Documentation
Request Syntax
client.describe_space(
DomainId='string',
SpaceName='string'
)
string
[REQUIRED]
The ID of the associated domain.
string
[REQUIRED]
The name of the space.
dict
Response Syntax
{
'DomainId': 'string',
'SpaceArn': 'string',
'SpaceName': 'string',
'HomeEfsFileSystemUid': 'string',
'Status': 'Deleting'|'Failed'|'InService'|'Pending'|'Updating'|'Update_Failed'|'Delete_Failed',
'LastModifiedTime': datetime(2015, 1, 1),
'CreationTime': datetime(2015, 1, 1),
'FailureReason': 'string',
'SpaceSettings': {
'JupyterServerAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': 'string',
'SageMakerImageVersionAlias': 'string',
'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.g6.xlarge'|'ml.g6.2xlarge'|'ml.g6.4xlarge'|'ml.g6.8xlarge'|'ml.g6.12xlarge'|'ml.g6.16xlarge'|'ml.g6.24xlarge'|'ml.g6.48xlarge'|'ml.g6e.xlarge'|'ml.g6e.2xlarge'|'ml.g6e.4xlarge'|'ml.g6e.8xlarge'|'ml.g6e.12xlarge'|'ml.g6e.16xlarge'|'ml.g6e.24xlarge'|'ml.g6e.48xlarge'|'ml.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.p5.48xlarge'|'ml.p5en.48xlarge'|'ml.p6-b200.48xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.r6i.large'|'ml.r6i.xlarge'|'ml.r6i.2xlarge'|'ml.r6i.4xlarge'|'ml.r6i.8xlarge'|'ml.r6i.12xlarge'|'ml.r6i.16xlarge'|'ml.r6i.24xlarge'|'ml.r6i.32xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge'|'ml.m6id.large'|'ml.m6id.xlarge'|'ml.m6id.2xlarge'|'ml.m6id.4xlarge'|'ml.m6id.8xlarge'|'ml.m6id.12xlarge'|'ml.m6id.16xlarge'|'ml.m6id.24xlarge'|'ml.m6id.32xlarge'|'ml.c6id.large'|'ml.c6id.xlarge'|'ml.c6id.2xlarge'|'ml.c6id.4xlarge'|'ml.c6id.8xlarge'|'ml.c6id.12xlarge'|'ml.c6id.16xlarge'|'ml.c6id.24xlarge'|'ml.c6id.32xlarge'|'ml.r6id.large'|'ml.r6id.xlarge'|'ml.r6id.2xlarge'|'ml.r6id.4xlarge'|'ml.r6id.8xlarge'|'ml.r6id.12xlarge'|'ml.r6id.16xlarge'|'ml.r6id.24xlarge'|'ml.r6id.32xlarge'|'ml.p5.4xlarge',
'LifecycleConfigArn': 'string',
'TrainingPlanArn': 'string'
},
'LifecycleConfigArns': [
'string',
],
'CodeRepositories': [
{
'RepositoryUrl': 'string'
},
]
},
'KernelGatewayAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': 'string',
'SageMakerImageVersionAlias': 'string',
'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.g6.xlarge'|'ml.g6.2xlarge'|'ml.g6.4xlarge'|'ml.g6.8xlarge'|'ml.g6.12xlarge'|'ml.g6.16xlarge'|'ml.g6.24xlarge'|'ml.g6.48xlarge'|'ml.g6e.xlarge'|'ml.g6e.2xlarge'|'ml.g6e.4xlarge'|'ml.g6e.8xlarge'|'ml.g6e.12xlarge'|'ml.g6e.16xlarge'|'ml.g6e.24xlarge'|'ml.g6e.48xlarge'|'ml.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.p5.48xlarge'|'ml.p5en.48xlarge'|'ml.p6-b200.48xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.r6i.large'|'ml.r6i.xlarge'|'ml.r6i.2xlarge'|'ml.r6i.4xlarge'|'ml.r6i.8xlarge'|'ml.r6i.12xlarge'|'ml.r6i.16xlarge'|'ml.r6i.24xlarge'|'ml.r6i.32xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge'|'ml.m6id.large'|'ml.m6id.xlarge'|'ml.m6id.2xlarge'|'ml.m6id.4xlarge'|'ml.m6id.8xlarge'|'ml.m6id.12xlarge'|'ml.m6id.16xlarge'|'ml.m6id.24xlarge'|'ml.m6id.32xlarge'|'ml.c6id.large'|'ml.c6id.xlarge'|'ml.c6id.2xlarge'|'ml.c6id.4xlarge'|'ml.c6id.8xlarge'|'ml.c6id.12xlarge'|'ml.c6id.16xlarge'|'ml.c6id.24xlarge'|'ml.c6id.32xlarge'|'ml.r6id.large'|'ml.r6id.xlarge'|'ml.r6id.2xlarge'|'ml.r6id.4xlarge'|'ml.r6id.8xlarge'|'ml.r6id.12xlarge'|'ml.r6id.16xlarge'|'ml.r6id.24xlarge'|'ml.r6id.32xlarge'|'ml.p5.4xlarge',
'LifecycleConfigArn': 'string',
'TrainingPlanArn': 'string'
},
'CustomImages': [
{
'ImageName': 'string',
'ImageVersionNumber': 123,
'AppImageConfigName': 'string'
},
],
'LifecycleConfigArns': [
'string',
]
},
'CodeEditorAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': 'string',
'SageMakerImageVersionAlias': 'string',
'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.g6.xlarge'|'ml.g6.2xlarge'|'ml.g6.4xlarge'|'ml.g6.8xlarge'|'ml.g6.12xlarge'|'ml.g6.16xlarge'|'ml.g6.24xlarge'|'ml.g6.48xlarge'|'ml.g6e.xlarge'|'ml.g6e.2xlarge'|'ml.g6e.4xlarge'|'ml.g6e.8xlarge'|'ml.g6e.12xlarge'|'ml.g6e.16xlarge'|'ml.g6e.24xlarge'|'ml.g6e.48xlarge'|'ml.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.p5.48xlarge'|'ml.p5en.48xlarge'|'ml.p6-b200.48xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.r6i.large'|'ml.r6i.xlarge'|'ml.r6i.2xlarge'|'ml.r6i.4xlarge'|'ml.r6i.8xlarge'|'ml.r6i.12xlarge'|'ml.r6i.16xlarge'|'ml.r6i.24xlarge'|'ml.r6i.32xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge'|'ml.m6id.large'|'ml.m6id.xlarge'|'ml.m6id.2xlarge'|'ml.m6id.4xlarge'|'ml.m6id.8xlarge'|'ml.m6id.12xlarge'|'ml.m6id.16xlarge'|'ml.m6id.24xlarge'|'ml.m6id.32xlarge'|'ml.c6id.large'|'ml.c6id.xlarge'|'ml.c6id.2xlarge'|'ml.c6id.4xlarge'|'ml.c6id.8xlarge'|'ml.c6id.12xlarge'|'ml.c6id.16xlarge'|'ml.c6id.24xlarge'|'ml.c6id.32xlarge'|'ml.r6id.large'|'ml.r6id.xlarge'|'ml.r6id.2xlarge'|'ml.r6id.4xlarge'|'ml.r6id.8xlarge'|'ml.r6id.12xlarge'|'ml.r6id.16xlarge'|'ml.r6id.24xlarge'|'ml.r6id.32xlarge'|'ml.p5.4xlarge',
'LifecycleConfigArn': 'string',
'TrainingPlanArn': 'string'
},
'AppLifecycleManagement': {
'IdleSettings': {
'IdleTimeoutInMinutes': 123
}
}
},
'JupyterLabAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': 'string',
'SageMakerImageVersionAlias': 'string',
'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.g6.xlarge'|'ml.g6.2xlarge'|'ml.g6.4xlarge'|'ml.g6.8xlarge'|'ml.g6.12xlarge'|'ml.g6.16xlarge'|'ml.g6.24xlarge'|'ml.g6.48xlarge'|'ml.g6e.xlarge'|'ml.g6e.2xlarge'|'ml.g6e.4xlarge'|'ml.g6e.8xlarge'|'ml.g6e.12xlarge'|'ml.g6e.16xlarge'|'ml.g6e.24xlarge'|'ml.g6e.48xlarge'|'ml.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.p5.48xlarge'|'ml.p5en.48xlarge'|'ml.p6-b200.48xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.r6i.large'|'ml.r6i.xlarge'|'ml.r6i.2xlarge'|'ml.r6i.4xlarge'|'ml.r6i.8xlarge'|'ml.r6i.12xlarge'|'ml.r6i.16xlarge'|'ml.r6i.24xlarge'|'ml.r6i.32xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge'|'ml.m6id.large'|'ml.m6id.xlarge'|'ml.m6id.2xlarge'|'ml.m6id.4xlarge'|'ml.m6id.8xlarge'|'ml.m6id.12xlarge'|'ml.m6id.16xlarge'|'ml.m6id.24xlarge'|'ml.m6id.32xlarge'|'ml.c6id.large'|'ml.c6id.xlarge'|'ml.c6id.2xlarge'|'ml.c6id.4xlarge'|'ml.c6id.8xlarge'|'ml.c6id.12xlarge'|'ml.c6id.16xlarge'|'ml.c6id.24xlarge'|'ml.c6id.32xlarge'|'ml.r6id.large'|'ml.r6id.xlarge'|'ml.r6id.2xlarge'|'ml.r6id.4xlarge'|'ml.r6id.8xlarge'|'ml.r6id.12xlarge'|'ml.r6id.16xlarge'|'ml.r6id.24xlarge'|'ml.r6id.32xlarge'|'ml.p5.4xlarge',
'LifecycleConfigArn': 'string',
'TrainingPlanArn': 'string'
},
'CodeRepositories': [
{
'RepositoryUrl': 'string'
},
],
'AppLifecycleManagement': {
'IdleSettings': {
'IdleTimeoutInMinutes': 123
}
}
},
'AppType': 'JupyterServer'|'KernelGateway'|'DetailedProfiler'|'TensorBoard'|'CodeEditor'|'JupyterLab'|'RStudioServerPro'|'RSessionGateway'|'Canvas',
'SpaceStorageSettings': {
'EbsStorageSettings': {
'EbsVolumeSizeInGb': 123
}
},
'SpaceManagedResources': 'ENABLED'|'DISABLED',
'CustomFileSystems': [
{
'EFSFileSystem': {
'FileSystemId': 'string'
},
'FSxLustreFileSystem': {
'FileSystemId': 'string'
},
'S3FileSystem': {
'S3Uri': 'string'
}
},
],
'RemoteAccess': 'ENABLED'|'DISABLED'
},
'OwnershipSettings': {
'OwnerUserProfileName': 'string'
},
'SpaceSharingSettings': {
'SharingType': 'Private'|'Shared'
},
'SpaceDisplayName': 'string',
'Url': 'string'
}
Response Structure
(dict) --
DomainId (string) --
The ID of the associated domain.
SpaceArn (string) --
The space's Amazon Resource Name (ARN).
SpaceName (string) --
The name of the space.
HomeEfsFileSystemUid (string) --
The ID of the space's profile in the Amazon EFS volume.
Status (string) --
The status.
LastModifiedTime (datetime) --
The last modified time.
CreationTime (datetime) --
The creation time.
FailureReason (string) --
The failure reason.
SpaceSettings (dict) --
A collection of space settings.
JupyterServerAppSettings (dict) --
The JupyterServer app settings.
DefaultResourceSpec (dict) --
The default instance type and the Amazon Resource Name (ARN) of the default SageMaker AI image used by the JupyterServer app. If you use the LifecycleConfigArns parameter, then this parameter is also required.
SageMakerImageArn (string) --
The ARN of the SageMaker AI image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance. To clear the value set for SageMakerImageVersionArn, pass None as the value.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
TrainingPlanArn (string) --
The ARN of the SageMaker AI Training Plan to use for this app. When you specify a training plan, the app launches on reserved GPU capacity. This field is supported for JupyterLab and CodeEditor app types.
For more information about how to reserve GPU capacity with SageMaker AI Training Plans, see Using training plans in Studio applications.
LifecycleConfigArns (list) --
The Amazon Resource Name (ARN) of the Lifecycle Configurations attached to the JupyterServerApp. If you use this parameter, the DefaultResourceSpec parameter is also required.
(string) --
CodeRepositories (list) --
A list of Git repositories that SageMaker AI automatically displays to users for cloning in the JupyterServer application.
(dict) --
A Git repository that SageMaker AI automatically displays to users for cloning in the JupyterServer application.
RepositoryUrl (string) --
The URL of the Git repository.
KernelGatewayAppSettings (dict) --
The KernelGateway app settings.
DefaultResourceSpec (dict) --
The default instance type and the Amazon Resource Name (ARN) of the default SageMaker AI image used by the KernelGateway app.
SageMakerImageArn (string) --
The ARN of the SageMaker AI image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance. To clear the value set for SageMakerImageVersionArn, pass None as the value.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
TrainingPlanArn (string) --
The ARN of the SageMaker AI Training Plan to use for this app. When you specify a training plan, the app launches on reserved GPU capacity. This field is supported for JupyterLab and CodeEditor app types.
For more information about how to reserve GPU capacity with SageMaker AI Training Plans, see Using training plans in Studio applications.
CustomImages (list) --
A list of custom SageMaker AI images that are configured to run as a KernelGateway app.
The maximum number of custom images are as follows.
On a domain level: 200
On a space level: 5
On a user profile level: 5
(dict) --
A custom SageMaker AI image. For more information, see Bring your own SageMaker AI image.
ImageName (string) --
The name of the CustomImage. Must be unique to your account.
ImageVersionNumber (integer) --
The version number of the CustomImage.
AppImageConfigName (string) --
The name of the AppImageConfig.
LifecycleConfigArns (list) --
The Amazon Resource Name (ARN) of the Lifecycle Configurations attached to the the user profile or domain.
(string) --
CodeEditorAppSettings (dict) --
The Code Editor application settings.
DefaultResourceSpec (dict) --
Specifies the ARN's of a SageMaker AI image and SageMaker AI image version, and the instance type that the version runs on.
SageMakerImageArn (string) --
The ARN of the SageMaker AI image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance. To clear the value set for SageMakerImageVersionArn, pass None as the value.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
TrainingPlanArn (string) --
The ARN of the SageMaker AI Training Plan to use for this app. When you specify a training plan, the app launches on reserved GPU capacity. This field is supported for JupyterLab and CodeEditor app types.
For more information about how to reserve GPU capacity with SageMaker AI Training Plans, see Using training plans in Studio applications.
AppLifecycleManagement (dict) --
Settings that are used to configure and manage the lifecycle of CodeEditor applications in a space.
IdleSettings (dict) --
Settings related to idle shutdown of Studio applications.
IdleTimeoutInMinutes (integer) --
The time that SageMaker waits after the application becomes idle before shutting it down.
JupyterLabAppSettings (dict) --
The settings for the JupyterLab application.
DefaultResourceSpec (dict) --
Specifies the ARN's of a SageMaker AI image and SageMaker AI image version, and the instance type that the version runs on.
SageMakerImageArn (string) --
The ARN of the SageMaker AI image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance. To clear the value set for SageMakerImageVersionArn, pass None as the value.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
TrainingPlanArn (string) --
The ARN of the SageMaker AI Training Plan to use for this app. When you specify a training plan, the app launches on reserved GPU capacity. This field is supported for JupyterLab and CodeEditor app types.
For more information about how to reserve GPU capacity with SageMaker AI Training Plans, see Using training plans in Studio applications.
CodeRepositories (list) --
A list of Git repositories that SageMaker automatically displays to users for cloning in the JupyterLab application.
(dict) --
A Git repository that SageMaker AI automatically displays to users for cloning in the JupyterServer application.
RepositoryUrl (string) --
The URL of the Git repository.
AppLifecycleManagement (dict) --
Settings that are used to configure and manage the lifecycle of JupyterLab applications in a space.
IdleSettings (dict) --
Settings related to idle shutdown of Studio applications.
IdleTimeoutInMinutes (integer) --
The time that SageMaker waits after the application becomes idle before shutting it down.
AppType (string) --
The type of app created within the space.
If using the UpdateSpace API, you can't change the app type of your space by specifying a different value for this field.
SpaceStorageSettings (dict) --
The storage settings for a space.
EbsStorageSettings (dict) --
A collection of EBS storage settings for a space.
EbsVolumeSizeInGb (integer) --
The size of an EBS storage volume for a space.
SpaceManagedResources (string) --
If you enable this option, SageMaker AI creates the following resources on your behalf when you create the space:
The user profile that possesses the space.
The app that the space contains.
CustomFileSystems (list) --
A file system, created by you, that you assign to a space for an Amazon SageMaker AI Domain. Permitted users can access this file system in Amazon SageMaker AI Studio.
(dict) --
A file system, created by you, that you assign to a user profile or space for an Amazon SageMaker AI Domain. Permitted users can access this file system in Amazon SageMaker AI Studio.
EFSFileSystem (dict) --
A custom file system in Amazon EFS.
FileSystemId (string) --
The ID of your Amazon EFS file system.
FSxLustreFileSystem (dict) --
A custom file system in Amazon FSx for Lustre.
FileSystemId (string) --
Amazon FSx for Lustre file system ID.
S3FileSystem (dict) --
A custom file system in Amazon S3. This is only supported in Amazon SageMaker Unified Studio.
S3Uri (string) --
The Amazon S3 URI that specifies the location in S3 where files are stored, which is mounted within the Studio environment. For example: s3://<bucket-name>/<prefix>/.
RemoteAccess (string) --
A setting that enables or disables remote access for a SageMaker space. When enabled, this allows you to connect to the remote space from your local IDE.
OwnershipSettings (dict) --
The collection of ownership settings for a space.
OwnerUserProfileName (string) --
The user profile who is the owner of the space.
SpaceSharingSettings (dict) --
The collection of space sharing settings for a space.
SharingType (string) --
Specifies the sharing type of the space.
SpaceDisplayName (string) --
The name of the space that appears in the Amazon SageMaker Studio UI.
Url (string) --
Returns the URL of the space. If the space is created with Amazon Web Services IAM Identity Center (Successor to Amazon Web Services Single Sign-On) authentication, users can navigate to the URL after appending the respective redirect parameter for the application type to be federated through Amazon Web Services IAM Identity Center.
The following application types are supported:
Studio Classic: &redirect=JupyterServer
JupyterLab: &redirect=JupyterLab
Code Editor, based on Code-OSS, Visual Studio Code - Open Source: &redirect=CodeEditor
{'TargetResources': {'studio-apps'}}
Retrieves detailed information about a specific training plan.
See also: AWS API Documentation
Request Syntax
client.describe_training_plan(
TrainingPlanName='string'
)
string
[REQUIRED]
The name of the training plan to describe.
dict
Response Syntax
{
'TrainingPlanArn': 'string',
'TrainingPlanName': 'string',
'Status': 'Pending'|'Active'|'Scheduled'|'Expired'|'Failed',
'StatusMessage': 'string',
'DurationHours': 123,
'DurationMinutes': 123,
'StartTime': datetime(2015, 1, 1),
'EndTime': datetime(2015, 1, 1),
'UpfrontFee': 'string',
'CurrencyCode': 'string',
'TotalInstanceCount': 123,
'AvailableInstanceCount': 123,
'InUseInstanceCount': 123,
'UnhealthyInstanceCount': 123,
'AvailableSpareInstanceCount': 123,
'TotalUltraServerCount': 123,
'TargetResources': [
'training-job'|'hyperpod-cluster'|'endpoint'|'studio-apps',
],
'ReservedCapacitySummaries': [
{
'ReservedCapacityArn': 'string',
'ReservedCapacityType': 'UltraServer'|'Instance',
'UltraServerType': 'string',
'UltraServerCount': 123,
'InstanceType': 'ml.p4d.24xlarge'|'ml.p5.48xlarge'|'ml.p5e.48xlarge'|'ml.p5en.48xlarge'|'ml.trn1.32xlarge'|'ml.trn2.48xlarge'|'ml.p6-b200.48xlarge'|'ml.p4de.24xlarge'|'ml.p6e-gb200.36xlarge'|'ml.p5.4xlarge'|'ml.p6-b300.48xlarge',
'TotalInstanceCount': 123,
'Status': 'Pending'|'Active'|'Scheduled'|'Expired'|'Failed',
'AvailabilityZone': 'string',
'DurationHours': 123,
'DurationMinutes': 123,
'StartTime': datetime(2015, 1, 1),
'EndTime': datetime(2015, 1, 1)
},
]
}
Response Structure
(dict) --
TrainingPlanArn (string) --
The Amazon Resource Name (ARN); of the training plan.
TrainingPlanName (string) --
The name of the training plan.
Status (string) --
The current status of the training plan (e.g., Pending, Active, Expired). To see the complete list of status values available for a training plan, refer to the Status attribute within the TrainingPlanSummary object.
StatusMessage (string) --
A message providing additional information about the current status of the training plan.
DurationHours (integer) --
The number of whole hours in the total duration for this training plan.
DurationMinutes (integer) --
The additional minutes beyond whole hours in the total duration for this training plan.
StartTime (datetime) --
The start time of the training plan.
EndTime (datetime) --
The end time of the training plan.
UpfrontFee (string) --
The upfront fee for the training plan.
CurrencyCode (string) --
The currency code for the upfront fee (e.g., USD).
TotalInstanceCount (integer) --
The total number of instances reserved in this training plan.
AvailableInstanceCount (integer) --
The number of instances currently available for use in this training plan.
InUseInstanceCount (integer) --
The number of instances currently in use from this training plan.
UnhealthyInstanceCount (integer) --
The number of instances in the training plan that are currently in an unhealthy state.
AvailableSpareInstanceCount (integer) --
The number of available spare instances in the training plan.
TotalUltraServerCount (integer) --
The total number of UltraServers reserved to this training plan.
TargetResources (list) --
The target resources (e.g., SageMaker Training Jobs, SageMaker HyperPod, SageMaker Endpoints, Studio apps) that can use this training plan.
Training plans are specific to their target resource.
A training plan designed for SageMaker training jobs can only be used to schedule and run training jobs.
A training plan for HyperPod clusters can be used exclusively to provide compute resources to a cluster's instance group.
A training plan for SageMaker endpoints can be used exclusively to provide compute resources to SageMaker endpoints for model deployment.
A training plan for Studio apps can be used to launch JupyterLab and Code Editor apps on reserved training plan capacity.
(string) --
ReservedCapacitySummaries (list) --
The list of Reserved Capacity providing the underlying compute resources of the plan.
(dict) --
Details of a reserved capacity for the training plan.
For more information about how to reserve GPU capacity for your SageMaker HyperPod clusters using Amazon SageMaker Training Plan, see ``CreateTrainingPlan ``.
ReservedCapacityArn (string) --
The Amazon Resource Name (ARN); of the reserved capacity.
ReservedCapacityType (string) --
The type of reserved capacity.
UltraServerType (string) --
The type of UltraServer included in this reserved capacity, such as ml.u-p6e-gb200x72.
UltraServerCount (integer) --
The number of UltraServers included in this reserved capacity.
InstanceType (string) --
The instance type for the reserved capacity.
TotalInstanceCount (integer) --
The total number of instances in the reserved capacity.
Status (string) --
The current status of the reserved capacity.
AvailabilityZone (string) --
The availability zone for the reserved capacity.
DurationHours (integer) --
The number of whole hours in the total duration for this reserved capacity.
DurationMinutes (integer) --
The additional minutes beyond whole hours in the total duration for this reserved capacity.
StartTime (datetime) --
The start time of the reserved capacity.
EndTime (datetime) --
The end time of the reserved capacity.
{'UserSettings': {'CodeEditorAppSettings': {'DefaultResourceSpec': {'TrainingPlanArn': 'string'}},
'JupyterLabAppSettings': {'DefaultResourceSpec': {'TrainingPlanArn': 'string'}},
'JupyterServerAppSettings': {'DefaultResourceSpec': {'TrainingPlanArn': 'string'}},
'KernelGatewayAppSettings': {'DefaultResourceSpec': {'TrainingPlanArn': 'string'}},
'RSessionAppSettings': {'DefaultResourceSpec': {'TrainingPlanArn': 'string'}},
'StudioWebPortalSettings': {'ExecutionRoleSessionNameMode': 'STATIC '
'| '
'USER_IDENTITY'},
'TensorBoardAppSettings': {'DefaultResourceSpec': {'TrainingPlanArn': 'string'}}}}
Describes a user profile. For more information, see CreateUserProfile.
See also: AWS API Documentation
Request Syntax
client.describe_user_profile(
DomainId='string',
UserProfileName='string'
)
string
[REQUIRED]
The domain ID.
string
[REQUIRED]
The user profile name. This value is not case sensitive.
dict
Response Syntax
{
'DomainId': 'string',
'UserProfileArn': 'string',
'UserProfileName': 'string',
'HomeEfsFileSystemUid': 'string',
'Status': 'Deleting'|'Failed'|'InService'|'Pending'|'Updating'|'Update_Failed'|'Delete_Failed',
'LastModifiedTime': datetime(2015, 1, 1),
'CreationTime': datetime(2015, 1, 1),
'FailureReason': 'string',
'SingleSignOnUserIdentifier': 'string',
'SingleSignOnUserValue': 'string',
'UserSettings': {
'ExecutionRole': 'string',
'SecurityGroups': [
'string',
],
'SharingSettings': {
'NotebookOutputOption': 'Allowed'|'Disabled',
'S3OutputPath': 'string',
'S3KmsKeyId': 'string'
},
'JupyterServerAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': 'string',
'SageMakerImageVersionAlias': 'string',
'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.g6.xlarge'|'ml.g6.2xlarge'|'ml.g6.4xlarge'|'ml.g6.8xlarge'|'ml.g6.12xlarge'|'ml.g6.16xlarge'|'ml.g6.24xlarge'|'ml.g6.48xlarge'|'ml.g6e.xlarge'|'ml.g6e.2xlarge'|'ml.g6e.4xlarge'|'ml.g6e.8xlarge'|'ml.g6e.12xlarge'|'ml.g6e.16xlarge'|'ml.g6e.24xlarge'|'ml.g6e.48xlarge'|'ml.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.p5.48xlarge'|'ml.p5en.48xlarge'|'ml.p6-b200.48xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.r6i.large'|'ml.r6i.xlarge'|'ml.r6i.2xlarge'|'ml.r6i.4xlarge'|'ml.r6i.8xlarge'|'ml.r6i.12xlarge'|'ml.r6i.16xlarge'|'ml.r6i.24xlarge'|'ml.r6i.32xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge'|'ml.m6id.large'|'ml.m6id.xlarge'|'ml.m6id.2xlarge'|'ml.m6id.4xlarge'|'ml.m6id.8xlarge'|'ml.m6id.12xlarge'|'ml.m6id.16xlarge'|'ml.m6id.24xlarge'|'ml.m6id.32xlarge'|'ml.c6id.large'|'ml.c6id.xlarge'|'ml.c6id.2xlarge'|'ml.c6id.4xlarge'|'ml.c6id.8xlarge'|'ml.c6id.12xlarge'|'ml.c6id.16xlarge'|'ml.c6id.24xlarge'|'ml.c6id.32xlarge'|'ml.r6id.large'|'ml.r6id.xlarge'|'ml.r6id.2xlarge'|'ml.r6id.4xlarge'|'ml.r6id.8xlarge'|'ml.r6id.12xlarge'|'ml.r6id.16xlarge'|'ml.r6id.24xlarge'|'ml.r6id.32xlarge'|'ml.p5.4xlarge',
'LifecycleConfigArn': 'string',
'TrainingPlanArn': 'string'
},
'LifecycleConfigArns': [
'string',
],
'CodeRepositories': [
{
'RepositoryUrl': 'string'
},
]
},
'KernelGatewayAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': 'string',
'SageMakerImageVersionAlias': 'string',
'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.g6.xlarge'|'ml.g6.2xlarge'|'ml.g6.4xlarge'|'ml.g6.8xlarge'|'ml.g6.12xlarge'|'ml.g6.16xlarge'|'ml.g6.24xlarge'|'ml.g6.48xlarge'|'ml.g6e.xlarge'|'ml.g6e.2xlarge'|'ml.g6e.4xlarge'|'ml.g6e.8xlarge'|'ml.g6e.12xlarge'|'ml.g6e.16xlarge'|'ml.g6e.24xlarge'|'ml.g6e.48xlarge'|'ml.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.p5.48xlarge'|'ml.p5en.48xlarge'|'ml.p6-b200.48xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.r6i.large'|'ml.r6i.xlarge'|'ml.r6i.2xlarge'|'ml.r6i.4xlarge'|'ml.r6i.8xlarge'|'ml.r6i.12xlarge'|'ml.r6i.16xlarge'|'ml.r6i.24xlarge'|'ml.r6i.32xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge'|'ml.m6id.large'|'ml.m6id.xlarge'|'ml.m6id.2xlarge'|'ml.m6id.4xlarge'|'ml.m6id.8xlarge'|'ml.m6id.12xlarge'|'ml.m6id.16xlarge'|'ml.m6id.24xlarge'|'ml.m6id.32xlarge'|'ml.c6id.large'|'ml.c6id.xlarge'|'ml.c6id.2xlarge'|'ml.c6id.4xlarge'|'ml.c6id.8xlarge'|'ml.c6id.12xlarge'|'ml.c6id.16xlarge'|'ml.c6id.24xlarge'|'ml.c6id.32xlarge'|'ml.r6id.large'|'ml.r6id.xlarge'|'ml.r6id.2xlarge'|'ml.r6id.4xlarge'|'ml.r6id.8xlarge'|'ml.r6id.12xlarge'|'ml.r6id.16xlarge'|'ml.r6id.24xlarge'|'ml.r6id.32xlarge'|'ml.p5.4xlarge',
'LifecycleConfigArn': 'string',
'TrainingPlanArn': 'string'
},
'CustomImages': [
{
'ImageName': 'string',
'ImageVersionNumber': 123,
'AppImageConfigName': 'string'
},
],
'LifecycleConfigArns': [
'string',
]
},
'TensorBoardAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': 'string',
'SageMakerImageVersionAlias': 'string',
'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.g6.xlarge'|'ml.g6.2xlarge'|'ml.g6.4xlarge'|'ml.g6.8xlarge'|'ml.g6.12xlarge'|'ml.g6.16xlarge'|'ml.g6.24xlarge'|'ml.g6.48xlarge'|'ml.g6e.xlarge'|'ml.g6e.2xlarge'|'ml.g6e.4xlarge'|'ml.g6e.8xlarge'|'ml.g6e.12xlarge'|'ml.g6e.16xlarge'|'ml.g6e.24xlarge'|'ml.g6e.48xlarge'|'ml.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.p5.48xlarge'|'ml.p5en.48xlarge'|'ml.p6-b200.48xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.r6i.large'|'ml.r6i.xlarge'|'ml.r6i.2xlarge'|'ml.r6i.4xlarge'|'ml.r6i.8xlarge'|'ml.r6i.12xlarge'|'ml.r6i.16xlarge'|'ml.r6i.24xlarge'|'ml.r6i.32xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge'|'ml.m6id.large'|'ml.m6id.xlarge'|'ml.m6id.2xlarge'|'ml.m6id.4xlarge'|'ml.m6id.8xlarge'|'ml.m6id.12xlarge'|'ml.m6id.16xlarge'|'ml.m6id.24xlarge'|'ml.m6id.32xlarge'|'ml.c6id.large'|'ml.c6id.xlarge'|'ml.c6id.2xlarge'|'ml.c6id.4xlarge'|'ml.c6id.8xlarge'|'ml.c6id.12xlarge'|'ml.c6id.16xlarge'|'ml.c6id.24xlarge'|'ml.c6id.32xlarge'|'ml.r6id.large'|'ml.r6id.xlarge'|'ml.r6id.2xlarge'|'ml.r6id.4xlarge'|'ml.r6id.8xlarge'|'ml.r6id.12xlarge'|'ml.r6id.16xlarge'|'ml.r6id.24xlarge'|'ml.r6id.32xlarge'|'ml.p5.4xlarge',
'LifecycleConfigArn': 'string',
'TrainingPlanArn': 'string'
}
},
'RStudioServerProAppSettings': {
'AccessStatus': 'ENABLED'|'DISABLED',
'UserGroup': 'R_STUDIO_ADMIN'|'R_STUDIO_USER'
},
'RSessionAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': 'string',
'SageMakerImageVersionAlias': 'string',
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],
'HiddenInstanceTypes': [
'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.g6.xlarge'|'ml.g6.2xlarge'|'ml.g6.4xlarge'|'ml.g6.8xlarge'|'ml.g6.12xlarge'|'ml.g6.16xlarge'|'ml.g6.24xlarge'|'ml.g6.48xlarge'|'ml.g6e.xlarge'|'ml.g6e.2xlarge'|'ml.g6e.4xlarge'|'ml.g6e.8xlarge'|'ml.g6e.12xlarge'|'ml.g6e.16xlarge'|'ml.g6e.24xlarge'|'ml.g6e.48xlarge'|'ml.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.p5.48xlarge'|'ml.p5en.48xlarge'|'ml.p6-b200.48xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.r6i.large'|'ml.r6i.xlarge'|'ml.r6i.2xlarge'|'ml.r6i.4xlarge'|'ml.r6i.8xlarge'|'ml.r6i.12xlarge'|'ml.r6i.16xlarge'|'ml.r6i.24xlarge'|'ml.r6i.32xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge'|'ml.m6id.large'|'ml.m6id.xlarge'|'ml.m6id.2xlarge'|'ml.m6id.4xlarge'|'ml.m6id.8xlarge'|'ml.m6id.12xlarge'|'ml.m6id.16xlarge'|'ml.m6id.24xlarge'|'ml.m6id.32xlarge'|'ml.c6id.large'|'ml.c6id.xlarge'|'ml.c6id.2xlarge'|'ml.c6id.4xlarge'|'ml.c6id.8xlarge'|'ml.c6id.12xlarge'|'ml.c6id.16xlarge'|'ml.c6id.24xlarge'|'ml.c6id.32xlarge'|'ml.r6id.large'|'ml.r6id.xlarge'|'ml.r6id.2xlarge'|'ml.r6id.4xlarge'|'ml.r6id.8xlarge'|'ml.r6id.12xlarge'|'ml.r6id.16xlarge'|'ml.r6id.24xlarge'|'ml.r6id.32xlarge'|'ml.p5.4xlarge',
],
'HiddenSageMakerImageVersionAliases': [
{
'SageMakerImageName': 'sagemaker_distribution',
'VersionAliases': [
'string',
]
},
],
'ExecutionRoleSessionNameMode': 'STATIC'|'USER_IDENTITY'
},
'AutoMountHomeEFS': 'Enabled'|'Disabled'|'DefaultAsDomain'
}
}
Response Structure
(dict) --
DomainId (string) --
The ID of the domain that contains the profile.
UserProfileArn (string) --
The user profile Amazon Resource Name (ARN).
UserProfileName (string) --
The user profile name.
HomeEfsFileSystemUid (string) --
The ID of the user's profile in the Amazon Elastic File System volume.
Status (string) --
The status.
LastModifiedTime (datetime) --
The last modified time.
CreationTime (datetime) --
The creation time.
FailureReason (string) --
The failure reason.
SingleSignOnUserIdentifier (string) --
The IAM Identity Center user identifier.
SingleSignOnUserValue (string) --
The IAM Identity Center user value.
UserSettings (dict) --
A collection of settings.
ExecutionRole (string) --
The execution role for the user.
SageMaker applies this setting only to private spaces that the user creates in the domain. SageMaker doesn't apply this setting to shared spaces.
SecurityGroups (list) --
The security groups for the Amazon Virtual Private Cloud (VPC) that the domain uses for communication.
Optional when the CreateDomain.AppNetworkAccessType parameter is set to PublicInternetOnly.
Required when the CreateDomain.AppNetworkAccessType parameter is set to VpcOnly, unless specified as part of the DefaultUserSettings for the domain.
Amazon SageMaker AI adds a security group to allow NFS traffic from Amazon SageMaker AI Studio. Therefore, the number of security groups that you can specify is one less than the maximum number shown.
SageMaker applies these settings only to private spaces that the user creates in the domain. SageMaker doesn't apply these settings to shared spaces.
(string) --
SharingSettings (dict) --
Specifies options for sharing Amazon SageMaker AI Studio notebooks.
NotebookOutputOption (string) --
Whether to include the notebook cell output when sharing the notebook. The default is Disabled.
S3OutputPath (string) --
When NotebookOutputOption is Allowed, the Amazon S3 bucket used to store the shared notebook snapshots.
S3KmsKeyId (string) --
When NotebookOutputOption is Allowed, the Amazon Web Services Key Management Service (KMS) encryption key ID used to encrypt the notebook cell output in the Amazon S3 bucket.
JupyterServerAppSettings (dict) --
The Jupyter server's app settings.
DefaultResourceSpec (dict) --
The default instance type and the Amazon Resource Name (ARN) of the default SageMaker AI image used by the JupyterServer app. If you use the LifecycleConfigArns parameter, then this parameter is also required.
SageMakerImageArn (string) --
The ARN of the SageMaker AI image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance. To clear the value set for SageMakerImageVersionArn, pass None as the value.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
TrainingPlanArn (string) --
The ARN of the SageMaker AI Training Plan to use for this app. When you specify a training plan, the app launches on reserved GPU capacity. This field is supported for JupyterLab and CodeEditor app types.
For more information about how to reserve GPU capacity with SageMaker AI Training Plans, see Using training plans in Studio applications.
LifecycleConfigArns (list) --
The Amazon Resource Name (ARN) of the Lifecycle Configurations attached to the JupyterServerApp. If you use this parameter, the DefaultResourceSpec parameter is also required.
(string) --
CodeRepositories (list) --
A list of Git repositories that SageMaker AI automatically displays to users for cloning in the JupyterServer application.
(dict) --
A Git repository that SageMaker AI automatically displays to users for cloning in the JupyterServer application.
RepositoryUrl (string) --
The URL of the Git repository.
KernelGatewayAppSettings (dict) --
The kernel gateway app settings.
DefaultResourceSpec (dict) --
The default instance type and the Amazon Resource Name (ARN) of the default SageMaker AI image used by the KernelGateway app.
SageMakerImageArn (string) --
The ARN of the SageMaker AI image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance. To clear the value set for SageMakerImageVersionArn, pass None as the value.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
TrainingPlanArn (string) --
The ARN of the SageMaker AI Training Plan to use for this app. When you specify a training plan, the app launches on reserved GPU capacity. This field is supported for JupyterLab and CodeEditor app types.
For more information about how to reserve GPU capacity with SageMaker AI Training Plans, see Using training plans in Studio applications.
CustomImages (list) --
A list of custom SageMaker AI images that are configured to run as a KernelGateway app.
The maximum number of custom images are as follows.
On a domain level: 200
On a space level: 5
On a user profile level: 5
(dict) --
A custom SageMaker AI image. For more information, see Bring your own SageMaker AI image.
ImageName (string) --
The name of the CustomImage. Must be unique to your account.
ImageVersionNumber (integer) --
The version number of the CustomImage.
AppImageConfigName (string) --
The name of the AppImageConfig.
LifecycleConfigArns (list) --
The Amazon Resource Name (ARN) of the Lifecycle Configurations attached to the the user profile or domain.
(string) --
TensorBoardAppSettings (dict) --
The TensorBoard app settings.
DefaultResourceSpec (dict) --
The default instance type and the Amazon Resource Name (ARN) of the SageMaker AI image created on the instance.
SageMakerImageArn (string) --
The ARN of the SageMaker AI image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance. To clear the value set for SageMakerImageVersionArn, pass None as the value.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
TrainingPlanArn (string) --
The ARN of the SageMaker AI Training Plan to use for this app. When you specify a training plan, the app launches on reserved GPU capacity. This field is supported for JupyterLab and CodeEditor app types.
For more information about how to reserve GPU capacity with SageMaker AI Training Plans, see Using training plans in Studio applications.
RStudioServerProAppSettings (dict) --
A collection of settings that configure user interaction with the RStudioServerPro app.
AccessStatus (string) --
Indicates whether the current user has access to the RStudioServerPro app.
UserGroup (string) --
The level of permissions that the user has within the RStudioServerPro app. This value defaults to User. The Admin value allows the user access to the RStudio Administrative Dashboard.
RSessionAppSettings (dict) --
A collection of settings that configure the RSessionGateway app.
DefaultResourceSpec (dict) --
Specifies the ARN's of a SageMaker AI image and SageMaker AI image version, and the instance type that the version runs on.
SageMakerImageArn (string) --
The ARN of the SageMaker AI image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance. To clear the value set for SageMakerImageVersionArn, pass None as the value.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
TrainingPlanArn (string) --
The ARN of the SageMaker AI Training Plan to use for this app. When you specify a training plan, the app launches on reserved GPU capacity. This field is supported for JupyterLab and CodeEditor app types.
For more information about how to reserve GPU capacity with SageMaker AI Training Plans, see Using training plans in Studio applications.
CustomImages (list) --
A list of custom SageMaker AI images that are configured to run as a RSession app.
(dict) --
A custom SageMaker AI image. For more information, see Bring your own SageMaker AI image.
ImageName (string) --
The name of the CustomImage. Must be unique to your account.
ImageVersionNumber (integer) --
The version number of the CustomImage.
AppImageConfigName (string) --
The name of the AppImageConfig.
CanvasAppSettings (dict) --
The Canvas app settings.
SageMaker applies these settings only to private spaces that SageMaker creates for the Canvas app.
TimeSeriesForecastingSettings (dict) --
Time series forecast settings for the SageMaker Canvas application.
Status (string) --
Describes whether time series forecasting is enabled or disabled in the Canvas application.
AmazonForecastRoleArn (string) --
The IAM role that Canvas passes to Amazon Forecast for time series forecasting. By default, Canvas uses the execution role specified in the UserProfile that launches the Canvas application. If an execution role is not specified in the UserProfile, Canvas uses the execution role specified in the Domain that owns the UserProfile. To allow time series forecasting, this IAM role should have the AmazonSageMakerCanvasForecastAccess policy attached and forecast.amazonaws.com added in the trust relationship as a service principal.
ModelRegisterSettings (dict) --
The model registry settings for the SageMaker Canvas application.
Status (string) --
Describes whether the integration to the model registry is enabled or disabled in the Canvas application.
CrossAccountModelRegisterRoleArn (string) --
The Amazon Resource Name (ARN) of the SageMaker model registry account. Required only to register model versions created by a different SageMaker Canvas Amazon Web Services account than the Amazon Web Services account in which SageMaker model registry is set up.
WorkspaceSettings (dict) --
The workspace settings for the SageMaker Canvas application.
S3ArtifactPath (string) --
The Amazon S3 bucket used to store artifacts generated by Canvas. Updating the Amazon S3 location impacts existing configuration settings, and Canvas users no longer have access to their artifacts. Canvas users must log out and log back in to apply the new location.
S3KmsKeyId (string) --
The Amazon Web Services Key Management Service (KMS) encryption key ID that is used to encrypt artifacts generated by Canvas in the Amazon S3 bucket.
IdentityProviderOAuthSettings (list) --
The settings for connecting to an external data source with OAuth.
(dict) --
The Amazon SageMaker Canvas application setting where you configure OAuth for connecting to an external data source, such as Snowflake.
DataSourceName (string) --
The name of the data source that you're connecting to. Canvas currently supports OAuth for Snowflake and Salesforce Data Cloud.
Status (string) --
Describes whether OAuth for a data source is enabled or disabled in the Canvas application.
SecretArn (string) --
The ARN of an Amazon Web Services Secrets Manager secret that stores the credentials from your identity provider, such as the client ID and secret, authorization URL, and token URL.
DirectDeploySettings (dict) --
The model deployment settings for the SageMaker Canvas application.
Status (string) --
Describes whether model deployment permissions are enabled or disabled in the Canvas application.
KendraSettings (dict) --
The settings for document querying.
Status (string) --
Describes whether the document querying feature is enabled or disabled in the Canvas application.
GenerativeAiSettings (dict) --
The generative AI settings for the SageMaker Canvas application.
AmazonBedrockRoleArn (string) --
The ARN of an Amazon Web Services IAM role that allows fine-tuning of large language models (LLMs) in Amazon Bedrock. The IAM role should have Amazon S3 read and write permissions, as well as a trust relationship that establishes bedrock.amazonaws.com as a service principal.
EmrServerlessSettings (dict) --
The settings for running Amazon EMR Serverless data processing jobs in SageMaker Canvas.
ExecutionRoleArn (string) --
The Amazon Resource Name (ARN) of the Amazon Web Services IAM role that is assumed for running Amazon EMR Serverless jobs in SageMaker Canvas. This role should have the necessary permissions to read and write data attached and a trust relationship with EMR Serverless.
Status (string) --
Describes whether Amazon EMR Serverless job capabilities are enabled or disabled in the SageMaker Canvas application.
CodeEditorAppSettings (dict) --
The Code Editor application settings.
SageMaker applies these settings only to private spaces that the user creates in the domain. SageMaker doesn't apply these settings to shared spaces.
DefaultResourceSpec (dict) --
Specifies the ARN's of a SageMaker AI image and SageMaker AI image version, and the instance type that the version runs on.
SageMakerImageArn (string) --
The ARN of the SageMaker AI image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance. To clear the value set for SageMakerImageVersionArn, pass None as the value.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
TrainingPlanArn (string) --
The ARN of the SageMaker AI Training Plan to use for this app. When you specify a training plan, the app launches on reserved GPU capacity. This field is supported for JupyterLab and CodeEditor app types.
For more information about how to reserve GPU capacity with SageMaker AI Training Plans, see Using training plans in Studio applications.
CustomImages (list) --
A list of custom SageMaker images that are configured to run as a Code Editor app.
(dict) --
A custom SageMaker AI image. For more information, see Bring your own SageMaker AI image.
ImageName (string) --
The name of the CustomImage. Must be unique to your account.
ImageVersionNumber (integer) --
The version number of the CustomImage.
AppImageConfigName (string) --
The name of the AppImageConfig.
LifecycleConfigArns (list) --
The Amazon Resource Name (ARN) of the Code Editor application lifecycle configuration.
(string) --
AppLifecycleManagement (dict) --
Settings that are used to configure and manage the lifecycle of CodeEditor applications.
IdleSettings (dict) --
Settings related to idle shutdown of Studio applications.
LifecycleManagement (string) --
Indicates whether idle shutdown is activated for the application type.
IdleTimeoutInMinutes (integer) --
The time that SageMaker waits after the application becomes idle before shutting it down.
MinIdleTimeoutInMinutes (integer) --
The minimum value in minutes that custom idle shutdown can be set to by the user.
MaxIdleTimeoutInMinutes (integer) --
The maximum value in minutes that custom idle shutdown can be set to by the user.
BuiltInLifecycleConfigArn (string) --
The lifecycle configuration that runs before the default lifecycle configuration. It can override changes made in the default lifecycle configuration.
JupyterLabAppSettings (dict) --
The settings for the JupyterLab application.
SageMaker applies these settings only to private spaces that the user creates in the domain. SageMaker doesn't apply these settings to shared spaces.
DefaultResourceSpec (dict) --
Specifies the ARN's of a SageMaker AI image and SageMaker AI image version, and the instance type that the version runs on.
SageMakerImageArn (string) --
The ARN of the SageMaker AI image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance. To clear the value set for SageMakerImageVersionArn, pass None as the value.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
TrainingPlanArn (string) --
The ARN of the SageMaker AI Training Plan to use for this app. When you specify a training plan, the app launches on reserved GPU capacity. This field is supported for JupyterLab and CodeEditor app types.
For more information about how to reserve GPU capacity with SageMaker AI Training Plans, see Using training plans in Studio applications.
CustomImages (list) --
A list of custom SageMaker images that are configured to run as a JupyterLab app.
(dict) --
A custom SageMaker AI image. For more information, see Bring your own SageMaker AI image.
ImageName (string) --
The name of the CustomImage. Must be unique to your account.
ImageVersionNumber (integer) --
The version number of the CustomImage.
AppImageConfigName (string) --
The name of the AppImageConfig.
LifecycleConfigArns (list) --
The Amazon Resource Name (ARN) of the lifecycle configurations attached to the user profile or domain. To remove a lifecycle config, you must set LifecycleConfigArns to an empty list.
(string) --
CodeRepositories (list) --
A list of Git repositories that SageMaker automatically displays to users for cloning in the JupyterLab application.
(dict) --
A Git repository that SageMaker AI automatically displays to users for cloning in the JupyterServer application.
RepositoryUrl (string) --
The URL of the Git repository.
AppLifecycleManagement (dict) --
Indicates whether idle shutdown is activated for JupyterLab applications.
IdleSettings (dict) --
Settings related to idle shutdown of Studio applications.
LifecycleManagement (string) --
Indicates whether idle shutdown is activated for the application type.
IdleTimeoutInMinutes (integer) --
The time that SageMaker waits after the application becomes idle before shutting it down.
MinIdleTimeoutInMinutes (integer) --
The minimum value in minutes that custom idle shutdown can be set to by the user.
MaxIdleTimeoutInMinutes (integer) --
The maximum value in minutes that custom idle shutdown can be set to by the user.
EmrSettings (dict) --
The configuration parameters that specify the IAM roles assumed by the execution role of SageMaker (assumable roles) and the cluster instances or job execution environments (execution roles or runtime roles) to manage and access resources required for running Amazon EMR clusters or Amazon EMR Serverless applications.
AssumableRoleArns (list) --
An array of Amazon Resource Names (ARNs) of the IAM roles that the execution role of SageMaker can assume for performing operations or tasks related to Amazon EMR clusters or Amazon EMR Serverless applications. These roles define the permissions and access policies required when performing Amazon EMR-related operations, such as listing, connecting to, or terminating Amazon EMR clusters or Amazon EMR Serverless applications. They are typically used in cross-account access scenarios, where the Amazon EMR resources (clusters or serverless applications) are located in a different Amazon Web Services account than the SageMaker domain.
(string) --
ExecutionRoleArns (list) --
An array of Amazon Resource Names (ARNs) of the IAM roles used by the Amazon EMR cluster instances or job execution environments to access other Amazon Web Services services and resources needed during the runtime of your Amazon EMR or Amazon EMR Serverless workloads, such as Amazon S3 for data access, Amazon CloudWatch for logging, or other Amazon Web Services services based on the particular workload requirements.
(string) --
BuiltInLifecycleConfigArn (string) --
The lifecycle configuration that runs before the default lifecycle configuration. It can override changes made in the default lifecycle configuration.
SpaceStorageSettings (dict) --
The storage settings for a space.
SageMaker applies these settings only to private spaces that the user creates in the domain. SageMaker doesn't apply these settings to shared spaces.
DefaultEbsStorageSettings (dict) --
The default EBS storage settings for a space.
DefaultEbsVolumeSizeInGb (integer) --
The default size of the EBS storage volume for a space.
MaximumEbsVolumeSizeInGb (integer) --
The maximum size of the EBS storage volume for a space.
DefaultLandingUri (string) --
The default experience that the user is directed to when accessing the domain. The supported values are:
studio::: Indicates that Studio is the default experience. This value can only be passed if StudioWebPortal is set to ENABLED.
app:JupyterServer:: Indicates that Studio Classic is the default experience.
StudioWebPortal (string) --
Whether the user can access Studio. If this value is set to DISABLED, the user cannot access Studio, even if that is the default experience for the domain.
CustomPosixUserConfig (dict) --
Details about the POSIX identity that is used for file system operations.
SageMaker applies these settings only to private spaces that the user creates in the domain. SageMaker doesn't apply these settings to shared spaces.
Uid (integer) --
The POSIX user ID.
Gid (integer) --
The POSIX group ID.
CustomFileSystemConfigs (list) --
The settings for assigning a custom file system to a user profile. Permitted users can access this file system in Amazon SageMaker AI Studio.
SageMaker applies these settings only to private spaces that the user creates in the domain. SageMaker doesn't apply these settings to shared spaces.
(dict) --
The settings for assigning a custom file system to a user profile or space for an Amazon SageMaker AI Domain. Permitted users can access this file system in Amazon SageMaker AI Studio.
EFSFileSystemConfig (dict) --
The settings for a custom Amazon EFS file system.
FileSystemId (string) --
The ID of your Amazon EFS file system.
FileSystemPath (string) --
The path to the file system directory that is accessible in Amazon SageMaker AI Studio. Permitted users can access only this directory and below.
FSxLustreFileSystemConfig (dict) --
The settings for a custom Amazon FSx for Lustre file system.
FileSystemId (string) --
The globally unique, 17-digit, ID of the file system, assigned by Amazon FSx for Lustre.
FileSystemPath (string) --
The path to the file system directory that is accessible in Amazon SageMaker Studio. Permitted users can access only this directory and below.
S3FileSystemConfig (dict) --
Configuration settings for a custom Amazon S3 file system.
MountPath (string) --
The file system path where the Amazon S3 storage location will be mounted within the Amazon SageMaker Studio environment.
S3Uri (string) --
The Amazon S3 URI of the S3 file system configuration.
StudioWebPortalSettings (dict) --
Studio settings. If these settings are applied on a user level, they take priority over the settings applied on a domain level.
HiddenMlTools (list) --
The machine learning tools that are hidden from the Studio left navigation pane.
(string) --
HiddenAppTypes (list) --
The Applications supported in Studio that are hidden from the Studio left navigation pane.
(string) --
HiddenInstanceTypes (list) --
The instance types you are hiding from the Studio user interface.
(string) --
HiddenSageMakerImageVersionAliases (list) --
The version aliases you are hiding from the Studio user interface.
(dict) --
The SageMaker images that are hidden from the Studio user interface. You must specify the SageMaker image name and version aliases.
SageMakerImageName (string) --
The SageMaker image name that you are hiding from the Studio user interface.
VersionAliases (list) --
The version aliases you are hiding from the Studio user interface.
(string) --
ExecutionRoleSessionNameMode (string) --
The execution role session name mode. If this value is set to USER_IDENTITY, the session name of the execution role corresponds to the user's identity. For IAM domains, the session name is the IAM session name used to generate the presigned URL. For IAM Identity Center domains, the session name is the username of the associated IAM Identity Center user. If this value is set to STATIC or is not set, the session name defaults to SageMaker.
AutoMountHomeEFS (string) --
Indicates whether auto-mounting of an EFS volume is supported for the user profile. The DefaultAsDomain value is only supported for user profiles. Do not use the DefaultAsDomain value when setting this parameter for a domain.
SageMaker applies this setting only to private spaces that the user creates in the domain. SageMaker doesn't apply this setting to shared spaces.
{'Apps': {'ResourceSpec': {'TrainingPlanArn': 'string'}}}
Lists apps.
See also: AWS API Documentation
Request Syntax
client.list_apps(
NextToken='string',
MaxResults=123,
SortOrder='Ascending'|'Descending',
SortBy='CreationTime',
DomainIdEquals='string',
UserProfileNameEquals='string',
SpaceNameEquals='string'
)
string
If the previous response was truncated, you will receive this token. Use it in your next request to receive the next set of results.
integer
This parameter defines the maximum number of results that can be return in a single response. The MaxResults parameter is an upper bound, not a target. If there are more results available than the value specified, a NextToken is provided in the response. The NextToken indicates that the user should get the next set of results by providing this token as a part of a subsequent call. The default value for MaxResults is 10.
string
The sort order for the results. The default is Ascending.
string
The parameter by which to sort the results. The default is CreationTime.
string
A parameter to search for the domain ID.
string
A parameter to search by user profile name. If SpaceNameEquals is set, then this value cannot be set.
string
A parameter to search by space name. If UserProfileNameEquals is set, then this value cannot be set.
dict
Response Syntax
{
'Apps': [
{
'DomainId': 'string',
'UserProfileName': 'string',
'SpaceName': 'string',
'AppType': 'JupyterServer'|'KernelGateway'|'DetailedProfiler'|'TensorBoard'|'CodeEditor'|'JupyterLab'|'RStudioServerPro'|'RSessionGateway'|'Canvas',
'AppName': 'string',
'Status': 'Deleted'|'Deleting'|'Failed'|'InService'|'Pending',
'CreationTime': datetime(2015, 1, 1),
'ResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': 'string',
'SageMakerImageVersionAlias': 'string',
'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.g6.xlarge'|'ml.g6.2xlarge'|'ml.g6.4xlarge'|'ml.g6.8xlarge'|'ml.g6.12xlarge'|'ml.g6.16xlarge'|'ml.g6.24xlarge'|'ml.g6.48xlarge'|'ml.g6e.xlarge'|'ml.g6e.2xlarge'|'ml.g6e.4xlarge'|'ml.g6e.8xlarge'|'ml.g6e.12xlarge'|'ml.g6e.16xlarge'|'ml.g6e.24xlarge'|'ml.g6e.48xlarge'|'ml.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.p5.48xlarge'|'ml.p5en.48xlarge'|'ml.p6-b200.48xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.r6i.large'|'ml.r6i.xlarge'|'ml.r6i.2xlarge'|'ml.r6i.4xlarge'|'ml.r6i.8xlarge'|'ml.r6i.12xlarge'|'ml.r6i.16xlarge'|'ml.r6i.24xlarge'|'ml.r6i.32xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge'|'ml.m6id.large'|'ml.m6id.xlarge'|'ml.m6id.2xlarge'|'ml.m6id.4xlarge'|'ml.m6id.8xlarge'|'ml.m6id.12xlarge'|'ml.m6id.16xlarge'|'ml.m6id.24xlarge'|'ml.m6id.32xlarge'|'ml.c6id.large'|'ml.c6id.xlarge'|'ml.c6id.2xlarge'|'ml.c6id.4xlarge'|'ml.c6id.8xlarge'|'ml.c6id.12xlarge'|'ml.c6id.16xlarge'|'ml.c6id.24xlarge'|'ml.c6id.32xlarge'|'ml.r6id.large'|'ml.r6id.xlarge'|'ml.r6id.2xlarge'|'ml.r6id.4xlarge'|'ml.r6id.8xlarge'|'ml.r6id.12xlarge'|'ml.r6id.16xlarge'|'ml.r6id.24xlarge'|'ml.r6id.32xlarge'|'ml.p5.4xlarge',
'LifecycleConfigArn': 'string',
'TrainingPlanArn': 'string'
}
},
],
'NextToken': 'string'
}
Response Structure
(dict) --
Apps (list) --
The list of apps.
(dict) --
Details about an Amazon SageMaker AI app.
DomainId (string) --
The domain ID.
UserProfileName (string) --
The user profile name.
SpaceName (string) --
The name of the space.
AppType (string) --
The type of app.
AppName (string) --
The name of the app.
Status (string) --
The status.
CreationTime (datetime) --
The creation time.
ResourceSpec (dict) --
Specifies the ARN's of a SageMaker AI image and SageMaker AI image version, and the instance type that the version runs on.
SageMakerImageArn (string) --
The ARN of the SageMaker AI image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance. To clear the value set for SageMakerImageVersionArn, pass None as the value.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
TrainingPlanArn (string) --
The ARN of the SageMaker AI Training Plan to use for this app. When you specify a training plan, the app launches on reserved GPU capacity. This field is supported for JupyterLab and CodeEditor app types.
For more information about how to reserve GPU capacity with SageMaker AI Training Plans, see Using training plans in Studio applications.
NextToken (string) --
If the previous response was truncated, you will receive this token. Use it in your next request to receive the next set of results.
{'Events': {'EventLevel': 'Info | Warn | Error'}}
Retrieves a list of event summaries for a specified HyperPod cluster. The operation supports filtering, sorting, and pagination of results. This functionality is only supported when the NodeProvisioningMode is set to Continuous.
See also: AWS API Documentation
Request Syntax
client.list_cluster_events(
ClusterName='string',
InstanceGroupName='string',
NodeId='string',
EventTimeAfter=datetime(2015, 1, 1),
EventTimeBefore=datetime(2015, 1, 1),
SortBy='EventTime',
SortOrder='Ascending'|'Descending',
ResourceType='Cluster'|'InstanceGroup'|'Instance',
MaxResults=123,
NextToken='string'
)
string
[REQUIRED]
The name or Amazon Resource Name (ARN) of the HyperPod cluster for which to list events.
string
The name of the instance group to filter events. If specified, only events related to this instance group are returned.
string
The EC2 instance ID to filter events. If specified, only events related to this instance are returned.
datetime
The start of the time range for filtering events. Only events that occurred after this time are included in the results.
datetime
The end of the time range for filtering events. Only events that occurred before this time are included in the results.
string
The field to use for sorting the event list. Currently, the only supported value is EventTime.
string
The order in which to sort the results. Valid values are Ascending or Descending (the default is Descending).
string
The type of resource for which to filter events. Valid values are Cluster, InstanceGroup, or Instance.
integer
The maximum number of events to return in the response. Valid range is 1 to 100.
string
A token to retrieve the next set of results. This token is obtained from the output of a previous ListClusterEvents call.
dict
Response Syntax
{
'NextToken': 'string',
'Events': [
{
'EventId': 'string',
'ClusterArn': 'string',
'ClusterName': 'string',
'InstanceGroupName': 'string',
'InstanceId': 'string',
'ResourceType': 'Cluster'|'InstanceGroup'|'Instance',
'EventTime': datetime(2015, 1, 1),
'Description': 'string',
'EventLevel': 'Info'|'Warn'|'Error'
},
]
}
Response Structure
(dict) --
NextToken (string) --
A token to retrieve the next set of results. Include this token in subsequent ListClusterEvents calls to fetch more events.
Events (list) --
A list of event summaries matching the specified criteria.
(dict) --
A summary of an event in a HyperPod cluster.
EventId (string) --
The unique identifier (UUID) of the event.
ClusterArn (string) --
The Amazon Resource Name (ARN) of the HyperPod cluster associated with the event.
ClusterName (string) --
The name of the HyperPod cluster associated with the event.
InstanceGroupName (string) --
The name of the instance group associated with the event, if applicable.
InstanceId (string) --
The Amazon Elastic Compute Cloud (EC2) instance ID associated with the event, if applicable.
ResourceType (string) --
The type of resource associated with the event. Valid values are Cluster, InstanceGroup, or Instance.
EventTime (datetime) --
The timestamp when the event occurred.
Description (string) --
A brief, human-readable description of the event.
EventLevel (string) --
The severity level of the event. Valid values are Info, Warn, and Error.
{'ModelPackageGroupSummaryList': {'ManagedConfiguration': {'ManagedStorageType': 'Restricted'}}}
Gets a list of the model groups in your Amazon Web Services account.
See also: AWS API Documentation
Request Syntax
client.list_model_package_groups(
CreationTimeAfter=datetime(2015, 1, 1),
CreationTimeBefore=datetime(2015, 1, 1),
MaxResults=123,
NameContains='string',
NextToken='string',
SortBy='Name'|'CreationTime',
SortOrder='Ascending'|'Descending',
CrossAccountFilterOption='SameAccount'|'CrossAccount'
)
datetime
A filter that returns only model groups created after the specified time.
datetime
A filter that returns only model groups created before the specified time.
integer
The maximum number of results to return in the response.
string
A string in the model group name. This filter returns only model groups whose name contains the specified string.
string
If the result of the previous ListModelPackageGroups request was truncated, the response includes a NextToken. To retrieve the next set of model groups, use the token in the next request.
string
The field to sort results by. The default is CreationTime.
string
The sort order for results. The default is Ascending.
string
A filter that returns either model groups shared with you or model groups in your own account. When the value is CrossAccount, the results show the resources made discoverable to you from other accounts. When the value is SameAccount or null, the results show resources from your account. The default is SameAccount.
dict
Response Syntax
{
'ModelPackageGroupSummaryList': [
{
'ModelPackageGroupName': 'string',
'ModelPackageGroupArn': 'string',
'ModelPackageGroupDescription': 'string',
'CreationTime': datetime(2015, 1, 1),
'ModelPackageGroupStatus': 'Pending'|'InProgress'|'Completed'|'Failed'|'Deleting'|'DeleteFailed',
'ManagedConfiguration': {
'ManagedStorageType': 'Restricted'
}
},
],
'NextToken': 'string'
}
Response Structure
(dict) --
ModelPackageGroupSummaryList (list) --
A list of summaries of the model groups in your Amazon Web Services account.
(dict) --
Summary information about a model group.
ModelPackageGroupName (string) --
The name of the model group.
ModelPackageGroupArn (string) --
The Amazon Resource Name (ARN) of the model group.
ModelPackageGroupDescription (string) --
A description of the model group.
CreationTime (datetime) --
The time that the model group was created.
ModelPackageGroupStatus (string) --
The status of the model group.
ManagedConfiguration (dict) --
The managed configuration of the model package group.
ManagedStorageType (string) --
The storage type of the model package.
NextToken (string) --
If the response is truncated, SageMaker returns this token. To retrieve the next set of model groups, use it in the subsequent request.
{'TrainingPlanSummaries': {'TargetResources': {'studio-apps'}}}
Retrieves a list of training plans for the current account.
See also: AWS API Documentation
Request Syntax
client.list_training_plans(
NextToken='string',
MaxResults=123,
StartTimeAfter=datetime(2015, 1, 1),
StartTimeBefore=datetime(2015, 1, 1),
SortBy='TrainingPlanName'|'StartTime'|'Status',
SortOrder='Ascending'|'Descending',
Filters=[
{
'Name': 'Status',
'Value': 'string'
},
]
)
string
A token to continue pagination if more results are available.
integer
The maximum number of results to return in the response.
datetime
Filter to list only training plans with an actual start time after this date.
datetime
Filter to list only training plans with an actual start time before this date.
string
The training plan field to sort the results by (e.g., StartTime, Status).
string
The order to sort the results (Ascending or Descending).
list
Additional filters to apply to the list of training plans.
(dict) --
A filter to apply when listing or searching for training plans.
For more information about how to reserve GPU capacity for your SageMaker HyperPod clusters using Amazon SageMaker Training Plan, see ``CreateTrainingPlan ``.
Name (string) -- [REQUIRED]
The name of the filter field (e.g., Status, InstanceType).
Value (string) -- [REQUIRED]
The value to filter by for the specified field.
dict
Response Syntax
{
'NextToken': 'string',
'TrainingPlanSummaries': [
{
'TrainingPlanArn': 'string',
'TrainingPlanName': 'string',
'Status': 'Pending'|'Active'|'Scheduled'|'Expired'|'Failed',
'StatusMessage': 'string',
'DurationHours': 123,
'DurationMinutes': 123,
'StartTime': datetime(2015, 1, 1),
'EndTime': datetime(2015, 1, 1),
'UpfrontFee': 'string',
'CurrencyCode': 'string',
'TotalInstanceCount': 123,
'AvailableInstanceCount': 123,
'InUseInstanceCount': 123,
'TotalUltraServerCount': 123,
'TargetResources': [
'training-job'|'hyperpod-cluster'|'endpoint'|'studio-apps',
],
'ReservedCapacitySummaries': [
{
'ReservedCapacityArn': 'string',
'ReservedCapacityType': 'UltraServer'|'Instance',
'UltraServerType': 'string',
'UltraServerCount': 123,
'InstanceType': 'ml.p4d.24xlarge'|'ml.p5.48xlarge'|'ml.p5e.48xlarge'|'ml.p5en.48xlarge'|'ml.trn1.32xlarge'|'ml.trn2.48xlarge'|'ml.p6-b200.48xlarge'|'ml.p4de.24xlarge'|'ml.p6e-gb200.36xlarge'|'ml.p5.4xlarge'|'ml.p6-b300.48xlarge',
'TotalInstanceCount': 123,
'Status': 'Pending'|'Active'|'Scheduled'|'Expired'|'Failed',
'AvailabilityZone': 'string',
'DurationHours': 123,
'DurationMinutes': 123,
'StartTime': datetime(2015, 1, 1),
'EndTime': datetime(2015, 1, 1)
},
]
},
]
}
Response Structure
(dict) --
NextToken (string) --
A token to continue pagination if more results are available.
TrainingPlanSummaries (list) --
A list of summary information for the training plans.
(dict) --
Details of the training plan.
For more information about how to reserve GPU capacity for your SageMaker HyperPod clusters using Amazon SageMaker Training Plan, see ``CreateTrainingPlan ``.
TrainingPlanArn (string) --
The Amazon Resource Name (ARN); of the training plan.
TrainingPlanName (string) --
The name of the training plan.
Status (string) --
The current status of the training plan (e.g., Pending, Active, Expired). To see the complete list of status values available for a training plan, refer to the Status attribute within the TrainingPlanSummary object.
StatusMessage (string) --
A message providing additional information about the current status of the training plan.
DurationHours (integer) --
The number of whole hours in the total duration for this training plan.
DurationMinutes (integer) --
The additional minutes beyond whole hours in the total duration for this training plan.
StartTime (datetime) --
The start time of the training plan.
EndTime (datetime) --
The end time of the training plan.
UpfrontFee (string) --
The upfront fee for the training plan.
CurrencyCode (string) --
The currency code for the upfront fee (e.g., USD).
TotalInstanceCount (integer) --
The total number of instances reserved in this training plan.
AvailableInstanceCount (integer) --
The number of instances currently available for use in this training plan.
InUseInstanceCount (integer) --
The number of instances currently in use from this training plan.
TotalUltraServerCount (integer) --
The total number of UltraServers allocated to this training plan.
TargetResources (list) --
The target resources (e.g., training jobs, HyperPod clusters, Endpoints, Studio apps) that can use this training plan.
Training plans are specific to their target resource.
A training plan designed for SageMaker training jobs can only be used to schedule and run training jobs.
A training plan for HyperPod clusters can be used exclusively to provide compute resources to a cluster's instance group.
A training plan for SageMaker endpoints can be used exclusively to provide compute resources to SageMaker endpoints for model deployment.
A training plan for Studio apps can be used to launch JupyterLab and Code Editor apps on reserved training plan capacity.
(string) --
ReservedCapacitySummaries (list) --
A list of reserved capacities associated with this training plan, including details such as instance types, counts, and availability zones.
(dict) --
Details of a reserved capacity for the training plan.
For more information about how to reserve GPU capacity for your SageMaker HyperPod clusters using Amazon SageMaker Training Plan, see ``CreateTrainingPlan ``.
ReservedCapacityArn (string) --
The Amazon Resource Name (ARN); of the reserved capacity.
ReservedCapacityType (string) --
The type of reserved capacity.
UltraServerType (string) --
The type of UltraServer included in this reserved capacity, such as ml.u-p6e-gb200x72.
UltraServerCount (integer) --
The number of UltraServers included in this reserved capacity.
InstanceType (string) --
The instance type for the reserved capacity.
TotalInstanceCount (integer) --
The total number of instances in the reserved capacity.
Status (string) --
The current status of the reserved capacity.
AvailabilityZone (string) --
The availability zone for the reserved capacity.
DurationHours (integer) --
The number of whole hours in the total duration for this reserved capacity.
DurationMinutes (integer) --
The additional minutes beyond whole hours in the total duration for this reserved capacity.
StartTime (datetime) --
The start time of the reserved capacity.
EndTime (datetime) --
The end time of the reserved capacity.
{'Results': {'TrainingJob': {'WarmPoolStatus': {'ResourceRetainedBillableTimeInSeconds': 'integer',
'ReusedByJob': 'string',
'Status': 'Available | '
'Terminated | Reused '
'| InUse'}},
'TrialComponent': {'SourceDetail': {'TrainingJob': {'WarmPoolStatus': {'ResourceRetainedBillableTimeInSeconds': 'integer',
'ReusedByJob': 'string',
'Status': 'Available '
'| '
'Terminated '
'| '
'Reused '
'| '
'InUse'}}}}}}
Finds SageMaker resources that match a search query. Matching resources are returned as a list of SearchRecord objects in the response. You can sort the search results by any resource property in a ascending or descending order.
You can query against the following value types: numeric, text, Boolean, and timestamp.
See also: AWS API Documentation
Request Syntax
client.search(
Resource='TrainingJob'|'Experiment'|'ExperimentTrial'|'ExperimentTrialComponent'|'Endpoint'|'Model'|'ModelPackage'|'ModelPackageGroup'|'Pipeline'|'PipelineExecution'|'FeatureGroup'|'FeatureMetadata'|'Image'|'ImageVersion'|'Project'|'HyperParameterTuningJob'|'ModelCard'|'PipelineVersion',
SearchExpression={
'Filters': [
{
'Name': 'string',
'Operator': 'Equals'|'NotEquals'|'GreaterThan'|'GreaterThanOrEqualTo'|'LessThan'|'LessThanOrEqualTo'|'Contains'|'Exists'|'NotExists'|'In',
'Value': 'string'
},
],
'NestedFilters': [
{
'NestedPropertyName': 'string',
'Filters': [
{
'Name': 'string',
'Operator': 'Equals'|'NotEquals'|'GreaterThan'|'GreaterThanOrEqualTo'|'LessThan'|'LessThanOrEqualTo'|'Contains'|'Exists'|'NotExists'|'In',
'Value': 'string'
},
]
},
],
'SubExpressions': [
{'... recursive ...'},
],
'Operator': 'And'|'Or'
},
SortBy='string',
SortOrder='Ascending'|'Descending',
NextToken='string',
MaxResults=123,
CrossAccountFilterOption='SameAccount'|'CrossAccount',
VisibilityConditions=[
{
'Key': 'string',
'Value': 'string'
},
]
)
string
[REQUIRED]
The name of the SageMaker resource to search for.
dict
A Boolean conditional statement. Resources must satisfy this condition to be included in search results. You must provide at least one subexpression, filter, or nested filter. The maximum number of recursive SubExpressions, NestedFilters, and Filters that can be included in a SearchExpression object is 50.
Filters (list) --
A list of filter objects.
(dict) --
A conditional statement for a search expression that includes a resource property, a Boolean operator, and a value. Resources that match the statement are returned in the results from the Search API.
If you specify a Value, but not an Operator, SageMaker uses the equals operator.
In search, there are several property types:
Metrics
To define a metric filter, enter a value using the form "Metrics.<name>", where <name> is a metric name. For example, the following filter searches for training jobs with an "accuracy" metric greater than "0.9":
{
"Name": "Metrics.accuracy",
"Operator": "GreaterThan",
"Value": "0.9"
}
HyperParameters
To define a hyperparameter filter, enter a value with the form "HyperParameters.<name>". Decimal hyperparameter values are treated as a decimal in a comparison if the specified Value is also a decimal value. If the specified Value is an integer, the decimal hyperparameter values are treated as integers. For example, the following filter is satisfied by training jobs with a "learning_rate" hyperparameter that is less than "0.5":
{
"Name": "HyperParameters.learning_rate",
"Operator": "LessThan",
"Value": "0.5"
}
Tags
To define a tag filter, enter a value with the form Tags.<key>.
Name (string) -- [REQUIRED]
A resource property name. For example, TrainingJobName. For valid property names, see SearchRecord. You must specify a valid property for the resource.
Operator (string) --
A Boolean binary operator that is used to evaluate the filter. The operator field contains one of the following values:
Equals
The value of Name equals Value.
NotEquals
The value of Name doesn't equal Value.
Exists
The Name property exists.
NotExists
The Name property does not exist.
GreaterThan
The value of Name is greater than Value. Not supported for text properties.
GreaterThanOrEqualTo
The value of Name is greater than or equal to Value. Not supported for text properties.
LessThan
The value of Name is less than Value. Not supported for text properties.
LessThanOrEqualTo
The value of Name is less than or equal to Value. Not supported for text properties.
In
The value of Name is one of the comma delimited strings in Value. Only supported for text properties.
Contains
The value of Name contains the string Value. Only supported for text properties.
A SearchExpression can include the Contains operator multiple times when the value of Name is one of the following:
Experiment.DisplayName
Experiment.ExperimentName
Experiment.Tags
Trial.DisplayName
Trial.TrialName
Trial.Tags
TrialComponent.DisplayName
TrialComponent.TrialComponentName
TrialComponent.Tags
TrialComponent.InputArtifacts
TrialComponent.OutputArtifacts
A SearchExpression can include only one Contains operator for all other values of Name. In these cases, if you include multiple Contains operators in the SearchExpression, the result is the following error message: " 'CONTAINS' operator usage limit of 1 exceeded."
Value (string) --
A value used with Name and Operator to determine which resources satisfy the filter's condition. For numerical properties, Value must be an integer or floating-point decimal. For timestamp properties, Value must be an ISO 8601 date-time string of the following format: YYYY-mm-dd'T'HH:MM:SS.
NestedFilters (list) --
A list of nested filter objects.
(dict) --
A list of nested Filter objects. A resource must satisfy the conditions of all filters to be included in the results returned from the Search API.
For example, to filter on a training job's InputDataConfig property with a specific channel name and S3Uri prefix, define the following filters:
'{Name:"InputDataConfig.ChannelName", "Operator":"Equals", "Value":"train"}',
'{Name:"InputDataConfig.DataSource.S3DataSource.S3Uri", "Operator":"Contains", "Value":"mybucket/catdata"}'
NestedPropertyName (string) -- [REQUIRED]
The name of the property to use in the nested filters. The value must match a listed property name, such as InputDataConfig.
Filters (list) -- [REQUIRED]
A list of filters. Each filter acts on a property. Filters must contain at least one Filters value. For example, a NestedFilters call might include a filter on the PropertyName parameter of the InputDataConfig property: InputDataConfig.DataSource.S3DataSource.S3Uri.
(dict) --
A conditional statement for a search expression that includes a resource property, a Boolean operator, and a value. Resources that match the statement are returned in the results from the Search API.
If you specify a Value, but not an Operator, SageMaker uses the equals operator.
In search, there are several property types:
Metrics
To define a metric filter, enter a value using the form "Metrics.<name>", where <name> is a metric name. For example, the following filter searches for training jobs with an "accuracy" metric greater than "0.9":
{
"Name": "Metrics.accuracy",
"Operator": "GreaterThan",
"Value": "0.9"
}
HyperParameters
To define a hyperparameter filter, enter a value with the form "HyperParameters.<name>". Decimal hyperparameter values are treated as a decimal in a comparison if the specified Value is also a decimal value. If the specified Value is an integer, the decimal hyperparameter values are treated as integers. For example, the following filter is satisfied by training jobs with a "learning_rate" hyperparameter that is less than "0.5":
{
"Name": "HyperParameters.learning_rate",
"Operator": "LessThan",
"Value": "0.5"
}
Tags
To define a tag filter, enter a value with the form Tags.<key>.
Name (string) -- [REQUIRED]
A resource property name. For example, TrainingJobName. For valid property names, see SearchRecord. You must specify a valid property for the resource.
Operator (string) --
A Boolean binary operator that is used to evaluate the filter. The operator field contains one of the following values:
Equals
The value of Name equals Value.
NotEquals
The value of Name doesn't equal Value.
Exists
The Name property exists.
NotExists
The Name property does not exist.
GreaterThan
The value of Name is greater than Value. Not supported for text properties.
GreaterThanOrEqualTo
The value of Name is greater than or equal to Value. Not supported for text properties.
LessThan
The value of Name is less than Value. Not supported for text properties.
LessThanOrEqualTo
The value of Name is less than or equal to Value. Not supported for text properties.
In
The value of Name is one of the comma delimited strings in Value. Only supported for text properties.
Contains
The value of Name contains the string Value. Only supported for text properties.
A SearchExpression can include the Contains operator multiple times when the value of Name is one of the following:
Experiment.DisplayName
Experiment.ExperimentName
Experiment.Tags
Trial.DisplayName
Trial.TrialName
Trial.Tags
TrialComponent.DisplayName
TrialComponent.TrialComponentName
TrialComponent.Tags
TrialComponent.InputArtifacts
TrialComponent.OutputArtifacts
A SearchExpression can include only one Contains operator for all other values of Name. In these cases, if you include multiple Contains operators in the SearchExpression, the result is the following error message: " 'CONTAINS' operator usage limit of 1 exceeded."
Value (string) --
A value used with Name and Operator to determine which resources satisfy the filter's condition. For numerical properties, Value must be an integer or floating-point decimal. For timestamp properties, Value must be an ISO 8601 date-time string of the following format: YYYY-mm-dd'T'HH:MM:SS.
SubExpressions (list) --
A list of search expression objects.
(dict) --
A multi-expression that searches for the specified resource or resources in a search. All resource objects that satisfy the expression's condition are included in the search results. You must specify at least one subexpression, filter, or nested filter. A SearchExpression can contain up to twenty elements.
A SearchExpression contains the following components:
A list of Filter objects. Each filter defines a simple Boolean expression comprised of a resource property name, Boolean operator, and value.
A list of NestedFilter objects. Each nested filter defines a list of Boolean expressions using a list of resource properties. A nested filter is satisfied if a single object in the list satisfies all Boolean expressions.
A list of SearchExpression objects. A search expression object can be nested in a list of search expression objects.
A Boolean operator: And or Or.
Operator (string) --
A Boolean operator used to evaluate the search expression. If you want every conditional statement in all lists to be satisfied for the entire search expression to be true, specify And. If only a single conditional statement needs to be true for the entire search expression to be true, specify Or. The default value is And.
string
The name of the resource property used to sort the SearchResults. The default is LastModifiedTime.
string
How SearchResults are ordered. Valid values are Ascending or Descending. The default is Descending.
string
If more than MaxResults resources match the specified SearchExpression, the response includes a NextToken. The NextToken can be passed to the next SearchRequest to continue retrieving results.
integer
The maximum number of results to return.
string
A cross account filter option. When the value is "CrossAccount" the search results will only include resources made discoverable to you from other accounts. When the value is "SameAccount" or null the search results will only include resources from your account. Default is null. For more information on searching for resources made discoverable to your account, see Search discoverable resources in the SageMaker Developer Guide. The maximum number of ``ResourceCatalog``s viewable is 1000.
list
Limits the results of your search request to the resources that you can access.
(dict) --
The list of key-value pairs used to filter your search results. If a search result contains a key from your list, it is included in the final search response if the value associated with the key in the result matches the value you specified. If the value doesn't match, the result is excluded from the search response. Any resources that don't have a key from the list that you've provided will also be included in the search response.
Key (string) --
The key that specifies the tag that you're using to filter the search results. It must be in the following format: Tags.<key>.
Value (string) --
The value for the tag that you're using to filter the search results.
dict
Response Syntax
# This section is too large to render. # Please see the AWS API Documentation linked below.
Response Structure
# This section is too large to render. # Please see the AWS API Documentation linked below.
{'TargetResources': {'studio-apps'}}
Response {'TrainingPlanOfferings': {'TargetResources': {'studio-apps'}}}
Searches for available training plan offerings based on specified criteria.
Users search for available plan offerings based on their requirements (e.g., instance type, count, start time, duration).
And then, they create a plan that best matches their needs using the ID of the plan offering they want to use.
For more information about how to reserve GPU capacity for your SageMaker training jobs or SageMaker HyperPod clusters using Amazon SageMaker Training Plan , see ``CreateTrainingPlan ``.
See also: AWS API Documentation
Request Syntax
client.search_training_plan_offerings(
InstanceType='ml.p4d.24xlarge'|'ml.p5.48xlarge'|'ml.p5e.48xlarge'|'ml.p5en.48xlarge'|'ml.trn1.32xlarge'|'ml.trn2.48xlarge'|'ml.p6-b200.48xlarge'|'ml.p4de.24xlarge'|'ml.p6e-gb200.36xlarge'|'ml.p5.4xlarge'|'ml.p6-b300.48xlarge',
InstanceCount=123,
UltraServerType='string',
UltraServerCount=123,
StartTimeAfter=datetime(2015, 1, 1),
EndTimeBefore=datetime(2015, 1, 1),
DurationHours=123,
TargetResources=[
'training-job'|'hyperpod-cluster'|'endpoint'|'studio-apps',
],
TrainingPlanArn='string'
)
string
The type of instance you want to search for in the available training plan offerings. This field allows you to filter the search results based on the specific compute resources you require for your SageMaker training jobs or SageMaker HyperPod clusters. When searching for training plan offerings, specifying the instance type helps you find Reserved Instances that match your computational needs.
integer
The number of instances you want to reserve in the training plan offerings. This allows you to specify the quantity of compute resources needed for your SageMaker training jobs or SageMaker HyperPod clusters, helping you find reserved capacity offerings that match your requirements.
string
The type of UltraServer to search for, such as ml.u-p6e-gb200x72.
integer
The number of UltraServers to search for.
datetime
A filter to search for training plan offerings with a start time after a specified date.
datetime
A filter to search for reserved capacity offerings with an end time before a specified date.
integer
The desired duration in hours for the training plan offerings.
list
The target resources (e.g., SageMaker Training Jobs, SageMaker HyperPod, SageMaker Endpoints, Studio apps) to search for in the offerings.
Training plans are specific to their target resource.
A training plan designed for SageMaker training jobs can only be used to schedule and run training jobs.
A training plan for HyperPod clusters can be used exclusively to provide compute resources to a cluster's instance group.
A training plan for SageMaker endpoints can be used exclusively to provide compute resources to SageMaker endpoints for model deployment.
A training plan for Studio apps can be used to launch JupyterLab and Code Editor apps on reserved training plan capacity.
(string) --
string
The Amazon Resource Name (ARN); of an existing training plan to search for extension offerings. When specified, the API returns extension offerings that can be used to extend the specified training plan.
dict
Response Syntax
{
'TrainingPlanOfferings': [
{
'TrainingPlanOfferingId': 'string',
'TargetResources': [
'training-job'|'hyperpod-cluster'|'endpoint'|'studio-apps',
],
'RequestedStartTimeAfter': datetime(2015, 1, 1),
'RequestedEndTimeBefore': datetime(2015, 1, 1),
'DurationHours': 123,
'DurationMinutes': 123,
'UpfrontFee': 'string',
'CurrencyCode': 'string',
'ReservedCapacityOfferings': [
{
'ReservedCapacityType': 'UltraServer'|'Instance',
'UltraServerType': 'string',
'UltraServerCount': 123,
'InstanceType': 'ml.p4d.24xlarge'|'ml.p5.48xlarge'|'ml.p5e.48xlarge'|'ml.p5en.48xlarge'|'ml.trn1.32xlarge'|'ml.trn2.48xlarge'|'ml.p6-b200.48xlarge'|'ml.p4de.24xlarge'|'ml.p6e-gb200.36xlarge'|'ml.p5.4xlarge'|'ml.p6-b300.48xlarge',
'InstanceCount': 123,
'AvailabilityZone': 'string',
'DurationHours': 123,
'DurationMinutes': 123,
'StartTime': datetime(2015, 1, 1),
'EndTime': datetime(2015, 1, 1),
'ExtensionStartTime': datetime(2015, 1, 1),
'ExtensionEndTime': datetime(2015, 1, 1)
},
]
},
],
'TrainingPlanExtensionOfferings': [
{
'TrainingPlanExtensionOfferingId': 'string',
'AvailabilityZone': 'string',
'StartDate': datetime(2015, 1, 1),
'EndDate': datetime(2015, 1, 1),
'DurationHours': 123,
'UpfrontFee': 'string',
'CurrencyCode': 'string'
},
]
}
Response Structure
(dict) --
TrainingPlanOfferings (list) --
A list of training plan offerings that match the search criteria.
(dict) --
Details about a training plan offering.
For more information about how to reserve GPU capacity for your SageMaker HyperPod clusters using Amazon SageMaker Training Plan, see ``CreateTrainingPlan ``.
TrainingPlanOfferingId (string) --
The unique identifier for this training plan offering.
TargetResources (list) --
The target resources (e.g., SageMaker Training Jobs, SageMaker HyperPod, SageMaker Endpoints, Studio apps) for this training plan offering.
Training plans are specific to their target resource.
A training plan designed for SageMaker training jobs can only be used to schedule and run training jobs.
A training plan for HyperPod clusters can be used exclusively to provide compute resources to a cluster's instance group.
A training plan for SageMaker endpoints can be used exclusively to provide compute resources to SageMaker endpoints for model deployment.
A training plan for Studio apps can be used to launch JupyterLab and Code Editor apps on reserved training plan capacity.
(string) --
RequestedStartTimeAfter (datetime) --
The requested start time that the user specified when searching for the training plan offering.
RequestedEndTimeBefore (datetime) --
The requested end time that the user specified when searching for the training plan offering.
DurationHours (integer) --
The number of whole hours in the total duration for this training plan offering.
DurationMinutes (integer) --
The additional minutes beyond whole hours in the total duration for this training plan offering.
UpfrontFee (string) --
The upfront fee for this training plan offering.
CurrencyCode (string) --
The currency code for the upfront fee (e.g., USD).
ReservedCapacityOfferings (list) --
A list of reserved capacity offerings associated with this training plan offering.
(dict) --
Details about a reserved capacity offering for a training plan offering.
For more information about how to reserve GPU capacity for your SageMaker HyperPod clusters using Amazon SageMaker Training Plan, see ``CreateTrainingPlan ``.
ReservedCapacityType (string) --
The type of reserved capacity offering.
UltraServerType (string) --
The type of UltraServer included in this reserved capacity offering, such as ml.u-p6e-gb200x72.
UltraServerCount (integer) --
The number of UltraServers included in this reserved capacity offering.
InstanceType (string) --
The instance type for the reserved capacity offering.
InstanceCount (integer) --
The number of instances in the reserved capacity offering.
AvailabilityZone (string) --
The availability zone for the reserved capacity offering.
DurationHours (integer) --
The number of whole hours in the total duration for this reserved capacity offering.
DurationMinutes (integer) --
The additional minutes beyond whole hours in the total duration for this reserved capacity offering.
StartTime (datetime) --
The start time of the reserved capacity offering.
EndTime (datetime) --
The end time of the reserved capacity offering.
ExtensionStartTime (datetime) --
The start time of the extension for the reserved capacity offering.
ExtensionEndTime (datetime) --
The end time of the extension for the reserved capacity offering.
TrainingPlanExtensionOfferings (list) --
A list of extension offerings available for the specified training plan. These offerings can be used with the ExtendTrainingPlan API to extend an existing training plan.
(dict) --
Details about an available extension offering for a training plan. Use the offering ID with the ExtendTrainingPlan API to extend a training plan.
TrainingPlanExtensionOfferingId (string) --
The unique identifier for this extension offering.
AvailabilityZone (string) --
The Availability Zone for this extension offering.
StartDate (datetime) --
The start date of this extension offering.
EndDate (datetime) --
The end date of this extension offering.
DurationHours (integer) --
The duration of this extension offering in hours.
UpfrontFee (string) --
The upfront fee for this extension offering.
CurrencyCode (string) --
The currency code for the upfront fee (e.g., USD).
{'DesiredModelVariants': {'InfrastructureConfig': {'RealTimeInferenceConfig': {'InstanceType': {'ml.c4.large',
'ml.c5.large',
'ml.c5d.large',
'ml.c6g.12xlarge',
'ml.c6g.16xlarge',
'ml.c6g.2xlarge',
'ml.c6g.4xlarge',
'ml.c6g.8xlarge',
'ml.c6g.large',
'ml.c6g.xlarge',
'ml.c6gd.12xlarge',
'ml.c6gd.16xlarge',
'ml.c6gd.2xlarge',
'ml.c6gd.4xlarge',
'ml.c6gd.8xlarge',
'ml.c6gd.large',
'ml.c6gd.xlarge',
'ml.c6gn.12xlarge',
'ml.c6gn.16xlarge',
'ml.c6gn.2xlarge',
'ml.c6gn.4xlarge',
'ml.c6gn.8xlarge',
'ml.c6gn.large',
'ml.c6gn.xlarge',
'ml.c6in.12xlarge',
'ml.c6in.16xlarge',
'ml.c6in.24xlarge',
'ml.c6in.2xlarge',
'ml.c6in.32xlarge',
'ml.c6in.4xlarge',
'ml.c6in.8xlarge',
'ml.c6in.large',
'ml.c6in.xlarge',
'ml.c7g.12xlarge',
'ml.c7g.16xlarge',
'ml.c7g.2xlarge',
'ml.c7g.4xlarge',
'ml.c7g.8xlarge',
'ml.c7g.large',
'ml.c7g.xlarge',
'ml.c8g.12xlarge',
'ml.c8g.16xlarge',
'ml.c8g.24xlarge',
'ml.c8g.2xlarge',
'ml.c8g.48xlarge',
'ml.c8g.4xlarge',
'ml.c8g.8xlarge',
'ml.c8g.large',
'ml.c8g.medium',
'ml.c8g.xlarge',
'ml.dl1.24xlarge',
'ml.g6e.12xlarge',
'ml.g6e.16xlarge',
'ml.g6e.24xlarge',
'ml.g6e.2xlarge',
'ml.g6e.48xlarge',
'ml.g6e.4xlarge',
'ml.g6e.8xlarge',
'ml.g6e.xlarge',
'ml.g7e.12xlarge',
'ml.g7e.24xlarge',
'ml.g7e.2xlarge',
'ml.g7e.48xlarge',
'ml.g7e.4xlarge',
'ml.g7e.8xlarge',
'ml.m5.large',
'ml.m6g.12xlarge',
'ml.m6g.16xlarge',
'ml.m6g.2xlarge',
'ml.m6g.4xlarge',
'ml.m6g.8xlarge',
'ml.m6g.large',
'ml.m6g.xlarge',
'ml.m6gd.12xlarge',
'ml.m6gd.16xlarge',
'ml.m6gd.2xlarge',
'ml.m6gd.4xlarge',
'ml.m6gd.8xlarge',
'ml.m6gd.large',
'ml.m6gd.xlarge',
'ml.m8g.12xlarge',
'ml.m8g.16xlarge',
'ml.m8g.24xlarge',
'ml.m8g.2xlarge',
'ml.m8g.48xlarge',
'ml.m8g.4xlarge',
'ml.m8g.8xlarge',
'ml.m8g.large',
'ml.m8g.medium',
'ml.m8g.xlarge',
'ml.p5.4xlarge',
'ml.p5e.48xlarge',
'ml.p5en.48xlarge',
'ml.p6-b300.48xlarge',
'ml.p6e-gb200.36xlarge',
'ml.r5d.12xlarge',
'ml.r5d.24xlarge',
'ml.r5d.2xlarge',
'ml.r5d.4xlarge',
'ml.r5d.large',
'ml.r5d.xlarge',
'ml.r6g.12xlarge',
'ml.r6g.16xlarge',
'ml.r6g.2xlarge',
'ml.r6g.4xlarge',
'ml.r6g.8xlarge',
'ml.r6g.large',
'ml.r6g.xlarge',
'ml.r6gd.12xlarge',
'ml.r6gd.16xlarge',
'ml.r6gd.2xlarge',
'ml.r6gd.4xlarge',
'ml.r6gd.8xlarge',
'ml.r6gd.large',
'ml.r6gd.xlarge',
'ml.r7gd.12xlarge',
'ml.r7gd.16xlarge',
'ml.r7gd.2xlarge',
'ml.r7gd.4xlarge',
'ml.r7gd.8xlarge',
'ml.r7gd.large',
'ml.r7gd.medium',
'ml.r7gd.xlarge',
'ml.r8g.12xlarge',
'ml.r8g.16xlarge',
'ml.r8g.24xlarge',
'ml.r8g.2xlarge',
'ml.r8g.48xlarge',
'ml.r8g.4xlarge',
'ml.r8g.8xlarge',
'ml.r8g.large',
'ml.r8g.medium',
'ml.r8g.xlarge',
'ml.trn2.48xlarge'}}}}}
Stops an inference experiment.
See also: AWS API Documentation
Request Syntax
client.stop_inference_experiment(
Name='string',
ModelVariantActions={
'string': 'Retain'|'Remove'|'Promote'
},
DesiredModelVariants=[
{
'ModelName': 'string',
'VariantName': 'string',
'InfrastructureConfig': {
'InfrastructureType': 'RealTimeInference',
'RealTimeInferenceConfig': {
'InstanceType': 'ml.t2.medium'|'ml.t2.large'|'ml.t2.xlarge'|'ml.t2.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.12xlarge'|'ml.m5d.24xlarge'|'ml.c4.large'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5d.large'|'ml.c5d.xlarge'|'ml.c5d.2xlarge'|'ml.c5d.4xlarge'|'ml.c5d.9xlarge'|'ml.c5d.18xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.12xlarge'|'ml.r5.24xlarge'|'ml.r5d.large'|'ml.r5d.xlarge'|'ml.r5d.2xlarge'|'ml.r5d.4xlarge'|'ml.r5d.12xlarge'|'ml.r5d.24xlarge'|'ml.inf1.xlarge'|'ml.inf1.2xlarge'|'ml.inf1.6xlarge'|'ml.inf1.24xlarge'|'ml.dl1.24xlarge'|'ml.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.r6i.large'|'ml.r6i.xlarge'|'ml.r6i.2xlarge'|'ml.r6i.4xlarge'|'ml.r6i.8xlarge'|'ml.r6i.12xlarge'|'ml.r6i.16xlarge'|'ml.r6i.24xlarge'|'ml.r6i.32xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.12xlarge'|'ml.g5.16xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.g6.xlarge'|'ml.g6.2xlarge'|'ml.g6.4xlarge'|'ml.g6.8xlarge'|'ml.g6.12xlarge'|'ml.g6.16xlarge'|'ml.g6.24xlarge'|'ml.g6.48xlarge'|'ml.r8g.medium'|'ml.r8g.large'|'ml.r8g.xlarge'|'ml.r8g.2xlarge'|'ml.r8g.4xlarge'|'ml.r8g.8xlarge'|'ml.r8g.12xlarge'|'ml.r8g.16xlarge'|'ml.r8g.24xlarge'|'ml.r8g.48xlarge'|'ml.g6e.xlarge'|'ml.g6e.2xlarge'|'ml.g6e.4xlarge'|'ml.g6e.8xlarge'|'ml.g6e.12xlarge'|'ml.g6e.16xlarge'|'ml.g6e.24xlarge'|'ml.g6e.48xlarge'|'ml.g7e.2xlarge'|'ml.g7e.4xlarge'|'ml.g7e.8xlarge'|'ml.g7e.12xlarge'|'ml.g7e.24xlarge'|'ml.g7e.48xlarge'|'ml.p4d.24xlarge'|'ml.c7g.large'|'ml.c7g.xlarge'|'ml.c7g.2xlarge'|'ml.c7g.4xlarge'|'ml.c7g.8xlarge'|'ml.c7g.12xlarge'|'ml.c7g.16xlarge'|'ml.m6g.large'|'ml.m6g.xlarge'|'ml.m6g.2xlarge'|'ml.m6g.4xlarge'|'ml.m6g.8xlarge'|'ml.m6g.12xlarge'|'ml.m6g.16xlarge'|'ml.m6gd.large'|'ml.m6gd.xlarge'|'ml.m6gd.2xlarge'|'ml.m6gd.4xlarge'|'ml.m6gd.8xlarge'|'ml.m6gd.12xlarge'|'ml.m6gd.16xlarge'|'ml.c6g.large'|'ml.c6g.xlarge'|'ml.c6g.2xlarge'|'ml.c6g.4xlarge'|'ml.c6g.8xlarge'|'ml.c6g.12xlarge'|'ml.c6g.16xlarge'|'ml.c6gd.large'|'ml.c6gd.xlarge'|'ml.c6gd.2xlarge'|'ml.c6gd.4xlarge'|'ml.c6gd.8xlarge'|'ml.c6gd.12xlarge'|'ml.c6gd.16xlarge'|'ml.c6gn.large'|'ml.c6gn.xlarge'|'ml.c6gn.2xlarge'|'ml.c6gn.4xlarge'|'ml.c6gn.8xlarge'|'ml.c6gn.12xlarge'|'ml.c6gn.16xlarge'|'ml.r6g.large'|'ml.r6g.xlarge'|'ml.r6g.2xlarge'|'ml.r6g.4xlarge'|'ml.r6g.8xlarge'|'ml.r6g.12xlarge'|'ml.r6g.16xlarge'|'ml.r6gd.large'|'ml.r6gd.xlarge'|'ml.r6gd.2xlarge'|'ml.r6gd.4xlarge'|'ml.r6gd.8xlarge'|'ml.r6gd.12xlarge'|'ml.r6gd.16xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.trn2.48xlarge'|'ml.inf2.xlarge'|'ml.inf2.8xlarge'|'ml.inf2.24xlarge'|'ml.inf2.48xlarge'|'ml.p5.48xlarge'|'ml.p5e.48xlarge'|'ml.p5en.48xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge'|'ml.c8g.medium'|'ml.c8g.large'|'ml.c8g.xlarge'|'ml.c8g.2xlarge'|'ml.c8g.4xlarge'|'ml.c8g.8xlarge'|'ml.c8g.12xlarge'|'ml.c8g.16xlarge'|'ml.c8g.24xlarge'|'ml.c8g.48xlarge'|'ml.r7gd.medium'|'ml.r7gd.large'|'ml.r7gd.xlarge'|'ml.r7gd.2xlarge'|'ml.r7gd.4xlarge'|'ml.r7gd.8xlarge'|'ml.r7gd.12xlarge'|'ml.r7gd.16xlarge'|'ml.m8g.medium'|'ml.m8g.large'|'ml.m8g.xlarge'|'ml.m8g.2xlarge'|'ml.m8g.4xlarge'|'ml.m8g.8xlarge'|'ml.m8g.12xlarge'|'ml.m8g.16xlarge'|'ml.m8g.24xlarge'|'ml.m8g.48xlarge'|'ml.c6in.large'|'ml.c6in.xlarge'|'ml.c6in.2xlarge'|'ml.c6in.4xlarge'|'ml.c6in.8xlarge'|'ml.c6in.12xlarge'|'ml.c6in.16xlarge'|'ml.c6in.24xlarge'|'ml.c6in.32xlarge'|'ml.p6-b200.48xlarge'|'ml.p6-b300.48xlarge'|'ml.p6e-gb200.36xlarge'|'ml.p5.4xlarge',
'InstanceCount': 123
}
}
},
],
DesiredState='Completed'|'Cancelled',
Reason='string'
)
string
[REQUIRED]
The name of the inference experiment to stop.
dict
[REQUIRED]
Array of key-value pairs, with names of variants mapped to actions. The possible actions are the following:
Promote - Promote the shadow variant to a production variant
Remove - Delete the variant
Retain - Keep the variant as it is
(string) --
(string) --
list
An array of ModelVariantConfig objects. There is one for each variant that you want to deploy after the inference experiment stops. Each ModelVariantConfig describes the infrastructure configuration for deploying the corresponding variant.
(dict) --
Contains information about the deployment options of a model.
ModelName (string) -- [REQUIRED]
The name of the Amazon SageMaker Model entity.
VariantName (string) -- [REQUIRED]
The name of the variant.
InfrastructureConfig (dict) -- [REQUIRED]
The configuration for the infrastructure that the model will be deployed to.
InfrastructureType (string) -- [REQUIRED]
The inference option to which to deploy your model. Possible values are the following:
RealTime: Deploy to real-time inference.
RealTimeInferenceConfig (dict) -- [REQUIRED]
The infrastructure configuration for deploying the model to real-time inference.
InstanceType (string) -- [REQUIRED]
The instance type the model is deployed to.
InstanceCount (integer) -- [REQUIRED]
The number of instances of the type specified by InstanceType.
string
The desired state of the experiment after stopping. The possible states are the following:
Completed: The experiment completed successfully
Cancelled: The experiment was canceled
string
The reason for stopping the experiment.
dict
Response Syntax
{
'InferenceExperimentArn': 'string'
}
Response Structure
(dict) --
InferenceExperimentArn (string) --
The ARN of the stopped inference experiment.
{'DefaultSpaceSettings': {'JupyterLabAppSettings': {'DefaultResourceSpec': {'TrainingPlanArn': 'string'}},
'JupyterServerAppSettings': {'DefaultResourceSpec': {'TrainingPlanArn': 'string'}},
'KernelGatewayAppSettings': {'DefaultResourceSpec': {'TrainingPlanArn': 'string'}}},
'DefaultUserSettings': {'CodeEditorAppSettings': {'DefaultResourceSpec': {'TrainingPlanArn': 'string'}},
'JupyterLabAppSettings': {'DefaultResourceSpec': {'TrainingPlanArn': 'string'}},
'JupyterServerAppSettings': {'DefaultResourceSpec': {'TrainingPlanArn': 'string'}},
'KernelGatewayAppSettings': {'DefaultResourceSpec': {'TrainingPlanArn': 'string'}},
'RSessionAppSettings': {'DefaultResourceSpec': {'TrainingPlanArn': 'string'}},
'StudioWebPortalSettings': {'ExecutionRoleSessionNameMode': 'STATIC '
'| '
'USER_IDENTITY'},
'TensorBoardAppSettings': {'DefaultResourceSpec': {'TrainingPlanArn': 'string'}}},
'DomainSettingsForUpdate': {'RStudioServerProDomainSettingsForUpdate': {'DefaultResourceSpec': {'TrainingPlanArn': 'string'}}}}
Updates the default settings for new user profiles in the domain.
See also: AWS API Documentation
Request Syntax
client.update_domain(
DomainId='string',
DefaultUserSettings={
'ExecutionRole': 'string',
'SecurityGroups': [
'string',
],
'SharingSettings': {
'NotebookOutputOption': 'Allowed'|'Disabled',
'S3OutputPath': 'string',
'S3KmsKeyId': 'string'
},
'JupyterServerAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': 'string',
'SageMakerImageVersionAlias': 'string',
'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.g6.xlarge'|'ml.g6.2xlarge'|'ml.g6.4xlarge'|'ml.g6.8xlarge'|'ml.g6.12xlarge'|'ml.g6.16xlarge'|'ml.g6.24xlarge'|'ml.g6.48xlarge'|'ml.g6e.xlarge'|'ml.g6e.2xlarge'|'ml.g6e.4xlarge'|'ml.g6e.8xlarge'|'ml.g6e.12xlarge'|'ml.g6e.16xlarge'|'ml.g6e.24xlarge'|'ml.g6e.48xlarge'|'ml.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.p5.48xlarge'|'ml.p5en.48xlarge'|'ml.p6-b200.48xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.r6i.large'|'ml.r6i.xlarge'|'ml.r6i.2xlarge'|'ml.r6i.4xlarge'|'ml.r6i.8xlarge'|'ml.r6i.12xlarge'|'ml.r6i.16xlarge'|'ml.r6i.24xlarge'|'ml.r6i.32xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge'|'ml.m6id.large'|'ml.m6id.xlarge'|'ml.m6id.2xlarge'|'ml.m6id.4xlarge'|'ml.m6id.8xlarge'|'ml.m6id.12xlarge'|'ml.m6id.16xlarge'|'ml.m6id.24xlarge'|'ml.m6id.32xlarge'|'ml.c6id.large'|'ml.c6id.xlarge'|'ml.c6id.2xlarge'|'ml.c6id.4xlarge'|'ml.c6id.8xlarge'|'ml.c6id.12xlarge'|'ml.c6id.16xlarge'|'ml.c6id.24xlarge'|'ml.c6id.32xlarge'|'ml.r6id.large'|'ml.r6id.xlarge'|'ml.r6id.2xlarge'|'ml.r6id.4xlarge'|'ml.r6id.8xlarge'|'ml.r6id.12xlarge'|'ml.r6id.16xlarge'|'ml.r6id.24xlarge'|'ml.r6id.32xlarge'|'ml.p5.4xlarge',
'LifecycleConfigArn': 'string',
'TrainingPlanArn': 'string'
},
'LifecycleConfigArns': [
'string',
],
'CodeRepositories': [
{
'RepositoryUrl': 'string'
},
]
},
'KernelGatewayAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': 'string',
'SageMakerImageVersionAlias': 'string',
'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.g6.xlarge'|'ml.g6.2xlarge'|'ml.g6.4xlarge'|'ml.g6.8xlarge'|'ml.g6.12xlarge'|'ml.g6.16xlarge'|'ml.g6.24xlarge'|'ml.g6.48xlarge'|'ml.g6e.xlarge'|'ml.g6e.2xlarge'|'ml.g6e.4xlarge'|'ml.g6e.8xlarge'|'ml.g6e.12xlarge'|'ml.g6e.16xlarge'|'ml.g6e.24xlarge'|'ml.g6e.48xlarge'|'ml.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.p5.48xlarge'|'ml.p5en.48xlarge'|'ml.p6-b200.48xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.r6i.large'|'ml.r6i.xlarge'|'ml.r6i.2xlarge'|'ml.r6i.4xlarge'|'ml.r6i.8xlarge'|'ml.r6i.12xlarge'|'ml.r6i.16xlarge'|'ml.r6i.24xlarge'|'ml.r6i.32xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge'|'ml.m6id.large'|'ml.m6id.xlarge'|'ml.m6id.2xlarge'|'ml.m6id.4xlarge'|'ml.m6id.8xlarge'|'ml.m6id.12xlarge'|'ml.m6id.16xlarge'|'ml.m6id.24xlarge'|'ml.m6id.32xlarge'|'ml.c6id.large'|'ml.c6id.xlarge'|'ml.c6id.2xlarge'|'ml.c6id.4xlarge'|'ml.c6id.8xlarge'|'ml.c6id.12xlarge'|'ml.c6id.16xlarge'|'ml.c6id.24xlarge'|'ml.c6id.32xlarge'|'ml.r6id.large'|'ml.r6id.xlarge'|'ml.r6id.2xlarge'|'ml.r6id.4xlarge'|'ml.r6id.8xlarge'|'ml.r6id.12xlarge'|'ml.r6id.16xlarge'|'ml.r6id.24xlarge'|'ml.r6id.32xlarge'|'ml.p5.4xlarge',
'LifecycleConfigArn': 'string',
'TrainingPlanArn': 'string'
},
'CustomImages': [
{
'ImageName': 'string',
'ImageVersionNumber': 123,
'AppImageConfigName': 'string'
},
],
'LifecycleConfigArns': [
'string',
]
},
'TensorBoardAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': 'string',
'SageMakerImageVersionAlias': 'string',
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'SageMakerImageVersionArn': 'string',
'SageMakerImageVersionAlias': 'string',
'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.g6.xlarge'|'ml.g6.2xlarge'|'ml.g6.4xlarge'|'ml.g6.8xlarge'|'ml.g6.12xlarge'|'ml.g6.16xlarge'|'ml.g6.24xlarge'|'ml.g6.48xlarge'|'ml.g6e.xlarge'|'ml.g6e.2xlarge'|'ml.g6e.4xlarge'|'ml.g6e.8xlarge'|'ml.g6e.12xlarge'|'ml.g6e.16xlarge'|'ml.g6e.24xlarge'|'ml.g6e.48xlarge'|'ml.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.p5.48xlarge'|'ml.p5en.48xlarge'|'ml.p6-b200.48xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.r6i.large'|'ml.r6i.xlarge'|'ml.r6i.2xlarge'|'ml.r6i.4xlarge'|'ml.r6i.8xlarge'|'ml.r6i.12xlarge'|'ml.r6i.16xlarge'|'ml.r6i.24xlarge'|'ml.r6i.32xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge'|'ml.m6id.large'|'ml.m6id.xlarge'|'ml.m6id.2xlarge'|'ml.m6id.4xlarge'|'ml.m6id.8xlarge'|'ml.m6id.12xlarge'|'ml.m6id.16xlarge'|'ml.m6id.24xlarge'|'ml.m6id.32xlarge'|'ml.c6id.large'|'ml.c6id.xlarge'|'ml.c6id.2xlarge'|'ml.c6id.4xlarge'|'ml.c6id.8xlarge'|'ml.c6id.12xlarge'|'ml.c6id.16xlarge'|'ml.c6id.24xlarge'|'ml.c6id.32xlarge'|'ml.r6id.large'|'ml.r6id.xlarge'|'ml.r6id.2xlarge'|'ml.r6id.4xlarge'|'ml.r6id.8xlarge'|'ml.r6id.12xlarge'|'ml.r6id.16xlarge'|'ml.r6id.24xlarge'|'ml.r6id.32xlarge'|'ml.p5.4xlarge',
'LifecycleConfigArn': 'string',
'TrainingPlanArn': 'string'
},
'CustomImages': [
{
'ImageName': 'string',
'ImageVersionNumber': 123,
'AppImageConfigName': 'string'
},
],
'LifecycleConfigArns': [
'string',
],
'CodeRepositories': [
{
'RepositoryUrl': 'string'
},
],
'AppLifecycleManagement': {
'IdleSettings': {
'LifecycleManagement': 'ENABLED'|'DISABLED',
'IdleTimeoutInMinutes': 123,
'MinIdleTimeoutInMinutes': 123,
'MaxIdleTimeoutInMinutes': 123
}
},
'EmrSettings': {
'AssumableRoleArns': [
'string',
],
'ExecutionRoleArns': [
'string',
]
},
'BuiltInLifecycleConfigArn': 'string'
},
'SpaceStorageSettings': {
'DefaultEbsStorageSettings': {
'DefaultEbsVolumeSizeInGb': 123,
'MaximumEbsVolumeSizeInGb': 123
}
},
'DefaultLandingUri': 'string',
'StudioWebPortal': 'ENABLED'|'DISABLED',
'CustomPosixUserConfig': {
'Uid': 123,
'Gid': 123
},
'CustomFileSystemConfigs': [
{
'EFSFileSystemConfig': {
'FileSystemId': 'string',
'FileSystemPath': 'string'
},
'FSxLustreFileSystemConfig': {
'FileSystemId': 'string',
'FileSystemPath': 'string'
},
'S3FileSystemConfig': {
'MountPath': 'string',
'S3Uri': 'string'
}
},
],
'StudioWebPortalSettings': {
'HiddenMlTools': [
'DataWrangler'|'FeatureStore'|'EmrClusters'|'AutoMl'|'Experiments'|'Training'|'ModelEvaluation'|'Pipelines'|'Models'|'JumpStart'|'InferenceRecommender'|'Endpoints'|'Projects'|'InferenceOptimization'|'PerformanceEvaluation'|'LakeraGuard'|'Comet'|'DeepchecksLLMEvaluation'|'Fiddler'|'HyperPodClusters'|'RunningInstances'|'Datasets'|'Evaluators',
],
'HiddenAppTypes': [
'JupyterServer'|'KernelGateway'|'DetailedProfiler'|'TensorBoard'|'CodeEditor'|'JupyterLab'|'RStudioServerPro'|'RSessionGateway'|'Canvas',
],
'HiddenInstanceTypes': [
'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.g6.xlarge'|'ml.g6.2xlarge'|'ml.g6.4xlarge'|'ml.g6.8xlarge'|'ml.g6.12xlarge'|'ml.g6.16xlarge'|'ml.g6.24xlarge'|'ml.g6.48xlarge'|'ml.g6e.xlarge'|'ml.g6e.2xlarge'|'ml.g6e.4xlarge'|'ml.g6e.8xlarge'|'ml.g6e.12xlarge'|'ml.g6e.16xlarge'|'ml.g6e.24xlarge'|'ml.g6e.48xlarge'|'ml.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.p5.48xlarge'|'ml.p5en.48xlarge'|'ml.p6-b200.48xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.r6i.large'|'ml.r6i.xlarge'|'ml.r6i.2xlarge'|'ml.r6i.4xlarge'|'ml.r6i.8xlarge'|'ml.r6i.12xlarge'|'ml.r6i.16xlarge'|'ml.r6i.24xlarge'|'ml.r6i.32xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge'|'ml.m6id.large'|'ml.m6id.xlarge'|'ml.m6id.2xlarge'|'ml.m6id.4xlarge'|'ml.m6id.8xlarge'|'ml.m6id.12xlarge'|'ml.m6id.16xlarge'|'ml.m6id.24xlarge'|'ml.m6id.32xlarge'|'ml.c6id.large'|'ml.c6id.xlarge'|'ml.c6id.2xlarge'|'ml.c6id.4xlarge'|'ml.c6id.8xlarge'|'ml.c6id.12xlarge'|'ml.c6id.16xlarge'|'ml.c6id.24xlarge'|'ml.c6id.32xlarge'|'ml.r6id.large'|'ml.r6id.xlarge'|'ml.r6id.2xlarge'|'ml.r6id.4xlarge'|'ml.r6id.8xlarge'|'ml.r6id.12xlarge'|'ml.r6id.16xlarge'|'ml.r6id.24xlarge'|'ml.r6id.32xlarge'|'ml.p5.4xlarge',
],
'HiddenSageMakerImageVersionAliases': [
{
'SageMakerImageName': 'sagemaker_distribution',
'VersionAliases': [
'string',
]
},
],
'ExecutionRoleSessionNameMode': 'STATIC'|'USER_IDENTITY'
},
'AutoMountHomeEFS': 'Enabled'|'Disabled'|'DefaultAsDomain'
},
DomainSettingsForUpdate={
'RStudioServerProDomainSettingsForUpdate': {
'DomainExecutionRoleArn': 'string',
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': 'string',
'SageMakerImageVersionAlias': 'string',
'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.g6.xlarge'|'ml.g6.2xlarge'|'ml.g6.4xlarge'|'ml.g6.8xlarge'|'ml.g6.12xlarge'|'ml.g6.16xlarge'|'ml.g6.24xlarge'|'ml.g6.48xlarge'|'ml.g6e.xlarge'|'ml.g6e.2xlarge'|'ml.g6e.4xlarge'|'ml.g6e.8xlarge'|'ml.g6e.12xlarge'|'ml.g6e.16xlarge'|'ml.g6e.24xlarge'|'ml.g6e.48xlarge'|'ml.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.p5.48xlarge'|'ml.p5en.48xlarge'|'ml.p6-b200.48xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.r6i.large'|'ml.r6i.xlarge'|'ml.r6i.2xlarge'|'ml.r6i.4xlarge'|'ml.r6i.8xlarge'|'ml.r6i.12xlarge'|'ml.r6i.16xlarge'|'ml.r6i.24xlarge'|'ml.r6i.32xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge'|'ml.m6id.large'|'ml.m6id.xlarge'|'ml.m6id.2xlarge'|'ml.m6id.4xlarge'|'ml.m6id.8xlarge'|'ml.m6id.12xlarge'|'ml.m6id.16xlarge'|'ml.m6id.24xlarge'|'ml.m6id.32xlarge'|'ml.c6id.large'|'ml.c6id.xlarge'|'ml.c6id.2xlarge'|'ml.c6id.4xlarge'|'ml.c6id.8xlarge'|'ml.c6id.12xlarge'|'ml.c6id.16xlarge'|'ml.c6id.24xlarge'|'ml.c6id.32xlarge'|'ml.r6id.large'|'ml.r6id.xlarge'|'ml.r6id.2xlarge'|'ml.r6id.4xlarge'|'ml.r6id.8xlarge'|'ml.r6id.12xlarge'|'ml.r6id.16xlarge'|'ml.r6id.24xlarge'|'ml.r6id.32xlarge'|'ml.p5.4xlarge',
'LifecycleConfigArn': 'string',
'TrainingPlanArn': 'string'
},
'RStudioConnectUrl': 'string',
'RStudioPackageManagerUrl': 'string'
},
'ExecutionRoleIdentityConfig': 'USER_PROFILE_NAME'|'DISABLED',
'SecurityGroupIds': [
'string',
],
'TrustedIdentityPropagationSettings': {
'Status': 'ENABLED'|'DISABLED'
},
'DockerSettings': {
'EnableDockerAccess': 'ENABLED'|'DISABLED',
'VpcOnlyTrustedAccounts': [
'string',
],
'RootlessDocker': 'ENABLED'|'DISABLED'
},
'AmazonQSettings': {
'Status': 'ENABLED'|'DISABLED',
'QProfileArn': 'string'
},
'UnifiedStudioSettings': {
'StudioWebPortalAccess': 'ENABLED'|'DISABLED',
'DomainAccountId': 'string',
'DomainRegion': 'string',
'DomainId': 'string',
'ProjectId': 'string',
'EnvironmentId': 'string',
'ProjectS3Path': 'string',
'SingleSignOnApplicationArn': 'string'
},
'IpAddressType': 'ipv4'|'dualstack'
},
AppSecurityGroupManagement='Service'|'Customer',
DefaultSpaceSettings={
'ExecutionRole': 'string',
'SecurityGroups': [
'string',
],
'JupyterServerAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': 'string',
'SageMakerImageVersionAlias': 'string',
'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.g6.xlarge'|'ml.g6.2xlarge'|'ml.g6.4xlarge'|'ml.g6.8xlarge'|'ml.g6.12xlarge'|'ml.g6.16xlarge'|'ml.g6.24xlarge'|'ml.g6.48xlarge'|'ml.g6e.xlarge'|'ml.g6e.2xlarge'|'ml.g6e.4xlarge'|'ml.g6e.8xlarge'|'ml.g6e.12xlarge'|'ml.g6e.16xlarge'|'ml.g6e.24xlarge'|'ml.g6e.48xlarge'|'ml.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.p5.48xlarge'|'ml.p5en.48xlarge'|'ml.p6-b200.48xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.r6i.large'|'ml.r6i.xlarge'|'ml.r6i.2xlarge'|'ml.r6i.4xlarge'|'ml.r6i.8xlarge'|'ml.r6i.12xlarge'|'ml.r6i.16xlarge'|'ml.r6i.24xlarge'|'ml.r6i.32xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge'|'ml.m6id.large'|'ml.m6id.xlarge'|'ml.m6id.2xlarge'|'ml.m6id.4xlarge'|'ml.m6id.8xlarge'|'ml.m6id.12xlarge'|'ml.m6id.16xlarge'|'ml.m6id.24xlarge'|'ml.m6id.32xlarge'|'ml.c6id.large'|'ml.c6id.xlarge'|'ml.c6id.2xlarge'|'ml.c6id.4xlarge'|'ml.c6id.8xlarge'|'ml.c6id.12xlarge'|'ml.c6id.16xlarge'|'ml.c6id.24xlarge'|'ml.c6id.32xlarge'|'ml.r6id.large'|'ml.r6id.xlarge'|'ml.r6id.2xlarge'|'ml.r6id.4xlarge'|'ml.r6id.8xlarge'|'ml.r6id.12xlarge'|'ml.r6id.16xlarge'|'ml.r6id.24xlarge'|'ml.r6id.32xlarge'|'ml.p5.4xlarge',
'LifecycleConfigArn': 'string',
'TrainingPlanArn': 'string'
},
'LifecycleConfigArns': [
'string',
],
'CodeRepositories': [
{
'RepositoryUrl': 'string'
},
]
},
'KernelGatewayAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': 'string',
'SageMakerImageVersionAlias': 'string',
'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.g6.xlarge'|'ml.g6.2xlarge'|'ml.g6.4xlarge'|'ml.g6.8xlarge'|'ml.g6.12xlarge'|'ml.g6.16xlarge'|'ml.g6.24xlarge'|'ml.g6.48xlarge'|'ml.g6e.xlarge'|'ml.g6e.2xlarge'|'ml.g6e.4xlarge'|'ml.g6e.8xlarge'|'ml.g6e.12xlarge'|'ml.g6e.16xlarge'|'ml.g6e.24xlarge'|'ml.g6e.48xlarge'|'ml.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.p5.48xlarge'|'ml.p5en.48xlarge'|'ml.p6-b200.48xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.r6i.large'|'ml.r6i.xlarge'|'ml.r6i.2xlarge'|'ml.r6i.4xlarge'|'ml.r6i.8xlarge'|'ml.r6i.12xlarge'|'ml.r6i.16xlarge'|'ml.r6i.24xlarge'|'ml.r6i.32xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge'|'ml.m6id.large'|'ml.m6id.xlarge'|'ml.m6id.2xlarge'|'ml.m6id.4xlarge'|'ml.m6id.8xlarge'|'ml.m6id.12xlarge'|'ml.m6id.16xlarge'|'ml.m6id.24xlarge'|'ml.m6id.32xlarge'|'ml.c6id.large'|'ml.c6id.xlarge'|'ml.c6id.2xlarge'|'ml.c6id.4xlarge'|'ml.c6id.8xlarge'|'ml.c6id.12xlarge'|'ml.c6id.16xlarge'|'ml.c6id.24xlarge'|'ml.c6id.32xlarge'|'ml.r6id.large'|'ml.r6id.xlarge'|'ml.r6id.2xlarge'|'ml.r6id.4xlarge'|'ml.r6id.8xlarge'|'ml.r6id.12xlarge'|'ml.r6id.16xlarge'|'ml.r6id.24xlarge'|'ml.r6id.32xlarge'|'ml.p5.4xlarge',
'LifecycleConfigArn': 'string',
'TrainingPlanArn': 'string'
},
'CustomImages': [
{
'ImageName': 'string',
'ImageVersionNumber': 123,
'AppImageConfigName': 'string'
},
],
'LifecycleConfigArns': [
'string',
]
},
'JupyterLabAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': 'string',
'SageMakerImageVersionAlias': 'string',
'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.g6.xlarge'|'ml.g6.2xlarge'|'ml.g6.4xlarge'|'ml.g6.8xlarge'|'ml.g6.12xlarge'|'ml.g6.16xlarge'|'ml.g6.24xlarge'|'ml.g6.48xlarge'|'ml.g6e.xlarge'|'ml.g6e.2xlarge'|'ml.g6e.4xlarge'|'ml.g6e.8xlarge'|'ml.g6e.12xlarge'|'ml.g6e.16xlarge'|'ml.g6e.24xlarge'|'ml.g6e.48xlarge'|'ml.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.p5.48xlarge'|'ml.p5en.48xlarge'|'ml.p6-b200.48xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.r6i.large'|'ml.r6i.xlarge'|'ml.r6i.2xlarge'|'ml.r6i.4xlarge'|'ml.r6i.8xlarge'|'ml.r6i.12xlarge'|'ml.r6i.16xlarge'|'ml.r6i.24xlarge'|'ml.r6i.32xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge'|'ml.m6id.large'|'ml.m6id.xlarge'|'ml.m6id.2xlarge'|'ml.m6id.4xlarge'|'ml.m6id.8xlarge'|'ml.m6id.12xlarge'|'ml.m6id.16xlarge'|'ml.m6id.24xlarge'|'ml.m6id.32xlarge'|'ml.c6id.large'|'ml.c6id.xlarge'|'ml.c6id.2xlarge'|'ml.c6id.4xlarge'|'ml.c6id.8xlarge'|'ml.c6id.12xlarge'|'ml.c6id.16xlarge'|'ml.c6id.24xlarge'|'ml.c6id.32xlarge'|'ml.r6id.large'|'ml.r6id.xlarge'|'ml.r6id.2xlarge'|'ml.r6id.4xlarge'|'ml.r6id.8xlarge'|'ml.r6id.12xlarge'|'ml.r6id.16xlarge'|'ml.r6id.24xlarge'|'ml.r6id.32xlarge'|'ml.p5.4xlarge',
'LifecycleConfigArn': 'string',
'TrainingPlanArn': 'string'
},
'CustomImages': [
{
'ImageName': 'string',
'ImageVersionNumber': 123,
'AppImageConfigName': 'string'
},
],
'LifecycleConfigArns': [
'string',
],
'CodeRepositories': [
{
'RepositoryUrl': 'string'
},
],
'AppLifecycleManagement': {
'IdleSettings': {
'LifecycleManagement': 'ENABLED'|'DISABLED',
'IdleTimeoutInMinutes': 123,
'MinIdleTimeoutInMinutes': 123,
'MaxIdleTimeoutInMinutes': 123
}
},
'EmrSettings': {
'AssumableRoleArns': [
'string',
],
'ExecutionRoleArns': [
'string',
]
},
'BuiltInLifecycleConfigArn': 'string'
},
'SpaceStorageSettings': {
'DefaultEbsStorageSettings': {
'DefaultEbsVolumeSizeInGb': 123,
'MaximumEbsVolumeSizeInGb': 123
}
},
'CustomPosixUserConfig': {
'Uid': 123,
'Gid': 123
},
'CustomFileSystemConfigs': [
{
'EFSFileSystemConfig': {
'FileSystemId': 'string',
'FileSystemPath': 'string'
},
'FSxLustreFileSystemConfig': {
'FileSystemId': 'string',
'FileSystemPath': 'string'
},
'S3FileSystemConfig': {
'MountPath': 'string',
'S3Uri': 'string'
}
},
]
},
SubnetIds=[
'string',
],
AppNetworkAccessType='PublicInternetOnly'|'VpcOnly',
TagPropagation='ENABLED'|'DISABLED',
VpcId='string'
)
string
[REQUIRED]
The ID of the domain to be updated.
dict
A collection of settings.
ExecutionRole (string) --
The execution role for the user.
SageMaker applies this setting only to private spaces that the user creates in the domain. SageMaker doesn't apply this setting to shared spaces.
SecurityGroups (list) --
The security groups for the Amazon Virtual Private Cloud (VPC) that the domain uses for communication.
Optional when the CreateDomain.AppNetworkAccessType parameter is set to PublicInternetOnly.
Required when the CreateDomain.AppNetworkAccessType parameter is set to VpcOnly, unless specified as part of the DefaultUserSettings for the domain.
Amazon SageMaker AI adds a security group to allow NFS traffic from Amazon SageMaker AI Studio. Therefore, the number of security groups that you can specify is one less than the maximum number shown.
SageMaker applies these settings only to private spaces that the user creates in the domain. SageMaker doesn't apply these settings to shared spaces.
(string) --
SharingSettings (dict) --
Specifies options for sharing Amazon SageMaker AI Studio notebooks.
NotebookOutputOption (string) --
Whether to include the notebook cell output when sharing the notebook. The default is Disabled.
S3OutputPath (string) --
When NotebookOutputOption is Allowed, the Amazon S3 bucket used to store the shared notebook snapshots.
S3KmsKeyId (string) --
When NotebookOutputOption is Allowed, the Amazon Web Services Key Management Service (KMS) encryption key ID used to encrypt the notebook cell output in the Amazon S3 bucket.
JupyterServerAppSettings (dict) --
The Jupyter server's app settings.
DefaultResourceSpec (dict) --
The default instance type and the Amazon Resource Name (ARN) of the default SageMaker AI image used by the JupyterServer app. If you use the LifecycleConfigArns parameter, then this parameter is also required.
SageMakerImageArn (string) --
The ARN of the SageMaker AI image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance. To clear the value set for SageMakerImageVersionArn, pass None as the value.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
TrainingPlanArn (string) --
The ARN of the SageMaker AI Training Plan to use for this app. When you specify a training plan, the app launches on reserved GPU capacity. This field is supported for JupyterLab and CodeEditor app types.
For more information about how to reserve GPU capacity with SageMaker AI Training Plans, see Using training plans in Studio applications.
LifecycleConfigArns (list) --
The Amazon Resource Name (ARN) of the Lifecycle Configurations attached to the JupyterServerApp. If you use this parameter, the DefaultResourceSpec parameter is also required.
(string) --
CodeRepositories (list) --
A list of Git repositories that SageMaker AI automatically displays to users for cloning in the JupyterServer application.
(dict) --
A Git repository that SageMaker AI automatically displays to users for cloning in the JupyterServer application.
RepositoryUrl (string) -- [REQUIRED]
The URL of the Git repository.
KernelGatewayAppSettings (dict) --
The kernel gateway app settings.
DefaultResourceSpec (dict) --
The default instance type and the Amazon Resource Name (ARN) of the default SageMaker AI image used by the KernelGateway app.
SageMakerImageArn (string) --
The ARN of the SageMaker AI image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance. To clear the value set for SageMakerImageVersionArn, pass None as the value.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
TrainingPlanArn (string) --
The ARN of the SageMaker AI Training Plan to use for this app. When you specify a training plan, the app launches on reserved GPU capacity. This field is supported for JupyterLab and CodeEditor app types.
For more information about how to reserve GPU capacity with SageMaker AI Training Plans, see Using training plans in Studio applications.
CustomImages (list) --
A list of custom SageMaker AI images that are configured to run as a KernelGateway app.
The maximum number of custom images are as follows.
On a domain level: 200
On a space level: 5
On a user profile level: 5
(dict) --
A custom SageMaker AI image. For more information, see Bring your own SageMaker AI image.
ImageName (string) -- [REQUIRED]
The name of the CustomImage. Must be unique to your account.
ImageVersionNumber (integer) --
The version number of the CustomImage.
AppImageConfigName (string) -- [REQUIRED]
The name of the AppImageConfig.
LifecycleConfigArns (list) --
The Amazon Resource Name (ARN) of the Lifecycle Configurations attached to the the user profile or domain.
(string) --
TensorBoardAppSettings (dict) --
The TensorBoard app settings.
DefaultResourceSpec (dict) --
The default instance type and the Amazon Resource Name (ARN) of the SageMaker AI image created on the instance.
SageMakerImageArn (string) --
The ARN of the SageMaker AI image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance. To clear the value set for SageMakerImageVersionArn, pass None as the value.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
TrainingPlanArn (string) --
The ARN of the SageMaker AI Training Plan to use for this app. When you specify a training plan, the app launches on reserved GPU capacity. This field is supported for JupyterLab and CodeEditor app types.
For more information about how to reserve GPU capacity with SageMaker AI Training Plans, see Using training plans in Studio applications.
RStudioServerProAppSettings (dict) --
A collection of settings that configure user interaction with the RStudioServerPro app.
AccessStatus (string) --
Indicates whether the current user has access to the RStudioServerPro app.
UserGroup (string) --
The level of permissions that the user has within the RStudioServerPro app. This value defaults to User. The Admin value allows the user access to the RStudio Administrative Dashboard.
RSessionAppSettings (dict) --
A collection of settings that configure the RSessionGateway app.
DefaultResourceSpec (dict) --
Specifies the ARN's of a SageMaker AI image and SageMaker AI image version, and the instance type that the version runs on.
SageMakerImageArn (string) --
The ARN of the SageMaker AI image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance. To clear the value set for SageMakerImageVersionArn, pass None as the value.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
TrainingPlanArn (string) --
The ARN of the SageMaker AI Training Plan to use for this app. When you specify a training plan, the app launches on reserved GPU capacity. This field is supported for JupyterLab and CodeEditor app types.
For more information about how to reserve GPU capacity with SageMaker AI Training Plans, see Using training plans in Studio applications.
CustomImages (list) --
A list of custom SageMaker AI images that are configured to run as a RSession app.
(dict) --
A custom SageMaker AI image. For more information, see Bring your own SageMaker AI image.
ImageName (string) -- [REQUIRED]
The name of the CustomImage. Must be unique to your account.
ImageVersionNumber (integer) --
The version number of the CustomImage.
AppImageConfigName (string) -- [REQUIRED]
The name of the AppImageConfig.
CanvasAppSettings (dict) --
The Canvas app settings.
SageMaker applies these settings only to private spaces that SageMaker creates for the Canvas app.
TimeSeriesForecastingSettings (dict) --
Time series forecast settings for the SageMaker Canvas application.
Status (string) --
Describes whether time series forecasting is enabled or disabled in the Canvas application.
AmazonForecastRoleArn (string) --
The IAM role that Canvas passes to Amazon Forecast for time series forecasting. By default, Canvas uses the execution role specified in the UserProfile that launches the Canvas application. If an execution role is not specified in the UserProfile, Canvas uses the execution role specified in the Domain that owns the UserProfile. To allow time series forecasting, this IAM role should have the AmazonSageMakerCanvasForecastAccess policy attached and forecast.amazonaws.com added in the trust relationship as a service principal.
ModelRegisterSettings (dict) --
The model registry settings for the SageMaker Canvas application.
Status (string) --
Describes whether the integration to the model registry is enabled or disabled in the Canvas application.
CrossAccountModelRegisterRoleArn (string) --
The Amazon Resource Name (ARN) of the SageMaker model registry account. Required only to register model versions created by a different SageMaker Canvas Amazon Web Services account than the Amazon Web Services account in which SageMaker model registry is set up.
WorkspaceSettings (dict) --
The workspace settings for the SageMaker Canvas application.
S3ArtifactPath (string) --
The Amazon S3 bucket used to store artifacts generated by Canvas. Updating the Amazon S3 location impacts existing configuration settings, and Canvas users no longer have access to their artifacts. Canvas users must log out and log back in to apply the new location.
S3KmsKeyId (string) --
The Amazon Web Services Key Management Service (KMS) encryption key ID that is used to encrypt artifacts generated by Canvas in the Amazon S3 bucket.
IdentityProviderOAuthSettings (list) --
The settings for connecting to an external data source with OAuth.
(dict) --
The Amazon SageMaker Canvas application setting where you configure OAuth for connecting to an external data source, such as Snowflake.
DataSourceName (string) --
The name of the data source that you're connecting to. Canvas currently supports OAuth for Snowflake and Salesforce Data Cloud.
Status (string) --
Describes whether OAuth for a data source is enabled or disabled in the Canvas application.
SecretArn (string) --
The ARN of an Amazon Web Services Secrets Manager secret that stores the credentials from your identity provider, such as the client ID and secret, authorization URL, and token URL.
DirectDeploySettings (dict) --
The model deployment settings for the SageMaker Canvas application.
Status (string) --
Describes whether model deployment permissions are enabled or disabled in the Canvas application.
KendraSettings (dict) --
The settings for document querying.
Status (string) --
Describes whether the document querying feature is enabled or disabled in the Canvas application.
GenerativeAiSettings (dict) --
The generative AI settings for the SageMaker Canvas application.
AmazonBedrockRoleArn (string) --
The ARN of an Amazon Web Services IAM role that allows fine-tuning of large language models (LLMs) in Amazon Bedrock. The IAM role should have Amazon S3 read and write permissions, as well as a trust relationship that establishes bedrock.amazonaws.com as a service principal.
EmrServerlessSettings (dict) --
The settings for running Amazon EMR Serverless data processing jobs in SageMaker Canvas.
ExecutionRoleArn (string) --
The Amazon Resource Name (ARN) of the Amazon Web Services IAM role that is assumed for running Amazon EMR Serverless jobs in SageMaker Canvas. This role should have the necessary permissions to read and write data attached and a trust relationship with EMR Serverless.
Status (string) --
Describes whether Amazon EMR Serverless job capabilities are enabled or disabled in the SageMaker Canvas application.
CodeEditorAppSettings (dict) --
The Code Editor application settings.
SageMaker applies these settings only to private spaces that the user creates in the domain. SageMaker doesn't apply these settings to shared spaces.
DefaultResourceSpec (dict) --
Specifies the ARN's of a SageMaker AI image and SageMaker AI image version, and the instance type that the version runs on.
SageMakerImageArn (string) --
The ARN of the SageMaker AI image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance. To clear the value set for SageMakerImageVersionArn, pass None as the value.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
TrainingPlanArn (string) --
The ARN of the SageMaker AI Training Plan to use for this app. When you specify a training plan, the app launches on reserved GPU capacity. This field is supported for JupyterLab and CodeEditor app types.
For more information about how to reserve GPU capacity with SageMaker AI Training Plans, see Using training plans in Studio applications.
CustomImages (list) --
A list of custom SageMaker images that are configured to run as a Code Editor app.
(dict) --
A custom SageMaker AI image. For more information, see Bring your own SageMaker AI image.
ImageName (string) -- [REQUIRED]
The name of the CustomImage. Must be unique to your account.
ImageVersionNumber (integer) --
The version number of the CustomImage.
AppImageConfigName (string) -- [REQUIRED]
The name of the AppImageConfig.
LifecycleConfigArns (list) --
The Amazon Resource Name (ARN) of the Code Editor application lifecycle configuration.
(string) --
AppLifecycleManagement (dict) --
Settings that are used to configure and manage the lifecycle of CodeEditor applications.
IdleSettings (dict) --
Settings related to idle shutdown of Studio applications.
LifecycleManagement (string) --
Indicates whether idle shutdown is activated for the application type.
IdleTimeoutInMinutes (integer) --
The time that SageMaker waits after the application becomes idle before shutting it down.
MinIdleTimeoutInMinutes (integer) --
The minimum value in minutes that custom idle shutdown can be set to by the user.
MaxIdleTimeoutInMinutes (integer) --
The maximum value in minutes that custom idle shutdown can be set to by the user.
BuiltInLifecycleConfigArn (string) --
The lifecycle configuration that runs before the default lifecycle configuration. It can override changes made in the default lifecycle configuration.
JupyterLabAppSettings (dict) --
The settings for the JupyterLab application.
SageMaker applies these settings only to private spaces that the user creates in the domain. SageMaker doesn't apply these settings to shared spaces.
DefaultResourceSpec (dict) --
Specifies the ARN's of a SageMaker AI image and SageMaker AI image version, and the instance type that the version runs on.
SageMakerImageArn (string) --
The ARN of the SageMaker AI image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance. To clear the value set for SageMakerImageVersionArn, pass None as the value.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
TrainingPlanArn (string) --
The ARN of the SageMaker AI Training Plan to use for this app. When you specify a training plan, the app launches on reserved GPU capacity. This field is supported for JupyterLab and CodeEditor app types.
For more information about how to reserve GPU capacity with SageMaker AI Training Plans, see Using training plans in Studio applications.
CustomImages (list) --
A list of custom SageMaker images that are configured to run as a JupyterLab app.
(dict) --
A custom SageMaker AI image. For more information, see Bring your own SageMaker AI image.
ImageName (string) -- [REQUIRED]
The name of the CustomImage. Must be unique to your account.
ImageVersionNumber (integer) --
The version number of the CustomImage.
AppImageConfigName (string) -- [REQUIRED]
The name of the AppImageConfig.
LifecycleConfigArns (list) --
The Amazon Resource Name (ARN) of the lifecycle configurations attached to the user profile or domain. To remove a lifecycle config, you must set LifecycleConfigArns to an empty list.
(string) --
CodeRepositories (list) --
A list of Git repositories that SageMaker automatically displays to users for cloning in the JupyterLab application.
(dict) --
A Git repository that SageMaker AI automatically displays to users for cloning in the JupyterServer application.
RepositoryUrl (string) -- [REQUIRED]
The URL of the Git repository.
AppLifecycleManagement (dict) --
Indicates whether idle shutdown is activated for JupyterLab applications.
IdleSettings (dict) --
Settings related to idle shutdown of Studio applications.
LifecycleManagement (string) --
Indicates whether idle shutdown is activated for the application type.
IdleTimeoutInMinutes (integer) --
The time that SageMaker waits after the application becomes idle before shutting it down.
MinIdleTimeoutInMinutes (integer) --
The minimum value in minutes that custom idle shutdown can be set to by the user.
MaxIdleTimeoutInMinutes (integer) --
The maximum value in minutes that custom idle shutdown can be set to by the user.
EmrSettings (dict) --
The configuration parameters that specify the IAM roles assumed by the execution role of SageMaker (assumable roles) and the cluster instances or job execution environments (execution roles or runtime roles) to manage and access resources required for running Amazon EMR clusters or Amazon EMR Serverless applications.
AssumableRoleArns (list) --
An array of Amazon Resource Names (ARNs) of the IAM roles that the execution role of SageMaker can assume for performing operations or tasks related to Amazon EMR clusters or Amazon EMR Serverless applications. These roles define the permissions and access policies required when performing Amazon EMR-related operations, such as listing, connecting to, or terminating Amazon EMR clusters or Amazon EMR Serverless applications. They are typically used in cross-account access scenarios, where the Amazon EMR resources (clusters or serverless applications) are located in a different Amazon Web Services account than the SageMaker domain.
(string) --
ExecutionRoleArns (list) --
An array of Amazon Resource Names (ARNs) of the IAM roles used by the Amazon EMR cluster instances or job execution environments to access other Amazon Web Services services and resources needed during the runtime of your Amazon EMR or Amazon EMR Serverless workloads, such as Amazon S3 for data access, Amazon CloudWatch for logging, or other Amazon Web Services services based on the particular workload requirements.
(string) --
BuiltInLifecycleConfigArn (string) --
The lifecycle configuration that runs before the default lifecycle configuration. It can override changes made in the default lifecycle configuration.
SpaceStorageSettings (dict) --
The storage settings for a space.
SageMaker applies these settings only to private spaces that the user creates in the domain. SageMaker doesn't apply these settings to shared spaces.
DefaultEbsStorageSettings (dict) --
The default EBS storage settings for a space.
DefaultEbsVolumeSizeInGb (integer) -- [REQUIRED]
The default size of the EBS storage volume for a space.
MaximumEbsVolumeSizeInGb (integer) -- [REQUIRED]
The maximum size of the EBS storage volume for a space.
DefaultLandingUri (string) --
The default experience that the user is directed to when accessing the domain. The supported values are:
studio::: Indicates that Studio is the default experience. This value can only be passed if StudioWebPortal is set to ENABLED.
app:JupyterServer:: Indicates that Studio Classic is the default experience.
StudioWebPortal (string) --
Whether the user can access Studio. If this value is set to DISABLED, the user cannot access Studio, even if that is the default experience for the domain.
CustomPosixUserConfig (dict) --
Details about the POSIX identity that is used for file system operations.
SageMaker applies these settings only to private spaces that the user creates in the domain. SageMaker doesn't apply these settings to shared spaces.
Uid (integer) -- [REQUIRED]
The POSIX user ID.
Gid (integer) -- [REQUIRED]
The POSIX group ID.
CustomFileSystemConfigs (list) --
The settings for assigning a custom file system to a user profile. Permitted users can access this file system in Amazon SageMaker AI Studio.
SageMaker applies these settings only to private spaces that the user creates in the domain. SageMaker doesn't apply these settings to shared spaces.
(dict) --
The settings for assigning a custom file system to a user profile or space for an Amazon SageMaker AI Domain. Permitted users can access this file system in Amazon SageMaker AI Studio.
EFSFileSystemConfig (dict) --
The settings for a custom Amazon EFS file system.
FileSystemId (string) -- [REQUIRED]
The ID of your Amazon EFS file system.
FileSystemPath (string) --
The path to the file system directory that is accessible in Amazon SageMaker AI Studio. Permitted users can access only this directory and below.
FSxLustreFileSystemConfig (dict) --
The settings for a custom Amazon FSx for Lustre file system.
FileSystemId (string) -- [REQUIRED]
The globally unique, 17-digit, ID of the file system, assigned by Amazon FSx for Lustre.
FileSystemPath (string) --
The path to the file system directory that is accessible in Amazon SageMaker Studio. Permitted users can access only this directory and below.
S3FileSystemConfig (dict) --
Configuration settings for a custom Amazon S3 file system.
MountPath (string) --
The file system path where the Amazon S3 storage location will be mounted within the Amazon SageMaker Studio environment.
S3Uri (string) -- [REQUIRED]
The Amazon S3 URI of the S3 file system configuration.
StudioWebPortalSettings (dict) --
Studio settings. If these settings are applied on a user level, they take priority over the settings applied on a domain level.
HiddenMlTools (list) --
The machine learning tools that are hidden from the Studio left navigation pane.
(string) --
HiddenAppTypes (list) --
The Applications supported in Studio that are hidden from the Studio left navigation pane.
(string) --
HiddenInstanceTypes (list) --
The instance types you are hiding from the Studio user interface.
(string) --
HiddenSageMakerImageVersionAliases (list) --
The version aliases you are hiding from the Studio user interface.
(dict) --
The SageMaker images that are hidden from the Studio user interface. You must specify the SageMaker image name and version aliases.
SageMakerImageName (string) --
The SageMaker image name that you are hiding from the Studio user interface.
VersionAliases (list) --
The version aliases you are hiding from the Studio user interface.
(string) --
ExecutionRoleSessionNameMode (string) --
The execution role session name mode. If this value is set to USER_IDENTITY, the session name of the execution role corresponds to the user's identity. For IAM domains, the session name is the IAM session name used to generate the presigned URL. For IAM Identity Center domains, the session name is the username of the associated IAM Identity Center user. If this value is set to STATIC or is not set, the session name defaults to SageMaker.
AutoMountHomeEFS (string) --
Indicates whether auto-mounting of an EFS volume is supported for the user profile. The DefaultAsDomain value is only supported for user profiles. Do not use the DefaultAsDomain value when setting this parameter for a domain.
SageMaker applies this setting only to private spaces that the user creates in the domain. SageMaker doesn't apply this setting to shared spaces.
dict
A collection of DomainSettings configuration values to update.
RStudioServerProDomainSettingsForUpdate (dict) --
A collection of RStudioServerPro Domain-level app settings to update. A single RStudioServerPro application is created for a domain.
DomainExecutionRoleArn (string) -- [REQUIRED]
The execution role for the RStudioServerPro Domain-level app.
DefaultResourceSpec (dict) --
Specifies the ARN's of a SageMaker AI image and SageMaker AI image version, and the instance type that the version runs on.
SageMakerImageArn (string) --
The ARN of the SageMaker AI image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance. To clear the value set for SageMakerImageVersionArn, pass None as the value.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
TrainingPlanArn (string) --
The ARN of the SageMaker AI Training Plan to use for this app. When you specify a training plan, the app launches on reserved GPU capacity. This field is supported for JupyterLab and CodeEditor app types.
For more information about how to reserve GPU capacity with SageMaker AI Training Plans, see Using training plans in Studio applications.
RStudioConnectUrl (string) --
A URL pointing to an RStudio Connect server.
RStudioPackageManagerUrl (string) --
A URL pointing to an RStudio Package Manager server.
ExecutionRoleIdentityConfig (string) --
The configuration for attaching a SageMaker AI user profile name to the execution role as a sts:SourceIdentity key. This configuration can only be modified if there are no apps in the InService or Pending state.
SecurityGroupIds (list) --
The security groups for the Amazon Virtual Private Cloud that the Domain uses for communication between Domain-level apps and user apps.
(string) --
TrustedIdentityPropagationSettings (dict) --
The Trusted Identity Propagation (TIP) settings for the SageMaker domain. These settings determine how user identities from IAM Identity Center are propagated through the domain to TIP enabled Amazon Web Services services.
Status (string) -- [REQUIRED]
The status of Trusted Identity Propagation (TIP) at the SageMaker domain level.
When disabled, standard IAM role-based access is used.
When enabled:
User identities from IAM Identity Center are propagated through the application to TIP enabled Amazon Web Services services.
New applications or existing applications that are automatically patched, will use the domain level configuration.
DockerSettings (dict) --
A collection of settings that configure the domain's Docker interaction.
EnableDockerAccess (string) --
Indicates whether the domain can access Docker.
VpcOnlyTrustedAccounts (list) --
The list of Amazon Web Services accounts that are trusted when the domain is created in VPC-only mode.
(string) --
RootlessDocker (string) --
Indicates whether to use rootless Docker.
AmazonQSettings (dict) --
A collection of settings that configure the Amazon Q experience within the domain.
Status (string) --
Whether Amazon Q has been enabled within the domain.
QProfileArn (string) --
The ARN of the Amazon Q profile used within the domain.
UnifiedStudioSettings (dict) --
The settings that apply to an SageMaker AI domain when you use it in Amazon SageMaker Unified Studio.
StudioWebPortalAccess (string) --
Sets whether you can access the domain in Amazon SageMaker Studio:
ENABLED
You can access the domain in Amazon SageMaker Studio. If you migrate the domain to Amazon SageMaker Unified Studio, you can access it in both studio interfaces.
DISABLED
You can't access the domain in Amazon SageMaker Studio. If you migrate the domain to Amazon SageMaker Unified Studio, you can access it only in that studio interface.
To migrate a domain to Amazon SageMaker Unified Studio, you specify the UnifiedStudioSettings data type when you use the UpdateDomain action.
DomainAccountId (string) --
The ID of the Amazon Web Services account that has the Amazon SageMaker Unified Studio domain. The default value, if you don't specify an ID, is the ID of the account that has the Amazon SageMaker AI domain.
DomainRegion (string) --
The Amazon Web Services Region where the domain is located in Amazon SageMaker Unified Studio. The default value, if you don't specify a Region, is the Region where the Amazon SageMaker AI domain is located.
DomainId (string) --
The ID of the Amazon SageMaker Unified Studio domain associated with this domain.
ProjectId (string) --
The ID of the Amazon SageMaker Unified Studio project that corresponds to the domain.
EnvironmentId (string) --
The ID of the environment that Amazon SageMaker Unified Studio associates with the domain.
ProjectS3Path (string) --
The location where Amazon S3 stores temporary execution data and other artifacts for the project that corresponds to the domain.
SingleSignOnApplicationArn (string) --
The ARN of the Amazon DataZone application managed by Amazon SageMaker Unified Studio in the Amazon Web Services IAM Identity Center.
IpAddressType (string) --
The IP address type for the domain. Specify ipv4 for IPv4-only connectivity or dualstack for both IPv4 and IPv6 connectivity. When you specify dualstack, the subnet must support IPv6 CIDR blocks.
string
The entity that creates and manages the required security groups for inter-app communication in VPCOnly mode. Required when CreateDomain.AppNetworkAccessType is VPCOnly and DomainSettings.RStudioServerProDomainSettings.DomainExecutionRoleArn is provided. If setting up the domain for use with RStudio, this value must be set to Service.
dict
The default settings for shared spaces that users create in the domain.
ExecutionRole (string) --
The ARN of the execution role for the space.
SecurityGroups (list) --
The security group IDs for the Amazon VPC that the space uses for communication.
(string) --
JupyterServerAppSettings (dict) --
The JupyterServer app settings.
DefaultResourceSpec (dict) --
The default instance type and the Amazon Resource Name (ARN) of the default SageMaker AI image used by the JupyterServer app. If you use the LifecycleConfigArns parameter, then this parameter is also required.
SageMakerImageArn (string) --
The ARN of the SageMaker AI image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance. To clear the value set for SageMakerImageVersionArn, pass None as the value.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
TrainingPlanArn (string) --
The ARN of the SageMaker AI Training Plan to use for this app. When you specify a training plan, the app launches on reserved GPU capacity. This field is supported for JupyterLab and CodeEditor app types.
For more information about how to reserve GPU capacity with SageMaker AI Training Plans, see Using training plans in Studio applications.
LifecycleConfigArns (list) --
The Amazon Resource Name (ARN) of the Lifecycle Configurations attached to the JupyterServerApp. If you use this parameter, the DefaultResourceSpec parameter is also required.
(string) --
CodeRepositories (list) --
A list of Git repositories that SageMaker AI automatically displays to users for cloning in the JupyterServer application.
(dict) --
A Git repository that SageMaker AI automatically displays to users for cloning in the JupyterServer application.
RepositoryUrl (string) -- [REQUIRED]
The URL of the Git repository.
KernelGatewayAppSettings (dict) --
The KernelGateway app settings.
DefaultResourceSpec (dict) --
The default instance type and the Amazon Resource Name (ARN) of the default SageMaker AI image used by the KernelGateway app.
SageMakerImageArn (string) --
The ARN of the SageMaker AI image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance. To clear the value set for SageMakerImageVersionArn, pass None as the value.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
TrainingPlanArn (string) --
The ARN of the SageMaker AI Training Plan to use for this app. When you specify a training plan, the app launches on reserved GPU capacity. This field is supported for JupyterLab and CodeEditor app types.
For more information about how to reserve GPU capacity with SageMaker AI Training Plans, see Using training plans in Studio applications.
CustomImages (list) --
A list of custom SageMaker AI images that are configured to run as a KernelGateway app.
The maximum number of custom images are as follows.
On a domain level: 200
On a space level: 5
On a user profile level: 5
(dict) --
A custom SageMaker AI image. For more information, see Bring your own SageMaker AI image.
ImageName (string) -- [REQUIRED]
The name of the CustomImage. Must be unique to your account.
ImageVersionNumber (integer) --
The version number of the CustomImage.
AppImageConfigName (string) -- [REQUIRED]
The name of the AppImageConfig.
LifecycleConfigArns (list) --
The Amazon Resource Name (ARN) of the Lifecycle Configurations attached to the the user profile or domain.
(string) --
JupyterLabAppSettings (dict) --
The settings for the JupyterLab application.
DefaultResourceSpec (dict) --
Specifies the ARN's of a SageMaker AI image and SageMaker AI image version, and the instance type that the version runs on.
SageMakerImageArn (string) --
The ARN of the SageMaker AI image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance. To clear the value set for SageMakerImageVersionArn, pass None as the value.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
TrainingPlanArn (string) --
The ARN of the SageMaker AI Training Plan to use for this app. When you specify a training plan, the app launches on reserved GPU capacity. This field is supported for JupyterLab and CodeEditor app types.
For more information about how to reserve GPU capacity with SageMaker AI Training Plans, see Using training plans in Studio applications.
CustomImages (list) --
A list of custom SageMaker images that are configured to run as a JupyterLab app.
(dict) --
A custom SageMaker AI image. For more information, see Bring your own SageMaker AI image.
ImageName (string) -- [REQUIRED]
The name of the CustomImage. Must be unique to your account.
ImageVersionNumber (integer) --
The version number of the CustomImage.
AppImageConfigName (string) -- [REQUIRED]
The name of the AppImageConfig.
LifecycleConfigArns (list) --
The Amazon Resource Name (ARN) of the lifecycle configurations attached to the user profile or domain. To remove a lifecycle config, you must set LifecycleConfigArns to an empty list.
(string) --
CodeRepositories (list) --
A list of Git repositories that SageMaker automatically displays to users for cloning in the JupyterLab application.
(dict) --
A Git repository that SageMaker AI automatically displays to users for cloning in the JupyterServer application.
RepositoryUrl (string) -- [REQUIRED]
The URL of the Git repository.
AppLifecycleManagement (dict) --
Indicates whether idle shutdown is activated for JupyterLab applications.
IdleSettings (dict) --
Settings related to idle shutdown of Studio applications.
LifecycleManagement (string) --
Indicates whether idle shutdown is activated for the application type.
IdleTimeoutInMinutes (integer) --
The time that SageMaker waits after the application becomes idle before shutting it down.
MinIdleTimeoutInMinutes (integer) --
The minimum value in minutes that custom idle shutdown can be set to by the user.
MaxIdleTimeoutInMinutes (integer) --
The maximum value in minutes that custom idle shutdown can be set to by the user.
EmrSettings (dict) --
The configuration parameters that specify the IAM roles assumed by the execution role of SageMaker (assumable roles) and the cluster instances or job execution environments (execution roles or runtime roles) to manage and access resources required for running Amazon EMR clusters or Amazon EMR Serverless applications.
AssumableRoleArns (list) --
An array of Amazon Resource Names (ARNs) of the IAM roles that the execution role of SageMaker can assume for performing operations or tasks related to Amazon EMR clusters or Amazon EMR Serverless applications. These roles define the permissions and access policies required when performing Amazon EMR-related operations, such as listing, connecting to, or terminating Amazon EMR clusters or Amazon EMR Serverless applications. They are typically used in cross-account access scenarios, where the Amazon EMR resources (clusters or serverless applications) are located in a different Amazon Web Services account than the SageMaker domain.
(string) --
ExecutionRoleArns (list) --
An array of Amazon Resource Names (ARNs) of the IAM roles used by the Amazon EMR cluster instances or job execution environments to access other Amazon Web Services services and resources needed during the runtime of your Amazon EMR or Amazon EMR Serverless workloads, such as Amazon S3 for data access, Amazon CloudWatch for logging, or other Amazon Web Services services based on the particular workload requirements.
(string) --
BuiltInLifecycleConfigArn (string) --
The lifecycle configuration that runs before the default lifecycle configuration. It can override changes made in the default lifecycle configuration.
SpaceStorageSettings (dict) --
The default storage settings for a space.
DefaultEbsStorageSettings (dict) --
The default EBS storage settings for a space.
DefaultEbsVolumeSizeInGb (integer) -- [REQUIRED]
The default size of the EBS storage volume for a space.
MaximumEbsVolumeSizeInGb (integer) -- [REQUIRED]
The maximum size of the EBS storage volume for a space.
CustomPosixUserConfig (dict) --
Details about the POSIX identity that is used for file system operations.
Uid (integer) -- [REQUIRED]
The POSIX user ID.
Gid (integer) -- [REQUIRED]
The POSIX group ID.
CustomFileSystemConfigs (list) --
The settings for assigning a custom file system to a domain. Permitted users can access this file system in Amazon SageMaker AI Studio.
(dict) --
The settings for assigning a custom file system to a user profile or space for an Amazon SageMaker AI Domain. Permitted users can access this file system in Amazon SageMaker AI Studio.
EFSFileSystemConfig (dict) --
The settings for a custom Amazon EFS file system.
FileSystemId (string) -- [REQUIRED]
The ID of your Amazon EFS file system.
FileSystemPath (string) --
The path to the file system directory that is accessible in Amazon SageMaker AI Studio. Permitted users can access only this directory and below.
FSxLustreFileSystemConfig (dict) --
The settings for a custom Amazon FSx for Lustre file system.
FileSystemId (string) -- [REQUIRED]
The globally unique, 17-digit, ID of the file system, assigned by Amazon FSx for Lustre.
FileSystemPath (string) --
The path to the file system directory that is accessible in Amazon SageMaker Studio. Permitted users can access only this directory and below.
S3FileSystemConfig (dict) --
Configuration settings for a custom Amazon S3 file system.
MountPath (string) --
The file system path where the Amazon S3 storage location will be mounted within the Amazon SageMaker Studio environment.
S3Uri (string) -- [REQUIRED]
The Amazon S3 URI of the S3 file system configuration.
list
The VPC subnets that Studio uses for communication.
If removing subnets, ensure there are no apps in the InService, Pending, or Deleting state.
(string) --
string
Specifies the VPC used for non-EFS traffic.
PublicInternetOnly - Non-EFS traffic is through a VPC managed by Amazon SageMaker AI, which allows direct internet access.
VpcOnly - All Studio traffic is through the specified VPC and subnets.
This configuration can only be modified if there are no apps in the InService, Pending, or Deleting state. The configuration cannot be updated if DomainSettings.RStudioServerProDomainSettings.DomainExecutionRoleArn is already set or DomainSettings.RStudioServerProDomainSettings.DomainExecutionRoleArn is provided as part of the same request.
string
Indicates whether custom tag propagation is supported for the domain. Defaults to DISABLED.
string
The identifier for the VPC used by the domain for network communication. Use this field only when adding VPC configuration to a SageMaker AI domain used in Amazon SageMaker Unified Studio that was created without VPC settings. SageMaker AI doesn't automatically apply VPC updates to existing applications. Stop and restart your applications to apply the changes.
dict
Response Syntax
{
'DomainArn': 'string'
}
Response Structure
(dict) --
DomainArn (string) --
The Amazon Resource Name (ARN) of the domain.
{'ModelVariants': {'InfrastructureConfig': {'RealTimeInferenceConfig': {'InstanceType': {'ml.c4.large',
'ml.c5.large',
'ml.c5d.large',
'ml.c6g.12xlarge',
'ml.c6g.16xlarge',
'ml.c6g.2xlarge',
'ml.c6g.4xlarge',
'ml.c6g.8xlarge',
'ml.c6g.large',
'ml.c6g.xlarge',
'ml.c6gd.12xlarge',
'ml.c6gd.16xlarge',
'ml.c6gd.2xlarge',
'ml.c6gd.4xlarge',
'ml.c6gd.8xlarge',
'ml.c6gd.large',
'ml.c6gd.xlarge',
'ml.c6gn.12xlarge',
'ml.c6gn.16xlarge',
'ml.c6gn.2xlarge',
'ml.c6gn.4xlarge',
'ml.c6gn.8xlarge',
'ml.c6gn.large',
'ml.c6gn.xlarge',
'ml.c6in.12xlarge',
'ml.c6in.16xlarge',
'ml.c6in.24xlarge',
'ml.c6in.2xlarge',
'ml.c6in.32xlarge',
'ml.c6in.4xlarge',
'ml.c6in.8xlarge',
'ml.c6in.large',
'ml.c6in.xlarge',
'ml.c7g.12xlarge',
'ml.c7g.16xlarge',
'ml.c7g.2xlarge',
'ml.c7g.4xlarge',
'ml.c7g.8xlarge',
'ml.c7g.large',
'ml.c7g.xlarge',
'ml.c8g.12xlarge',
'ml.c8g.16xlarge',
'ml.c8g.24xlarge',
'ml.c8g.2xlarge',
'ml.c8g.48xlarge',
'ml.c8g.4xlarge',
'ml.c8g.8xlarge',
'ml.c8g.large',
'ml.c8g.medium',
'ml.c8g.xlarge',
'ml.dl1.24xlarge',
'ml.g6e.12xlarge',
'ml.g6e.16xlarge',
'ml.g6e.24xlarge',
'ml.g6e.2xlarge',
'ml.g6e.48xlarge',
'ml.g6e.4xlarge',
'ml.g6e.8xlarge',
'ml.g6e.xlarge',
'ml.g7e.12xlarge',
'ml.g7e.24xlarge',
'ml.g7e.2xlarge',
'ml.g7e.48xlarge',
'ml.g7e.4xlarge',
'ml.g7e.8xlarge',
'ml.m5.large',
'ml.m6g.12xlarge',
'ml.m6g.16xlarge',
'ml.m6g.2xlarge',
'ml.m6g.4xlarge',
'ml.m6g.8xlarge',
'ml.m6g.large',
'ml.m6g.xlarge',
'ml.m6gd.12xlarge',
'ml.m6gd.16xlarge',
'ml.m6gd.2xlarge',
'ml.m6gd.4xlarge',
'ml.m6gd.8xlarge',
'ml.m6gd.large',
'ml.m6gd.xlarge',
'ml.m8g.12xlarge',
'ml.m8g.16xlarge',
'ml.m8g.24xlarge',
'ml.m8g.2xlarge',
'ml.m8g.48xlarge',
'ml.m8g.4xlarge',
'ml.m8g.8xlarge',
'ml.m8g.large',
'ml.m8g.medium',
'ml.m8g.xlarge',
'ml.p5.4xlarge',
'ml.p5e.48xlarge',
'ml.p5en.48xlarge',
'ml.p6-b300.48xlarge',
'ml.p6e-gb200.36xlarge',
'ml.r5d.12xlarge',
'ml.r5d.24xlarge',
'ml.r5d.2xlarge',
'ml.r5d.4xlarge',
'ml.r5d.large',
'ml.r5d.xlarge',
'ml.r6g.12xlarge',
'ml.r6g.16xlarge',
'ml.r6g.2xlarge',
'ml.r6g.4xlarge',
'ml.r6g.8xlarge',
'ml.r6g.large',
'ml.r6g.xlarge',
'ml.r6gd.12xlarge',
'ml.r6gd.16xlarge',
'ml.r6gd.2xlarge',
'ml.r6gd.4xlarge',
'ml.r6gd.8xlarge',
'ml.r6gd.large',
'ml.r6gd.xlarge',
'ml.r7gd.12xlarge',
'ml.r7gd.16xlarge',
'ml.r7gd.2xlarge',
'ml.r7gd.4xlarge',
'ml.r7gd.8xlarge',
'ml.r7gd.large',
'ml.r7gd.medium',
'ml.r7gd.xlarge',
'ml.r8g.12xlarge',
'ml.r8g.16xlarge',
'ml.r8g.24xlarge',
'ml.r8g.2xlarge',
'ml.r8g.48xlarge',
'ml.r8g.4xlarge',
'ml.r8g.8xlarge',
'ml.r8g.large',
'ml.r8g.medium',
'ml.r8g.xlarge',
'ml.trn2.48xlarge'}}}}}
Updates an inference experiment that you created. The status of the inference experiment has to be either Created, Running. For more information on the status of an inference experiment, see DescribeInferenceExperiment.
See also: AWS API Documentation
Request Syntax
client.update_inference_experiment(
Name='string',
Schedule={
'StartTime': datetime(2015, 1, 1),
'EndTime': datetime(2015, 1, 1)
},
Description='string',
ModelVariants=[
{
'ModelName': 'string',
'VariantName': 'string',
'InfrastructureConfig': {
'InfrastructureType': 'RealTimeInference',
'RealTimeInferenceConfig': {
'InstanceType': 'ml.t2.medium'|'ml.t2.large'|'ml.t2.xlarge'|'ml.t2.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.12xlarge'|'ml.m5d.24xlarge'|'ml.c4.large'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5d.large'|'ml.c5d.xlarge'|'ml.c5d.2xlarge'|'ml.c5d.4xlarge'|'ml.c5d.9xlarge'|'ml.c5d.18xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.12xlarge'|'ml.r5.24xlarge'|'ml.r5d.large'|'ml.r5d.xlarge'|'ml.r5d.2xlarge'|'ml.r5d.4xlarge'|'ml.r5d.12xlarge'|'ml.r5d.24xlarge'|'ml.inf1.xlarge'|'ml.inf1.2xlarge'|'ml.inf1.6xlarge'|'ml.inf1.24xlarge'|'ml.dl1.24xlarge'|'ml.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.r6i.large'|'ml.r6i.xlarge'|'ml.r6i.2xlarge'|'ml.r6i.4xlarge'|'ml.r6i.8xlarge'|'ml.r6i.12xlarge'|'ml.r6i.16xlarge'|'ml.r6i.24xlarge'|'ml.r6i.32xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.12xlarge'|'ml.g5.16xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.g6.xlarge'|'ml.g6.2xlarge'|'ml.g6.4xlarge'|'ml.g6.8xlarge'|'ml.g6.12xlarge'|'ml.g6.16xlarge'|'ml.g6.24xlarge'|'ml.g6.48xlarge'|'ml.r8g.medium'|'ml.r8g.large'|'ml.r8g.xlarge'|'ml.r8g.2xlarge'|'ml.r8g.4xlarge'|'ml.r8g.8xlarge'|'ml.r8g.12xlarge'|'ml.r8g.16xlarge'|'ml.r8g.24xlarge'|'ml.r8g.48xlarge'|'ml.g6e.xlarge'|'ml.g6e.2xlarge'|'ml.g6e.4xlarge'|'ml.g6e.8xlarge'|'ml.g6e.12xlarge'|'ml.g6e.16xlarge'|'ml.g6e.24xlarge'|'ml.g6e.48xlarge'|'ml.g7e.2xlarge'|'ml.g7e.4xlarge'|'ml.g7e.8xlarge'|'ml.g7e.12xlarge'|'ml.g7e.24xlarge'|'ml.g7e.48xlarge'|'ml.p4d.24xlarge'|'ml.c7g.large'|'ml.c7g.xlarge'|'ml.c7g.2xlarge'|'ml.c7g.4xlarge'|'ml.c7g.8xlarge'|'ml.c7g.12xlarge'|'ml.c7g.16xlarge'|'ml.m6g.large'|'ml.m6g.xlarge'|'ml.m6g.2xlarge'|'ml.m6g.4xlarge'|'ml.m6g.8xlarge'|'ml.m6g.12xlarge'|'ml.m6g.16xlarge'|'ml.m6gd.large'|'ml.m6gd.xlarge'|'ml.m6gd.2xlarge'|'ml.m6gd.4xlarge'|'ml.m6gd.8xlarge'|'ml.m6gd.12xlarge'|'ml.m6gd.16xlarge'|'ml.c6g.large'|'ml.c6g.xlarge'|'ml.c6g.2xlarge'|'ml.c6g.4xlarge'|'ml.c6g.8xlarge'|'ml.c6g.12xlarge'|'ml.c6g.16xlarge'|'ml.c6gd.large'|'ml.c6gd.xlarge'|'ml.c6gd.2xlarge'|'ml.c6gd.4xlarge'|'ml.c6gd.8xlarge'|'ml.c6gd.12xlarge'|'ml.c6gd.16xlarge'|'ml.c6gn.large'|'ml.c6gn.xlarge'|'ml.c6gn.2xlarge'|'ml.c6gn.4xlarge'|'ml.c6gn.8xlarge'|'ml.c6gn.12xlarge'|'ml.c6gn.16xlarge'|'ml.r6g.large'|'ml.r6g.xlarge'|'ml.r6g.2xlarge'|'ml.r6g.4xlarge'|'ml.r6g.8xlarge'|'ml.r6g.12xlarge'|'ml.r6g.16xlarge'|'ml.r6gd.large'|'ml.r6gd.xlarge'|'ml.r6gd.2xlarge'|'ml.r6gd.4xlarge'|'ml.r6gd.8xlarge'|'ml.r6gd.12xlarge'|'ml.r6gd.16xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.trn2.48xlarge'|'ml.inf2.xlarge'|'ml.inf2.8xlarge'|'ml.inf2.24xlarge'|'ml.inf2.48xlarge'|'ml.p5.48xlarge'|'ml.p5e.48xlarge'|'ml.p5en.48xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge'|'ml.c8g.medium'|'ml.c8g.large'|'ml.c8g.xlarge'|'ml.c8g.2xlarge'|'ml.c8g.4xlarge'|'ml.c8g.8xlarge'|'ml.c8g.12xlarge'|'ml.c8g.16xlarge'|'ml.c8g.24xlarge'|'ml.c8g.48xlarge'|'ml.r7gd.medium'|'ml.r7gd.large'|'ml.r7gd.xlarge'|'ml.r7gd.2xlarge'|'ml.r7gd.4xlarge'|'ml.r7gd.8xlarge'|'ml.r7gd.12xlarge'|'ml.r7gd.16xlarge'|'ml.m8g.medium'|'ml.m8g.large'|'ml.m8g.xlarge'|'ml.m8g.2xlarge'|'ml.m8g.4xlarge'|'ml.m8g.8xlarge'|'ml.m8g.12xlarge'|'ml.m8g.16xlarge'|'ml.m8g.24xlarge'|'ml.m8g.48xlarge'|'ml.c6in.large'|'ml.c6in.xlarge'|'ml.c6in.2xlarge'|'ml.c6in.4xlarge'|'ml.c6in.8xlarge'|'ml.c6in.12xlarge'|'ml.c6in.16xlarge'|'ml.c6in.24xlarge'|'ml.c6in.32xlarge'|'ml.p6-b200.48xlarge'|'ml.p6-b300.48xlarge'|'ml.p6e-gb200.36xlarge'|'ml.p5.4xlarge',
'InstanceCount': 123
}
}
},
],
DataStorageConfig={
'Destination': 'string',
'KmsKey': 'string',
'ContentType': {
'CsvContentTypes': [
'string',
],
'JsonContentTypes': [
'string',
]
}
},
ShadowModeConfig={
'SourceModelVariantName': 'string',
'ShadowModelVariants': [
{
'ShadowModelVariantName': 'string',
'SamplingPercentage': 123
},
]
}
)
string
[REQUIRED]
The name of the inference experiment to be updated.
dict
The duration for which the inference experiment will run. If the status of the inference experiment is Created, then you can update both the start and end dates. If the status of the inference experiment is Running, then you can update only the end date.
StartTime (datetime) --
The timestamp at which the inference experiment started or will start.
EndTime (datetime) --
The timestamp at which the inference experiment ended or will end.
string
The description of the inference experiment.
list
An array of ModelVariantConfig objects. There is one for each variant, whose infrastructure configuration you want to update.
(dict) --
Contains information about the deployment options of a model.
ModelName (string) -- [REQUIRED]
The name of the Amazon SageMaker Model entity.
VariantName (string) -- [REQUIRED]
The name of the variant.
InfrastructureConfig (dict) -- [REQUIRED]
The configuration for the infrastructure that the model will be deployed to.
InfrastructureType (string) -- [REQUIRED]
The inference option to which to deploy your model. Possible values are the following:
RealTime: Deploy to real-time inference.
RealTimeInferenceConfig (dict) -- [REQUIRED]
The infrastructure configuration for deploying the model to real-time inference.
InstanceType (string) -- [REQUIRED]
The instance type the model is deployed to.
InstanceCount (integer) -- [REQUIRED]
The number of instances of the type specified by InstanceType.
dict
The Amazon S3 location and configuration for storing inference request and response data.
Destination (string) -- [REQUIRED]
The Amazon S3 bucket where the inference request and response data is stored.
KmsKey (string) --
The Amazon Web Services Key Management Service key that Amazon SageMaker uses to encrypt captured data at rest using Amazon S3 server-side encryption.
ContentType (dict) --
Configuration specifying how to treat different headers. If no headers are specified Amazon SageMaker AI will by default base64 encode when capturing the data.
CsvContentTypes (list) --
The list of all content type headers that Amazon SageMaker AI will treat as CSV and capture accordingly.
(string) --
JsonContentTypes (list) --
The list of all content type headers that SageMaker AI will treat as JSON and capture accordingly.
(string) --
dict
The configuration of ShadowMode inference experiment type. Use this field to specify a production variant which takes all the inference requests, and a shadow variant to which Amazon SageMaker replicates a percentage of the inference requests. For the shadow variant also specify the percentage of requests that Amazon SageMaker replicates.
SourceModelVariantName (string) -- [REQUIRED]
The name of the production variant, which takes all the inference requests.
ShadowModelVariants (list) -- [REQUIRED]
List of shadow variant configurations.
(dict) --
The name and sampling percentage of a shadow variant.
ShadowModelVariantName (string) -- [REQUIRED]
The name of the shadow variant.
SamplingPercentage (integer) -- [REQUIRED]
The percentage of inference requests that Amazon SageMaker replicates from the production variant to the shadow variant.
dict
Response Syntax
{
'InferenceExperimentArn': 'string'
}
Response Structure
(dict) --
InferenceExperimentArn (string) --
The ARN of the updated inference experiment.
{'SpaceSettings': {'CodeEditorAppSettings': {'DefaultResourceSpec': {'TrainingPlanArn': 'string'}},
'JupyterLabAppSettings': {'DefaultResourceSpec': {'TrainingPlanArn': 'string'}},
'JupyterServerAppSettings': {'DefaultResourceSpec': {'TrainingPlanArn': 'string'}},
'KernelGatewayAppSettings': {'DefaultResourceSpec': {'TrainingPlanArn': 'string'}}}}
Updates the settings of a space.
See also: AWS API Documentation
Request Syntax
client.update_space(
DomainId='string',
SpaceName='string',
SpaceSettings={
'JupyterServerAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': 'string',
'SageMakerImageVersionAlias': 'string',
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},
],
'RemoteAccess': 'ENABLED'|'DISABLED'
},
SpaceDisplayName='string'
)
string
[REQUIRED]
The ID of the associated domain.
string
[REQUIRED]
The name of the space.
dict
A collection of space settings.
JupyterServerAppSettings (dict) --
The JupyterServer app settings.
DefaultResourceSpec (dict) --
The default instance type and the Amazon Resource Name (ARN) of the default SageMaker AI image used by the JupyterServer app. If you use the LifecycleConfigArns parameter, then this parameter is also required.
SageMakerImageArn (string) --
The ARN of the SageMaker AI image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance. To clear the value set for SageMakerImageVersionArn, pass None as the value.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
TrainingPlanArn (string) --
The ARN of the SageMaker AI Training Plan to use for this app. When you specify a training plan, the app launches on reserved GPU capacity. This field is supported for JupyterLab and CodeEditor app types.
For more information about how to reserve GPU capacity with SageMaker AI Training Plans, see Using training plans in Studio applications.
LifecycleConfigArns (list) --
The Amazon Resource Name (ARN) of the Lifecycle Configurations attached to the JupyterServerApp. If you use this parameter, the DefaultResourceSpec parameter is also required.
(string) --
CodeRepositories (list) --
A list of Git repositories that SageMaker AI automatically displays to users for cloning in the JupyterServer application.
(dict) --
A Git repository that SageMaker AI automatically displays to users for cloning in the JupyterServer application.
RepositoryUrl (string) -- [REQUIRED]
The URL of the Git repository.
KernelGatewayAppSettings (dict) --
The KernelGateway app settings.
DefaultResourceSpec (dict) --
The default instance type and the Amazon Resource Name (ARN) of the default SageMaker AI image used by the KernelGateway app.
SageMakerImageArn (string) --
The ARN of the SageMaker AI image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance. To clear the value set for SageMakerImageVersionArn, pass None as the value.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
TrainingPlanArn (string) --
The ARN of the SageMaker AI Training Plan to use for this app. When you specify a training plan, the app launches on reserved GPU capacity. This field is supported for JupyterLab and CodeEditor app types.
For more information about how to reserve GPU capacity with SageMaker AI Training Plans, see Using training plans in Studio applications.
CustomImages (list) --
A list of custom SageMaker AI images that are configured to run as a KernelGateway app.
The maximum number of custom images are as follows.
On a domain level: 200
On a space level: 5
On a user profile level: 5
(dict) --
A custom SageMaker AI image. For more information, see Bring your own SageMaker AI image.
ImageName (string) -- [REQUIRED]
The name of the CustomImage. Must be unique to your account.
ImageVersionNumber (integer) --
The version number of the CustomImage.
AppImageConfigName (string) -- [REQUIRED]
The name of the AppImageConfig.
LifecycleConfigArns (list) --
The Amazon Resource Name (ARN) of the Lifecycle Configurations attached to the the user profile or domain.
(string) --
CodeEditorAppSettings (dict) --
The Code Editor application settings.
DefaultResourceSpec (dict) --
Specifies the ARN's of a SageMaker AI image and SageMaker AI image version, and the instance type that the version runs on.
SageMakerImageArn (string) --
The ARN of the SageMaker AI image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance. To clear the value set for SageMakerImageVersionArn, pass None as the value.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
TrainingPlanArn (string) --
The ARN of the SageMaker AI Training Plan to use for this app. When you specify a training plan, the app launches on reserved GPU capacity. This field is supported for JupyterLab and CodeEditor app types.
For more information about how to reserve GPU capacity with SageMaker AI Training Plans, see Using training plans in Studio applications.
AppLifecycleManagement (dict) --
Settings that are used to configure and manage the lifecycle of CodeEditor applications in a space.
IdleSettings (dict) --
Settings related to idle shutdown of Studio applications.
IdleTimeoutInMinutes (integer) --
The time that SageMaker waits after the application becomes idle before shutting it down.
JupyterLabAppSettings (dict) --
The settings for the JupyterLab application.
DefaultResourceSpec (dict) --
Specifies the ARN's of a SageMaker AI image and SageMaker AI image version, and the instance type that the version runs on.
SageMakerImageArn (string) --
The ARN of the SageMaker AI image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance. To clear the value set for SageMakerImageVersionArn, pass None as the value.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
TrainingPlanArn (string) --
The ARN of the SageMaker AI Training Plan to use for this app. When you specify a training plan, the app launches on reserved GPU capacity. This field is supported for JupyterLab and CodeEditor app types.
For more information about how to reserve GPU capacity with SageMaker AI Training Plans, see Using training plans in Studio applications.
CodeRepositories (list) --
A list of Git repositories that SageMaker automatically displays to users for cloning in the JupyterLab application.
(dict) --
A Git repository that SageMaker AI automatically displays to users for cloning in the JupyterServer application.
RepositoryUrl (string) -- [REQUIRED]
The URL of the Git repository.
AppLifecycleManagement (dict) --
Settings that are used to configure and manage the lifecycle of JupyterLab applications in a space.
IdleSettings (dict) --
Settings related to idle shutdown of Studio applications.
IdleTimeoutInMinutes (integer) --
The time that SageMaker waits after the application becomes idle before shutting it down.
AppType (string) --
The type of app created within the space.
If using the UpdateSpace API, you can't change the app type of your space by specifying a different value for this field.
SpaceStorageSettings (dict) --
The storage settings for a space.
EbsStorageSettings (dict) --
A collection of EBS storage settings for a space.
EbsVolumeSizeInGb (integer) -- [REQUIRED]
The size of an EBS storage volume for a space.
SpaceManagedResources (string) --
If you enable this option, SageMaker AI creates the following resources on your behalf when you create the space:
The user profile that possesses the space.
The app that the space contains.
CustomFileSystems (list) --
A file system, created by you, that you assign to a space for an Amazon SageMaker AI Domain. Permitted users can access this file system in Amazon SageMaker AI Studio.
(dict) --
A file system, created by you, that you assign to a user profile or space for an Amazon SageMaker AI Domain. Permitted users can access this file system in Amazon SageMaker AI Studio.
EFSFileSystem (dict) --
A custom file system in Amazon EFS.
FileSystemId (string) -- [REQUIRED]
The ID of your Amazon EFS file system.
FSxLustreFileSystem (dict) --
A custom file system in Amazon FSx for Lustre.
FileSystemId (string) -- [REQUIRED]
Amazon FSx for Lustre file system ID.
S3FileSystem (dict) --
A custom file system in Amazon S3. This is only supported in Amazon SageMaker Unified Studio.
S3Uri (string) -- [REQUIRED]
The Amazon S3 URI that specifies the location in S3 where files are stored, which is mounted within the Studio environment. For example: s3://<bucket-name>/<prefix>/.
RemoteAccess (string) --
A setting that enables or disables remote access for a SageMaker space. When enabled, this allows you to connect to the remote space from your local IDE.
string
The name of the space that appears in the Amazon SageMaker Studio UI.
dict
Response Syntax
{
'SpaceArn': 'string'
}
Response Structure
(dict) --
SpaceArn (string) --
The space's Amazon Resource Name (ARN).
{'UserSettings': {'CodeEditorAppSettings': {'DefaultResourceSpec': {'TrainingPlanArn': 'string'}},
'JupyterLabAppSettings': {'DefaultResourceSpec': {'TrainingPlanArn': 'string'}},
'JupyterServerAppSettings': {'DefaultResourceSpec': {'TrainingPlanArn': 'string'}},
'KernelGatewayAppSettings': {'DefaultResourceSpec': {'TrainingPlanArn': 'string'}},
'RSessionAppSettings': {'DefaultResourceSpec': {'TrainingPlanArn': 'string'}},
'StudioWebPortalSettings': {'ExecutionRoleSessionNameMode': 'STATIC '
'| '
'USER_IDENTITY'},
'TensorBoardAppSettings': {'DefaultResourceSpec': {'TrainingPlanArn': 'string'}}}}
Updates a user profile.
See also: AWS API Documentation
Request Syntax
client.update_user_profile(
DomainId='string',
UserProfileName='string',
UserSettings={
'ExecutionRole': 'string',
'SecurityGroups': [
'string',
],
'SharingSettings': {
'NotebookOutputOption': 'Allowed'|'Disabled',
'S3OutputPath': 'string',
'S3KmsKeyId': 'string'
},
'JupyterServerAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': 'string',
'SageMakerImageVersionAlias': 'string',
'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.g6.xlarge'|'ml.g6.2xlarge'|'ml.g6.4xlarge'|'ml.g6.8xlarge'|'ml.g6.12xlarge'|'ml.g6.16xlarge'|'ml.g6.24xlarge'|'ml.g6.48xlarge'|'ml.g6e.xlarge'|'ml.g6e.2xlarge'|'ml.g6e.4xlarge'|'ml.g6e.8xlarge'|'ml.g6e.12xlarge'|'ml.g6e.16xlarge'|'ml.g6e.24xlarge'|'ml.g6e.48xlarge'|'ml.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.p5.48xlarge'|'ml.p5en.48xlarge'|'ml.p6-b200.48xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.r6i.large'|'ml.r6i.xlarge'|'ml.r6i.2xlarge'|'ml.r6i.4xlarge'|'ml.r6i.8xlarge'|'ml.r6i.12xlarge'|'ml.r6i.16xlarge'|'ml.r6i.24xlarge'|'ml.r6i.32xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge'|'ml.m6id.large'|'ml.m6id.xlarge'|'ml.m6id.2xlarge'|'ml.m6id.4xlarge'|'ml.m6id.8xlarge'|'ml.m6id.12xlarge'|'ml.m6id.16xlarge'|'ml.m6id.24xlarge'|'ml.m6id.32xlarge'|'ml.c6id.large'|'ml.c6id.xlarge'|'ml.c6id.2xlarge'|'ml.c6id.4xlarge'|'ml.c6id.8xlarge'|'ml.c6id.12xlarge'|'ml.c6id.16xlarge'|'ml.c6id.24xlarge'|'ml.c6id.32xlarge'|'ml.r6id.large'|'ml.r6id.xlarge'|'ml.r6id.2xlarge'|'ml.r6id.4xlarge'|'ml.r6id.8xlarge'|'ml.r6id.12xlarge'|'ml.r6id.16xlarge'|'ml.r6id.24xlarge'|'ml.r6id.32xlarge'|'ml.p5.4xlarge',
'LifecycleConfigArn': 'string',
'TrainingPlanArn': 'string'
},
'LifecycleConfigArns': [
'string',
],
'CodeRepositories': [
{
'RepositoryUrl': 'string'
},
]
},
'KernelGatewayAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': 'string',
'SageMakerImageVersionAlias': 'string',
'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.g6.xlarge'|'ml.g6.2xlarge'|'ml.g6.4xlarge'|'ml.g6.8xlarge'|'ml.g6.12xlarge'|'ml.g6.16xlarge'|'ml.g6.24xlarge'|'ml.g6.48xlarge'|'ml.g6e.xlarge'|'ml.g6e.2xlarge'|'ml.g6e.4xlarge'|'ml.g6e.8xlarge'|'ml.g6e.12xlarge'|'ml.g6e.16xlarge'|'ml.g6e.24xlarge'|'ml.g6e.48xlarge'|'ml.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.p5.48xlarge'|'ml.p5en.48xlarge'|'ml.p6-b200.48xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.r6i.large'|'ml.r6i.xlarge'|'ml.r6i.2xlarge'|'ml.r6i.4xlarge'|'ml.r6i.8xlarge'|'ml.r6i.12xlarge'|'ml.r6i.16xlarge'|'ml.r6i.24xlarge'|'ml.r6i.32xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge'|'ml.m6id.large'|'ml.m6id.xlarge'|'ml.m6id.2xlarge'|'ml.m6id.4xlarge'|'ml.m6id.8xlarge'|'ml.m6id.12xlarge'|'ml.m6id.16xlarge'|'ml.m6id.24xlarge'|'ml.m6id.32xlarge'|'ml.c6id.large'|'ml.c6id.xlarge'|'ml.c6id.2xlarge'|'ml.c6id.4xlarge'|'ml.c6id.8xlarge'|'ml.c6id.12xlarge'|'ml.c6id.16xlarge'|'ml.c6id.24xlarge'|'ml.c6id.32xlarge'|'ml.r6id.large'|'ml.r6id.xlarge'|'ml.r6id.2xlarge'|'ml.r6id.4xlarge'|'ml.r6id.8xlarge'|'ml.r6id.12xlarge'|'ml.r6id.16xlarge'|'ml.r6id.24xlarge'|'ml.r6id.32xlarge'|'ml.p5.4xlarge',
'LifecycleConfigArn': 'string',
'TrainingPlanArn': 'string'
},
'CustomImages': [
{
'ImageName': 'string',
'ImageVersionNumber': 123,
'AppImageConfigName': 'string'
},
],
'LifecycleConfigArns': [
'string',
]
},
'TensorBoardAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': 'string',
'SageMakerImageVersionAlias': 'string',
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'SageMakerImageVersionArn': 'string',
'SageMakerImageVersionAlias': 'string',
'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.g6.xlarge'|'ml.g6.2xlarge'|'ml.g6.4xlarge'|'ml.g6.8xlarge'|'ml.g6.12xlarge'|'ml.g6.16xlarge'|'ml.g6.24xlarge'|'ml.g6.48xlarge'|'ml.g6e.xlarge'|'ml.g6e.2xlarge'|'ml.g6e.4xlarge'|'ml.g6e.8xlarge'|'ml.g6e.12xlarge'|'ml.g6e.16xlarge'|'ml.g6e.24xlarge'|'ml.g6e.48xlarge'|'ml.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.p5.48xlarge'|'ml.p5en.48xlarge'|'ml.p6-b200.48xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.r6i.large'|'ml.r6i.xlarge'|'ml.r6i.2xlarge'|'ml.r6i.4xlarge'|'ml.r6i.8xlarge'|'ml.r6i.12xlarge'|'ml.r6i.16xlarge'|'ml.r6i.24xlarge'|'ml.r6i.32xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge'|'ml.m6id.large'|'ml.m6id.xlarge'|'ml.m6id.2xlarge'|'ml.m6id.4xlarge'|'ml.m6id.8xlarge'|'ml.m6id.12xlarge'|'ml.m6id.16xlarge'|'ml.m6id.24xlarge'|'ml.m6id.32xlarge'|'ml.c6id.large'|'ml.c6id.xlarge'|'ml.c6id.2xlarge'|'ml.c6id.4xlarge'|'ml.c6id.8xlarge'|'ml.c6id.12xlarge'|'ml.c6id.16xlarge'|'ml.c6id.24xlarge'|'ml.c6id.32xlarge'|'ml.r6id.large'|'ml.r6id.xlarge'|'ml.r6id.2xlarge'|'ml.r6id.4xlarge'|'ml.r6id.8xlarge'|'ml.r6id.12xlarge'|'ml.r6id.16xlarge'|'ml.r6id.24xlarge'|'ml.r6id.32xlarge'|'ml.p5.4xlarge',
'LifecycleConfigArn': 'string',
'TrainingPlanArn': 'string'
},
'CustomImages': [
{
'ImageName': 'string',
'ImageVersionNumber': 123,
'AppImageConfigName': 'string'
},
],
'LifecycleConfigArns': [
'string',
],
'CodeRepositories': [
{
'RepositoryUrl': 'string'
},
],
'AppLifecycleManagement': {
'IdleSettings': {
'LifecycleManagement': 'ENABLED'|'DISABLED',
'IdleTimeoutInMinutes': 123,
'MinIdleTimeoutInMinutes': 123,
'MaxIdleTimeoutInMinutes': 123
}
},
'EmrSettings': {
'AssumableRoleArns': [
'string',
],
'ExecutionRoleArns': [
'string',
]
},
'BuiltInLifecycleConfigArn': 'string'
},
'SpaceStorageSettings': {
'DefaultEbsStorageSettings': {
'DefaultEbsVolumeSizeInGb': 123,
'MaximumEbsVolumeSizeInGb': 123
}
},
'DefaultLandingUri': 'string',
'StudioWebPortal': 'ENABLED'|'DISABLED',
'CustomPosixUserConfig': {
'Uid': 123,
'Gid': 123
},
'CustomFileSystemConfigs': [
{
'EFSFileSystemConfig': {
'FileSystemId': 'string',
'FileSystemPath': 'string'
},
'FSxLustreFileSystemConfig': {
'FileSystemId': 'string',
'FileSystemPath': 'string'
},
'S3FileSystemConfig': {
'MountPath': 'string',
'S3Uri': 'string'
}
},
],
'StudioWebPortalSettings': {
'HiddenMlTools': [
'DataWrangler'|'FeatureStore'|'EmrClusters'|'AutoMl'|'Experiments'|'Training'|'ModelEvaluation'|'Pipelines'|'Models'|'JumpStart'|'InferenceRecommender'|'Endpoints'|'Projects'|'InferenceOptimization'|'PerformanceEvaluation'|'LakeraGuard'|'Comet'|'DeepchecksLLMEvaluation'|'Fiddler'|'HyperPodClusters'|'RunningInstances'|'Datasets'|'Evaluators',
],
'HiddenAppTypes': [
'JupyterServer'|'KernelGateway'|'DetailedProfiler'|'TensorBoard'|'CodeEditor'|'JupyterLab'|'RStudioServerPro'|'RSessionGateway'|'Canvas',
],
'HiddenInstanceTypes': [
'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.g6.xlarge'|'ml.g6.2xlarge'|'ml.g6.4xlarge'|'ml.g6.8xlarge'|'ml.g6.12xlarge'|'ml.g6.16xlarge'|'ml.g6.24xlarge'|'ml.g6.48xlarge'|'ml.g6e.xlarge'|'ml.g6e.2xlarge'|'ml.g6e.4xlarge'|'ml.g6e.8xlarge'|'ml.g6e.12xlarge'|'ml.g6e.16xlarge'|'ml.g6e.24xlarge'|'ml.g6e.48xlarge'|'ml.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.p5.48xlarge'|'ml.p5en.48xlarge'|'ml.p6-b200.48xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.r6i.large'|'ml.r6i.xlarge'|'ml.r6i.2xlarge'|'ml.r6i.4xlarge'|'ml.r6i.8xlarge'|'ml.r6i.12xlarge'|'ml.r6i.16xlarge'|'ml.r6i.24xlarge'|'ml.r6i.32xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge'|'ml.m6id.large'|'ml.m6id.xlarge'|'ml.m6id.2xlarge'|'ml.m6id.4xlarge'|'ml.m6id.8xlarge'|'ml.m6id.12xlarge'|'ml.m6id.16xlarge'|'ml.m6id.24xlarge'|'ml.m6id.32xlarge'|'ml.c6id.large'|'ml.c6id.xlarge'|'ml.c6id.2xlarge'|'ml.c6id.4xlarge'|'ml.c6id.8xlarge'|'ml.c6id.12xlarge'|'ml.c6id.16xlarge'|'ml.c6id.24xlarge'|'ml.c6id.32xlarge'|'ml.r6id.large'|'ml.r6id.xlarge'|'ml.r6id.2xlarge'|'ml.r6id.4xlarge'|'ml.r6id.8xlarge'|'ml.r6id.12xlarge'|'ml.r6id.16xlarge'|'ml.r6id.24xlarge'|'ml.r6id.32xlarge'|'ml.p5.4xlarge',
],
'HiddenSageMakerImageVersionAliases': [
{
'SageMakerImageName': 'sagemaker_distribution',
'VersionAliases': [
'string',
]
},
],
'ExecutionRoleSessionNameMode': 'STATIC'|'USER_IDENTITY'
},
'AutoMountHomeEFS': 'Enabled'|'Disabled'|'DefaultAsDomain'
}
)
string
[REQUIRED]
The domain ID.
string
[REQUIRED]
The user profile name.
dict
A collection of settings.
ExecutionRole (string) --
The execution role for the user.
SageMaker applies this setting only to private spaces that the user creates in the domain. SageMaker doesn't apply this setting to shared spaces.
SecurityGroups (list) --
The security groups for the Amazon Virtual Private Cloud (VPC) that the domain uses for communication.
Optional when the CreateDomain.AppNetworkAccessType parameter is set to PublicInternetOnly.
Required when the CreateDomain.AppNetworkAccessType parameter is set to VpcOnly, unless specified as part of the DefaultUserSettings for the domain.
Amazon SageMaker AI adds a security group to allow NFS traffic from Amazon SageMaker AI Studio. Therefore, the number of security groups that you can specify is one less than the maximum number shown.
SageMaker applies these settings only to private spaces that the user creates in the domain. SageMaker doesn't apply these settings to shared spaces.
(string) --
SharingSettings (dict) --
Specifies options for sharing Amazon SageMaker AI Studio notebooks.
NotebookOutputOption (string) --
Whether to include the notebook cell output when sharing the notebook. The default is Disabled.
S3OutputPath (string) --
When NotebookOutputOption is Allowed, the Amazon S3 bucket used to store the shared notebook snapshots.
S3KmsKeyId (string) --
When NotebookOutputOption is Allowed, the Amazon Web Services Key Management Service (KMS) encryption key ID used to encrypt the notebook cell output in the Amazon S3 bucket.
JupyterServerAppSettings (dict) --
The Jupyter server's app settings.
DefaultResourceSpec (dict) --
The default instance type and the Amazon Resource Name (ARN) of the default SageMaker AI image used by the JupyterServer app. If you use the LifecycleConfigArns parameter, then this parameter is also required.
SageMakerImageArn (string) --
The ARN of the SageMaker AI image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance. To clear the value set for SageMakerImageVersionArn, pass None as the value.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
TrainingPlanArn (string) --
The ARN of the SageMaker AI Training Plan to use for this app. When you specify a training plan, the app launches on reserved GPU capacity. This field is supported for JupyterLab and CodeEditor app types.
For more information about how to reserve GPU capacity with SageMaker AI Training Plans, see Using training plans in Studio applications.
LifecycleConfigArns (list) --
The Amazon Resource Name (ARN) of the Lifecycle Configurations attached to the JupyterServerApp. If you use this parameter, the DefaultResourceSpec parameter is also required.
(string) --
CodeRepositories (list) --
A list of Git repositories that SageMaker AI automatically displays to users for cloning in the JupyterServer application.
(dict) --
A Git repository that SageMaker AI automatically displays to users for cloning in the JupyterServer application.
RepositoryUrl (string) -- [REQUIRED]
The URL of the Git repository.
KernelGatewayAppSettings (dict) --
The kernel gateway app settings.
DefaultResourceSpec (dict) --
The default instance type and the Amazon Resource Name (ARN) of the default SageMaker AI image used by the KernelGateway app.
SageMakerImageArn (string) --
The ARN of the SageMaker AI image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance. To clear the value set for SageMakerImageVersionArn, pass None as the value.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
TrainingPlanArn (string) --
The ARN of the SageMaker AI Training Plan to use for this app. When you specify a training plan, the app launches on reserved GPU capacity. This field is supported for JupyterLab and CodeEditor app types.
For more information about how to reserve GPU capacity with SageMaker AI Training Plans, see Using training plans in Studio applications.
CustomImages (list) --
A list of custom SageMaker AI images that are configured to run as a KernelGateway app.
The maximum number of custom images are as follows.
On a domain level: 200
On a space level: 5
On a user profile level: 5
(dict) --
A custom SageMaker AI image. For more information, see Bring your own SageMaker AI image.
ImageName (string) -- [REQUIRED]
The name of the CustomImage. Must be unique to your account.
ImageVersionNumber (integer) --
The version number of the CustomImage.
AppImageConfigName (string) -- [REQUIRED]
The name of the AppImageConfig.
LifecycleConfigArns (list) --
The Amazon Resource Name (ARN) of the Lifecycle Configurations attached to the the user profile or domain.
(string) --
TensorBoardAppSettings (dict) --
The TensorBoard app settings.
DefaultResourceSpec (dict) --
The default instance type and the Amazon Resource Name (ARN) of the SageMaker AI image created on the instance.
SageMakerImageArn (string) --
The ARN of the SageMaker AI image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance. To clear the value set for SageMakerImageVersionArn, pass None as the value.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
TrainingPlanArn (string) --
The ARN of the SageMaker AI Training Plan to use for this app. When you specify a training plan, the app launches on reserved GPU capacity. This field is supported for JupyterLab and CodeEditor app types.
For more information about how to reserve GPU capacity with SageMaker AI Training Plans, see Using training plans in Studio applications.
RStudioServerProAppSettings (dict) --
A collection of settings that configure user interaction with the RStudioServerPro app.
AccessStatus (string) --
Indicates whether the current user has access to the RStudioServerPro app.
UserGroup (string) --
The level of permissions that the user has within the RStudioServerPro app. This value defaults to User. The Admin value allows the user access to the RStudio Administrative Dashboard.
RSessionAppSettings (dict) --
A collection of settings that configure the RSessionGateway app.
DefaultResourceSpec (dict) --
Specifies the ARN's of a SageMaker AI image and SageMaker AI image version, and the instance type that the version runs on.
SageMakerImageArn (string) --
The ARN of the SageMaker AI image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance. To clear the value set for SageMakerImageVersionArn, pass None as the value.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
TrainingPlanArn (string) --
The ARN of the SageMaker AI Training Plan to use for this app. When you specify a training plan, the app launches on reserved GPU capacity. This field is supported for JupyterLab and CodeEditor app types.
For more information about how to reserve GPU capacity with SageMaker AI Training Plans, see Using training plans in Studio applications.
CustomImages (list) --
A list of custom SageMaker AI images that are configured to run as a RSession app.
(dict) --
A custom SageMaker AI image. For more information, see Bring your own SageMaker AI image.
ImageName (string) -- [REQUIRED]
The name of the CustomImage. Must be unique to your account.
ImageVersionNumber (integer) --
The version number of the CustomImage.
AppImageConfigName (string) -- [REQUIRED]
The name of the AppImageConfig.
CanvasAppSettings (dict) --
The Canvas app settings.
SageMaker applies these settings only to private spaces that SageMaker creates for the Canvas app.
TimeSeriesForecastingSettings (dict) --
Time series forecast settings for the SageMaker Canvas application.
Status (string) --
Describes whether time series forecasting is enabled or disabled in the Canvas application.
AmazonForecastRoleArn (string) --
The IAM role that Canvas passes to Amazon Forecast for time series forecasting. By default, Canvas uses the execution role specified in the UserProfile that launches the Canvas application. If an execution role is not specified in the UserProfile, Canvas uses the execution role specified in the Domain that owns the UserProfile. To allow time series forecasting, this IAM role should have the AmazonSageMakerCanvasForecastAccess policy attached and forecast.amazonaws.com added in the trust relationship as a service principal.
ModelRegisterSettings (dict) --
The model registry settings for the SageMaker Canvas application.
Status (string) --
Describes whether the integration to the model registry is enabled or disabled in the Canvas application.
CrossAccountModelRegisterRoleArn (string) --
The Amazon Resource Name (ARN) of the SageMaker model registry account. Required only to register model versions created by a different SageMaker Canvas Amazon Web Services account than the Amazon Web Services account in which SageMaker model registry is set up.
WorkspaceSettings (dict) --
The workspace settings for the SageMaker Canvas application.
S3ArtifactPath (string) --
The Amazon S3 bucket used to store artifacts generated by Canvas. Updating the Amazon S3 location impacts existing configuration settings, and Canvas users no longer have access to their artifacts. Canvas users must log out and log back in to apply the new location.
S3KmsKeyId (string) --
The Amazon Web Services Key Management Service (KMS) encryption key ID that is used to encrypt artifacts generated by Canvas in the Amazon S3 bucket.
IdentityProviderOAuthSettings (list) --
The settings for connecting to an external data source with OAuth.
(dict) --
The Amazon SageMaker Canvas application setting where you configure OAuth for connecting to an external data source, such as Snowflake.
DataSourceName (string) --
The name of the data source that you're connecting to. Canvas currently supports OAuth for Snowflake and Salesforce Data Cloud.
Status (string) --
Describes whether OAuth for a data source is enabled or disabled in the Canvas application.
SecretArn (string) --
The ARN of an Amazon Web Services Secrets Manager secret that stores the credentials from your identity provider, such as the client ID and secret, authorization URL, and token URL.
DirectDeploySettings (dict) --
The model deployment settings for the SageMaker Canvas application.
Status (string) --
Describes whether model deployment permissions are enabled or disabled in the Canvas application.
KendraSettings (dict) --
The settings for document querying.
Status (string) --
Describes whether the document querying feature is enabled or disabled in the Canvas application.
GenerativeAiSettings (dict) --
The generative AI settings for the SageMaker Canvas application.
AmazonBedrockRoleArn (string) --
The ARN of an Amazon Web Services IAM role that allows fine-tuning of large language models (LLMs) in Amazon Bedrock. The IAM role should have Amazon S3 read and write permissions, as well as a trust relationship that establishes bedrock.amazonaws.com as a service principal.
EmrServerlessSettings (dict) --
The settings for running Amazon EMR Serverless data processing jobs in SageMaker Canvas.
ExecutionRoleArn (string) --
The Amazon Resource Name (ARN) of the Amazon Web Services IAM role that is assumed for running Amazon EMR Serverless jobs in SageMaker Canvas. This role should have the necessary permissions to read and write data attached and a trust relationship with EMR Serverless.
Status (string) --
Describes whether Amazon EMR Serverless job capabilities are enabled or disabled in the SageMaker Canvas application.
CodeEditorAppSettings (dict) --
The Code Editor application settings.
SageMaker applies these settings only to private spaces that the user creates in the domain. SageMaker doesn't apply these settings to shared spaces.
DefaultResourceSpec (dict) --
Specifies the ARN's of a SageMaker AI image and SageMaker AI image version, and the instance type that the version runs on.
SageMakerImageArn (string) --
The ARN of the SageMaker AI image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance. To clear the value set for SageMakerImageVersionArn, pass None as the value.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
TrainingPlanArn (string) --
The ARN of the SageMaker AI Training Plan to use for this app. When you specify a training plan, the app launches on reserved GPU capacity. This field is supported for JupyterLab and CodeEditor app types.
For more information about how to reserve GPU capacity with SageMaker AI Training Plans, see Using training plans in Studio applications.
CustomImages (list) --
A list of custom SageMaker images that are configured to run as a Code Editor app.
(dict) --
A custom SageMaker AI image. For more information, see Bring your own SageMaker AI image.
ImageName (string) -- [REQUIRED]
The name of the CustomImage. Must be unique to your account.
ImageVersionNumber (integer) --
The version number of the CustomImage.
AppImageConfigName (string) -- [REQUIRED]
The name of the AppImageConfig.
LifecycleConfigArns (list) --
The Amazon Resource Name (ARN) of the Code Editor application lifecycle configuration.
(string) --
AppLifecycleManagement (dict) --
Settings that are used to configure and manage the lifecycle of CodeEditor applications.
IdleSettings (dict) --
Settings related to idle shutdown of Studio applications.
LifecycleManagement (string) --
Indicates whether idle shutdown is activated for the application type.
IdleTimeoutInMinutes (integer) --
The time that SageMaker waits after the application becomes idle before shutting it down.
MinIdleTimeoutInMinutes (integer) --
The minimum value in minutes that custom idle shutdown can be set to by the user.
MaxIdleTimeoutInMinutes (integer) --
The maximum value in minutes that custom idle shutdown can be set to by the user.
BuiltInLifecycleConfigArn (string) --
The lifecycle configuration that runs before the default lifecycle configuration. It can override changes made in the default lifecycle configuration.
JupyterLabAppSettings (dict) --
The settings for the JupyterLab application.
SageMaker applies these settings only to private spaces that the user creates in the domain. SageMaker doesn't apply these settings to shared spaces.
DefaultResourceSpec (dict) --
Specifies the ARN's of a SageMaker AI image and SageMaker AI image version, and the instance type that the version runs on.
SageMakerImageArn (string) --
The ARN of the SageMaker AI image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance. To clear the value set for SageMakerImageVersionArn, pass None as the value.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
TrainingPlanArn (string) --
The ARN of the SageMaker AI Training Plan to use for this app. When you specify a training plan, the app launches on reserved GPU capacity. This field is supported for JupyterLab and CodeEditor app types.
For more information about how to reserve GPU capacity with SageMaker AI Training Plans, see Using training plans in Studio applications.
CustomImages (list) --
A list of custom SageMaker images that are configured to run as a JupyterLab app.
(dict) --
A custom SageMaker AI image. For more information, see Bring your own SageMaker AI image.
ImageName (string) -- [REQUIRED]
The name of the CustomImage. Must be unique to your account.
ImageVersionNumber (integer) --
The version number of the CustomImage.
AppImageConfigName (string) -- [REQUIRED]
The name of the AppImageConfig.
LifecycleConfigArns (list) --
The Amazon Resource Name (ARN) of the lifecycle configurations attached to the user profile or domain. To remove a lifecycle config, you must set LifecycleConfigArns to an empty list.
(string) --
CodeRepositories (list) --
A list of Git repositories that SageMaker automatically displays to users for cloning in the JupyterLab application.
(dict) --
A Git repository that SageMaker AI automatically displays to users for cloning in the JupyterServer application.
RepositoryUrl (string) -- [REQUIRED]
The URL of the Git repository.
AppLifecycleManagement (dict) --
Indicates whether idle shutdown is activated for JupyterLab applications.
IdleSettings (dict) --
Settings related to idle shutdown of Studio applications.
LifecycleManagement (string) --
Indicates whether idle shutdown is activated for the application type.
IdleTimeoutInMinutes (integer) --
The time that SageMaker waits after the application becomes idle before shutting it down.
MinIdleTimeoutInMinutes (integer) --
The minimum value in minutes that custom idle shutdown can be set to by the user.
MaxIdleTimeoutInMinutes (integer) --
The maximum value in minutes that custom idle shutdown can be set to by the user.
EmrSettings (dict) --
The configuration parameters that specify the IAM roles assumed by the execution role of SageMaker (assumable roles) and the cluster instances or job execution environments (execution roles or runtime roles) to manage and access resources required for running Amazon EMR clusters or Amazon EMR Serverless applications.
AssumableRoleArns (list) --
An array of Amazon Resource Names (ARNs) of the IAM roles that the execution role of SageMaker can assume for performing operations or tasks related to Amazon EMR clusters or Amazon EMR Serverless applications. These roles define the permissions and access policies required when performing Amazon EMR-related operations, such as listing, connecting to, or terminating Amazon EMR clusters or Amazon EMR Serverless applications. They are typically used in cross-account access scenarios, where the Amazon EMR resources (clusters or serverless applications) are located in a different Amazon Web Services account than the SageMaker domain.
(string) --
ExecutionRoleArns (list) --
An array of Amazon Resource Names (ARNs) of the IAM roles used by the Amazon EMR cluster instances or job execution environments to access other Amazon Web Services services and resources needed during the runtime of your Amazon EMR or Amazon EMR Serverless workloads, such as Amazon S3 for data access, Amazon CloudWatch for logging, or other Amazon Web Services services based on the particular workload requirements.
(string) --
BuiltInLifecycleConfigArn (string) --
The lifecycle configuration that runs before the default lifecycle configuration. It can override changes made in the default lifecycle configuration.
SpaceStorageSettings (dict) --
The storage settings for a space.
SageMaker applies these settings only to private spaces that the user creates in the domain. SageMaker doesn't apply these settings to shared spaces.
DefaultEbsStorageSettings (dict) --
The default EBS storage settings for a space.
DefaultEbsVolumeSizeInGb (integer) -- [REQUIRED]
The default size of the EBS storage volume for a space.
MaximumEbsVolumeSizeInGb (integer) -- [REQUIRED]
The maximum size of the EBS storage volume for a space.
DefaultLandingUri (string) --
The default experience that the user is directed to when accessing the domain. The supported values are:
studio::: Indicates that Studio is the default experience. This value can only be passed if StudioWebPortal is set to ENABLED.
app:JupyterServer:: Indicates that Studio Classic is the default experience.
StudioWebPortal (string) --
Whether the user can access Studio. If this value is set to DISABLED, the user cannot access Studio, even if that is the default experience for the domain.
CustomPosixUserConfig (dict) --
Details about the POSIX identity that is used for file system operations.
SageMaker applies these settings only to private spaces that the user creates in the domain. SageMaker doesn't apply these settings to shared spaces.
Uid (integer) -- [REQUIRED]
The POSIX user ID.
Gid (integer) -- [REQUIRED]
The POSIX group ID.
CustomFileSystemConfigs (list) --
The settings for assigning a custom file system to a user profile. Permitted users can access this file system in Amazon SageMaker AI Studio.
SageMaker applies these settings only to private spaces that the user creates in the domain. SageMaker doesn't apply these settings to shared spaces.
(dict) --
The settings for assigning a custom file system to a user profile or space for an Amazon SageMaker AI Domain. Permitted users can access this file system in Amazon SageMaker AI Studio.
EFSFileSystemConfig (dict) --
The settings for a custom Amazon EFS file system.
FileSystemId (string) -- [REQUIRED]
The ID of your Amazon EFS file system.
FileSystemPath (string) --
The path to the file system directory that is accessible in Amazon SageMaker AI Studio. Permitted users can access only this directory and below.
FSxLustreFileSystemConfig (dict) --
The settings for a custom Amazon FSx for Lustre file system.
FileSystemId (string) -- [REQUIRED]
The globally unique, 17-digit, ID of the file system, assigned by Amazon FSx for Lustre.
FileSystemPath (string) --
The path to the file system directory that is accessible in Amazon SageMaker Studio. Permitted users can access only this directory and below.
S3FileSystemConfig (dict) --
Configuration settings for a custom Amazon S3 file system.
MountPath (string) --
The file system path where the Amazon S3 storage location will be mounted within the Amazon SageMaker Studio environment.
S3Uri (string) -- [REQUIRED]
The Amazon S3 URI of the S3 file system configuration.
StudioWebPortalSettings (dict) --
Studio settings. If these settings are applied on a user level, they take priority over the settings applied on a domain level.
HiddenMlTools (list) --
The machine learning tools that are hidden from the Studio left navigation pane.
(string) --
HiddenAppTypes (list) --
The Applications supported in Studio that are hidden from the Studio left navigation pane.
(string) --
HiddenInstanceTypes (list) --
The instance types you are hiding from the Studio user interface.
(string) --
HiddenSageMakerImageVersionAliases (list) --
The version aliases you are hiding from the Studio user interface.
(dict) --
The SageMaker images that are hidden from the Studio user interface. You must specify the SageMaker image name and version aliases.
SageMakerImageName (string) --
The SageMaker image name that you are hiding from the Studio user interface.
VersionAliases (list) --
The version aliases you are hiding from the Studio user interface.
(string) --
ExecutionRoleSessionNameMode (string) --
The execution role session name mode. If this value is set to USER_IDENTITY, the session name of the execution role corresponds to the user's identity. For IAM domains, the session name is the IAM session name used to generate the presigned URL. For IAM Identity Center domains, the session name is the username of the associated IAM Identity Center user. If this value is set to STATIC or is not set, the session name defaults to SageMaker.
AutoMountHomeEFS (string) --
Indicates whether auto-mounting of an EFS volume is supported for the user profile. The DefaultAsDomain value is only supported for user profiles. Do not use the DefaultAsDomain value when setting this parameter for a domain.
SageMaker applies this setting only to private spaces that the user creates in the domain. SageMaker doesn't apply this setting to shared spaces.
dict
Response Syntax
{
'UserProfileArn': 'string'
}
Response Structure
(dict) --
UserProfileArn (string) --
The user profile Amazon Resource Name (ARN).