2024/07/09 - Amazon SageMaker Service - 5 new 6 updated api methods
Changes This release 1/ enables optimization jobs that allows customers to perform Ahead-of-time compilation and quantization. 2/ allows customers to control access to Amazon Q integration in SageMaker Studio. 3/ enables AdditionalModelDataSources for CreateModel action.
Lists the optimization jobs in your account and their properties.
See also: AWS API Documentation
Request Syntax
client.list_optimization_jobs(
NextToken='string',
MaxResults=123,
CreationTimeAfter=datetime(2015, 1, 1),
CreationTimeBefore=datetime(2015, 1, 1),
LastModifiedTimeAfter=datetime(2015, 1, 1),
LastModifiedTimeBefore=datetime(2015, 1, 1),
OptimizationContains='string',
NameContains='string',
StatusEquals='INPROGRESS'|'COMPLETED'|'FAILED'|'STARTING'|'STOPPING'|'STOPPED',
SortBy='Name'|'CreationTime'|'Status',
SortOrder='Ascending'|'Descending'
)
string
A token that you use to get the next set of results following a truncated response. If the response to the previous request was truncated, that response provides the value for this token.
integer
The maximum number of optimization jobs to return in the response. The default is 50.
datetime
Filters the results to only those optimization jobs that were created after the specified time.
datetime
Filters the results to only those optimization jobs that were created before the specified time.
datetime
Filters the results to only those optimization jobs that were updated after the specified time.
datetime
Filters the results to only those optimization jobs that were updated before the specified time.
string
Filters the results to only those optimization jobs that apply the specified optimization techniques. You can specify either Quantization or Compilation .
string
Filters the results to only those optimization jobs with a name that contains the specified string.
string
Filters the results to only those optimization jobs with the specified status.
string
The field by which to sort the optimization jobs in the response. The default is CreationTime
string
The sort order for results. The default is Ascending
dict
Response Syntax
{
'OptimizationJobSummaries': [
{
'OptimizationJobName': 'string',
'OptimizationJobArn': 'string',
'CreationTime': datetime(2015, 1, 1),
'OptimizationJobStatus': 'INPROGRESS'|'COMPLETED'|'FAILED'|'STARTING'|'STOPPING'|'STOPPED',
'OptimizationStartTime': datetime(2015, 1, 1),
'OptimizationEndTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'DeploymentInstanceType': 'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.p5.48xlarge'|'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.inf2.xlarge'|'ml.inf2.8xlarge'|'ml.inf2.24xlarge'|'ml.inf2.48xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge',
'OptimizationTypes': [
'string',
]
},
],
'NextToken': 'string'
}
Response Structure
(dict) --
OptimizationJobSummaries (list) --
A list of optimization jobs and their properties that matches any of the filters you specified in the request.
(dict) --
Summarizes an optimization job by providing some of its key properties.
OptimizationJobName (string) --
The name that you assigned to the optimization job.
OptimizationJobArn (string) --
The Amazon Resource Name (ARN) of the optimization job.
CreationTime (datetime) --
The time when you created the optimization job.
OptimizationJobStatus (string) --
The current status of the optimization job.
OptimizationStartTime (datetime) --
The time when the optimization job started.
OptimizationEndTime (datetime) --
The time when the optimization job finished processing.
LastModifiedTime (datetime) --
The time when the optimization job was last updated.
DeploymentInstanceType (string) --
The type of instance that hosts the optimized model that you create with the optimization job.
OptimizationTypes (list) --
The optimization techniques that are applied by the optimization job.
(string) --
NextToken (string) --
The token to use in a subsequent request to get the next set of results following a truncated response.
Creates a job that optimizes a model for inference performance. To create the job, you provide the location of a source model, and you provide the settings for the optimization techniques that you want the job to apply. When the job completes successfully, SageMaker uploads the new optimized model to the output destination that you specify.
For more information about how to use this action, and about the supported optimization techniques, see Optimize model inference with Amazon SageMaker.
See also: AWS API Documentation
Request Syntax
client.create_optimization_job(
OptimizationJobName='string',
RoleArn='string',
ModelSource={
'S3': {
'S3Uri': 'string',
'ModelAccessConfig': {
'AcceptEula': True|False
}
}
},
DeploymentInstanceType='ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.p5.48xlarge'|'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.inf2.xlarge'|'ml.inf2.8xlarge'|'ml.inf2.24xlarge'|'ml.inf2.48xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge',
OptimizationEnvironment={
'string': 'string'
},
OptimizationConfigs=[
{
'ModelQuantizationConfig': {
'Image': 'string',
'OverrideEnvironment': {
'string': 'string'
}
},
'ModelCompilationConfig': {
'Image': 'string',
'OverrideEnvironment': {
'string': 'string'
}
}
},
],
OutputConfig={
'KmsKeyId': 'string',
'S3OutputLocation': 'string'
},
StoppingCondition={
'MaxRuntimeInSeconds': 123,
'MaxWaitTimeInSeconds': 123,
'MaxPendingTimeInSeconds': 123
},
Tags=[
{
'Key': 'string',
'Value': 'string'
},
],
VpcConfig={
'SecurityGroupIds': [
'string',
],
'Subnets': [
'string',
]
}
)
string
[REQUIRED]
A custom name for the new optimization job.
string
[REQUIRED]
The Amazon Resource Name (ARN) of an IAM role that enables Amazon SageMaker to perform tasks on your behalf.
During model optimization, Amazon SageMaker needs your permission to:
Read input data from an S3 bucket
Write model artifacts to an S3 bucket
Write logs to Amazon CloudWatch Logs
Publish metrics to Amazon CloudWatch
You grant permissions for all of these tasks to an IAM role. To pass this role to Amazon SageMaker, the caller of this API must have the iam:PassRole permission. For more information, see Amazon SageMaker Roles.
dict
[REQUIRED]
The location of the source model to optimize with an optimization job.
S3 (dict) --
The Amazon S3 location of a source model to optimize with an optimization job.
S3Uri (string) --
An Amazon S3 URI that locates a source model to optimize with an optimization job.
ModelAccessConfig (dict) --
The access configuration settings for the source ML model for an optimization job, where you can accept the model end-user license agreement (EULA).
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.
string
[REQUIRED]
The type of instance that hosts the optimized model that you create with the optimization job.
dict
The environment variables to set in the model container.
(string) --
(string) --
list
[REQUIRED]
Settings for each of the optimization techniques that the job applies.
(dict) --
Settings for an optimization technique that you apply with a model optimization job.
Note
This is a Tagged Union structure. Only one of the following top level keys can be set: ModelQuantizationConfig, ModelCompilationConfig.
ModelQuantizationConfig (dict) --
Settings for the model quantization technique that's applied by a model optimization job.
Image (string) --
The URI of an LMI DLC in Amazon ECR. SageMaker uses this image to run the optimization.
OverrideEnvironment (dict) --
Environment variables that override the default ones in the model container.
(string) --
(string) --
ModelCompilationConfig (dict) --
Settings for the model compilation technique that's applied by a model optimization job.
Image (string) --
The URI of an LMI DLC in Amazon ECR. SageMaker uses this image to run the optimization.
OverrideEnvironment (dict) --
Environment variables that override the default ones in the model container.
(string) --
(string) --
dict
[REQUIRED]
Details for where to store the optimized model that you create with the optimization job.
KmsKeyId (string) --
The Amazon Resource Name (ARN) of a key in Amazon Web Services KMS. SageMaker uses they key to encrypt the artifacts of the optimized model when SageMaker uploads the model to Amazon S3.
S3OutputLocation (string) -- [REQUIRED]
The Amazon S3 URI for where to store the optimized model that you create with an optimization job.
dict
[REQUIRED]
Specifies a limit to how long a job can run. When the job reaches the time limit, SageMaker ends the job. Use this API to cap costs.
To stop a training job, SageMaker sends the algorithm the SIGTERM signal, which delays job termination for 120 seconds. Algorithms can use this 120-second window to save the model artifacts, so the results of training are not lost.
The training algorithms provided by SageMaker automatically save the intermediate results of a model training job when possible. This attempt to save artifacts is only a best effort case as model might not be in a state from which it can be saved. For example, if training has just started, the model might not be ready to save. When saved, this intermediate data is a valid model artifact. You can use it to create a model with CreateModel .
Note
The Neural Topic Model (NTM) currently does not support saving intermediate model artifacts. When training NTMs, make sure that the maximum runtime is sufficient for the training job to complete.
MaxRuntimeInSeconds (integer) --
The maximum length of time, in seconds, that a training or compilation job can run before it is stopped.
For compilation jobs, if the job does not complete during this time, a TimeOut error is generated. We recommend starting with 900 seconds and increasing as necessary based on your model.
For all other jobs, if the job does not complete during this time, SageMaker ends the job. When RetryStrategy is specified in the job request, MaxRuntimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt. The default value is 1 day. The maximum value is 28 days.
The maximum time that a TrainingJob can run in total, including any time spent publishing metrics or archiving and uploading models after it has been stopped, is 30 days.
MaxWaitTimeInSeconds (integer) --
The maximum length of time, in seconds, that a managed Spot training job has to complete. It is the amount of time spent waiting for Spot capacity plus the amount of time the job can run. It must be equal to or greater than MaxRuntimeInSeconds . If the job does not complete during this time, SageMaker ends the job.
When RetryStrategy is specified in the job request, MaxWaitTimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt.
MaxPendingTimeInSeconds (integer) --
The maximum length of time, in seconds, that a training or compilation job can be pending before it is stopped.
list
A list of key-value pairs associated with the optimization job. 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
A VPC in Amazon VPC that your optimized model has access to.
SecurityGroupIds (list) -- [REQUIRED]
The VPC security group IDs, in the form sg-xxxxxxxx . Specify the security groups for the VPC that is specified in the Subnets field.
(string) --
Subnets (list) -- [REQUIRED]
The ID of the subnets in the VPC to which you want to connect your optimized model.
(string) --
dict
Response Syntax
{
'OptimizationJobArn': 'string'
}
Response Structure
(dict) --
OptimizationJobArn (string) --
The Amazon Resource Name (ARN) of the optimization job.
Ends a running inference optimization job.
See also: AWS API Documentation
Request Syntax
client.stop_optimization_job(
OptimizationJobName='string'
)
string
[REQUIRED]
The name that you assigned to the optimization job.
None
Deletes an optimization job.
See also: AWS API Documentation
Request Syntax
client.delete_optimization_job(
OptimizationJobName='string'
)
string
[REQUIRED]
The name that you assigned to the optimization job.
None
Provides the properties of the specified optimization job.
See also: AWS API Documentation
Request Syntax
client.describe_optimization_job(
OptimizationJobName='string'
)
string
[REQUIRED]
The name that you assigned to the optimization job.
dict
Response Syntax
{
'OptimizationJobArn': 'string',
'OptimizationJobStatus': 'INPROGRESS'|'COMPLETED'|'FAILED'|'STARTING'|'STOPPING'|'STOPPED',
'OptimizationStartTime': datetime(2015, 1, 1),
'OptimizationEndTime': datetime(2015, 1, 1),
'CreationTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'FailureReason': 'string',
'OptimizationJobName': 'string',
'ModelSource': {
'S3': {
'S3Uri': 'string',
'ModelAccessConfig': {
'AcceptEula': True|False
}
}
},
'OptimizationEnvironment': {
'string': 'string'
},
'DeploymentInstanceType': 'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.p5.48xlarge'|'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.inf2.xlarge'|'ml.inf2.8xlarge'|'ml.inf2.24xlarge'|'ml.inf2.48xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge',
'OptimizationConfigs': [
{
'ModelQuantizationConfig': {
'Image': 'string',
'OverrideEnvironment': {
'string': 'string'
}
},
'ModelCompilationConfig': {
'Image': 'string',
'OverrideEnvironment': {
'string': 'string'
}
}
},
],
'OutputConfig': {
'KmsKeyId': 'string',
'S3OutputLocation': 'string'
},
'OptimizationOutput': {
'RecommendedInferenceImage': 'string'
},
'RoleArn': 'string',
'StoppingCondition': {
'MaxRuntimeInSeconds': 123,
'MaxWaitTimeInSeconds': 123,
'MaxPendingTimeInSeconds': 123
},
'VpcConfig': {
'SecurityGroupIds': [
'string',
],
'Subnets': [
'string',
]
}
}
Response Structure
(dict) --
OptimizationJobArn (string) --
The Amazon Resource Name (ARN) of the optimization job.
OptimizationJobStatus (string) --
The current status of the optimization job.
OptimizationStartTime (datetime) --
The time when the optimization job started.
OptimizationEndTime (datetime) --
The time when the optimization job finished processing.
CreationTime (datetime) --
The time when you created the optimization job.
LastModifiedTime (datetime) --
The time when the optimization job was last updated.
FailureReason (string) --
If the optimization job status is FAILED , the reason for the failure.
OptimizationJobName (string) --
The name that you assigned to the optimization job.
ModelSource (dict) --
The location of the source model to optimize with an optimization job.
S3 (dict) --
The Amazon S3 location of a source model to optimize with an optimization job.
S3Uri (string) --
An Amazon S3 URI that locates a source model to optimize with an optimization job.
ModelAccessConfig (dict) --
The access configuration settings for the source ML model for an optimization job, where you can accept the model end-user license agreement (EULA).
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.
OptimizationEnvironment (dict) --
The environment variables to set in the model container.
(string) --
(string) --
DeploymentInstanceType (string) --
The type of instance that hosts the optimized model that you create with the optimization job.
OptimizationConfigs (list) --
Settings for each of the optimization techniques that the job applies.
(dict) --
Settings for an optimization technique that you apply with a model optimization job.
Note
This is a Tagged Union structure. Only one of the following top level keys will be set: ModelQuantizationConfig, ModelCompilationConfig. If a client receives an unknown member it will set SDK_UNKNOWN_MEMBER as the top level key, which maps to the name or tag of the unknown member. The structure of SDK_UNKNOWN_MEMBER is as follows:
'SDK_UNKNOWN_MEMBER': {'name': 'UnknownMemberName'}
ModelQuantizationConfig (dict) --
Settings for the model quantization technique that's applied by a model optimization job.
Image (string) --
The URI of an LMI DLC in Amazon ECR. SageMaker uses this image to run the optimization.
OverrideEnvironment (dict) --
Environment variables that override the default ones in the model container.
(string) --
(string) --
ModelCompilationConfig (dict) --
Settings for the model compilation technique that's applied by a model optimization job.
Image (string) --
The URI of an LMI DLC in Amazon ECR. SageMaker uses this image to run the optimization.
OverrideEnvironment (dict) --
Environment variables that override the default ones in the model container.
(string) --
(string) --
OutputConfig (dict) --
Details for where to store the optimized model that you create with the optimization job.
KmsKeyId (string) --
The Amazon Resource Name (ARN) of a key in Amazon Web Services KMS. SageMaker uses they key to encrypt the artifacts of the optimized model when SageMaker uploads the model to Amazon S3.
S3OutputLocation (string) --
The Amazon S3 URI for where to store the optimized model that you create with an optimization job.
OptimizationOutput (dict) --
Output values produced by an optimization job.
RecommendedInferenceImage (string) --
The image that SageMaker recommends that you use to host the optimized model that you created with an optimization job.
RoleArn (string) --
The ARN of the IAM role that you assigned to the optimization job.
StoppingCondition (dict) --
Specifies a limit to how long a job can run. When the job reaches the time limit, SageMaker ends the job. Use this API to cap costs.
To stop a training job, SageMaker sends the algorithm the SIGTERM signal, which delays job termination for 120 seconds. Algorithms can use this 120-second window to save the model artifacts, so the results of training are not lost.
The training algorithms provided by SageMaker automatically save the intermediate results of a model training job when possible. This attempt to save artifacts is only a best effort case as model might not be in a state from which it can be saved. For example, if training has just started, the model might not be ready to save. When saved, this intermediate data is a valid model artifact. You can use it to create a model with CreateModel .
Note
The Neural Topic Model (NTM) currently does not support saving intermediate model artifacts. When training NTMs, make sure that the maximum runtime is sufficient for the training job to complete.
MaxRuntimeInSeconds (integer) --
The maximum length of time, in seconds, that a training or compilation job can run before it is stopped.
For compilation jobs, if the job does not complete during this time, a TimeOut error is generated. We recommend starting with 900 seconds and increasing as necessary based on your model.
For all other jobs, if the job does not complete during this time, SageMaker ends the job. When RetryStrategy is specified in the job request, MaxRuntimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt. The default value is 1 day. The maximum value is 28 days.
The maximum time that a TrainingJob can run in total, including any time spent publishing metrics or archiving and uploading models after it has been stopped, is 30 days.
MaxWaitTimeInSeconds (integer) --
The maximum length of time, in seconds, that a managed Spot training job has to complete. It is the amount of time spent waiting for Spot capacity plus the amount of time the job can run. It must be equal to or greater than MaxRuntimeInSeconds . If the job does not complete during this time, SageMaker ends the job.
When RetryStrategy is specified in the job request, MaxWaitTimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt.
MaxPendingTimeInSeconds (integer) --
The maximum length of time, in seconds, that a training or compilation job can be pending before it is stopped.
VpcConfig (dict) --
A VPC in Amazon VPC that your optimized model has access to.
SecurityGroupIds (list) --
The VPC security group IDs, in the form sg-xxxxxxxx . Specify the security groups for the VPC that is specified in the Subnets field.
(string) --
Subnets (list) --
The ID of the subnets in the VPC to which you want to connect your optimized model.
(string) --
{'DomainSettings': {'AmazonQSettings': {'QProfileArn': 'string',
'Status': 'ENABLED | DISABLED'}}}
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 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, 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 Studio notebook or to train or host models unless your VPC has an interface endpoint to the SageMaker API and runtime or a NAT gateway and your security groups allow outbound connections.
Warning
NFS traffic over TCP on port 2049 needs to be allowed in both inbound and outbound rules in order to launch a Amazon SageMaker Studio app successfully.
For more information, see Connect Amazon SageMaker 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',
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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.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.p5.48xlarge'|'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',
'LifecycleConfigArn': '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.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.p5.48xlarge'|'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',
'LifecycleConfigArn': 'string'
},
'CustomImages': [
{
'ImageName': 'string',
'ImageVersionNumber': 123,
'AppImageConfigName': 'string'
},
],
'LifecycleConfigArns': [
'string',
],
'CodeRepositories': [
{
'RepositoryUrl': 'string'
},
]
},
'SpaceStorageSettings': {
'DefaultEbsStorageSettings': {
'DefaultEbsVolumeSizeInGb': 123,
'MaximumEbsVolumeSizeInGb': 123
}
},
'CustomPosixUserConfig': {
'Uid': 123,
'Gid': 123
},
'CustomFileSystemConfigs': [
{
'EFSFileSystemConfig': {
'FileSystemId': 'string',
'FileSystemPath': '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.
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 adds a security group to allow NFS traffic from Amazon SageMaker Studio. Therefore, the number of security groups that you can specify is one less than the maximum number shown.
(string) --
SharingSettings (dict) --
Specifies options for sharing Amazon SageMaker 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 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 image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance.
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.
Note
JupyterServer apps only support the system value.
For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
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.
Note
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 JupyterServer application.
(dict) --
A Git repository that SageMaker 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 image used by the KernelGateway app.
Note
The Amazon SageMaker Studio UI does not use the default instance type value set here. The default instance type set here is used when Apps are created using the CLI or CloudFormation and the instance type parameter value is not passed.
SageMakerImageArn (string) --
The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance.
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.
Note
JupyterServer apps only support the system value.
For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
CustomImages (list) --
A list of custom SageMaker images that are configured to run as a KernelGateway app.
(dict) --
A custom SageMaker image. For more information, see Bring your own SageMaker 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.
Note
To remove a Lifecycle Config, you must set LifecycleConfigArns to an empty list.
(string) --
TensorBoardAppSettings (dict) --
The TensorBoard app settings.
DefaultResourceSpec (dict) --
The default instance type and the Amazon Resource Name (ARN) of the SageMaker image created on the instance.
SageMakerImageArn (string) --
The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance.
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.
Note
JupyterServer apps only support the system value.
For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
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 image and SageMaker image version, and the instance type that the version runs on.
SageMakerImageArn (string) --
The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance.
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.
Note
JupyterServer apps only support the system value.
For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
CustomImages (list) --
A list of custom SageMaker images that are configured to run as a RSession app.
(dict) --
A custom SageMaker image. For more information, see Bring your own SageMaker 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.
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.
CodeEditorAppSettings (dict) --
The Code Editor application settings.
DefaultResourceSpec (dict) --
Specifies the ARN's of a SageMaker image and SageMaker image version, and the instance type that the version runs on.
SageMakerImageArn (string) --
The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance.
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.
Note
JupyterServer apps only support the system value.
For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
CustomImages (list) --
A list of custom SageMaker images that are configured to run as a Code Editor app.
(dict) --
A custom SageMaker image. For more information, see Bring your own SageMaker 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) --
JupyterLabAppSettings (dict) --
The settings for the JupyterLab application.
DefaultResourceSpec (dict) --
Specifies the ARN's of a SageMaker image and SageMaker image version, and the instance type that the version runs on.
SageMakerImageArn (string) --
The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance.
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.
Note
JupyterServer apps only support the system value.
For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
CustomImages (list) --
A list of custom SageMaker images that are configured to run as a JupyterLab app.
(dict) --
A custom SageMaker image. For more information, see Bring your own SageMaker 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 automatically displays to users for cloning in the JupyterServer application.
RepositoryUrl (string) -- [REQUIRED]
The URL of the Git repository.
SpaceStorageSettings (dict) --
The 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.
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.
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 Studio.
(dict) --
The settings for assigning a custom file system to a user profile or space for an Amazon SageMaker Domain. Permitted users can access this file system in Amazon SageMaker Studio.
Note
This is a Tagged Union structure. Only one of the following top level keys can be set: EFSFileSystemConfig.
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 Studio. Permitted users can access only this directory and below.
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) --
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 image and SageMaker image version, and the instance type that the version runs on.
SageMakerImageArn (string) --
The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance.
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.
Note
JupyterServer apps only support the system value.
For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
ExecutionRoleIdentityConfig (string) --
The configuration for attaching a SageMaker user profile name to the execution role as a sts:SourceIdentity key.
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) --
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.
list
[REQUIRED]
The VPC subnets that the domain uses for communication.
(string) --
string
[REQUIRED]
The ID of the Amazon Virtual Private Cloud (VPC) that the domain uses for communication.
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, which allows direct internet access
VpcOnly - All traffic is through the specified VPC and subnets
string
Use KmsKeyId .
string
SageMaker 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 .
dict
The default settings used to create a space.
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 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 image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance.
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.
Note
JupyterServer apps only support the system value.
For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
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.
Note
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 JupyterServer application.
(dict) --
A Git repository that SageMaker 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 image used by the KernelGateway app.
Note
The Amazon SageMaker Studio UI does not use the default instance type value set here. The default instance type set here is used when Apps are created using the CLI or CloudFormation and the instance type parameter value is not passed.
SageMakerImageArn (string) --
The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance.
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.
Note
JupyterServer apps only support the system value.
For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
CustomImages (list) --
A list of custom SageMaker images that are configured to run as a KernelGateway app.
(dict) --
A custom SageMaker image. For more information, see Bring your own SageMaker 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.
Note
To remove a Lifecycle Config, you must set LifecycleConfigArns to an empty list.
(string) --
JupyterLabAppSettings (dict) --
The settings for the JupyterLab application.
DefaultResourceSpec (dict) --
Specifies the ARN's of a SageMaker image and SageMaker image version, and the instance type that the version runs on.
SageMakerImageArn (string) --
The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance.
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.
Note
JupyterServer apps only support the system value.
For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
CustomImages (list) --
A list of custom SageMaker images that are configured to run as a JupyterLab app.
(dict) --
A custom SageMaker image. For more information, see Bring your own SageMaker 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 automatically displays to users for cloning in the JupyterServer application.
RepositoryUrl (string) -- [REQUIRED]
The URL of the Git repository.
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 Studio.
(dict) --
The settings for assigning a custom file system to a user profile or space for an Amazon SageMaker Domain. Permitted users can access this file system in Amazon SageMaker Studio.
Note
This is a Tagged Union structure. Only one of the following top level keys can be set: EFSFileSystemConfig.
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 Studio. Permitted users can access only this directory and below.
dict
Response Syntax
{
'DomainArn': 'string',
'Url': 'string'
}
Response Structure
(dict) --
DomainArn (string) --
The Amazon Resource Name (ARN) of the created domain.
Url (string) --
The URL to the created domain.
{'Containers': {'AdditionalModelDataSources': [{'ChannelName': 'string',
'S3DataSource': {'CompressionType': 'None '
'| '
'Gzip',
'HubAccessConfig': {'HubContentArn': 'string'},
'ModelAccessConfig': {'AcceptEula': 'boolean'},
'S3DataType': 'S3Prefix '
'| '
'S3Object',
'S3Uri': 'string'}}]},
'PrimaryContainer': {'AdditionalModelDataSources': [{'ChannelName': 'string',
'S3DataSource': {'CompressionType': 'None '
'| '
'Gzip',
'HubAccessConfig': {'HubContentArn': 'string'},
'ModelAccessConfig': {'AcceptEula': 'boolean'},
'S3DataType': 'S3Prefix '
'| '
'S3Object',
'S3Uri': 'string'}}]}}
Creates a model in SageMaker. In the request, you name the model and describe a primary container. For the primary container, you specify the Docker image that contains inference code, artifacts (from prior training), and a custom environment map that the inference code uses when you deploy the model for predictions.
Use this API to create a model if you want to use SageMaker hosting services or run a batch transform job.
To host your model, you create an endpoint configuration with the CreateEndpointConfig API, and then create an endpoint with the CreateEndpoint API. SageMaker then deploys all of the containers that you defined for the model in the hosting environment.
To run a batch transform using your model, you start a job with the CreateTransformJob API. SageMaker uses your model and your dataset to get inferences which are then saved to a specified S3 location.
In the request, you also provide an IAM role that SageMaker can assume to access model artifacts and docker image for deployment on ML compute hosting instances or for batch transform jobs. In addition, you also use the IAM role to manage permissions the inference code needs. For example, if the inference code access any other Amazon Web Services resources, you grant necessary permissions via this role.
See also: AWS API Documentation
Request Syntax
client.create_model(
ModelName='string',
PrimaryContainer={
'ContainerHostname': 'string',
'Image': 'string',
'ImageConfig': {
'RepositoryAccessMode': 'Platform'|'Vpc',
'RepositoryAuthConfig': {
'RepositoryCredentialsProviderArn': 'string'
}
},
'Mode': 'SingleModel'|'MultiModel',
'ModelDataUrl': 'string',
'ModelDataSource': {
'S3DataSource': {
'S3Uri': 'string',
'S3DataType': 'S3Prefix'|'S3Object',
'CompressionType': 'None'|'Gzip',
'ModelAccessConfig': {
'AcceptEula': True|False
},
'HubAccessConfig': {
'HubContentArn': 'string'
}
}
},
'AdditionalModelDataSources': [
{
'ChannelName': 'string',
'S3DataSource': {
'S3Uri': 'string',
'S3DataType': 'S3Prefix'|'S3Object',
'CompressionType': 'None'|'Gzip',
'ModelAccessConfig': {
'AcceptEula': True|False
},
'HubAccessConfig': {
'HubContentArn': 'string'
}
}
},
],
'Environment': {
'string': 'string'
},
'ModelPackageName': 'string',
'InferenceSpecificationName': 'string',
'MultiModelConfig': {
'ModelCacheSetting': 'Enabled'|'Disabled'
}
},
Containers=[
{
'ContainerHostname': 'string',
'Image': 'string',
'ImageConfig': {
'RepositoryAccessMode': 'Platform'|'Vpc',
'RepositoryAuthConfig': {
'RepositoryCredentialsProviderArn': 'string'
}
},
'Mode': 'SingleModel'|'MultiModel',
'ModelDataUrl': 'string',
'ModelDataSource': {
'S3DataSource': {
'S3Uri': 'string',
'S3DataType': 'S3Prefix'|'S3Object',
'CompressionType': 'None'|'Gzip',
'ModelAccessConfig': {
'AcceptEula': True|False
},
'HubAccessConfig': {
'HubContentArn': 'string'
}
}
},
'AdditionalModelDataSources': [
{
'ChannelName': 'string',
'S3DataSource': {
'S3Uri': 'string',
'S3DataType': 'S3Prefix'|'S3Object',
'CompressionType': 'None'|'Gzip',
'ModelAccessConfig': {
'AcceptEula': True|False
},
'HubAccessConfig': {
'HubContentArn': 'string'
}
}
},
],
'Environment': {
'string': 'string'
},
'ModelPackageName': 'string',
'InferenceSpecificationName': 'string',
'MultiModelConfig': {
'ModelCacheSetting': 'Enabled'|'Disabled'
}
},
],
InferenceExecutionConfig={
'Mode': 'Serial'|'Direct'
},
ExecutionRoleArn='string',
Tags=[
{
'Key': 'string',
'Value': 'string'
},
],
VpcConfig={
'SecurityGroupIds': [
'string',
],
'Subnets': [
'string',
]
},
EnableNetworkIsolation=True|False
)
string
[REQUIRED]
The name of the new model.
dict
The location of the primary docker image containing inference code, associated artifacts, and custom environment map that the inference code uses when the model is deployed for predictions.
ContainerHostname (string) --
This parameter is ignored for models that contain only a PrimaryContainer .
When a ContainerDefinition is part of an inference pipeline, the value of the parameter uniquely identifies the container for the purposes of logging and metrics. For information, see Use Logs and Metrics to Monitor an Inference Pipeline. If you don't specify a value for this parameter for a ContainerDefinition that is part of an inference pipeline, a unique name is automatically assigned based on the position of the ContainerDefinition in the pipeline. If you specify a value for the ContainerHostName for any ContainerDefinition that is part of an inference pipeline, you must specify a value for the ContainerHostName parameter of every ContainerDefinition in that pipeline.
Image (string) --
The path where inference code is stored. This can be either in Amazon EC2 Container Registry or in a Docker registry that is accessible from the same VPC that you configure for your endpoint. 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.
Note
The model artifacts in an Amazon S3 bucket and the Docker image for inference container in Amazon EC2 Container Registry must be in the same region as the model or endpoint you are creating.
ImageConfig (dict) --
Specifies whether the model container is in Amazon ECR or a private Docker registry accessible from your Amazon Virtual Private Cloud (VPC). For information about storing containers in a private Docker registry, see Use a Private Docker Registry for Real-Time Inference Containers.
Note
The model artifacts in an Amazon S3 bucket and the Docker image for inference container in Amazon EC2 Container Registry must be in the same region as the model or endpoint you are creating.
RepositoryAccessMode (string) -- [REQUIRED]
Set this to one of the following values:
Platform - The model image is hosted in Amazon ECR.
Vpc - The model image is hosted in a private Docker registry in your VPC.
RepositoryAuthConfig (dict) --
(Optional) Specifies an authentication configuration for the private docker registry where your model image is hosted. Specify a value for this property only if you specified Vpc as the value for the RepositoryAccessMode field, and the private Docker registry where the model image is hosted requires authentication.
RepositoryCredentialsProviderArn (string) -- [REQUIRED]
The Amazon Resource Name (ARN) of an Amazon Web Services Lambda function that provides credentials to authenticate to the private Docker registry where your model image is hosted. For information about how to create an Amazon Web Services Lambda function, see Create a Lambda function with the console in the Amazon Web Services Lambda Developer Guide .
Mode (string) --
Whether the container hosts a single model or multiple models.
ModelDataUrl (string) --
The 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). The S3 path is required for SageMaker built-in algorithms, but not if you use your own algorithms. For more information on built-in algorithms, see Common Parameters.
Note
The model artifacts must be in an S3 bucket that is in the same region as the model or endpoint you are creating.
If you provide a value for this parameter, SageMaker uses Amazon Web Services Security Token Service to download model artifacts from the S3 path you provide. Amazon Web Services STS is activated in your Amazon Web Services account by default. If you previously deactivated Amazon Web Services STS for a region, you need to reactivate Amazon Web Services STS for that region. For more information, see Activating and Deactivating Amazon Web Services STS in an Amazon Web Services Region in the Amazon Web Services Identity and Access Management User Guide .
Warning
If you use a built-in algorithm to create a model, SageMaker requires that you provide a S3 path to the model artifacts in ModelDataUrl .
ModelDataSource (dict) --
Specifies the location of ML model data to deploy.
Note
Currently you cannot use ModelDataSource in conjunction with SageMaker batch transform, SageMaker serverless endpoints, SageMaker multi-model endpoints, and SageMaker Marketplace.
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.
AdditionalModelDataSources (list) --
Data sources that are available to your model in addition to the one that you specify for ModelDataSource when you use the CreateModel 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.
Environment (dict) --
The environment variables to set in the Docker container.
The maximum length of each key and value in the Environment map is 1024 bytes. The maximum length of all keys and values in the map, combined, is 32 KB. If you pass multiple containers to a CreateModel request, then the maximum length of all of their maps, combined, is also 32 KB.
(string) --
(string) --
ModelPackageName (string) --
The name or Amazon Resource Name (ARN) of the model package to use to create the model.
InferenceSpecificationName (string) --
The inference specification name in the model package version.
MultiModelConfig (dict) --
Specifies additional configuration for multi-model endpoints.
ModelCacheSetting (string) --
Whether to cache models for a multi-model endpoint. By default, multi-model endpoints cache models so that a model does not have to be loaded into memory each time it is invoked. Some use cases do not benefit from model caching. For example, if an endpoint hosts a large number of models that are each invoked infrequently, the endpoint might perform better if you disable model caching. To disable model caching, set the value of this parameter to Disabled .
list
Specifies the containers in the inference pipeline.
(dict) --
Describes the container, as part of model definition.
ContainerHostname (string) --
This parameter is ignored for models that contain only a PrimaryContainer .
When a ContainerDefinition is part of an inference pipeline, the value of the parameter uniquely identifies the container for the purposes of logging and metrics. For information, see Use Logs and Metrics to Monitor an Inference Pipeline. If you don't specify a value for this parameter for a ContainerDefinition that is part of an inference pipeline, a unique name is automatically assigned based on the position of the ContainerDefinition in the pipeline. If you specify a value for the ContainerHostName for any ContainerDefinition that is part of an inference pipeline, you must specify a value for the ContainerHostName parameter of every ContainerDefinition in that pipeline.
Image (string) --
The path where inference code is stored. This can be either in Amazon EC2 Container Registry or in a Docker registry that is accessible from the same VPC that you configure for your endpoint. 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.
Note
The model artifacts in an Amazon S3 bucket and the Docker image for inference container in Amazon EC2 Container Registry must be in the same region as the model or endpoint you are creating.
ImageConfig (dict) --
Specifies whether the model container is in Amazon ECR or a private Docker registry accessible from your Amazon Virtual Private Cloud (VPC). For information about storing containers in a private Docker registry, see Use a Private Docker Registry for Real-Time Inference Containers.
Note
The model artifacts in an Amazon S3 bucket and the Docker image for inference container in Amazon EC2 Container Registry must be in the same region as the model or endpoint you are creating.
RepositoryAccessMode (string) -- [REQUIRED]
Set this to one of the following values:
Platform - The model image is hosted in Amazon ECR.
Vpc - The model image is hosted in a private Docker registry in your VPC.
RepositoryAuthConfig (dict) --
(Optional) Specifies an authentication configuration for the private docker registry where your model image is hosted. Specify a value for this property only if you specified Vpc as the value for the RepositoryAccessMode field, and the private Docker registry where the model image is hosted requires authentication.
RepositoryCredentialsProviderArn (string) -- [REQUIRED]
The Amazon Resource Name (ARN) of an Amazon Web Services Lambda function that provides credentials to authenticate to the private Docker registry where your model image is hosted. For information about how to create an Amazon Web Services Lambda function, see Create a Lambda function with the console in the Amazon Web Services Lambda Developer Guide .
Mode (string) --
Whether the container hosts a single model or multiple models.
ModelDataUrl (string) --
The 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). The S3 path is required for SageMaker built-in algorithms, but not if you use your own algorithms. For more information on built-in algorithms, see Common Parameters.
Note
The model artifacts must be in an S3 bucket that is in the same region as the model or endpoint you are creating.
If you provide a value for this parameter, SageMaker uses Amazon Web Services Security Token Service to download model artifacts from the S3 path you provide. Amazon Web Services STS is activated in your Amazon Web Services account by default. If you previously deactivated Amazon Web Services STS for a region, you need to reactivate Amazon Web Services STS for that region. For more information, see Activating and Deactivating Amazon Web Services STS in an Amazon Web Services Region in the Amazon Web Services Identity and Access Management User Guide .
Warning
If you use a built-in algorithm to create a model, SageMaker requires that you provide a S3 path to the model artifacts in ModelDataUrl .
ModelDataSource (dict) --
Specifies the location of ML model data to deploy.
Note
Currently you cannot use ModelDataSource in conjunction with SageMaker batch transform, SageMaker serverless endpoints, SageMaker multi-model endpoints, and SageMaker Marketplace.
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.
AdditionalModelDataSources (list) --
Data sources that are available to your model in addition to the one that you specify for ModelDataSource when you use the CreateModel 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.
Environment (dict) --
The environment variables to set in the Docker container.
The maximum length of each key and value in the Environment map is 1024 bytes. The maximum length of all keys and values in the map, combined, is 32 KB. If you pass multiple containers to a CreateModel request, then the maximum length of all of their maps, combined, is also 32 KB.
(string) --
(string) --
ModelPackageName (string) --
The name or Amazon Resource Name (ARN) of the model package to use to create the model.
InferenceSpecificationName (string) --
The inference specification name in the model package version.
MultiModelConfig (dict) --
Specifies additional configuration for multi-model endpoints.
ModelCacheSetting (string) --
Whether to cache models for a multi-model endpoint. By default, multi-model endpoints cache models so that a model does not have to be loaded into memory each time it is invoked. Some use cases do not benefit from model caching. For example, if an endpoint hosts a large number of models that are each invoked infrequently, the endpoint might perform better if you disable model caching. To disable model caching, set the value of this parameter to Disabled .
dict
Specifies details of how containers in a multi-container endpoint are called.
Mode (string) -- [REQUIRED]
How containers in a multi-container are run. The following values are valid.
SERIAL - Containers run as a serial pipeline.
DIRECT - Only the individual container that you specify is run.
string
The Amazon Resource Name (ARN) of the IAM role that SageMaker can assume to access model artifacts and docker image for deployment on ML compute instances or for batch transform jobs. Deploying on ML compute instances is part of model hosting. For more information, see SageMaker Roles.
Note
To be able to pass this role to SageMaker, the caller of this API must have the iam:PassRole permission.
list
An 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 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
A VpcConfig object that specifies the VPC that you want your model to connect to. Control access to and from your model container by configuring the VPC. VpcConfig is used in hosting services and in batch transform. For more information, see Protect Endpoints by Using an Amazon Virtual Private Cloud and Protect Data in Batch Transform Jobs by Using an Amazon Virtual Private Cloud.
SecurityGroupIds (list) -- [REQUIRED]
The VPC security group IDs, in the form sg-xxxxxxxx . Specify the security groups for the VPC that is specified in the Subnets field.
(string) --
Subnets (list) -- [REQUIRED]
The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones.
(string) --
boolean
Isolates the model container. No inbound or outbound network calls can be made to or from the model container.
dict
Response Syntax
{
'ModelArn': 'string'
}
Response Structure
(dict) --
ModelArn (string) --
The ARN of the model created in SageMaker.
{'DomainSettings': {'AmazonQSettings': {'QProfileArn': 'string',
'Status': 'ENABLED | DISABLED'}}}
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.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.p5.48xlarge'|'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',
'LifecycleConfigArn': 'string'
},
'LifecycleConfigArns': [
'string',
],
'CodeRepositories': [
{
'RepositoryUrl': 'string'
},
]
},
'KernelGatewayAppSettings': {
'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.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.p5.48xlarge'|'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',
'LifecycleConfigArn': 'string'
},
'CustomImages': [
{
'ImageName': 'string',
'ImageVersionNumber': 123,
'AppImageConfigName': 'string'
},
],
'LifecycleConfigArns': [
'string',
],
'CodeRepositories': [
{
'RepositoryUrl': 'string'
},
]
},
'SpaceStorageSettings': {
'DefaultEbsStorageSettings': {
'DefaultEbsVolumeSizeInGb': 123,
'MaximumEbsVolumeSizeInGb': 123
}
},
'CustomPosixUserConfig': {
'Uid': 123,
'Gid': 123
},
'CustomFileSystemConfigs': [
{
'EFSFileSystemConfig': {
'FileSystemId': 'string',
'FileSystemPath': '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 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.
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 adds a security group to allow NFS traffic from Amazon SageMaker Studio. Therefore, the number of security groups that you can specify is one less than the maximum number shown.
(string) --
SharingSettings (dict) --
Specifies options for sharing Amazon SageMaker 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 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 image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance.
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.
Note
JupyterServer apps only support the system value.
For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
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.
Note
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 JupyterServer application.
(dict) --
A Git repository that SageMaker 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 image used by the KernelGateway app.
Note
The Amazon SageMaker Studio UI does not use the default instance type value set here. The default instance type set here is used when Apps are created using the CLI or CloudFormation and the instance type parameter value is not passed.
SageMakerImageArn (string) --
The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance.
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.
Note
JupyterServer apps only support the system value.
For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
CustomImages (list) --
A list of custom SageMaker images that are configured to run as a KernelGateway app.
(dict) --
A custom SageMaker image. For more information, see Bring your own SageMaker 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.
Note
To remove a Lifecycle Config, you must set LifecycleConfigArns to an empty list.
(string) --
TensorBoardAppSettings (dict) --
The TensorBoard app settings.
DefaultResourceSpec (dict) --
The default instance type and the Amazon Resource Name (ARN) of the SageMaker image created on the instance.
SageMakerImageArn (string) --
The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance.
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.
Note
JupyterServer apps only support the system value.
For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
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 image and SageMaker image version, and the instance type that the version runs on.
SageMakerImageArn (string) --
The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance.
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.
Note
JupyterServer apps only support the system value.
For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
CustomImages (list) --
A list of custom SageMaker images that are configured to run as a RSession app.
(dict) --
A custom SageMaker image. For more information, see Bring your own SageMaker 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.
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.
CodeEditorAppSettings (dict) --
The Code Editor application settings.
DefaultResourceSpec (dict) --
Specifies the ARN's of a SageMaker image and SageMaker image version, and the instance type that the version runs on.
SageMakerImageArn (string) --
The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance.
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.
Note
JupyterServer apps only support the system value.
For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
CustomImages (list) --
A list of custom SageMaker images that are configured to run as a Code Editor app.
(dict) --
A custom SageMaker image. For more information, see Bring your own SageMaker 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) --
JupyterLabAppSettings (dict) --
The settings for the JupyterLab application.
DefaultResourceSpec (dict) --
Specifies the ARN's of a SageMaker image and SageMaker image version, and the instance type that the version runs on.
SageMakerImageArn (string) --
The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance.
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.
Note
JupyterServer apps only support the system value.
For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
CustomImages (list) --
A list of custom SageMaker images that are configured to run as a JupyterLab app.
(dict) --
A custom SageMaker image. For more information, see Bring your own SageMaker 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 automatically displays to users for cloning in the JupyterServer application.
RepositoryUrl (string) --
The URL of the Git repository.
SpaceStorageSettings (dict) --
The 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.
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.
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 Studio.
(dict) --
The settings for assigning a custom file system to a user profile or space for an Amazon SageMaker Domain. Permitted users can access this file system in Amazon SageMaker Studio.
Note
This is a Tagged Union structure. Only one of the following top level keys will be set: EFSFileSystemConfig. If a client receives an unknown member it will set SDK_UNKNOWN_MEMBER as the top level key, which maps to the name or tag of the unknown member. The structure of SDK_UNKNOWN_MEMBER is as follows:
'SDK_UNKNOWN_MEMBER': {'name': 'UnknownMemberName'}
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 Studio. Permitted users can access only this directory and below.
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) --
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 image and SageMaker image version, and the instance type that the version runs on.
SageMakerImageArn (string) --
The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance.
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.
Note
JupyterServer apps only support the system value.
For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
ExecutionRoleIdentityConfig (string) --
The configuration for attaching a SageMaker user profile name to the execution role as a sts:SourceIdentity key.
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) --
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.
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, 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.
DefaultSpaceSettings (dict) --
The default settings used to create a space.
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 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 image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance.
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.
Note
JupyterServer apps only support the system value.
For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
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.
Note
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 JupyterServer application.
(dict) --
A Git repository that SageMaker 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 image used by the KernelGateway app.
Note
The Amazon SageMaker Studio UI does not use the default instance type value set here. The default instance type set here is used when Apps are created using the CLI or CloudFormation and the instance type parameter value is not passed.
SageMakerImageArn (string) --
The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance.
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.
Note
JupyterServer apps only support the system value.
For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
CustomImages (list) --
A list of custom SageMaker images that are configured to run as a KernelGateway app.
(dict) --
A custom SageMaker image. For more information, see Bring your own SageMaker 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.
Note
To remove a Lifecycle Config, you must set LifecycleConfigArns to an empty list.
(string) --
JupyterLabAppSettings (dict) --
The settings for the JupyterLab application.
DefaultResourceSpec (dict) --
Specifies the ARN's of a SageMaker image and SageMaker image version, and the instance type that the version runs on.
SageMakerImageArn (string) --
The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance.
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.
Note
JupyterServer apps only support the system value.
For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
CustomImages (list) --
A list of custom SageMaker images that are configured to run as a JupyterLab app.
(dict) --
A custom SageMaker image. For more information, see Bring your own SageMaker 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 automatically displays to users for cloning in the JupyterServer application.
RepositoryUrl (string) --
The URL of the Git repository.
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 Studio.
(dict) --
The settings for assigning a custom file system to a user profile or space for an Amazon SageMaker Domain. Permitted users can access this file system in Amazon SageMaker Studio.
Note
This is a Tagged Union structure. Only one of the following top level keys will be set: EFSFileSystemConfig. If a client receives an unknown member it will set SDK_UNKNOWN_MEMBER as the top level key, which maps to the name or tag of the unknown member. The structure of SDK_UNKNOWN_MEMBER is as follows:
'SDK_UNKNOWN_MEMBER': {'name': 'UnknownMemberName'}
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 Studio. Permitted users can access only this directory and below.
{'Containers': {'AdditionalModelDataSources': [{'ChannelName': 'string',
'S3DataSource': {'CompressionType': 'None '
'| '
'Gzip',
'HubAccessConfig': {'HubContentArn': 'string'},
'ModelAccessConfig': {'AcceptEula': 'boolean'},
'S3DataType': 'S3Prefix '
'| '
'S3Object',
'S3Uri': 'string'}}]},
'PrimaryContainer': {'AdditionalModelDataSources': [{'ChannelName': 'string',
'S3DataSource': {'CompressionType': 'None '
'| '
'Gzip',
'HubAccessConfig': {'HubContentArn': 'string'},
'ModelAccessConfig': {'AcceptEula': 'boolean'},
'S3DataType': 'S3Prefix '
'| '
'S3Object',
'S3Uri': 'string'}}]}}
Describes a model that you created using the CreateModel API.
See also: AWS API Documentation
Request Syntax
client.describe_model(
ModelName='string'
)
string
[REQUIRED]
The name of the model.
dict
Response Syntax
{
'ModelName': 'string',
'PrimaryContainer': {
'ContainerHostname': 'string',
'Image': 'string',
'ImageConfig': {
'RepositoryAccessMode': 'Platform'|'Vpc',
'RepositoryAuthConfig': {
'RepositoryCredentialsProviderArn': 'string'
}
},
'Mode': 'SingleModel'|'MultiModel',
'ModelDataUrl': 'string',
'ModelDataSource': {
'S3DataSource': {
'S3Uri': 'string',
'S3DataType': 'S3Prefix'|'S3Object',
'CompressionType': 'None'|'Gzip',
'ModelAccessConfig': {
'AcceptEula': True|False
},
'HubAccessConfig': {
'HubContentArn': 'string'
}
}
},
'AdditionalModelDataSources': [
{
'ChannelName': 'string',
'S3DataSource': {
'S3Uri': 'string',
'S3DataType': 'S3Prefix'|'S3Object',
'CompressionType': 'None'|'Gzip',
'ModelAccessConfig': {
'AcceptEula': True|False
},
'HubAccessConfig': {
'HubContentArn': 'string'
}
}
},
],
'Environment': {
'string': 'string'
},
'ModelPackageName': 'string',
'InferenceSpecificationName': 'string',
'MultiModelConfig': {
'ModelCacheSetting': 'Enabled'|'Disabled'
}
},
'Containers': [
{
'ContainerHostname': 'string',
'Image': 'string',
'ImageConfig': {
'RepositoryAccessMode': 'Platform'|'Vpc',
'RepositoryAuthConfig': {
'RepositoryCredentialsProviderArn': 'string'
}
},
'Mode': 'SingleModel'|'MultiModel',
'ModelDataUrl': 'string',
'ModelDataSource': {
'S3DataSource': {
'S3Uri': 'string',
'S3DataType': 'S3Prefix'|'S3Object',
'CompressionType': 'None'|'Gzip',
'ModelAccessConfig': {
'AcceptEula': True|False
},
'HubAccessConfig': {
'HubContentArn': 'string'
}
}
},
'AdditionalModelDataSources': [
{
'ChannelName': 'string',
'S3DataSource': {
'S3Uri': 'string',
'S3DataType': 'S3Prefix'|'S3Object',
'CompressionType': 'None'|'Gzip',
'ModelAccessConfig': {
'AcceptEula': True|False
},
'HubAccessConfig': {
'HubContentArn': 'string'
}
}
},
],
'Environment': {
'string': 'string'
},
'ModelPackageName': 'string',
'InferenceSpecificationName': 'string',
'MultiModelConfig': {
'ModelCacheSetting': 'Enabled'|'Disabled'
}
},
],
'InferenceExecutionConfig': {
'Mode': 'Serial'|'Direct'
},
'ExecutionRoleArn': 'string',
'VpcConfig': {
'SecurityGroupIds': [
'string',
],
'Subnets': [
'string',
]
},
'CreationTime': datetime(2015, 1, 1),
'ModelArn': 'string',
'EnableNetworkIsolation': True|False,
'DeploymentRecommendation': {
'RecommendationStatus': 'IN_PROGRESS'|'COMPLETED'|'FAILED'|'NOT_APPLICABLE',
'RealTimeInferenceRecommendations': [
{
'RecommendationId': 'string',
'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.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.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.inf2.xlarge'|'ml.inf2.8xlarge'|'ml.inf2.24xlarge'|'ml.inf2.48xlarge'|'ml.p5.48xlarge'|'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',
'Environment': {
'string': 'string'
}
},
]
}
}
Response Structure
(dict) --
ModelName (string) --
Name of the SageMaker model.
PrimaryContainer (dict) --
The location of the primary inference code, associated artifacts, and custom environment map that the inference code uses when it is deployed in production.
ContainerHostname (string) --
This parameter is ignored for models that contain only a PrimaryContainer .
When a ContainerDefinition is part of an inference pipeline, the value of the parameter uniquely identifies the container for the purposes of logging and metrics. For information, see Use Logs and Metrics to Monitor an Inference Pipeline. If you don't specify a value for this parameter for a ContainerDefinition that is part of an inference pipeline, a unique name is automatically assigned based on the position of the ContainerDefinition in the pipeline. If you specify a value for the ContainerHostName for any ContainerDefinition that is part of an inference pipeline, you must specify a value for the ContainerHostName parameter of every ContainerDefinition in that pipeline.
Image (string) --
The path where inference code is stored. This can be either in Amazon EC2 Container Registry or in a Docker registry that is accessible from the same VPC that you configure for your endpoint. 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.
Note
The model artifacts in an Amazon S3 bucket and the Docker image for inference container in Amazon EC2 Container Registry must be in the same region as the model or endpoint you are creating.
ImageConfig (dict) --
Specifies whether the model container is in Amazon ECR or a private Docker registry accessible from your Amazon Virtual Private Cloud (VPC). For information about storing containers in a private Docker registry, see Use a Private Docker Registry for Real-Time Inference Containers.
Note
The model artifacts in an Amazon S3 bucket and the Docker image for inference container in Amazon EC2 Container Registry must be in the same region as the model or endpoint you are creating.
RepositoryAccessMode (string) --
Set this to one of the following values:
Platform - The model image is hosted in Amazon ECR.
Vpc - The model image is hosted in a private Docker registry in your VPC.
RepositoryAuthConfig (dict) --
(Optional) Specifies an authentication configuration for the private docker registry where your model image is hosted. Specify a value for this property only if you specified Vpc as the value for the RepositoryAccessMode field, and the private Docker registry where the model image is hosted requires authentication.
RepositoryCredentialsProviderArn (string) --
The Amazon Resource Name (ARN) of an Amazon Web Services Lambda function that provides credentials to authenticate to the private Docker registry where your model image is hosted. For information about how to create an Amazon Web Services Lambda function, see Create a Lambda function with the console in the Amazon Web Services Lambda Developer Guide .
Mode (string) --
Whether the container hosts a single model or multiple models.
ModelDataUrl (string) --
The 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). The S3 path is required for SageMaker built-in algorithms, but not if you use your own algorithms. For more information on built-in algorithms, see Common Parameters.
Note
The model artifacts must be in an S3 bucket that is in the same region as the model or endpoint you are creating.
If you provide a value for this parameter, SageMaker uses Amazon Web Services Security Token Service to download model artifacts from the S3 path you provide. Amazon Web Services STS is activated in your Amazon Web Services account by default. If you previously deactivated Amazon Web Services STS for a region, you need to reactivate Amazon Web Services STS for that region. For more information, see Activating and Deactivating Amazon Web Services STS in an Amazon Web Services Region in the Amazon Web Services Identity and Access Management User Guide .
Warning
If you use a built-in algorithm to create a model, SageMaker requires that you provide a S3 path to the model artifacts in ModelDataUrl .
ModelDataSource (dict) --
Specifies the location of ML model data to deploy.
Note
Currently you cannot use ModelDataSource in conjunction with SageMaker batch transform, SageMaker serverless endpoints, SageMaker multi-model endpoints, and SageMaker Marketplace.
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.
AdditionalModelDataSources (list) --
Data sources that are available to your model in addition to the one that you specify for ModelDataSource when you use the CreateModel 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.
Environment (dict) --
The environment variables to set in the Docker container.
The maximum length of each key and value in the Environment map is 1024 bytes. The maximum length of all keys and values in the map, combined, is 32 KB. If you pass multiple containers to a CreateModel request, then the maximum length of all of their maps, combined, is also 32 KB.
(string) --
(string) --
ModelPackageName (string) --
The name or Amazon Resource Name (ARN) of the model package to use to create the model.
InferenceSpecificationName (string) --
The inference specification name in the model package version.
MultiModelConfig (dict) --
Specifies additional configuration for multi-model endpoints.
ModelCacheSetting (string) --
Whether to cache models for a multi-model endpoint. By default, multi-model endpoints cache models so that a model does not have to be loaded into memory each time it is invoked. Some use cases do not benefit from model caching. For example, if an endpoint hosts a large number of models that are each invoked infrequently, the endpoint might perform better if you disable model caching. To disable model caching, set the value of this parameter to Disabled .
Containers (list) --
The containers in the inference pipeline.
(dict) --
Describes the container, as part of model definition.
ContainerHostname (string) --
This parameter is ignored for models that contain only a PrimaryContainer .
When a ContainerDefinition is part of an inference pipeline, the value of the parameter uniquely identifies the container for the purposes of logging and metrics. For information, see Use Logs and Metrics to Monitor an Inference Pipeline. If you don't specify a value for this parameter for a ContainerDefinition that is part of an inference pipeline, a unique name is automatically assigned based on the position of the ContainerDefinition in the pipeline. If you specify a value for the ContainerHostName for any ContainerDefinition that is part of an inference pipeline, you must specify a value for the ContainerHostName parameter of every ContainerDefinition in that pipeline.
Image (string) --
The path where inference code is stored. This can be either in Amazon EC2 Container Registry or in a Docker registry that is accessible from the same VPC that you configure for your endpoint. 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.
Note
The model artifacts in an Amazon S3 bucket and the Docker image for inference container in Amazon EC2 Container Registry must be in the same region as the model or endpoint you are creating.
ImageConfig (dict) --
Specifies whether the model container is in Amazon ECR or a private Docker registry accessible from your Amazon Virtual Private Cloud (VPC). For information about storing containers in a private Docker registry, see Use a Private Docker Registry for Real-Time Inference Containers.
Note
The model artifacts in an Amazon S3 bucket and the Docker image for inference container in Amazon EC2 Container Registry must be in the same region as the model or endpoint you are creating.
RepositoryAccessMode (string) --
Set this to one of the following values:
Platform - The model image is hosted in Amazon ECR.
Vpc - The model image is hosted in a private Docker registry in your VPC.
RepositoryAuthConfig (dict) --
(Optional) Specifies an authentication configuration for the private docker registry where your model image is hosted. Specify a value for this property only if you specified Vpc as the value for the RepositoryAccessMode field, and the private Docker registry where the model image is hosted requires authentication.
RepositoryCredentialsProviderArn (string) --
The Amazon Resource Name (ARN) of an Amazon Web Services Lambda function that provides credentials to authenticate to the private Docker registry where your model image is hosted. For information about how to create an Amazon Web Services Lambda function, see Create a Lambda function with the console in the Amazon Web Services Lambda Developer Guide .
Mode (string) --
Whether the container hosts a single model or multiple models.
ModelDataUrl (string) --
The 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). The S3 path is required for SageMaker built-in algorithms, but not if you use your own algorithms. For more information on built-in algorithms, see Common Parameters.
Note
The model artifacts must be in an S3 bucket that is in the same region as the model or endpoint you are creating.
If you provide a value for this parameter, SageMaker uses Amazon Web Services Security Token Service to download model artifacts from the S3 path you provide. Amazon Web Services STS is activated in your Amazon Web Services account by default. If you previously deactivated Amazon Web Services STS for a region, you need to reactivate Amazon Web Services STS for that region. For more information, see Activating and Deactivating Amazon Web Services STS in an Amazon Web Services Region in the Amazon Web Services Identity and Access Management User Guide .
Warning
If you use a built-in algorithm to create a model, SageMaker requires that you provide a S3 path to the model artifacts in ModelDataUrl .
ModelDataSource (dict) --
Specifies the location of ML model data to deploy.
Note
Currently you cannot use ModelDataSource in conjunction with SageMaker batch transform, SageMaker serverless endpoints, SageMaker multi-model endpoints, and SageMaker Marketplace.
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.
AdditionalModelDataSources (list) --
Data sources that are available to your model in addition to the one that you specify for ModelDataSource when you use the CreateModel 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.
Environment (dict) --
The environment variables to set in the Docker container.
The maximum length of each key and value in the Environment map is 1024 bytes. The maximum length of all keys and values in the map, combined, is 32 KB. If you pass multiple containers to a CreateModel request, then the maximum length of all of their maps, combined, is also 32 KB.
(string) --
(string) --
ModelPackageName (string) --
The name or Amazon Resource Name (ARN) of the model package to use to create the model.
InferenceSpecificationName (string) --
The inference specification name in the model package version.
MultiModelConfig (dict) --
Specifies additional configuration for multi-model endpoints.
ModelCacheSetting (string) --
Whether to cache models for a multi-model endpoint. By default, multi-model endpoints cache models so that a model does not have to be loaded into memory each time it is invoked. Some use cases do not benefit from model caching. For example, if an endpoint hosts a large number of models that are each invoked infrequently, the endpoint might perform better if you disable model caching. To disable model caching, set the value of this parameter to Disabled .
InferenceExecutionConfig (dict) --
Specifies details of how containers in a multi-container endpoint are called.
Mode (string) --
How containers in a multi-container are run. The following values are valid.
SERIAL - Containers run as a serial pipeline.
DIRECT - Only the individual container that you specify is run.
ExecutionRoleArn (string) --
The Amazon Resource Name (ARN) of the IAM role that you specified for the model.
VpcConfig (dict) --
A VpcConfig object that specifies the VPC that this model has access to. For more information, see Protect Endpoints by Using an Amazon Virtual Private Cloud
SecurityGroupIds (list) --
The VPC security group IDs, in the form sg-xxxxxxxx . Specify the security groups for the VPC that is specified in the Subnets field.
(string) --
Subnets (list) --
The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones.
(string) --
CreationTime (datetime) --
A timestamp that shows when the model was created.
ModelArn (string) --
The Amazon Resource Name (ARN) of the model.
EnableNetworkIsolation (boolean) --
If True , no inbound or outbound network calls can be made to or from the model container.
DeploymentRecommendation (dict) --
A set of recommended deployment configurations for the model.
RecommendationStatus (string) --
Status of the deployment recommendation. The status NOT_APPLICABLE means that SageMaker is unable to provide a default recommendation for the model using the information provided. If the deployment status is IN_PROGRESS , retry your API call after a few seconds to get a COMPLETED deployment recommendation.
RealTimeInferenceRecommendations (list) --
A list of RealTimeInferenceRecommendation items.
(dict) --
The recommended configuration to use for Real-Time Inference.
RecommendationId (string) --
The recommendation ID which uniquely identifies each recommendation.
InstanceType (string) --
The recommended instance type for Real-Time Inference.
Environment (dict) --
The recommended environment variables to set in the model container for Real-Time Inference.
(string) --
(string) --
{'Results': {'Model': {'Model': {'Containers': {'AdditionalModelDataSources': [{'ChannelName': 'string',
'S3DataSource': {'CompressionType': 'None '
'| '
'Gzip',
'HubAccessConfig': {'HubContentArn': 'string'},
'ModelAccessConfig': {'AcceptEula': 'boolean'},
'S3DataType': 'S3Prefix '
'| '
'S3Object',
'S3Uri': 'string'}}]},
'PrimaryContainer': {'AdditionalModelDataSources': [{'ChannelName': 'string',
'S3DataSource': {'CompressionType': 'None '
'| '
'Gzip',
'HubAccessConfig': {'HubContentArn': 'string'},
'ModelAccessConfig': {'AcceptEula': 'boolean'},
'S3DataType': 'S3Prefix '
'| '
'S3Object',
'S3Uri': 'string'}}]}}}}}
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.
Note
The Search API may provide access to otherwise restricted data. See Amazon SageMaker API Permissions: Actions, Permissions, and Resources Reference for more information.
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',
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.
{'DomainSettingsForUpdate': {'AmazonQSettings': {'QProfileArn': 'string',
'Status': 'ENABLED | '
'DISABLED'}}}
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',
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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.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.p5.48xlarge'|'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',
'LifecycleConfigArn': '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.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.p5.48xlarge'|'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',
'LifecycleConfigArn': 'string'
},
'CustomImages': [
{
'ImageName': 'string',
'ImageVersionNumber': 123,
'AppImageConfigName': 'string'
},
],
'LifecycleConfigArns': [
'string',
],
'CodeRepositories': [
{
'RepositoryUrl': 'string'
},
]
},
'SpaceStorageSettings': {
'DefaultEbsStorageSettings': {
'DefaultEbsVolumeSizeInGb': 123,
'MaximumEbsVolumeSizeInGb': 123
}
},
'CustomPosixUserConfig': {
'Uid': 123,
'Gid': 123
},
'CustomFileSystemConfigs': [
{
'EFSFileSystemConfig': {
'FileSystemId': 'string',
'FileSystemPath': 'string'
}
},
]
},
SubnetIds=[
'string',
],
AppNetworkAccessType='PublicInternetOnly'|'VpcOnly'
)
string
[REQUIRED]
The ID of the domain to be updated.
dict
A collection of settings.
ExecutionRole (string) --
The execution role for the user.
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 adds a security group to allow NFS traffic from Amazon SageMaker Studio. Therefore, the number of security groups that you can specify is one less than the maximum number shown.
(string) --
SharingSettings (dict) --
Specifies options for sharing Amazon SageMaker 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 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 image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance.
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.
Note
JupyterServer apps only support the system value.
For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
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.
Note
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 JupyterServer application.
(dict) --
A Git repository that SageMaker 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 image used by the KernelGateway app.
Note
The Amazon SageMaker Studio UI does not use the default instance type value set here. The default instance type set here is used when Apps are created using the CLI or CloudFormation and the instance type parameter value is not passed.
SageMakerImageArn (string) --
The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance.
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.
Note
JupyterServer apps only support the system value.
For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
CustomImages (list) --
A list of custom SageMaker images that are configured to run as a KernelGateway app.
(dict) --
A custom SageMaker image. For more information, see Bring your own SageMaker 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.
Note
To remove a Lifecycle Config, you must set LifecycleConfigArns to an empty list.
(string) --
TensorBoardAppSettings (dict) --
The TensorBoard app settings.
DefaultResourceSpec (dict) --
The default instance type and the Amazon Resource Name (ARN) of the SageMaker image created on the instance.
SageMakerImageArn (string) --
The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance.
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.
Note
JupyterServer apps only support the system value.
For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
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 image and SageMaker image version, and the instance type that the version runs on.
SageMakerImageArn (string) --
The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance.
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.
Note
JupyterServer apps only support the system value.
For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
CustomImages (list) --
A list of custom SageMaker images that are configured to run as a RSession app.
(dict) --
A custom SageMaker image. For more information, see Bring your own SageMaker 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.
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.
CodeEditorAppSettings (dict) --
The Code Editor application settings.
DefaultResourceSpec (dict) --
Specifies the ARN's of a SageMaker image and SageMaker image version, and the instance type that the version runs on.
SageMakerImageArn (string) --
The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance.
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.
Note
JupyterServer apps only support the system value.
For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
CustomImages (list) --
A list of custom SageMaker images that are configured to run as a Code Editor app.
(dict) --
A custom SageMaker image. For more information, see Bring your own SageMaker 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) --
JupyterLabAppSettings (dict) --
The settings for the JupyterLab application.
DefaultResourceSpec (dict) --
Specifies the ARN's of a SageMaker image and SageMaker image version, and the instance type that the version runs on.
SageMakerImageArn (string) --
The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance.
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.
Note
JupyterServer apps only support the system value.
For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
CustomImages (list) --
A list of custom SageMaker images that are configured to run as a JupyterLab app.
(dict) --
A custom SageMaker image. For more information, see Bring your own SageMaker 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 automatically displays to users for cloning in the JupyterServer application.
RepositoryUrl (string) -- [REQUIRED]
The URL of the Git repository.
SpaceStorageSettings (dict) --
The 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.
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.
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 Studio.
(dict) --
The settings for assigning a custom file system to a user profile or space for an Amazon SageMaker Domain. Permitted users can access this file system in Amazon SageMaker Studio.
Note
This is a Tagged Union structure. Only one of the following top level keys can be set: EFSFileSystemConfig.
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 Studio. Permitted users can access only this directory and below.
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) --
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 image and SageMaker image version, and the instance type that the version runs on.
SageMakerImageArn (string) --
The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance.
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.
Note
JupyterServer apps only support the system value.
For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
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 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) --
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) --
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.
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 used to create a space within 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 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 image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance.
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.
Note
JupyterServer apps only support the system value.
For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
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.
Note
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 JupyterServer application.
(dict) --
A Git repository that SageMaker 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 image used by the KernelGateway app.
Note
The Amazon SageMaker Studio UI does not use the default instance type value set here. The default instance type set here is used when Apps are created using the CLI or CloudFormation and the instance type parameter value is not passed.
SageMakerImageArn (string) --
The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance.
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.
Note
JupyterServer apps only support the system value.
For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
CustomImages (list) --
A list of custom SageMaker images that are configured to run as a KernelGateway app.
(dict) --
A custom SageMaker image. For more information, see Bring your own SageMaker 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.
Note
To remove a Lifecycle Config, you must set LifecycleConfigArns to an empty list.
(string) --
JupyterLabAppSettings (dict) --
The settings for the JupyterLab application.
DefaultResourceSpec (dict) --
Specifies the ARN's of a SageMaker image and SageMaker image version, and the instance type that the version runs on.
SageMakerImageArn (string) --
The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance.
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.
Note
JupyterServer apps only support the system value.
For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
CustomImages (list) --
A list of custom SageMaker images that are configured to run as a JupyterLab app.
(dict) --
A custom SageMaker image. For more information, see Bring your own SageMaker 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 automatically displays to users for cloning in the JupyterServer application.
RepositoryUrl (string) -- [REQUIRED]
The URL of the Git repository.
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 Studio.
(dict) --
The settings for assigning a custom file system to a user profile or space for an Amazon SageMaker Domain. Permitted users can access this file system in Amazon SageMaker Studio.
Note
This is a Tagged Union structure. Only one of the following top level keys can be set: EFSFileSystemConfig.
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 Studio. Permitted users can access only this directory and below.
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, 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.
dict
Response Syntax
{
'DomainArn': 'string'
}
Response Structure
(dict) --
DomainArn (string) --
The Amazon Resource Name (ARN) of the domain.