2020/07/24 - Amazon SageMaker Service - 3 new12 updated api methods
Changes Update sagemaker client to latest version
Use this operation to delete a workforce.
If you want to create a new workforce in an AWS Region where the a workforce already exists, use this operation to delete the existing workforce and then use to create a new workforce.
See also: AWS API Documentation
Request Syntax
client.delete_workforce(
WorkforceName='string'
)
string
[REQUIRED]
The name of the workforce.
dict
Response Syntax
{}
Response Structure
(dict) --
Use this operation to list all private and vendor workforces in an AWS Region. Note that you can only have one private workforce per AWS Region.
See also: AWS API Documentation
Request Syntax
client.list_workforces(
SortBy='Name'|'CreateDate',
SortOrder='Ascending'|'Descending',
NameContains='string',
NextToken='string',
MaxResults=123
)
string
Sort workforces using the workforce name or creation date.
string
Sort workforces in ascending or descending order.
string
A filter you can use to search for workforces using part of the workforce name.
string
A token to resume pagination.
integer
The maximum number of workforces returned in the response.
dict
Response Syntax
{
'Workforces': [
{
'WorkforceName': 'string',
'WorkforceArn': 'string',
'LastUpdatedDate': datetime(2015, 1, 1),
'SourceIpConfig': {
'Cidrs': [
'string',
]
},
'SubDomain': 'string',
'CognitoConfig': {
'UserPool': 'string',
'ClientId': 'string'
},
'OidcConfig': {
'ClientId': 'string',
'Issuer': 'string',
'AuthorizationEndpoint': 'string',
'TokenEndpoint': 'string',
'UserInfoEndpoint': 'string',
'LogoutEndpoint': 'string',
'JwksUri': 'string'
},
'CreateDate': datetime(2015, 1, 1)
},
],
'NextToken': 'string'
}
Response Structure
(dict) --
Workforces (list) --
A list containing information about your workforce.
(dict) --
A single private workforce, which is automatically created when you create your first private work team. You can create one private work force in each AWS Region. By default, any workforce-related API operation used in a specific region will apply to the workforce created in that region. To learn how to create a private workforce, see Create a Private Workforce.
WorkforceName (string) --
The name of the private workforce.
WorkforceArn (string) --
The Amazon Resource Name (ARN) of the private workforce.
LastUpdatedDate (datetime) --
The most recent date that was used to successfully add one or more IP address ranges ( CIDRs) to a private workforce's allow list.
SourceIpConfig (dict) --
A list of one to ten IP address ranges ( CIDRs) to be added to the workforce allow list.
Cidrs (list) --
A list of one to ten Classless Inter-Domain Routing (CIDR) values.
Maximum: Ten CIDR values
(string) --
SubDomain (string) --
The subdomain for your OIDC Identity Provider.
CognitoConfig (dict) --
The configuration of an Amazon Cognito workforce. A single Cognito workforce is created using and corresponds to a single Amazon Cognito user pool.
UserPool (string) --
A user pool is a user directory in Amazon Cognito. With a user pool, your users can sign in to your web or mobile app through Amazon Cognito. Your users can also sign in through social identity providers like Google, Facebook, Amazon, or Apple, and through SAML identity providers.
ClientId (string) --
The client ID for your Amazon Cognito user pool.
OidcConfig (dict) --
The configuration of an OIDC Identity Provider (IdP) private workforce.
ClientId (string) --
The OIDC IdP client ID used to configure your private workforce.
Issuer (string) --
The OIDC IdP issuer used to configure your private workforce.
AuthorizationEndpoint (string) --
The OIDC IdP authorization endpoint used to configure your private workforce.
TokenEndpoint (string) --
The OIDC IdP token endpoint used to configure your private workforce.
UserInfoEndpoint (string) --
The OIDC IdP user information endpoint used to configure your private workforce.
LogoutEndpoint (string) --
The OIDC IdP logout endpoint used to configure your private workforce.
JwksUri (string) --
The OIDC IdP JSON Web Key Set (Jwks) URI used to configure your private workforce.
CreateDate (datetime) --
The date that the workforce is created.
NextToken (string) --
A token to resume pagination.
Use this operation to create a workforce. This operation will return an error if a workforce already exists in the AWS Region that you specify. You can only create one workforce in each AWS Region.
If you want to create a new workforce in an AWS Region where the a workforce already exists, use the API operation to delete the existing workforce and then use this operation to create a new workforce.
To create a private workforce using Amazon Cognito, you must specify a Cognito user pool in CognitoConfig. You can also create an Amazon Cognito workforce using the Amazon SageMaker console. For more information, see Create a Private Workforce (Amazon Cognito).
To create a private workforce using your own OIDC Identity Provider (IdP), specify your IdP configuration in OidcConfig. You must create a OIDC IdP workforce using this API operation. For more information, see Create a Private Workforce (OIDC IdP).
See also: AWS API Documentation
Request Syntax
client.create_workforce(
CognitoConfig={
'UserPool': 'string',
'ClientId': 'string'
},
OidcConfig={
'ClientId': 'string',
'ClientSecret': 'string',
'Issuer': 'string',
'AuthorizationEndpoint': 'string',
'TokenEndpoint': 'string',
'UserInfoEndpoint': 'string',
'LogoutEndpoint': 'string',
'JwksUri': 'string'
},
SourceIpConfig={
'Cidrs': [
'string',
]
},
WorkforceName='string',
Tags=[
{
'Key': 'string',
'Value': 'string'
},
]
)
dict
Use this parameter to configure an Amazon Cognito private workforce. A single Cognito workforce is created using and corresponds to a single Amazon Cognito user pool.
Do not use OidcConfig if you specify values for CognitoConfig.
UserPool (string) -- [REQUIRED]
A user pool is a user directory in Amazon Cognito. With a user pool, your users can sign in to your web or mobile app through Amazon Cognito. Your users can also sign in through social identity providers like Google, Facebook, Amazon, or Apple, and through SAML identity providers.
ClientId (string) -- [REQUIRED]
The client ID for your Amazon Cognito user pool.
dict
Use this parameter to configure a private workforce using your own OIDC Identity Provider. Do not use CognitoConfig if you specify values for OidcConfig.
ClientId (string) -- [REQUIRED]
The OIDC IdP client ID used to configure your private workforce.
ClientSecret (string) -- [REQUIRED]
The OIDC IdP client secret used to configure your private workforce.
Issuer (string) -- [REQUIRED]
The OIDC IdP issuer used to configure your private workforce.
AuthorizationEndpoint (string) -- [REQUIRED]
The OIDC IdP authorization endpoint used to configure your private workforce.
TokenEndpoint (string) -- [REQUIRED]
The OIDC IdP token endpoint used to configure your private workforce.
UserInfoEndpoint (string) -- [REQUIRED]
The OIDC IdP user information endpoint used to configure your private workforce.
LogoutEndpoint (string) -- [REQUIRED]
The OIDC IdP logout endpoint used to configure your private workforce.
JwksUri (string) -- [REQUIRED]
The OIDC IdP JSON Web Key Set (Jwks) URI used to configure your private workforce.
dict
A list of IP address ranges ( CIDRs). Used to create an allow list of IP addresses for a private workforce. For more information, see .
Cidrs (list) -- [REQUIRED]
A list of one to ten Classless Inter-Domain Routing (CIDR) values.
Maximum: Ten CIDR values
(string) --
string
[REQUIRED]
The name of the private workforce.
list
An array of key-value pairs that contain metadata to help you categorize and organize our workforce. Each tag consists of a key and a value, both of which you define.
(dict) --
Describes a tag.
Key (string) -- [REQUIRED]
The tag key.
Value (string) -- [REQUIRED]
The tag value.
dict
Response Syntax
{
'WorkforceArn': 'string'
}
Response Structure
(dict) --
WorkforceArn (string) --
The Amazon Resource Name (ARN) of the workforce.
{'OutputConfig': {'CompilerOptions': 'string',
'TargetDevice': {'ml_g4dn', 'x86_win32', 'x86_win64'},
'TargetPlatform': {'Accelerator': 'INTEL_GRAPHICS | MALI | '
'NVIDIA',
'Arch': 'X86_64 | X86 | ARM64 | ARM_EABI '
'| ARM_EABIHF',
'Os': 'ANDROID | LINUX'}}}
Starts a model compilation job. After the model has been compiled, Amazon SageMaker saves the resulting model artifacts to an Amazon Simple Storage Service (Amazon S3) bucket that you specify.
If you choose to host your model using Amazon SageMaker hosting services, you can use the resulting model artifacts as part of the model. You can also use the artifacts with AWS IoT Greengrass. In that case, deploy them as an ML resource.
In the request body, you provide the following:
A name for the compilation job
Information about the input model artifacts
The output location for the compiled model and the device (target) that the model runs on
The Amazon Resource Name (ARN) of the IAM role that Amazon SageMaker assumes to perform the model compilation job.
You can also provide a Tag to track the model compilation job's resource use and costs. The response body contains the CompilationJobArn for the compiled job.
To stop a model compilation job, use StopCompilationJob. To get information about a particular model compilation job, use DescribeCompilationJob. To get information about multiple model compilation jobs, use ListCompilationJobs.
See also: AWS API Documentation
Request Syntax
client.create_compilation_job(
CompilationJobName='string',
RoleArn='string',
InputConfig={
'S3Uri': 'string',
'DataInputConfig': 'string',
'Framework': 'TENSORFLOW'|'KERAS'|'MXNET'|'ONNX'|'PYTORCH'|'XGBOOST'|'TFLITE'
},
OutputConfig={
'S3OutputLocation': 'string',
'TargetDevice': 'lambda'|'ml_m4'|'ml_m5'|'ml_c4'|'ml_c5'|'ml_p2'|'ml_p3'|'ml_g4dn'|'ml_inf1'|'jetson_tx1'|'jetson_tx2'|'jetson_nano'|'jetson_xavier'|'rasp3b'|'imx8qm'|'deeplens'|'rk3399'|'rk3288'|'aisage'|'sbe_c'|'qcs605'|'qcs603'|'sitara_am57x'|'amba_cv22'|'x86_win32'|'x86_win64',
'TargetPlatform': {
'Os': 'ANDROID'|'LINUX',
'Arch': 'X86_64'|'X86'|'ARM64'|'ARM_EABI'|'ARM_EABIHF',
'Accelerator': 'INTEL_GRAPHICS'|'MALI'|'NVIDIA'
},
'CompilerOptions': 'string'
},
StoppingCondition={
'MaxRuntimeInSeconds': 123,
'MaxWaitTimeInSeconds': 123
}
)
string
[REQUIRED]
A name for the model compilation job. The name must be unique within the AWS Region and within your AWS account.
string
[REQUIRED]
The Amazon Resource Name (ARN) of an IAM role that enables Amazon SageMaker to perform tasks on your behalf.
During model compilation, 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]
Provides information about the location of input model artifacts, the name and shape of the expected data inputs, and the framework in which the model was trained.
S3Uri (string) -- [REQUIRED]
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).
DataInputConfig (string) -- [REQUIRED]
Specifies the name and shape of the expected data inputs for your trained model with a JSON dictionary form. The data inputs are InputConfig$Framework specific.
TensorFlow: You must specify the name and shape (NHWC format) of the expected data inputs using a dictionary format for your trained model. The dictionary formats required for the console and CLI are different.
Examples for one input:
If using the console, {"input":[1,1024,1024,3]}
If using the CLI, {\"input\":[1,1024,1024,3]}
Examples for two inputs:
If using the console, {"data1": [1,28,28,1], "data2":[1,28,28,1]}
If using the CLI, {\"data1\": [1,28,28,1], \"data2\":[1,28,28,1]}
KERAS: You must specify the name and shape (NCHW format) of expected data inputs using a dictionary format for your trained model. Note that while Keras model artifacts should be uploaded in NHWC (channel-last) format, DataInputConfig should be specified in NCHW (channel-first) format. The dictionary formats required for the console and CLI are different.
Examples for one input:
If using the console, {"input_1":[1,3,224,224]}
If using the CLI, {\"input_1\":[1,3,224,224]}
Examples for two inputs:
If using the console, {"input_1": [1,3,224,224], "input_2":[1,3,224,224]}
If using the CLI, {\"input_1\": [1,3,224,224], \"input_2\":[1,3,224,224]}
MXNET/ONNX: You must specify the name and shape (NCHW format) of the expected data inputs in order using a dictionary format for your trained model. The dictionary formats required for the console and CLI are different.
Examples for one input:
If using the console, {"data":[1,3,1024,1024]}
If using the CLI, {\"data\":[1,3,1024,1024]}
Examples for two inputs:
If using the console, {"var1": [1,1,28,28], "var2":[1,1,28,28]}
If using the CLI, {\"var1\": [1,1,28,28], \"var2\":[1,1,28,28]}
PyTorch: You can either specify the name and shape (NCHW format) of expected data inputs in order using a dictionary format for your trained model or you can specify the shape only using a list format. The dictionary formats required for the console and CLI are different. The list formats for the console and CLI are the same.
Examples for one input in dictionary format:
If using the console, {"input0":[1,3,224,224]}
If using the CLI, {\"input0\":[1,3,224,224]}
Example for one input in list format: [[1,3,224,224]]
Examples for two inputs in dictionary format:
If using the console, {"input0":[1,3,224,224], "input1":[1,3,224,224]}
If using the CLI, {\"input0\":[1,3,224,224], \"input1\":[1,3,224,224]}
Example for two inputs in list format: [[1,3,224,224], [1,3,224,224]]
XGBOOST: input data name and shape are not needed.
Framework (string) -- [REQUIRED]
Identifies the framework in which the model was trained. For example: TENSORFLOW.
dict
[REQUIRED]
Provides information about the output location for the compiled model and the target device the model runs on.
S3OutputLocation (string) -- [REQUIRED]
Identifies the S3 bucket where you want Amazon SageMaker to store the model artifacts. For example, s3://bucket-name/key-name-prefix.
TargetDevice (string) --
Identifies the target device or the machine learning instance that you want to run your model on after the compilation has completed. Alternatively, you can specify OS, architecture, and accelerator using TargetPlatform fields. It can be used instead of TargetPlatform.
TargetPlatform (dict) --
Contains information about a target platform that you want your model to run on, such as OS, architecture, and accelerators. It is an alternative of TargetDevice.
The following examples show how to configure the TargetPlatform and CompilerOptions JSON strings for popular target platforms:
Raspberry Pi 3 Model B+ "TargetPlatform": {"Os": "LINUX", "Arch": "ARM_EABIHF"}, "CompilerOptions": {'mattr': ['+neon']}
Jetson TX2 "TargetPlatform": {"Os": "LINUX", "Arch": "ARM64", "Accelerator": "NVIDIA"}, "CompilerOptions": {'gpu-code': 'sm_62', 'trt-ver': '6.0.1', 'cuda-ver': '10.0'}
EC2 m5.2xlarge instance OS "TargetPlatform": {"Os": "LINUX", "Arch": "X86_64", "Accelerator": "NVIDIA"}, "CompilerOptions": {'mcpu': 'skylake-avx512'}
RK3399 "TargetPlatform": {"Os": "LINUX", "Arch": "ARM64", "Accelerator": "MALI"}
ARMv7 phone (CPU) "TargetPlatform": {"Os": "ANDROID", "Arch": "ARM_EABI"}, "CompilerOptions": {'ANDROID_PLATFORM': 25, 'mattr': ['+neon']}
ARMv8 phone (CPU) "TargetPlatform": {"Os": "ANDROID", "Arch": "ARM64"}, "CompilerOptions": {'ANDROID_PLATFORM': 29}
Os (string) -- [REQUIRED]
Specifies a target platform OS.
LINUX: Linux-based operating systems.
ANDROID: Android operating systems. Android API level can be specified using the ANDROID_PLATFORM compiler option. For example, "CompilerOptions": {'ANDROID_PLATFORM': 28}
Arch (string) -- [REQUIRED]
Specifies a target platform architecture.
X86_64: 64-bit version of the x86 instruction set.
X86: 32-bit version of the x86 instruction set.
ARM64: ARMv8 64-bit CPU.
ARM_EABIHF: ARMv7 32-bit, Hard Float.
ARM_EABI: ARMv7 32-bit, Soft Float. Used by Android 32-bit ARM platform.
Accelerator (string) --
Specifies a target platform accelerator (optional).
NVIDIA: Nvidia graphics processing unit. It also requires gpu-code, trt-ver, cuda-ver compiler options
MALI: ARM Mali graphics processor
INTEL_GRAPHICS: Integrated Intel graphics
CompilerOptions (string) --
Specifies additional parameters for compiler options in JSON format. The compiler options are TargetPlatform specific. It is required for NVIDIA accelerators and highly recommended for CPU compliations. For any other cases, it is optional to specify CompilerOptions.
CPU: Compilation for CPU supports the following compiler options.
mcpu: CPU micro-architecture. For example, {'mcpu': 'skylake-avx512'}
mattr: CPU flags. For example, {'mattr': ['+neon', '+vfpv4']}
ARM: Details of ARM CPU compilations.
NEON: NEON is an implementation of the Advanced SIMD extension used in ARMv7 processors. For example, add {'mattr': ['+neon']} to the compiler options if compiling for ARM 32-bit platform with the NEON support.
NVIDIA: Compilation for NVIDIA GPU supports the following compiler options.
gpu_code: Specifies the targeted architecture.
trt-ver: Specifies the TensorRT versions in x.y.z. format.
cuda-ver: Specifies the CUDA version in x.y format.
For example, {'gpu-code': 'sm_72', 'trt-ver': '6.0.1', 'cuda-ver': '10.1'}
ANDROID: Compilation for the Android OS supports the following compiler options:
ANDROID_PLATFORM: Specifies the Android API levels. Available levels range from 21 to 29. For example, {'ANDROID_PLATFORM': 28}.
mattr: Add {'mattr': ['+neon']} to compiler options if compiling for ARM 32-bit platform with NEON support.
dict
[REQUIRED]
Specifies a limit to how long a model compilation job can run. When the job reaches the time limit, Amazon SageMaker ends the compilation job. Use this API to cap model training costs.
MaxRuntimeInSeconds (integer) --
The maximum length of time, in seconds, that the training or compilation job can run. If job does not complete during this time, Amazon SageMaker ends the job. If value is not specified, default value is 1 day. The maximum value is 28 days.
MaxWaitTimeInSeconds (integer) --
The maximum length of time, in seconds, how long you are willing to wait for a managed spot training job to complete. It is the amount of time spent waiting for Spot capacity plus the amount of time the training job runs. It must be equal to or greater than MaxRuntimeInSeconds.
dict
Response Syntax
{
'CompilationJobArn': 'string'
}
Response Structure
(dict) --
CompilationJobArn (string) --
If the action is successful, the service sends back an HTTP 200 response. Amazon SageMaker returns the following data in JSON format:
CompilationJobArn: The Amazon Resource Name (ARN) of the compiled job.
{'MemberDefinitions': {'OidcMemberDefinition': {'Groups': ['string']}},
'WorkforceName': 'string'}
Creates a new work team for labeling your data. A work team is defined by one or more Amazon Cognito user pools. You must first create the user pools before you can create a work team.
You cannot create more than 25 work teams in an account and region.
See also: AWS API Documentation
Request Syntax
client.create_workteam(
WorkteamName='string',
WorkforceName='string',
MemberDefinitions=[
{
'CognitoMemberDefinition': {
'UserPool': 'string',
'UserGroup': 'string',
'ClientId': 'string'
},
'OidcMemberDefinition': {
'Groups': [
'string',
]
}
},
],
Description='string',
NotificationConfiguration={
'NotificationTopicArn': 'string'
},
Tags=[
{
'Key': 'string',
'Value': 'string'
},
]
)
string
[REQUIRED]
The name of the work team. Use this name to identify the work team.
string
The name of the workforce.
list
[REQUIRED]
A list of MemberDefinition objects that contains objects that identify the Amazon Cognito user pool that makes up the work team. For more information, see Amazon Cognito User Pools.
All of the CognitoMemberDefinition objects that make up the member definition must have the same ClientId and UserPool values.
(dict) --
Defines the Amazon Cognito user group that is part of a work team.
CognitoMemberDefinition (dict) --
The Amazon Cognito user group that is part of the work team.
UserPool (string) -- [REQUIRED]
An identifier for a user pool. The user pool must be in the same region as the service that you are calling.
UserGroup (string) -- [REQUIRED]
An identifier for a user group.
ClientId (string) -- [REQUIRED]
An identifier for an application client. You must create the app client ID using Amazon Cognito.
OidcMemberDefinition (dict) --
A list user groups that exist in your OIDC Identity Provider (IdP). One to ten groups can be used to create a single private work team. When you add a user group to the list of Groups, you can add that user group to one or more private work teams. If you add a user group to a private work team, all workers in that user group are added to the work team.
Groups (list) -- [REQUIRED]
A list of comma seperated strings that identifies user groups in your OIDC IdP. Each user group is made up of a group of private workers.
(string) --
string
[REQUIRED]
A description of the work team.
dict
Configures notification of workers regarding available or expiring work items.
NotificationTopicArn (string) --
The ARN for the SNS topic to which notifications should be published.
list
An array of key-value pairs.
For more information, see Resource Tag and Using Cost Allocation Tags in the AWS Billing and Cost Management User Guide.
(dict) --
Describes a tag.
Key (string) -- [REQUIRED]
The tag key.
Value (string) -- [REQUIRED]
The tag value.
dict
Response Syntax
{
'WorkteamArn': 'string'
}
Response Structure
(dict) --
WorkteamArn (string) --
The Amazon Resource Name (ARN) of the work team. You can use this ARN to identify the work team.
{'OutputConfig': {'CompilerOptions': 'string',
'TargetDevice': {'ml_g4dn', 'x86_win32', 'x86_win64'},
'TargetPlatform': {'Accelerator': 'INTEL_GRAPHICS | MALI | '
'NVIDIA',
'Arch': 'X86_64 | X86 | ARM64 | ARM_EABI '
'| ARM_EABIHF',
'Os': 'ANDROID | LINUX'}}}
Returns information about a model compilation job.
To create a model compilation job, use CreateCompilationJob. To get information about multiple model compilation jobs, use ListCompilationJobs.
See also: AWS API Documentation
Request Syntax
client.describe_compilation_job(
CompilationJobName='string'
)
string
[REQUIRED]
The name of the model compilation job that you want information about.
dict
Response Syntax
{
'CompilationJobName': 'string',
'CompilationJobArn': 'string',
'CompilationJobStatus': 'INPROGRESS'|'COMPLETED'|'FAILED'|'STARTING'|'STOPPING'|'STOPPED',
'CompilationStartTime': datetime(2015, 1, 1),
'CompilationEndTime': datetime(2015, 1, 1),
'StoppingCondition': {
'MaxRuntimeInSeconds': 123,
'MaxWaitTimeInSeconds': 123
},
'CreationTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'FailureReason': 'string',
'ModelArtifacts': {
'S3ModelArtifacts': 'string'
},
'RoleArn': 'string',
'InputConfig': {
'S3Uri': 'string',
'DataInputConfig': 'string',
'Framework': 'TENSORFLOW'|'KERAS'|'MXNET'|'ONNX'|'PYTORCH'|'XGBOOST'|'TFLITE'
},
'OutputConfig': {
'S3OutputLocation': 'string',
'TargetDevice': 'lambda'|'ml_m4'|'ml_m5'|'ml_c4'|'ml_c5'|'ml_p2'|'ml_p3'|'ml_g4dn'|'ml_inf1'|'jetson_tx1'|'jetson_tx2'|'jetson_nano'|'jetson_xavier'|'rasp3b'|'imx8qm'|'deeplens'|'rk3399'|'rk3288'|'aisage'|'sbe_c'|'qcs605'|'qcs603'|'sitara_am57x'|'amba_cv22'|'x86_win32'|'x86_win64',
'TargetPlatform': {
'Os': 'ANDROID'|'LINUX',
'Arch': 'X86_64'|'X86'|'ARM64'|'ARM_EABI'|'ARM_EABIHF',
'Accelerator': 'INTEL_GRAPHICS'|'MALI'|'NVIDIA'
},
'CompilerOptions': 'string'
}
}
Response Structure
(dict) --
CompilationJobName (string) --
The name of the model compilation job.
CompilationJobArn (string) --
The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker assumes to perform the model compilation job.
CompilationJobStatus (string) --
The status of the model compilation job.
CompilationStartTime (datetime) --
The time when the model compilation job started the CompilationJob instances.
You are billed for the time between this timestamp and the timestamp in the DescribeCompilationJobResponse$CompilationEndTime field. In Amazon CloudWatch Logs, the start time might be later than this time. That's because it takes time to download the compilation job, which depends on the size of the compilation job container.
CompilationEndTime (datetime) --
The time when the model compilation job on a compilation job instance ended. For a successful or stopped job, this is when the job's model artifacts have finished uploading. For a failed job, this is when Amazon SageMaker detected that the job failed.
StoppingCondition (dict) --
Specifies a limit to how long a model compilation job can run. When the job reaches the time limit, Amazon SageMaker ends the compilation job. Use this API to cap model training costs.
MaxRuntimeInSeconds (integer) --
The maximum length of time, in seconds, that the training or compilation job can run. If job does not complete during this time, Amazon SageMaker ends the job. If value is not specified, default value is 1 day. The maximum value is 28 days.
MaxWaitTimeInSeconds (integer) --
The maximum length of time, in seconds, how long you are willing to wait for a managed spot training job to complete. It is the amount of time spent waiting for Spot capacity plus the amount of time the training job runs. It must be equal to or greater than MaxRuntimeInSeconds.
CreationTime (datetime) --
The time that the model compilation job was created.
LastModifiedTime (datetime) --
The time that the status of the model compilation job was last modified.
FailureReason (string) --
If a model compilation job failed, the reason it failed.
ModelArtifacts (dict) --
Information about the location in Amazon S3 that has been configured for storing the model artifacts used in the compilation job.
S3ModelArtifacts (string) --
The path of the S3 object that contains the model artifacts. For example, s3://bucket-name/keynameprefix/model.tar.gz.
RoleArn (string) --
The Amazon Resource Name (ARN) of the model compilation job.
InputConfig (dict) --
Information about the location in Amazon S3 of the input model artifacts, the name and shape of the expected data inputs, and the framework in which the model was trained.
S3Uri (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).
DataInputConfig (string) --
Specifies the name and shape of the expected data inputs for your trained model with a JSON dictionary form. The data inputs are InputConfig$Framework specific.
TensorFlow: You must specify the name and shape (NHWC format) of the expected data inputs using a dictionary format for your trained model. The dictionary formats required for the console and CLI are different.
Examples for one input:
If using the console, {"input":[1,1024,1024,3]}
If using the CLI, {\"input\":[1,1024,1024,3]}
Examples for two inputs:
If using the console, {"data1": [1,28,28,1], "data2":[1,28,28,1]}
If using the CLI, {\"data1\": [1,28,28,1], \"data2\":[1,28,28,1]}
KERAS: You must specify the name and shape (NCHW format) of expected data inputs using a dictionary format for your trained model. Note that while Keras model artifacts should be uploaded in NHWC (channel-last) format, DataInputConfig should be specified in NCHW (channel-first) format. The dictionary formats required for the console and CLI are different.
Examples for one input:
If using the console, {"input_1":[1,3,224,224]}
If using the CLI, {\"input_1\":[1,3,224,224]}
Examples for two inputs:
If using the console, {"input_1": [1,3,224,224], "input_2":[1,3,224,224]}
If using the CLI, {\"input_1\": [1,3,224,224], \"input_2\":[1,3,224,224]}
MXNET/ONNX: You must specify the name and shape (NCHW format) of the expected data inputs in order using a dictionary format for your trained model. The dictionary formats required for the console and CLI are different.
Examples for one input:
If using the console, {"data":[1,3,1024,1024]}
If using the CLI, {\"data\":[1,3,1024,1024]}
Examples for two inputs:
If using the console, {"var1": [1,1,28,28], "var2":[1,1,28,28]}
If using the CLI, {\"var1\": [1,1,28,28], \"var2\":[1,1,28,28]}
PyTorch: You can either specify the name and shape (NCHW format) of expected data inputs in order using a dictionary format for your trained model or you can specify the shape only using a list format. The dictionary formats required for the console and CLI are different. The list formats for the console and CLI are the same.
Examples for one input in dictionary format:
If using the console, {"input0":[1,3,224,224]}
If using the CLI, {\"input0\":[1,3,224,224]}
Example for one input in list format: [[1,3,224,224]]
Examples for two inputs in dictionary format:
If using the console, {"input0":[1,3,224,224], "input1":[1,3,224,224]}
If using the CLI, {\"input0\":[1,3,224,224], \"input1\":[1,3,224,224]}
Example for two inputs in list format: [[1,3,224,224], [1,3,224,224]]
XGBOOST: input data name and shape are not needed.
Framework (string) --
Identifies the framework in which the model was trained. For example: TENSORFLOW.
OutputConfig (dict) --
Information about the output location for the compiled model and the target device that the model runs on.
S3OutputLocation (string) --
Identifies the S3 bucket where you want Amazon SageMaker to store the model artifacts. For example, s3://bucket-name/key-name-prefix.
TargetDevice (string) --
Identifies the target device or the machine learning instance that you want to run your model on after the compilation has completed. Alternatively, you can specify OS, architecture, and accelerator using TargetPlatform fields. It can be used instead of TargetPlatform.
TargetPlatform (dict) --
Contains information about a target platform that you want your model to run on, such as OS, architecture, and accelerators. It is an alternative of TargetDevice.
The following examples show how to configure the TargetPlatform and CompilerOptions JSON strings for popular target platforms:
Raspberry Pi 3 Model B+ "TargetPlatform": {"Os": "LINUX", "Arch": "ARM_EABIHF"}, "CompilerOptions": {'mattr': ['+neon']}
Jetson TX2 "TargetPlatform": {"Os": "LINUX", "Arch": "ARM64", "Accelerator": "NVIDIA"}, "CompilerOptions": {'gpu-code': 'sm_62', 'trt-ver': '6.0.1', 'cuda-ver': '10.0'}
EC2 m5.2xlarge instance OS "TargetPlatform": {"Os": "LINUX", "Arch": "X86_64", "Accelerator": "NVIDIA"}, "CompilerOptions": {'mcpu': 'skylake-avx512'}
RK3399 "TargetPlatform": {"Os": "LINUX", "Arch": "ARM64", "Accelerator": "MALI"}
ARMv7 phone (CPU) "TargetPlatform": {"Os": "ANDROID", "Arch": "ARM_EABI"}, "CompilerOptions": {'ANDROID_PLATFORM': 25, 'mattr': ['+neon']}
ARMv8 phone (CPU) "TargetPlatform": {"Os": "ANDROID", "Arch": "ARM64"}, "CompilerOptions": {'ANDROID_PLATFORM': 29}
Os (string) --
Specifies a target platform OS.
LINUX: Linux-based operating systems.
ANDROID: Android operating systems. Android API level can be specified using the ANDROID_PLATFORM compiler option. For example, "CompilerOptions": {'ANDROID_PLATFORM': 28}
Arch (string) --
Specifies a target platform architecture.
X86_64: 64-bit version of the x86 instruction set.
X86: 32-bit version of the x86 instruction set.
ARM64: ARMv8 64-bit CPU.
ARM_EABIHF: ARMv7 32-bit, Hard Float.
ARM_EABI: ARMv7 32-bit, Soft Float. Used by Android 32-bit ARM platform.
Accelerator (string) --
Specifies a target platform accelerator (optional).
NVIDIA: Nvidia graphics processing unit. It also requires gpu-code, trt-ver, cuda-ver compiler options
MALI: ARM Mali graphics processor
INTEL_GRAPHICS: Integrated Intel graphics
CompilerOptions (string) --
Specifies additional parameters for compiler options in JSON format. The compiler options are TargetPlatform specific. It is required for NVIDIA accelerators and highly recommended for CPU compliations. For any other cases, it is optional to specify CompilerOptions.
CPU: Compilation for CPU supports the following compiler options.
mcpu: CPU micro-architecture. For example, {'mcpu': 'skylake-avx512'}
mattr: CPU flags. For example, {'mattr': ['+neon', '+vfpv4']}
ARM: Details of ARM CPU compilations.
NEON: NEON is an implementation of the Advanced SIMD extension used in ARMv7 processors. For example, add {'mattr': ['+neon']} to the compiler options if compiling for ARM 32-bit platform with the NEON support.
NVIDIA: Compilation for NVIDIA GPU supports the following compiler options.
gpu_code: Specifies the targeted architecture.
trt-ver: Specifies the TensorRT versions in x.y.z. format.
cuda-ver: Specifies the CUDA version in x.y format.
For example, {'gpu-code': 'sm_72', 'trt-ver': '6.0.1', 'cuda-ver': '10.1'}
ANDROID: Compilation for the Android OS supports the following compiler options:
ANDROID_PLATFORM: Specifies the Android API levels. Available levels range from 21 to 29. For example, {'ANDROID_PLATFORM': 28}.
mattr: Add {'mattr': ['+neon']} to compiler options if compiling for ARM 32-bit platform with NEON support.
{'LabelingJobStatus': {'Initializing'}}
Gets information about a labeling job.
See also: AWS API Documentation
Request Syntax
client.describe_labeling_job(
LabelingJobName='string'
)
string
[REQUIRED]
The name of the labeling job to return information for.
dict
Response Syntax
{
'LabelingJobStatus': 'Initializing'|'InProgress'|'Completed'|'Failed'|'Stopping'|'Stopped',
'LabelCounters': {
'TotalLabeled': 123,
'HumanLabeled': 123,
'MachineLabeled': 123,
'FailedNonRetryableError': 123,
'Unlabeled': 123
},
'FailureReason': 'string',
'CreationTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'JobReferenceCode': 'string',
'LabelingJobName': 'string',
'LabelingJobArn': 'string',
'LabelAttributeName': 'string',
'InputConfig': {
'DataSource': {
'S3DataSource': {
'ManifestS3Uri': 'string'
}
},
'DataAttributes': {
'ContentClassifiers': [
'FreeOfPersonallyIdentifiableInformation'|'FreeOfAdultContent',
]
}
},
'OutputConfig': {
'S3OutputPath': 'string',
'KmsKeyId': 'string'
},
'RoleArn': 'string',
'LabelCategoryConfigS3Uri': 'string',
'StoppingConditions': {
'MaxHumanLabeledObjectCount': 123,
'MaxPercentageOfInputDatasetLabeled': 123
},
'LabelingJobAlgorithmsConfig': {
'LabelingJobAlgorithmSpecificationArn': 'string',
'InitialActiveLearningModelArn': 'string',
'LabelingJobResourceConfig': {
'VolumeKmsKeyId': 'string'
}
},
'HumanTaskConfig': {
'WorkteamArn': 'string',
'UiConfig': {
'UiTemplateS3Uri': 'string',
'HumanTaskUiArn': 'string'
},
'PreHumanTaskLambdaArn': 'string',
'TaskKeywords': [
'string',
],
'TaskTitle': 'string',
'TaskDescription': 'string',
'NumberOfHumanWorkersPerDataObject': 123,
'TaskTimeLimitInSeconds': 123,
'TaskAvailabilityLifetimeInSeconds': 123,
'MaxConcurrentTaskCount': 123,
'AnnotationConsolidationConfig': {
'AnnotationConsolidationLambdaArn': 'string'
},
'PublicWorkforceTaskPrice': {
'AmountInUsd': {
'Dollars': 123,
'Cents': 123,
'TenthFractionsOfACent': 123
}
}
},
'Tags': [
{
'Key': 'string',
'Value': 'string'
},
],
'LabelingJobOutput': {
'OutputDatasetS3Uri': 'string',
'FinalActiveLearningModelArn': 'string'
}
}
Response Structure
(dict) --
LabelingJobStatus (string) --
The processing status of the labeling job.
LabelCounters (dict) --
Provides a breakdown of the number of data objects labeled by humans, the number of objects labeled by machine, the number of objects than couldn't be labeled, and the total number of objects labeled.
TotalLabeled (integer) --
The total number of objects labeled.
HumanLabeled (integer) --
The total number of objects labeled by a human worker.
MachineLabeled (integer) --
The total number of objects labeled by automated data labeling.
FailedNonRetryableError (integer) --
The total number of objects that could not be labeled due to an error.
Unlabeled (integer) --
The total number of objects not yet labeled.
FailureReason (string) --
If the job failed, the reason that it failed.
CreationTime (datetime) --
The date and time that the labeling job was created.
LastModifiedTime (datetime) --
The date and time that the labeling job was last updated.
JobReferenceCode (string) --
A unique identifier for work done as part of a labeling job.
LabelingJobName (string) --
The name assigned to the labeling job when it was created.
LabelingJobArn (string) --
The Amazon Resource Name (ARN) of the labeling job.
LabelAttributeName (string) --
The attribute used as the label in the output manifest file.
InputConfig (dict) --
Input configuration information for the labeling job, such as the Amazon S3 location of the data objects and the location of the manifest file that describes the data objects.
DataSource (dict) --
The location of the input data.
S3DataSource (dict) --
The Amazon S3 location of the input data objects.
ManifestS3Uri (string) --
The Amazon S3 location of the manifest file that describes the input data objects.
DataAttributes (dict) --
Attributes of the data specified by the customer.
ContentClassifiers (list) --
Declares that your content is free of personally identifiable information or adult content. Amazon SageMaker may restrict the Amazon Mechanical Turk workers that can view your task based on this information.
(string) --
OutputConfig (dict) --
The location of the job's output data and the AWS Key Management Service key ID for the key used to encrypt the output data, if any.
S3OutputPath (string) --
The Amazon S3 location to write output data.
KmsKeyId (string) --
The AWS Key Management Service ID of the key used to encrypt the output data, if any.
If you use a KMS key ID or an alias of your master key, the Amazon SageMaker execution role must include permissions to call kms:Encrypt. If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account. Amazon SageMaker uses server-side encryption with KMS-managed keys for LabelingJobOutputConfig. If you use a bucket policy with an s3:PutObject permission that only allows objects with server-side encryption, set the condition key of s3:x-amz-server-side-encryption to "aws:kms". For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.
The KMS key policy must grant permission to the IAM role that you specify in your CreateLabelingJob request. For more information, see Using Key Policies in AWS KMS in the AWS Key Management Service Developer Guide.
RoleArn (string) --
The Amazon Resource Name (ARN) that Amazon SageMaker assumes to perform tasks on your behalf during data labeling.
LabelCategoryConfigS3Uri (string) --
The S3 location of the JSON file that defines the categories used to label data objects. Please note the following label-category limits:
Semantic segmentation labeling jobs using automated labeling: 20 labels
Box bounding labeling jobs (all): 10 labels
The file is a JSON structure in the following format:
{
"document-version": "2018-11-28"
"labels": [
{
"label": "label 1"
},
{
"label": "label 2"
},
...
{
"label": "label n"
}
]
}
StoppingConditions (dict) --
A set of conditions for stopping a labeling job. If any of the conditions are met, the job is automatically stopped.
MaxHumanLabeledObjectCount (integer) --
The maximum number of objects that can be labeled by human workers.
MaxPercentageOfInputDatasetLabeled (integer) --
The maximum number of input data objects that should be labeled.
LabelingJobAlgorithmsConfig (dict) --
Configuration information for automated data labeling.
LabelingJobAlgorithmSpecificationArn (string) --
Specifies the Amazon Resource Name (ARN) of the algorithm used for auto-labeling. You must select one of the following ARNs:
Image classification arn:aws:sagemaker:region:027400017018:labeling-job-algorithm-specification/image-classification
Text classification arn:aws:sagemaker:region:027400017018:labeling-job-algorithm-specification/text-classification
Object detection arn:aws:sagemaker:region:027400017018:labeling-job-algorithm-specification/object-detection
Semantic Segmentation arn:aws:sagemaker:region:027400017018:labeling-job-algorithm-specification/semantic-segmentation
InitialActiveLearningModelArn (string) --
At the end of an auto-label job Amazon SageMaker Ground Truth sends the Amazon Resource Nam (ARN) of the final model used for auto-labeling. You can use this model as the starting point for subsequent similar jobs by providing the ARN of the model here.
LabelingJobResourceConfig (dict) --
Provides configuration information for a labeling job.
VolumeKmsKeyId (string) --
The AWS Key Management Service (AWS KMS) key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the training job. The VolumeKmsKeyId can be any of the following formats:
// KMS Key ID "1234abcd-12ab-34cd-56ef-1234567890ab"
// Amazon Resource Name (ARN) of a KMS Key "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
HumanTaskConfig (dict) --
Configuration information required for human workers to complete a labeling task.
WorkteamArn (string) --
The Amazon Resource Name (ARN) of the work team assigned to complete the tasks.
UiConfig (dict) --
Information about the user interface that workers use to complete the labeling task.
UiTemplateS3Uri (string) --
The Amazon S3 bucket location of the UI template, or worker task template. This is the template used to render the worker UI and tools for labeling job tasks. For more information about the contents of a UI template, see Creating Your Custom Labeling Task Template.
HumanTaskUiArn (string) --
The ARN of the worker task template used to render the worker UI and tools for labeling job tasks.
Use this parameter when you are creating a labeling job for 3D point cloud and video fram labeling jobs. Use your labeling job task type to select one of the following ARN's and use it with this parameter when you create a labeling job. Replace aws-region with the AWS region you are creating your labeling job in.
3D Point Cloud HumanTaskUiArns
Use this HumanTaskUiArn for 3D point cloud object detection and 3D point cloud object detection adjustment labeling jobs.
arn:aws:sagemaker:aws-region:394669845002:human-task-ui/PointCloudObjectDetection
Use this HumanTaskUiArn for 3D point cloud object tracking and 3D point cloud object tracking adjustment labeling jobs.
arn:aws:sagemaker:aws-region:394669845002:human-task-ui/PointCloudObjectTracking
Use this HumanTaskUiArn for 3D point cloud semantic segmentation and 3D point cloud semantic segmentation adjustment labeling jobs.
arn:aws:sagemaker:aws-region:394669845002:human-task-ui/PointCloudSemanticSegmentation
Video Frame HumanTaskUiArns
Use this HumanTaskUiArn for video frame object detection and video frame object detection adjustment labeling jobs.
arn:aws:sagemaker:region:394669845002:human-task-ui/VideoObjectDetection
Use this HumanTaskUiArn for video frame object tracking and video frame object tracking adjustment labeling jobs.
arn:aws:sagemaker:aws-region:394669845002:human-task-ui/VideoObjectTracking
PreHumanTaskLambdaArn (string) --
The Amazon Resource Name (ARN) of a Lambda function that is run before a data object is sent to a human worker. Use this function to provide input to a custom labeling job.
For built-in task types, use one of the following Amazon SageMaker Ground Truth Lambda function ARNs for PreHumanTaskLambdaArn. For custom labeling workflows, see Pre-annotation Lambda.
Bounding box - Finds the most similar boxes from different workers based on the Jaccard index of the boxes.
arn:aws:lambda:us-east-1:432418664414:function:PRE-BoundingBox
arn:aws:lambda:us-east-2:266458841044:function:PRE-BoundingBox
arn:aws:lambda:us-west-2:081040173940:function:PRE-BoundingBox
arn:aws:lambda:ca-central-1:918755190332:function:PRE-BoundingBox
arn:aws:lambda:eu-west-1:568282634449:function:PRE-BoundingBox
arn:aws:lambda:eu-west-2:487402164563:function:PRE-BoundingBox
arn:aws:lambda:eu-central-1:203001061592:function:PRE-BoundingBox
arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-BoundingBox
arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-BoundingBox
arn:aws:lambda:ap-south-1:565803892007:function:PRE-BoundingBox
arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-BoundingBox
arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-BoundingBox
Image classification - Uses a variant of the Expectation Maximization approach to estimate the true class of an image based on annotations from individual workers.
arn:aws:lambda:us-east-1:432418664414:function:PRE-ImageMultiClass
arn:aws:lambda:us-east-2:266458841044:function:PRE-ImageMultiClass
arn:aws:lambda:us-west-2:081040173940:function:PRE-ImageMultiClass
arn:aws:lambda:ca-central-1:918755190332:function:PRE-ImageMultiClass
arn:aws:lambda:eu-west-1:568282634449:function:PRE-ImageMultiClass
arn:aws:lambda:eu-west-2:487402164563:function:PRE-ImageMultiClass
arn:aws:lambda:eu-central-1:203001061592:function:PRE-ImageMultiClass
arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-ImageMultiClass
arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-ImageMultiClass
arn:aws:lambda:ap-south-1:565803892007:function:PRE-ImageMultiClass
arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-ImageMultiClass
arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-ImageMultiClass
Multi-label image classification - Uses a variant of the Expectation Maximization approach to estimate the true classes of an image based on annotations from individual workers.
arn:aws:lambda:us-east-1:432418664414:function:PRE-ImageMultiClassMultiLabel
arn:aws:lambda:us-east-2:266458841044:function:PRE-ImageMultiClassMultiLabel
arn:aws:lambda:us-west-2:081040173940:function:PRE-ImageMultiClassMultiLabel
arn:aws:lambda:ca-central-1:918755190332:function:PRE-ImageMultiClassMultiLabel
arn:aws:lambda:eu-west-1:568282634449:function:PRE-ImageMultiClassMultiLabel
arn:aws:lambda:eu-west-2:487402164563:function:PRE-ImageMultiClassMultiLabel
arn:aws:lambda:eu-central-1:203001061592:function:PRE-ImageMultiClassMultiLabel
arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-ImageMultiClassMultiLabel
arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-ImageMultiClassMultiLabel
arn:aws:lambda:ap-south-1:565803892007:function:PRE-ImageMultiClassMultiLabel
arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-ImageMultiClassMultiLabel
arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-ImageMultiClassMultiLabel
Semantic segmentation - Treats each pixel in an image as a multi-class classification and treats pixel annotations from workers as "votes" for the correct label.
arn:aws:lambda:us-east-1:432418664414:function:PRE-SemanticSegmentation
arn:aws:lambda:us-east-2:266458841044:function:PRE-SemanticSegmentation
arn:aws:lambda:us-west-2:081040173940:function:PRE-SemanticSegmentation
arn:aws:lambda:ca-central-1:918755190332:function:PRE-SemanticSegmentation
arn:aws:lambda:eu-west-1:568282634449:function:PRE-SemanticSegmentation
arn:aws:lambda:eu-west-2:487402164563:function:PRE-SemanticSegmentation
arn:aws:lambda:eu-central-1:203001061592:function:PRE-SemanticSegmentation
arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-SemanticSegmentation
arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-SemanticSegmentation
arn:aws:lambda:ap-south-1:565803892007:function:PRE-SemanticSegmentation
arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-SemanticSegmentation
arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-SemanticSegmentation
Text classification - Uses a variant of the Expectation Maximization approach to estimate the true class of text based on annotations from individual workers.
arn:aws:lambda:us-east-1:432418664414:function:PRE-TextMultiClass
arn:aws:lambda:us-east-2:266458841044:function:PRE-TextMultiClass
arn:aws:lambda:us-west-2:081040173940:function:PRE-TextMultiClass
arn:aws:lambda:ca-central-1:918755190332:function:PRE-TextMultiClass
arn:aws:lambda:eu-west-1:568282634449:function:PRE-TextMultiClass
arn:aws:lambda:eu-west-2:487402164563:function:PRE-TextMultiClass
arn:aws:lambda:eu-central-1:203001061592:function:PRE-TextMultiClass
arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-TextMultiClass
arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-TextMultiClass
arn:aws:lambda:ap-south-1:565803892007:function:PRE-TextMultiClass
arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-TextMultiClass
arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-TextMultiClass
Multi-label text classification - Uses a variant of the Expectation Maximization approach to estimate the true classes of text based on annotations from individual workers.
arn:aws:lambda:us-east-1:432418664414:function:PRE-TextMultiClassMultiLabel
arn:aws:lambda:us-east-2:266458841044:function:PRE-TextMultiClassMultiLabel
arn:aws:lambda:us-west-2:081040173940:function:PRE-TextMultiClassMultiLabel
arn:aws:lambda:ca-central-1:918755190332:function:PRE-TextMultiClassMultiLabel
arn:aws:lambda:eu-west-1:568282634449:function:PRE-TextMultiClassMultiLabel
arn:aws:lambda:eu-west-2:487402164563:function:PRE-TextMultiClassMultiLabel
arn:aws:lambda:eu-central-1:203001061592:function:PRE-TextMultiClassMultiLabel
arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-TextMultiClassMultiLabel
arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-TextMultiClassMultiLabel
arn:aws:lambda:ap-south-1:565803892007:function:PRE-TextMultiClassMultiLabel
arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-TextMultiClassMultiLabel
arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-TextMultiClassMultiLabel
Named entity recognition - Groups similar selections and calculates aggregate boundaries, resolving to most-assigned label.
arn:aws:lambda:us-east-1:432418664414:function:PRE-NamedEntityRecognition
arn:aws:lambda:us-east-2:266458841044:function:PRE-NamedEntityRecognition
arn:aws:lambda:us-west-2:081040173940:function:PRE-NamedEntityRecognition
arn:aws:lambda:ca-central-1:918755190332:function:PRE-NamedEntityRecognition
arn:aws:lambda:eu-west-1:568282634449:function:PRE-NamedEntityRecognition
arn:aws:lambda:eu-west-2:487402164563:function:PRE-NamedEntityRecognition
arn:aws:lambda:eu-central-1:203001061592:function:PRE-NamedEntityRecognition
arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-NamedEntityRecognition
arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-NamedEntityRecognition
arn:aws:lambda:ap-south-1:565803892007:function:PRE-NamedEntityRecognition
arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-NamedEntityRecognition
arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-NamedEntityRecognition
Video Classification - Use this task type when you need workers to classify videos using predefined labels that you specify. Workers are shown videos and are asked to choose one label for each video.
arn:aws:lambda:us-east-1:432418664414:function:PRE-VideoMultiClass
arn:aws:lambda:us-east-2:266458841044:function:PRE-VideoMultiClass
arn:aws:lambda:us-west-2:081040173940:function:PRE-VideoMultiClass
arn:aws:lambda:eu-west-1:568282634449:function:PRE-VideoMultiClass
arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-VideoMultiClass
arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-VideoMultiClass
arn:aws:lambda:ap-south-1:565803892007:function:PRE-VideoMultiClass
arn:aws:lambda:eu-central-1:203001061592:function:PRE-VideoMultiClass
arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-VideoMultiClass
arn:aws:lambda:eu-west-2:487402164563:function:PRE-VideoMultiClass
arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-VideoMultiClass
arn:aws:lambda:ca-central-1:918755190332:function:PRE-VideoMultiClass
Video Frame Object Detection - Use this task type to have workers identify and locate objects in a sequence of video frames (images extracted from a video) using bounding boxes. For example, you can use this task to ask workers to identify and localize various objects in a series of video frames, such as cars, bikes, and pedestrians.
arn:aws:lambda:us-east-1:432418664414:function:PRE-VideoObjectDetection
arn:aws:lambda:us-east-2:266458841044:function:PRE-VideoObjectDetection
arn:aws:lambda:us-west-2:081040173940:function:PRE-VideoObjectDetection
arn:aws:lambda:eu-west-1:568282634449:function:PRE-VideoObjectDetection
arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-VideoObjectDetection
arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-VideoObjectDetection
arn:aws:lambda:ap-south-1:565803892007:function:PRE-VideoObjectDetection
arn:aws:lambda:eu-central-1:203001061592:function:PRE-VideoObjectDetection
arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-VideoObjectDetection
arn:aws:lambda:eu-west-2:487402164563:function:PRE-VideoObjectDetection
arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-VideoObjectDetection
arn:aws:lambda:ca-central-1:918755190332:function:PRE-VideoObjectDetection
Video Frame Object Tracking - Use this task type to have workers track the movement of objects in a sequence of video frames (images extracted from a video) using bounding boxes. For example, you can use this task to ask workers to track the movement of objects, such as cars, bikes, and pedestrians.
arn:aws:lambda:us-east-1:432418664414:function:PRE-VideoObjectTracking
arn:aws:lambda:us-east-2:266458841044:function:PRE-VideoObjectTracking
arn:aws:lambda:us-west-2:081040173940:function:PRE-VideoObjectTracking
arn:aws:lambda:eu-west-1:568282634449:function:PRE-VideoObjectTracking
arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-VideoObjectTracking
arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-VideoObjectTracking
arn:aws:lambda:ap-south-1:565803892007:function:PRE-VideoObjectTracking
arn:aws:lambda:eu-central-1:203001061592:function:PRE-VideoObjectTracking
arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-VideoObjectTracking
arn:aws:lambda:eu-west-2:487402164563:function:PRE-VideoObjectTracking
arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-VideoObjectTracking
arn:aws:lambda:ca-central-1:918755190332:function:PRE-VideoObjectTracking
3D Point Cloud Modalities
Use the following pre-annotation lambdas for 3D point cloud labeling modality tasks. See 3D Point Cloud Task types to learn more.
3D Point Cloud Object Detection - Use this task type when you want workers to classify objects in a 3D point cloud by drawing 3D cuboids around objects. For example, you can use this task type to ask workers to identify different types of objects in a point cloud, such as cars, bikes, and pedestrians.
arn:aws:lambda:us-east-1:432418664414:function:PRE-3DPointCloudObjectDetection
arn:aws:lambda:us-east-2:266458841044:function:PRE-3DPointCloudObjectDetection
arn:aws:lambda:us-west-2:081040173940:function:PRE-3DPointCloudObjectDetection
arn:aws:lambda:eu-west-1:568282634449:function:PRE-3DPointCloudObjectDetection
arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-3DPointCloudObjectDetection
arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-3DPointCloudObjectDetection
arn:aws:lambda:ap-south-1:565803892007:function:PRE-3DPointCloudObjectDetection
arn:aws:lambda:eu-central-1:203001061592:function:PRE-3DPointCloudObjectDetection
arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-3DPointCloudObjectDetection
arn:aws:lambda:eu-west-2:487402164563:function:PRE-3DPointCloudObjectDetection
arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-3DPointCloudObjectDetection
arn:aws:lambda:ca-central-1:918755190332:function:PRE-3DPointCloudObjectDetection
3D Point Cloud Object Tracking - Use this task type when you want workers to draw 3D cuboids around objects that appear in a sequence of 3D point cloud frames. For example, you can use this task type to ask workers to track the movement of vehicles across multiple point cloud frames.
arn:aws:lambda:us-east-1:432418664414:function:PRE-3DPointCloudObjectTracking
arn:aws:lambda:us-east-2:266458841044:function:PRE-3DPointCloudObjectTracking
arn:aws:lambda:us-west-2:081040173940:function:PRE-3DPointCloudObjectTracking
arn:aws:lambda:eu-west-1:568282634449:function:PRE-3DPointCloudObjectTracking
arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-3DPointCloudObjectTracking
arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-3DPointCloudObjectTracking
arn:aws:lambda:ap-south-1:565803892007:function:PRE-3DPointCloudObjectTracking
arn:aws:lambda:eu-central-1:203001061592:function:PRE-3DPointCloudObjectTracking
arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-3DPointCloudObjectTracking
arn:aws:lambda:eu-west-2:487402164563:function:PRE-3DPointCloudObjectTracking
arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-3DPointCloudObjectTracking
arn:aws:lambda:ca-central-1:918755190332:function:PRE-3DPointCloudObjectTracking
3D Point Cloud Semantic Segmentation - Use this task type when you want workers to create a point-level semantic segmentation masks by painting objects in a 3D point cloud using different colors where each color is assigned to one of the classes you specify.
arn:aws:lambda:us-east-1:432418664414:function:PRE-3DPointCloudSemanticSegmentation
arn:aws:lambda:us-east-2:266458841044:function:PRE-3DPointCloudSemanticSegmentation
arn:aws:lambda:us-west-2:081040173940:function:PRE-3DPointCloudSemanticSegmentation
arn:aws:lambda:eu-west-1:568282634449:function:PRE-3DPointCloudSemanticSegmentation
arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-3DPointCloudSemanticSegmentation
arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-3DPointCloudSemanticSegmentation
arn:aws:lambda:ap-south-1:565803892007:function:PRE-3DPointCloudSemanticSegmentation
arn:aws:lambda:eu-central-1:203001061592:function:PRE-3DPointCloudSemanticSegmentation
arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-3DPointCloudSemanticSegmentation
arn:aws:lambda:eu-west-2:487402164563:function:PRE-3DPointCloudSemanticSegmentation
arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-3DPointCloudSemanticSegmentation
arn:aws:lambda:ca-central-1:918755190332:function:PRE-3DPointCloudSemanticSegmentation
Use the following ARNs for Label Verification and Adjustment Jobs
Use label verification and adjustment jobs to review and adjust labels. To learn more, see Verify and Adjust Labels.
Bounding box verification - Uses a variant of the Expectation Maximization approach to estimate the true class of verification judgement for bounding box labels based on annotations from individual workers.
arn:aws:lambda:us-east-1:432418664414:function:PRE-Adjustment3DPointCloudObjectTracking
arn:aws:lambda:us-east-2:266458841044:function:PRE-Adjustment3DPointCloudObjectTracking
arn:aws:lambda:us-west-2:081040173940:function:PRE-Adjustment3DPointCloudObjectTracking
arn:aws:lambda:eu-west-1:568282634449:function:PRE-Adjustment3DPointCloudObjectTracking
arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-Adjustment3DPointCloudObjectTracking
arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-Adjustment3DPointCloudObjectTracking
arn:aws:lambda:ap-south-1:565803892007:function:PRE-Adjustment3DPointCloudObjectTracking
arn:aws:lambda:eu-central-1:203001061592:function:PRE-Adjustment3DPointCloudObjectTracking
arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-Adjustment3DPointCloudObjectTracking
arn:aws:lambda:eu-west-2:487402164563:function:PRE-Adjustment3DPointCloudObjectTracking
arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-Adjustment3DPointCloudObjectTracking
arn:aws:lambda:ca-central-1:918755190332:function:PRE-Adjustment3DPointCloudObjectTracking
Bounding box adjustment - Finds the most similar boxes from different workers based on the Jaccard index of the adjusted annotations.
arn:aws:lambda:us-east-1:432418664414:function:PRE-AdjustmentBoundingBox
arn:aws:lambda:us-east-2:266458841044:function:PRE-AdjustmentBoundingBox
arn:aws:lambda:us-west-2:081040173940:function:PRE-AdjustmentBoundingBox
arn:aws:lambda:ca-central-1:918755190332:function:PRE-AdjustmentBoundingBox
arn:aws:lambda:eu-west-1:568282634449:function:PRE-AdjustmentBoundingBox
arn:aws:lambda:eu-west-2:487402164563:function:PRE-AdjustmentBoundingBox
arn:aws:lambda:eu-central-1:203001061592:function:PRE-AdjustmentBoundingBox
arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-AdjustmentBoundingBox
arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-AdjustmentBoundingBox
arn:aws:lambda:ap-south-1:565803892007:function:PRE-AdjustmentBoundingBox
arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-AdjustmentBoundingBox
arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-AdjustmentBoundingBox
Semantic segmentation verification - Uses a variant of the Expectation Maximization approach to estimate the true class of verification judgment for semantic segmentation labels based on annotations from individual workers.
arn:aws:lambda:us-east-1:432418664414:function:PRE-VerificationSemanticSegmentation
arn:aws:lambda:us-east-2:266458841044:function:PRE-VerificationSemanticSegmentation
arn:aws:lambda:us-west-2:081040173940:function:PRE-VerificationSemanticSegmentation
arn:aws:lambda:ca-central-1:918755190332:function:PRE-VerificationSemanticSegmentation
arn:aws:lambda:eu-west-1:568282634449:function:PRE-VerificationSemanticSegmentation
arn:aws:lambda:eu-west-2:487402164563:function:PRE-VerificationSemanticSegmentation
arn:aws:lambda:eu-central-1:203001061592:function:PRE-VerificationSemanticSegmentation
arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-VerificationSemanticSegmentation
arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-VerificationSemanticSegmentation
arn:aws:lambda:ap-south-1:565803892007:function:PRE-VerificationSemanticSegmentation
arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-VerificationSemanticSegmentation
arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-VerificationSemanticSegmentation
Semantic segmentation adjustment - Treats each pixel in an image as a multi-class classification and treats pixel adjusted annotations from workers as "votes" for the correct label.
arn:aws:lambda:us-east-1:432418664414:function:PRE-AdjustmentSemanticSegmentation
arn:aws:lambda:us-east-2:266458841044:function:PRE-AdjustmentSemanticSegmentation
arn:aws:lambda:us-west-2:081040173940:function:PRE-AdjustmentSemanticSegmentation
arn:aws:lambda:ca-central-1:918755190332:function:PRE-AdjustmentSemanticSegmentation
arn:aws:lambda:eu-west-1:568282634449:function:PRE-AdjustmentSemanticSegmentation
arn:aws:lambda:eu-west-2:487402164563:function:PRE-AdjustmentSemanticSegmentation
arn:aws:lambda:eu-central-1:203001061592:function:PRE-AdjustmentSemanticSegmentation
arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-AdjustmentSemanticSegmentation
arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-AdjustmentSemanticSegmentation
arn:aws:lambda:ap-south-1:565803892007:function:PRE-AdjustmentSemanticSegmentation
arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-AdjustmentSemanticSegmentation
arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-AdjustmentSemanticSegmentation
Video Frame Object Detection Adjustment - Use this task type when you want workers to adjust bounding boxes that workers have added to video frames to classify and localize objects in a sequence of video frames.
arn:aws:lambda:us-east-1:432418664414:function:PRE-AdjustmentVideoObjectDetection
arn:aws:lambda:us-east-2:266458841044:function:PRE-AdjustmentVideoObjectDetection
arn:aws:lambda:us-west-2:081040173940:function:PRE-AdjustmentVideoObjectDetection
arn:aws:lambda:eu-west-1:568282634449:function:PRE-AdjustmentVideoObjectDetection
arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-AdjustmentVideoObjectDetection
arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-AdjustmentVideoObjectDetection
arn:aws:lambda:ap-south-1:565803892007:function:PRE-AdjustmentVideoObjectDetection
arn:aws:lambda:eu-central-1:203001061592:function:PRE-AdjustmentVideoObjectDetection
arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-AdjustmentVideoObjectDetection
arn:aws:lambda:eu-west-2:487402164563:function:PRE-AdjustmentVideoObjectDetection
arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-AdjustmentVideoObjectDetection
arn:aws:lambda:ca-central-1:918755190332:function:PRE-AdjustmentVideoObjectDetection
Video Frame Object Tracking Adjustment - Use this task type when you want workers to adjust bounding boxes that workers have added to video frames to track object movement across a sequence of video frames.
arn:aws:lambda:us-east-1:432418664414:function:PRE-AdjustmentVideoObjectTracking
arn:aws:lambda:us-east-2:266458841044:function:PRE-AdjustmentVideoObjectTracking
arn:aws:lambda:us-west-2:081040173940:function:PRE-AdjustmentVideoObjectTracking
arn:aws:lambda:eu-west-1:568282634449:function:PRE-AdjustmentVideoObjectTracking
arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-AdjustmentVideoObjectTracking
arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-AdjustmentVideoObjectTracking
arn:aws:lambda:ap-south-1:565803892007:function:PRE-AdjustmentVideoObjectTracking
arn:aws:lambda:eu-central-1:203001061592:function:PRE-AdjustmentVideoObjectTracking
arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-AdjustmentVideoObjectTracking
arn:aws:lambda:eu-west-2:487402164563:function:PRE-AdjustmentVideoObjectTracking
arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-AdjustmentVideoObjectTracking
arn:aws:lambda:ca-central-1:918755190332:function:PRE-AdjustmentVideoObjectTracking
3D point cloud object detection adjustment - Adjust 3D cuboids in a point cloud frame.
arn:aws:lambda:us-east-1:432418664414:function:PRE-Adjustment3DPointCloudObjectDetection
arn:aws:lambda:us-east-2:266458841044:function:PRE-Adjustment3DPointCloudObjectDetection
arn:aws:lambda:us-west-2:081040173940:function:PRE-Adjustment3DPointCloudObjectDetection
arn:aws:lambda:eu-west-1:568282634449:function:PRE-Adjustment3DPointCloudObjectDetection
arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-Adjustment3DPointCloudObjectDetection
arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-Adjustment3DPointCloudObjectDetection
arn:aws:lambda:ap-south-1:565803892007:function:PRE-Adjustment3DPointCloudObjectDetection
arn:aws:lambda:eu-central-1:203001061592:function:PRE-Adjustment3DPointCloudObjectDetection
arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-Adjustment3DPointCloudObjectDetection
arn:aws:lambda:eu-west-2:487402164563:function:PRE-Adjustment3DPointCloudObjectDetection
arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-Adjustment3DPointCloudObjectDetection
arn:aws:lambda:ca-central-1:918755190332:function:PRE-Adjustment3DPointCloudObjectDetection
3D point cloud object tracking adjustment - Adjust 3D cuboids across a sequence of point cloud frames.
arn:aws:lambda:us-east-1:432418664414:function:PRE-Adjustment3DPointCloudObjectTracking
arn:aws:lambda:us-east-2:266458841044:function:PRE-Adjustment3DPointCloudObjectTracking
arn:aws:lambda:us-west-2:081040173940:function:PRE-Adjustment3DPointCloudObjectTracking
arn:aws:lambda:eu-west-1:568282634449:function:PRE-Adjustment3DPointCloudObjectTracking
arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-Adjustment3DPointCloudObjectTracking
arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-Adjustment3DPointCloudObjectTracking
arn:aws:lambda:ap-south-1:565803892007:function:PRE-Adjustment3DPointCloudObjectTracking
arn:aws:lambda:eu-central-1:203001061592:function:PRE-Adjustment3DPointCloudObjectTracking
arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-Adjustment3DPointCloudObjectTracking
arn:aws:lambda:eu-west-2:487402164563:function:PRE-Adjustment3DPointCloudObjectTracking
arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-Adjustment3DPointCloudObjectTracking
arn:aws:lambda:ca-central-1:918755190332:function:PRE-Adjustment3DPointCloudObjectTracking
3D point cloud semantic segmentation adjustment - Adjust semantic segmentation masks in a 3D point cloud.
arn:aws:lambda:us-east-1:432418664414:function:PRE-Adjustment3DPointCloudSemanticSegmentation
arn:aws:lambda:us-east-2:266458841044:function:PRE-Adjustment3DPointCloudSemanticSegmentation
arn:aws:lambda:us-west-2:081040173940:function:PRE-Adjustment3DPointCloudSemanticSegmentation
arn:aws:lambda:eu-west-1:568282634449:function:PRE-Adjustment3DPointCloudSemanticSegmentation
arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-Adjustment3DPointCloudSemanticSegmentation
arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-Adjustment3DPointCloudSemanticSegmentation
arn:aws:lambda:ap-south-1:565803892007:function:PRE-Adjustment3DPointCloudSemanticSegmentation
arn:aws:lambda:eu-central-1:203001061592:function:PRE-Adjustment3DPointCloudSemanticSegmentation
arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-Adjustment3DPointCloudSemanticSegmentation
arn:aws:lambda:eu-west-2:487402164563:function:PRE-Adjustment3DPointCloudSemanticSegmentation
arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-Adjustment3DPointCloudSemanticSegmentation
arn:aws:lambda:ca-central-1:918755190332:function:PRE-Adjustment3DPointCloudSemanticSegmentation
TaskKeywords (list) --
Keywords used to describe the task so that workers on Amazon Mechanical Turk can discover the task.
(string) --
TaskTitle (string) --
A title for the task for your human workers.
TaskDescription (string) --
A description of the task for your human workers.
NumberOfHumanWorkersPerDataObject (integer) --
The number of human workers that will label an object.
TaskTimeLimitInSeconds (integer) --
The amount of time that a worker has to complete a task.
TaskAvailabilityLifetimeInSeconds (integer) --
The length of time that a task remains available for labeling by human workers. If you choose the Amazon Mechanical Turk workforce, the maximum is 12 hours (43200). The default value is 864000 seconds (10 days). For private and vendor workforces, the maximum is as listed.
MaxConcurrentTaskCount (integer) --
Defines the maximum number of data objects that can be labeled by human workers at the same time. Also referred to as batch size. Each object may have more than one worker at one time. The default value is 1000 objects.
AnnotationConsolidationConfig (dict) --
Configures how labels are consolidated across human workers.
AnnotationConsolidationLambdaArn (string) --
The Amazon Resource Name (ARN) of a Lambda function implements the logic for annotation consolidation and to process output data.
This parameter is required for all labeling jobs. For built-in task types, use one of the following Amazon SageMaker Ground Truth Lambda function ARNs for AnnotationConsolidationLambdaArn. For custom labeling workflows, see Post-annotation Lambda.
Bounding box - Finds the most similar boxes from different workers based on the Jaccard index of the boxes.
arn:aws:lambda:us-east-1:432418664414:function:ACS-BoundingBox arn:aws:lambda:us-east-2:266458841044:function:ACS-BoundingBox arn:aws:lambda:us-west-2:081040173940:function:ACS-BoundingBox arn:aws:lambda:eu-west-1:568282634449:function:ACS-BoundingBox arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-BoundingBox arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-BoundingBox arn:aws:lambda:ap-south-1:565803892007:function:ACS-BoundingBox arn:aws:lambda:eu-central-1:203001061592:function:ACS-BoundingBox arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-BoundingBox arn:aws:lambda:eu-west-2:487402164563:function:ACS-BoundingBox arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-BoundingBox arn:aws:lambda:ca-central-1:918755190332:function:ACS-BoundingBox
Image classification - Uses a variant of the Expectation Maximization approach to estimate the true class of an image based on annotations from individual workers.
arn:aws:lambda:us-east-1:432418664414:function:ACS-ImageMultiClass arn:aws:lambda:us-east-2:266458841044:function:ACS-ImageMultiClass arn:aws:lambda:us-west-2:081040173940:function:ACS-ImageMultiClass arn:aws:lambda:eu-west-1:568282634449:function:ACS-ImageMultiClass arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-ImageMultiClass arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-ImageMultiClass arn:aws:lambda:ap-south-1:565803892007:function:ACS-ImageMultiClass arn:aws:lambda:eu-central-1:203001061592:function:ACS-ImageMultiClass arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-ImageMultiClass arn:aws:lambda:eu-west-2:487402164563:function:ACS-ImageMultiClass arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-ImageMultiClass arn:aws:lambda:ca-central-1:918755190332:function:ACS-ImageMultiClass
Multi-label image classification - Uses a variant of the Expectation Maximization approach to estimate the true classes of an image based on annotations from individual workers.
arn:aws:lambda:us-east-1:432418664414:function:ACS-ImageMultiClassMultiLabel arn:aws:lambda:us-east-2:266458841044:function:ACS-ImageMultiClassMultiLabel arn:aws:lambda:us-west-2:081040173940:function:ACS-ImageMultiClassMultiLabel arn:aws:lambda:eu-west-1:568282634449:function:ACS-ImageMultiClassMultiLabel arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-ImageMultiClassMultiLabel arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-ImageMultiClassMultiLabel arn:aws:lambda:ap-south-1:565803892007:function:ACS-ImageMultiClassMultiLabel arn:aws:lambda:eu-central-1:203001061592:function:ACS-ImageMultiClassMultiLabel arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-ImageMultiClassMultiLabel arn:aws:lambda:eu-west-2:487402164563:function:ACS-ImageMultiClassMultiLabel arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-ImageMultiClassMultiLabel arn:aws:lambda:ca-central-1:918755190332:function:ACS-ImageMultiClassMultiLabel
Semantic segmentation - Treats each pixel in an image as a multi-class classification and treats pixel annotations from workers as "votes" for the correct label.
arn:aws:lambda:us-east-1:432418664414:function:ACS-SemanticSegmentation arn:aws:lambda:us-east-2:266458841044:function:ACS-SemanticSegmentation arn:aws:lambda:us-west-2:081040173940:function:ACS-SemanticSegmentation arn:aws:lambda:eu-west-1:568282634449:function:ACS-SemanticSegmentation arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-SemanticSegmentation arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-SemanticSegmentation arn:aws:lambda:ap-south-1:565803892007:function:ACS-SemanticSegmentation arn:aws:lambda:eu-central-1:203001061592:function:ACS-SemanticSegmentation arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-SemanticSegmentation arn:aws:lambda:eu-west-2:487402164563:function:ACS-SemanticSegmentation arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-SemanticSegmentation arn:aws:lambda:ca-central-1:918755190332:function:ACS-SemanticSegmentation
Text classification - Uses a variant of the Expectation Maximization approach to estimate the true class of text based on annotations from individual workers.
arn:aws:lambda:us-east-1:432418664414:function:ACS-TextMultiClass arn:aws:lambda:us-east-2:266458841044:function:ACS-TextMultiClass arn:aws:lambda:us-west-2:081040173940:function:ACS-TextMultiClass arn:aws:lambda:eu-west-1:568282634449:function:ACS-TextMultiClass arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-TextMultiClass arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-TextMultiClass arn:aws:lambda:ap-south-1:565803892007:function:ACS-TextMultiClass arn:aws:lambda:eu-central-1:203001061592:function:ACS-TextMultiClass arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-TextMultiClass arn:aws:lambda:eu-west-2:487402164563:function:ACS-TextMultiClass arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-TextMultiClass arn:aws:lambda:ca-central-1:918755190332:function:ACS-TextMultiClass
Multi-label text classification - Uses a variant of the Expectation Maximization approach to estimate the true classes of text based on annotations from individual workers.
arn:aws:lambda:us-east-1:432418664414:function:ACS-TextMultiClassMultiLabel arn:aws:lambda:us-east-2:266458841044:function:ACS-TextMultiClassMultiLabel arn:aws:lambda:us-west-2:081040173940:function:ACS-TextMultiClassMultiLabel arn:aws:lambda:eu-west-1:568282634449:function:ACS-TextMultiClassMultiLabel arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-TextMultiClassMultiLabel arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-TextMultiClassMultiLabel arn:aws:lambda:ap-south-1:565803892007:function:ACS-TextMultiClassMultiLabel arn:aws:lambda:eu-central-1:203001061592:function:ACS-TextMultiClassMultiLabel arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-TextMultiClassMultiLabel arn:aws:lambda:eu-west-2:487402164563:function:ACS-TextMultiClassMultiLabel arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-TextMultiClassMultiLabel arn:aws:lambda:ca-central-1:918755190332:function:ACS-TextMultiClassMultiLabel
Named entity recognition - Groups similar selections and calculates aggregate boundaries, resolving to most-assigned label.
arn:aws:lambda:us-east-1:432418664414:function:ACS-NamedEntityRecognition arn:aws:lambda:us-east-2:266458841044:function:ACS-NamedEntityRecognition arn:aws:lambda:us-west-2:081040173940:function:ACS-NamedEntityRecognition arn:aws:lambda:eu-west-1:568282634449:function:ACS-NamedEntityRecognition arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-NamedEntityRecognition arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-NamedEntityRecognition arn:aws:lambda:ap-south-1:565803892007:function:ACS-NamedEntityRecognition arn:aws:lambda:eu-central-1:203001061592:function:ACS-NamedEntityRecognition arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-NamedEntityRecognition arn:aws:lambda:eu-west-2:487402164563:function:ACS-NamedEntityRecognition arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-NamedEntityRecognition arn:aws:lambda:ca-central-1:918755190332:function:ACS-NamedEntityRecognition
Named entity recognition - Groups similar selections and calculates aggregate boundaries, resolving to most-assigned label.
arn:aws:lambda:us-east-1:432418664414:function:ACS-NamedEntityRecognition arn:aws:lambda:us-east-2:266458841044:function:ACS-NamedEntityRecognition arn:aws:lambda:us-west-2:081040173940:function:ACS-NamedEntityRecognition arn:aws:lambda:eu-west-1:568282634449:function:ACS-NamedEntityRecognition arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-NamedEntityRecognition arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-NamedEntityRecognition arn:aws:lambda:ap-south-1:565803892007:function:ACS-NamedEntityRecognition arn:aws:lambda:eu-central-1:203001061592:function:ACS-NamedEntityRecognition arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-NamedEntityRecognition arn:aws:lambda:eu-west-2:487402164563:function:ACS-NamedEntityRecognition arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-NamedEntityRecognition arn:aws:lambda:ca-central-1:918755190332:function:ACS-NamedEntityRecognition
Video Classification - Use this task type when you need workers to classify videos using predefined labels that you specify. Workers are shown videos and are asked to choose one label for each video.
arn:aws:lambda:us-east-1:432418664414:function:ACS-VideoMultiClass arn:aws:lambda:us-east-2:266458841044:function:ACS-VideoMultiClass arn:aws:lambda:us-west-2:081040173940:function:ACS-VideoMultiClass arn:aws:lambda:eu-west-1:568282634449:function:ACS-VideoMultiClass arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-VideoMultiClass arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-VideoMultiClass arn:aws:lambda:ap-south-1:565803892007:function:ACS-VideoMultiClass arn:aws:lambda:eu-central-1:203001061592:function:ACS-VideoMultiClass arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-VideoMultiClass arn:aws:lambda:eu-west-2:487402164563:function:ACS-VideoMultiClass arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-VideoMultiClass arn:aws:lambda:ca-central-1:918755190332:function:ACS-VideoMultiClass
Video Frame Object Detection - Use this task type to have workers identify and locate objects in a sequence of video frames (images extracted from a video) using bounding boxes. For example, you can use this task to ask workers to identify and localize various objects in a series of video frames, such as cars, bikes, and pedestrians.
arn:aws:lambda:us-east-1:432418664414:function:ACS-VideoObjectDetection arn:aws:lambda:us-east-2:266458841044:function:ACS-VideoObjectDetection arn:aws:lambda:us-west-2:081040173940:function:ACS-VideoObjectDetection arn:aws:lambda:eu-west-1:568282634449:function:ACS-VideoObjectDetection arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-VideoObjectDetection arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-VideoObjectDetection arn:aws:lambda:ap-south-1:565803892007:function:ACS-VideoObjectDetection arn:aws:lambda:eu-central-1:203001061592:function:ACS-VideoObjectDetection arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-VideoObjectDetection arn:aws:lambda:eu-west-2:487402164563:function:ACS-VideoObjectDetection arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-VideoObjectDetection arn:aws:lambda:ca-central-1:918755190332:function:ACS-VideoObjectDetection
Video Frame Object Tracking - Use this task type to have workers track the movement of objects in a sequence of video frames (images extracted from a video) using bounding boxes. For example, you can use this task to ask workers to track the movement of objects, such as cars, bikes, and pedestrians.
arn:aws:lambda:us-east-1:432418664414:function:ACS-VideoObjectTracking arn:aws:lambda:us-east-2:266458841044:function:ACS-VideoObjectTracking arn:aws:lambda:us-west-2:081040173940:function:ACS-VideoObjectTracking arn:aws:lambda:eu-west-1:568282634449:function:ACS-VideoObjectTracking arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-VideoObjectTracking arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-VideoObjectTracking arn:aws:lambda:ap-south-1:565803892007:function:ACS-VideoObjectTracking arn:aws:lambda:eu-central-1:203001061592:function:ACS-VideoObjectTracking arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-VideoObjectTracking arn:aws:lambda:eu-west-2:487402164563:function:ACS-VideoObjectTracking arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-VideoObjectTracking arn:aws:lambda:ca-central-1:918755190332:function:ACS-VideoObjectTracking
3D point cloud object detection - Use this task type when you want workers to classify objects in a 3D point cloud by drawing 3D cuboids around objects. For example, you can use this task type to ask workers to identify different types of objects in a point cloud, such as cars, bikes, and pedestrians.
arn:aws:lambda:us-east-1:432418664414:function:ACS-3DPointCloudObjectDetection arn:aws:lambda:us-east-2:266458841044:function:ACS-3DPointCloudObjectDetection arn:aws:lambda:us-west-2:081040173940:function:ACS-3DPointCloudObjectDetection arn:aws:lambda:eu-west-1:568282634449:function:ACS-3DPointCloudObjectDetection arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-3DPointCloudObjectDetection arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-3DPointCloudObjectDetection arn:aws:lambda:ap-south-1:565803892007:function:ACS-3DPointCloudObjectDetection arn:aws:lambda:eu-central-1:203001061592:function:ACS-3DPointCloudObjectDetection arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-3DPointCloudObjectDetection arn:aws:lambda:eu-west-2:487402164563:function:ACS-3DPointCloudObjectDetection arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-3DPointCloudObjectDetection arn:aws:lambda:ca-central-1:918755190332:function:ACS-3DPointCloudObjectDetection
3D point cloud object tracking - Use this task type when you want workers to draw 3D cuboids around objects that appear in a sequence of 3D point cloud frames. For example, you can use this task type to ask workers to track the movement of vehicles across multiple point cloud frames.
arn:aws:lambda:us-east-1:432418664414:function:ACS-3DPointCloudObjectTracking arn:aws:lambda:us-east-2:266458841044:function:ACS-3DPointCloudObjectTracking arn:aws:lambda:us-west-2:081040173940:function:ACS-3DPointCloudObjectTracking arn:aws:lambda:eu-west-1:568282634449:function:ACS-3DPointCloudObjectTracking arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-3DPointCloudObjectTracking arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-3DPointCloudObjectTracking arn:aws:lambda:ap-south-1:565803892007:function:ACS-3DPointCloudObjectTracking arn:aws:lambda:eu-central-1:203001061592:function:ACS-3DPointCloudObjectTracking arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-3DPointCloudObjectTracking arn:aws:lambda:eu-west-2:487402164563:function:ACS-3DPointCloudObjectTracking arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-3DPointCloudObjectTracking arn:aws:lambda:ca-central-1:918755190332:function:ACS-3DPointCloudObjectTracking
3D point cloud semantic segmentation - Use this task type when you want workers to create a point-level semantic segmentation masks by painting objects in a 3D point cloud using different colors where each color is assigned to one of the classes you specify.
arn:aws:lambda:us-east-1:432418664414:function:ACS-3DPointCloudSemanticSegmentation arn:aws:lambda:us-east-2:266458841044:function:ACS-3DPointCloudSemanticSegmentation arn:aws:lambda:us-west-2:081040173940:function:ACS-3DPointCloudSemanticSegmentation arn:aws:lambda:eu-west-1:568282634449:function:ACS-3DPointCloudSemanticSegmentation arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-3DPointCloudSemanticSegmentation arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-3DPointCloudSemanticSegmentation arn:aws:lambda:ap-south-1:565803892007:function:ACS-3DPointCloudSemanticSegmentation arn:aws:lambda:eu-central-1:203001061592:function:ACS-3DPointCloudSemanticSegmentation arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-3DPointCloudSemanticSegmentation arn:aws:lambda:eu-west-2:487402164563:function:ACS-3DPointCloudSemanticSegmentation arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-3DPointCloudSemanticSegmentation arn:aws:lambda:ca-central-1:918755190332:function:ACS-3DPointCloudSemanticSegmentation
Use the following ARNs for Label Verification and Adjustment Jobs
Use label verification and adjustment jobs to review and adjust labels. To learn more, see Verify and Adjust Labels.
Semantic segmentation adjustment - Treats each pixel in an image as a multi-class classification and treats pixel adjusted annotations from workers as "votes" for the correct label.
arn:aws:lambda:us-east-1:432418664414:function:ACS-AdjustmentSemanticSegmentation arn:aws:lambda:us-east-2:266458841044:function:ACS-AdjustmentSemanticSegmentation arn:aws:lambda:us-west-2:081040173940:function:ACS-AdjustmentSemanticSegmentation arn:aws:lambda:eu-west-1:568282634449:function:ACS-AdjustmentSemanticSegmentation arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-AdjustmentSemanticSegmentation arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-AdjustmentSemanticSegmentation arn:aws:lambda:ap-south-1:565803892007:function:ACS-AdjustmentSemanticSegmentation arn:aws:lambda:eu-central-1:203001061592:function:ACS-AdjustmentSemanticSegmentation arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-AdjustmentSemanticSegmentation arn:aws:lambda:eu-west-2:487402164563:function:ACS-AdjustmentSemanticSegmentation arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-AdjustmentSemanticSegmentation arn:aws:lambda:ca-central-1:918755190332:function:ACS-AdjustmentSemanticSegmentation
Semantic segmentation verification - Uses a variant of the Expectation Maximization approach to estimate the true class of verification judgment for semantic segmentation labels based on annotations from individual workers.
arn:aws:lambda:us-east-1:432418664414:function:ACS-VerificationSemanticSegmentation arn:aws:lambda:us-east-2:266458841044:function:ACS-VerificationSemanticSegmentation arn:aws:lambda:us-west-2:081040173940:function:ACS-VerificationSemanticSegmentation arn:aws:lambda:eu-west-1:568282634449:function:ACS-VerificationSemanticSegmentation arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-VerificationSemanticSegmentation arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-VerificationSemanticSegmentation arn:aws:lambda:ap-south-1:565803892007:function:ACS-VerificationSemanticSegmentation arn:aws:lambda:eu-central-1:203001061592:function:ACS-VerificationSemanticSegmentation arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-VerificationSemanticSegmentation arn:aws:lambda:eu-west-2:487402164563:function:ACS-VerificationSemanticSegmentation arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-VerificationSemanticSegmentation arn:aws:lambda:ca-central-1:918755190332:function:ACS-VerificationSemanticSegmentation
Bounding box verification - Uses a variant of the Expectation Maximization approach to estimate the true class of verification judgement for bounding box labels based on annotations from individual workers.
arn:aws:lambda:us-east-1:432418664414:function:ACS-VerificationBoundingBox arn:aws:lambda:us-east-2:266458841044:function:ACS-VerificationBoundingBox arn:aws:lambda:us-west-2:081040173940:function:ACS-VerificationBoundingBox arn:aws:lambda:eu-west-1:568282634449:function:ACS-VerificationBoundingBox arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-VerificationBoundingBox arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-VerificationBoundingBox arn:aws:lambda:ap-south-1:565803892007:function:ACS-VerificationBoundingBox arn:aws:lambda:eu-central-1:203001061592:function:ACS-VerificationBoundingBox arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-VerificationBoundingBox arn:aws:lambda:eu-west-2:487402164563:function:ACS-VerificationBoundingBox arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-VerificationBoundingBox arn:aws:lambda:ca-central-1:918755190332:function:ACS-VerificationBoundingBox
Bounding box adjustment - Finds the most similar boxes from different workers based on the Jaccard index of the adjusted annotations.
arn:aws:lambda:us-east-1:432418664414:function:ACS-AdjustmentBoundingBox arn:aws:lambda:us-east-2:266458841044:function:ACS-AdjustmentBoundingBox arn:aws:lambda:us-west-2:081040173940:function:ACS-AdjustmentBoundingBox arn:aws:lambda:eu-west-1:568282634449:function:ACS-AdjustmentBoundingBox arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-AdjustmentBoundingBox arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-AdjustmentBoundingBox arn:aws:lambda:ap-south-1:565803892007:function:ACS-AdjustmentBoundingBox arn:aws:lambda:eu-central-1:203001061592:function:ACS-AdjustmentBoundingBox arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-AdjustmentBoundingBox arn:aws:lambda:eu-west-2:487402164563:function:ACS-AdjustmentBoundingBox arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-AdjustmentBoundingBox arn:aws:lambda:ca-central-1:918755190332:function:ACS-AdjustmentBoundingBox
Video Frame Object Detection Adjustment - Use this task type when you want workers to adjust bounding boxes that workers have added to video frames to classify and localize objects in a sequence of video frames.
arn:aws:lambda:us-east-1:432418664414:function:ACS-AdjustmentVideoObjectDetection arn:aws:lambda:us-east-2:266458841044:function:ACS-AdjustmentVideoObjectDetection arn:aws:lambda:us-west-2:081040173940:function:ACS-AdjustmentVideoObjectDetection arn:aws:lambda:eu-west-1:568282634449:function:ACS-AdjustmentVideoObjectDetection arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-AdjustmentVideoObjectDetection arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-AdjustmentVideoObjectDetection arn:aws:lambda:ap-south-1:565803892007:function:ACS-AdjustmentVideoObjectDetection arn:aws:lambda:eu-central-1:203001061592:function:ACS-AdjustmentVideoObjectDetection arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-AdjustmentVideoObjectDetection arn:aws:lambda:eu-west-2:487402164563:function:ACS-AdjustmentVideoObjectDetection arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-AdjustmentVideoObjectDetection arn:aws:lambda:ca-central-1:918755190332:function:ACS-AdjustmentVideoObjectDetection
Video Frame Object Tracking Adjustment - Use this task type when you want workers to adjust bounding boxes that workers have added to video frames to track object movement across a sequence of video frames.
arn:aws:lambda:us-east-1:432418664414:function:ACS-AdjustmentVideoObjectTracking arn:aws:lambda:us-east-2:266458841044:function:ACS-AdjustmentVideoObjectTracking arn:aws:lambda:us-west-2:081040173940:function:ACS-AdjustmentVideoObjectTracking arn:aws:lambda:eu-west-1:568282634449:function:ACS-AdjustmentVideoObjectTracking arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-AdjustmentVideoObjectTracking arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-AdjustmentVideoObjectTracking arn:aws:lambda:ap-south-1:565803892007:function:ACS-AdjustmentVideoObjectTracking arn:aws:lambda:eu-central-1:203001061592:function:ACS-AdjustmentVideoObjectTracking arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-AdjustmentVideoObjectTracking arn:aws:lambda:eu-west-2:487402164563:function:ACS-AdjustmentVideoObjectTracking arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-AdjustmentVideoObjectTracking arn:aws:lambda:ca-central-1:918755190332:function:ACS-AdjustmentVideoObjectTracking
3D point cloud object detection adjustment - Use this task type when you want workers to adjust 3D cuboids around objects in a 3D point cloud.
arn:aws:lambda:us-east-1:432418664414:function:ACS-Adjustment3DPointCloudObjectDetection arn:aws:lambda:us-east-2:266458841044:function:ACS-Adjustment3DPointCloudObjectDetection arn:aws:lambda:us-west-2:081040173940:function:ACS-Adjustment3DPointCloudObjectDetection arn:aws:lambda:eu-west-1:568282634449:function:ACS-Adjustment3DPointCloudObjectDetection arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-Adjustment3DPointCloudObjectDetection arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-Adjustment3DPointCloudObjectDetection arn:aws:lambda:ap-south-1:565803892007:function:ACS-Adjustment3DPointCloudObjectDetection arn:aws:lambda:eu-central-1:203001061592:function:ACS-Adjustment3DPointCloudObjectDetection arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-Adjustment3DPointCloudObjectDetection arn:aws:lambda:eu-west-2:487402164563:function:ACS-Adjustment3DPointCloudObjectDetection arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-Adjustment3DPointCloudObjectDetection arn:aws:lambda:ca-central-1:918755190332:function:ACS-Adjustment3DPointCloudObjectDetection
3D point cloud object tracking adjustment - Use this task type when you want workers to adjust 3D cuboids around objects that appear in a sequence of 3D point cloud frames.
arn:aws:lambda:us-east-1:432418664414:function:ACS-Adjustment3DPointCloudObjectTracking arn:aws:lambda:us-east-2:266458841044:function:ACS-Adjustment3DPointCloudObjectTracking arn:aws:lambda:us-west-2:081040173940:function:ACS-Adjustment3DPointCloudObjectTracking arn:aws:lambda:eu-west-1:568282634449:function:ACS-Adjustment3DPointCloudObjectTracking arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-Adjustment3DPointCloudObjectTracking arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-Adjustment3DPointCloudObjectTracking arn:aws:lambda:ap-south-1:565803892007:function:ACS-Adjustment3DPointCloudObjectTracking arn:aws:lambda:eu-central-1:203001061592:function:ACS-Adjustment3DPointCloudObjectTracking arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-Adjustment3DPointCloudObjectTracking arn:aws:lambda:eu-west-2:487402164563:function:ACS-Adjustment3DPointCloudObjectTracking arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-Adjustment3DPointCloudObjectTracking arn:aws:lambda:ca-central-1:918755190332:function:ACS-Adjustment3DPointCloudObjectTracking
3D point cloud semantic segmentation adjustment - Use this task type when you want workers to adjust a point-level semantic segmentation masks using a paint tool.
arn:aws:lambda:us-east-1:432418664414:function:ACS-Adjustment3DPointCloudSemanticSegmentation arn:aws:lambda:us-east-2:266458841044:function:ACS-Adjustment3DPointCloudSemanticSegmentation arn:aws:lambda:us-west-2:081040173940:function:ACS-Adjustment3DPointCloudSemanticSegmentation arn:aws:lambda:eu-west-1:568282634449:function:ACS-Adjustment3DPointCloudSemanticSegmentation arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-Adjustment3DPointCloudSemanticSegmentation arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-Adjustment3DPointCloudSemanticSegmentation arn:aws:lambda:ap-south-1:565803892007:function:ACS-Adjustment3DPointCloudSemanticSegmentation arn:aws:lambda:eu-central-1:203001061592:function:ACS-Adjustment3DPointCloudSemanticSegmentation arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-Adjustment3DPointCloudSemanticSegmentation arn:aws:lambda:eu-west-2:487402164563:function:ACS-Adjustment3DPointCloudSemanticSegmentation arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-Adjustment3DPointCloudSemanticSegmentation arn:aws:lambda:ca-central-1:918755190332:function:ACS-Adjustment3DPointCloudSemanticSegmentation
PublicWorkforceTaskPrice (dict) --
The price that you pay for each task performed by an Amazon Mechanical Turk worker.
AmountInUsd (dict) --
Defines the amount of money paid to an Amazon Mechanical Turk worker in United States dollars.
Dollars (integer) --
The whole number of dollars in the amount.
Cents (integer) --
The fractional portion, in cents, of the amount.
TenthFractionsOfACent (integer) --
Fractions of a cent, in tenths.
Tags (list) --
An array of key/value pairs. For more information, see Using Cost Allocation Tags in the AWS Billing and Cost Management User Guide.
(dict) --
Describes a tag.
Key (string) --
The tag key.
Value (string) --
The tag value.
LabelingJobOutput (dict) --
The location of the output produced by the labeling job.
OutputDatasetS3Uri (string) --
The Amazon S3 bucket location of the manifest file for labeled data.
FinalActiveLearningModelArn (string) --
The Amazon Resource Name (ARN) for the most recent Amazon SageMaker model trained as part of automated data labeling.
{'Workforce': {'CognitoConfig': {'ClientId': 'string', 'UserPool': 'string'},
'CreateDate': 'timestamp',
'OidcConfig': {'AuthorizationEndpoint': 'string',
'ClientId': 'string',
'Issuer': 'string',
'JwksUri': 'string',
'LogoutEndpoint': 'string',
'TokenEndpoint': 'string',
'UserInfoEndpoint': 'string'},
'SubDomain': 'string'}}
Lists private workforce information, including workforce name, Amazon Resource Name (ARN), and, if applicable, allowed IP address ranges ( CIDRs). Allowable IP address ranges are the IP addresses that workers can use to access tasks.
See also: AWS API Documentation
Request Syntax
client.describe_workforce(
WorkforceName='string'
)
string
[REQUIRED]
The name of the private workforce whose access you want to restrict. WorkforceName is automatically set to default when a workforce is created and cannot be modified.
dict
Response Syntax
{
'Workforce': {
'WorkforceName': 'string',
'WorkforceArn': 'string',
'LastUpdatedDate': datetime(2015, 1, 1),
'SourceIpConfig': {
'Cidrs': [
'string',
]
},
'SubDomain': 'string',
'CognitoConfig': {
'UserPool': 'string',
'ClientId': 'string'
},
'OidcConfig': {
'ClientId': 'string',
'Issuer': 'string',
'AuthorizationEndpoint': 'string',
'TokenEndpoint': 'string',
'UserInfoEndpoint': 'string',
'LogoutEndpoint': 'string',
'JwksUri': 'string'
},
'CreateDate': datetime(2015, 1, 1)
}
}
Response Structure
(dict) --
Workforce (dict) --
A single private workforce, which is automatically created when you create your first private work team. You can create one private work force in each AWS Region. By default, any workforce-related API operation used in a specific region will apply to the workforce created in that region. To learn how to create a private workforce, see Create a Private Workforce.
WorkforceName (string) --
The name of the private workforce.
WorkforceArn (string) --
The Amazon Resource Name (ARN) of the private workforce.
LastUpdatedDate (datetime) --
The most recent date that was used to successfully add one or more IP address ranges ( CIDRs) to a private workforce's allow list.
SourceIpConfig (dict) --
A list of one to ten IP address ranges ( CIDRs) to be added to the workforce allow list.
Cidrs (list) --
A list of one to ten Classless Inter-Domain Routing (CIDR) values.
Maximum: Ten CIDR values
(string) --
SubDomain (string) --
The subdomain for your OIDC Identity Provider.
CognitoConfig (dict) --
The configuration of an Amazon Cognito workforce. A single Cognito workforce is created using and corresponds to a single Amazon Cognito user pool.
UserPool (string) --
A user pool is a user directory in Amazon Cognito. With a user pool, your users can sign in to your web or mobile app through Amazon Cognito. Your users can also sign in through social identity providers like Google, Facebook, Amazon, or Apple, and through SAML identity providers.
ClientId (string) --
The client ID for your Amazon Cognito user pool.
OidcConfig (dict) --
The configuration of an OIDC Identity Provider (IdP) private workforce.
ClientId (string) --
The OIDC IdP client ID used to configure your private workforce.
Issuer (string) --
The OIDC IdP issuer used to configure your private workforce.
AuthorizationEndpoint (string) --
The OIDC IdP authorization endpoint used to configure your private workforce.
TokenEndpoint (string) --
The OIDC IdP token endpoint used to configure your private workforce.
UserInfoEndpoint (string) --
The OIDC IdP user information endpoint used to configure your private workforce.
LogoutEndpoint (string) --
The OIDC IdP logout endpoint used to configure your private workforce.
JwksUri (string) --
The OIDC IdP JSON Web Key Set (Jwks) URI used to configure your private workforce.
CreateDate (datetime) --
The date that the workforce is created.
{'Workteam': {'MemberDefinitions': {'OidcMemberDefinition': {'Groups': ['string']}},
'WorkforceArn': 'string'}}
Gets information about a specific work team. You can see information such as the create date, the last updated date, membership information, and the work team's Amazon Resource Name (ARN).
See also: AWS API Documentation
Request Syntax
client.describe_workteam(
WorkteamName='string'
)
string
[REQUIRED]
The name of the work team to return a description of.
dict
Response Syntax
{
'Workteam': {
'WorkteamName': 'string',
'MemberDefinitions': [
{
'CognitoMemberDefinition': {
'UserPool': 'string',
'UserGroup': 'string',
'ClientId': 'string'
},
'OidcMemberDefinition': {
'Groups': [
'string',
]
}
},
],
'WorkteamArn': 'string',
'WorkforceArn': 'string',
'ProductListingIds': [
'string',
],
'Description': 'string',
'SubDomain': 'string',
'CreateDate': datetime(2015, 1, 1),
'LastUpdatedDate': datetime(2015, 1, 1),
'NotificationConfiguration': {
'NotificationTopicArn': 'string'
}
}
}
Response Structure
(dict) --
Workteam (dict) --
A Workteam instance that contains information about the work team.
WorkteamName (string) --
The name of the work team.
MemberDefinitions (list) --
The Amazon Cognito user groups that make up the work team.
(dict) --
Defines the Amazon Cognito user group that is part of a work team.
CognitoMemberDefinition (dict) --
The Amazon Cognito user group that is part of the work team.
UserPool (string) --
An identifier for a user pool. The user pool must be in the same region as the service that you are calling.
UserGroup (string) --
An identifier for a user group.
ClientId (string) --
An identifier for an application client. You must create the app client ID using Amazon Cognito.
OidcMemberDefinition (dict) --
A list user groups that exist in your OIDC Identity Provider (IdP). One to ten groups can be used to create a single private work team. When you add a user group to the list of Groups, you can add that user group to one or more private work teams. If you add a user group to a private work team, all workers in that user group are added to the work team.
Groups (list) --
A list of comma seperated strings that identifies user groups in your OIDC IdP. Each user group is made up of a group of private workers.
(string) --
WorkteamArn (string) --
The Amazon Resource Name (ARN) that identifies the work team.
WorkforceArn (string) --
The Amazon Resource Name (ARN) of the workforce.
ProductListingIds (list) --
The Amazon Marketplace identifier for a vendor's work team.
(string) --
Description (string) --
A description of the work team.
SubDomain (string) --
The URI of the labeling job's user interface. Workers open this URI to start labeling your data objects.
CreateDate (datetime) --
The date and time that the work team was created (timestamp).
LastUpdatedDate (datetime) --
The date and time that the work team was last updated (timestamp).
NotificationConfiguration (dict) --
Configures SNS notifications of available or expiring work items for work teams.
NotificationTopicArn (string) --
The ARN for the SNS topic to which notifications should be published.
{'CompilationJobSummaries': {'CompilationTargetDevice': {'ml_g4dn',
'x86_win32',
'x86_win64'},
'CompilationTargetPlatformAccelerator': 'INTEL_GRAPHICS '
'| MALI | '
'NVIDIA',
'CompilationTargetPlatformArch': 'X86_64 | X86 | '
'ARM64 | '
'ARM_EABI | '
'ARM_EABIHF',
'CompilationTargetPlatformOs': 'ANDROID | LINUX'}}
Lists model compilation jobs that satisfy various filters.
To create a model compilation job, use CreateCompilationJob. To get information about a particular model compilation job you have created, use DescribeCompilationJob.
See also: AWS API Documentation
Request Syntax
client.list_compilation_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),
NameContains='string',
StatusEquals='INPROGRESS'|'COMPLETED'|'FAILED'|'STARTING'|'STOPPING'|'STOPPED',
SortBy='Name'|'CreationTime'|'Status',
SortOrder='Ascending'|'Descending'
)
string
If the result of the previous ListCompilationJobs request was truncated, the response includes a NextToken. To retrieve the next set of model compilation jobs, use the token in the next request.
integer
The maximum number of model compilation jobs to return in the response.
datetime
A filter that returns the model compilation jobs that were created after a specified time.
datetime
A filter that returns the model compilation jobs that were created before a specified time.
datetime
A filter that returns the model compilation jobs that were modified after a specified time.
datetime
A filter that returns the model compilation jobs that were modified before a specified time.
string
A filter that returns the model compilation jobs whose name contains a specified string.
string
A filter that retrieves model compilation jobs with a specific DescribeCompilationJobResponse$CompilationJobStatus status.
string
The field by which to sort results. The default is CreationTime.
string
The sort order for results. The default is Ascending.
dict
Response Syntax
{
'CompilationJobSummaries': [
{
'CompilationJobName': 'string',
'CompilationJobArn': 'string',
'CreationTime': datetime(2015, 1, 1),
'CompilationStartTime': datetime(2015, 1, 1),
'CompilationEndTime': datetime(2015, 1, 1),
'CompilationTargetDevice': 'lambda'|'ml_m4'|'ml_m5'|'ml_c4'|'ml_c5'|'ml_p2'|'ml_p3'|'ml_g4dn'|'ml_inf1'|'jetson_tx1'|'jetson_tx2'|'jetson_nano'|'jetson_xavier'|'rasp3b'|'imx8qm'|'deeplens'|'rk3399'|'rk3288'|'aisage'|'sbe_c'|'qcs605'|'qcs603'|'sitara_am57x'|'amba_cv22'|'x86_win32'|'x86_win64',
'CompilationTargetPlatformOs': 'ANDROID'|'LINUX',
'CompilationTargetPlatformArch': 'X86_64'|'X86'|'ARM64'|'ARM_EABI'|'ARM_EABIHF',
'CompilationTargetPlatformAccelerator': 'INTEL_GRAPHICS'|'MALI'|'NVIDIA',
'LastModifiedTime': datetime(2015, 1, 1),
'CompilationJobStatus': 'INPROGRESS'|'COMPLETED'|'FAILED'|'STARTING'|'STOPPING'|'STOPPED'
},
],
'NextToken': 'string'
}
Response Structure
(dict) --
CompilationJobSummaries (list) --
An array of CompilationJobSummary objects, each describing a model compilation job.
(dict) --
A summary of a model compilation job.
CompilationJobName (string) --
The name of the model compilation job that you want a summary for.
CompilationJobArn (string) --
The Amazon Resource Name (ARN) of the model compilation job.
CreationTime (datetime) --
The time when the model compilation job was created.
CompilationStartTime (datetime) --
The time when the model compilation job started.
CompilationEndTime (datetime) --
The time when the model compilation job completed.
CompilationTargetDevice (string) --
The type of device that the model will run on after the compilation job has completed.
CompilationTargetPlatformOs (string) --
The type of OS that the model will run on after the compilation job has completed.
CompilationTargetPlatformArch (string) --
The type of architecture that the model will run on after the compilation job has completed.
CompilationTargetPlatformAccelerator (string) --
The type of accelerator that the model will run on after the compilation job has completed.
LastModifiedTime (datetime) --
The time when the model compilation job was last modified.
CompilationJobStatus (string) --
The status of the model compilation job.
NextToken (string) --
If the response is truncated, Amazon SageMaker returns this NextToken. To retrieve the next set of model compilation jobs, use this token in the next request.
{'StatusEquals': {'Initializing'}}
Response {'LabelingJobSummaryList': {'LabelingJobStatus': {'Initializing'}}}
Gets a list of labeling jobs.
See also: AWS API Documentation
Request Syntax
client.list_labeling_jobs(
CreationTimeAfter=datetime(2015, 1, 1),
CreationTimeBefore=datetime(2015, 1, 1),
LastModifiedTimeAfter=datetime(2015, 1, 1),
LastModifiedTimeBefore=datetime(2015, 1, 1),
MaxResults=123,
NextToken='string',
NameContains='string',
SortBy='Name'|'CreationTime'|'Status',
SortOrder='Ascending'|'Descending',
StatusEquals='Initializing'|'InProgress'|'Completed'|'Failed'|'Stopping'|'Stopped'
)
datetime
A filter that returns only labeling jobs created after the specified time (timestamp).
datetime
A filter that returns only labeling jobs created before the specified time (timestamp).
datetime
A filter that returns only labeling jobs modified after the specified time (timestamp).
datetime
A filter that returns only labeling jobs modified before the specified time (timestamp).
integer
The maximum number of labeling jobs to return in each page of the response.
string
If the result of the previous ListLabelingJobs request was truncated, the response includes a NextToken. To retrieve the next set of labeling jobs, use the token in the next request.
string
A string in the labeling job name. This filter returns only labeling jobs whose name contains the specified string.
string
The field to sort results by. The default is CreationTime.
string
The sort order for results. The default is Ascending.
string
A filter that retrieves only labeling jobs with a specific status.
dict
Response Syntax
{
'LabelingJobSummaryList': [
{
'LabelingJobName': 'string',
'LabelingJobArn': 'string',
'CreationTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'LabelingJobStatus': 'Initializing'|'InProgress'|'Completed'|'Failed'|'Stopping'|'Stopped',
'LabelCounters': {
'TotalLabeled': 123,
'HumanLabeled': 123,
'MachineLabeled': 123,
'FailedNonRetryableError': 123,
'Unlabeled': 123
},
'WorkteamArn': 'string',
'PreHumanTaskLambdaArn': 'string',
'AnnotationConsolidationLambdaArn': 'string',
'FailureReason': 'string',
'LabelingJobOutput': {
'OutputDatasetS3Uri': 'string',
'FinalActiveLearningModelArn': 'string'
},
'InputConfig': {
'DataSource': {
'S3DataSource': {
'ManifestS3Uri': 'string'
}
},
'DataAttributes': {
'ContentClassifiers': [
'FreeOfPersonallyIdentifiableInformation'|'FreeOfAdultContent',
]
}
}
},
],
'NextToken': 'string'
}
Response Structure
(dict) --
LabelingJobSummaryList (list) --
An array of LabelingJobSummary objects, each describing a labeling job.
(dict) --
Provides summary information about a labeling job.
LabelingJobName (string) --
The name of the labeling job.
LabelingJobArn (string) --
The Amazon Resource Name (ARN) assigned to the labeling job when it was created.
CreationTime (datetime) --
The date and time that the job was created (timestamp).
LastModifiedTime (datetime) --
The date and time that the job was last modified (timestamp).
LabelingJobStatus (string) --
The current status of the labeling job.
LabelCounters (dict) --
Counts showing the progress of the labeling job.
TotalLabeled (integer) --
The total number of objects labeled.
HumanLabeled (integer) --
The total number of objects labeled by a human worker.
MachineLabeled (integer) --
The total number of objects labeled by automated data labeling.
FailedNonRetryableError (integer) --
The total number of objects that could not be labeled due to an error.
Unlabeled (integer) --
The total number of objects not yet labeled.
WorkteamArn (string) --
The Amazon Resource Name (ARN) of the work team assigned to the job.
PreHumanTaskLambdaArn (string) --
The Amazon Resource Name (ARN) of a Lambda function. The function is run before each data object is sent to a worker.
AnnotationConsolidationLambdaArn (string) --
The Amazon Resource Name (ARN) of the Lambda function used to consolidate the annotations from individual workers into a label for a data object. For more information, see Annotation Consolidation.
FailureReason (string) --
If the LabelingJobStatus field is Failed, this field contains a description of the error.
LabelingJobOutput (dict) --
The location of the output produced by the labeling job.
OutputDatasetS3Uri (string) --
The Amazon S3 bucket location of the manifest file for labeled data.
FinalActiveLearningModelArn (string) --
The Amazon Resource Name (ARN) for the most recent Amazon SageMaker model trained as part of automated data labeling.
InputConfig (dict) --
Input configuration for the labeling job.
DataSource (dict) --
The location of the input data.
S3DataSource (dict) --
The Amazon S3 location of the input data objects.
ManifestS3Uri (string) --
The Amazon S3 location of the manifest file that describes the input data objects.
DataAttributes (dict) --
Attributes of the data specified by the customer.
ContentClassifiers (list) --
Declares that your content is free of personally identifiable information or adult content. Amazon SageMaker may restrict the Amazon Mechanical Turk workers that can view your task based on this information.
(string) --
NextToken (string) --
If the response is truncated, Amazon SageMaker returns this token. To retrieve the next set of labeling jobs, use it in the subsequent request.
{'Workteams': {'MemberDefinitions': {'OidcMemberDefinition': {'Groups': ['string']}},
'WorkforceArn': 'string'}}
Gets a list of work teams that you have defined in a region. The list may be empty if no work team satisfies the filter specified in the NameContains parameter.
See also: AWS API Documentation
Request Syntax
client.list_workteams(
SortBy='Name'|'CreateDate',
SortOrder='Ascending'|'Descending',
NameContains='string',
NextToken='string',
MaxResults=123
)
string
The field to sort results by. The default is CreationTime.
string
The sort order for results. The default is Ascending.
string
A string in the work team's name. This filter returns only work teams whose name contains the specified string.
string
If the result of the previous ListWorkteams request was truncated, the response includes a NextToken. To retrieve the next set of labeling jobs, use the token in the next request.
integer
The maximum number of work teams to return in each page of the response.
dict
Response Syntax
{
'Workteams': [
{
'WorkteamName': 'string',
'MemberDefinitions': [
{
'CognitoMemberDefinition': {
'UserPool': 'string',
'UserGroup': 'string',
'ClientId': 'string'
},
'OidcMemberDefinition': {
'Groups': [
'string',
]
}
},
],
'WorkteamArn': 'string',
'WorkforceArn': 'string',
'ProductListingIds': [
'string',
],
'Description': 'string',
'SubDomain': 'string',
'CreateDate': datetime(2015, 1, 1),
'LastUpdatedDate': datetime(2015, 1, 1),
'NotificationConfiguration': {
'NotificationTopicArn': 'string'
}
},
],
'NextToken': 'string'
}
Response Structure
(dict) --
Workteams (list) --
An array of Workteam objects, each describing a work team.
(dict) --
Provides details about a labeling work team.
WorkteamName (string) --
The name of the work team.
MemberDefinitions (list) --
The Amazon Cognito user groups that make up the work team.
(dict) --
Defines the Amazon Cognito user group that is part of a work team.
CognitoMemberDefinition (dict) --
The Amazon Cognito user group that is part of the work team.
UserPool (string) --
An identifier for a user pool. The user pool must be in the same region as the service that you are calling.
UserGroup (string) --
An identifier for a user group.
ClientId (string) --
An identifier for an application client. You must create the app client ID using Amazon Cognito.
OidcMemberDefinition (dict) --
A list user groups that exist in your OIDC Identity Provider (IdP). One to ten groups can be used to create a single private work team. When you add a user group to the list of Groups, you can add that user group to one or more private work teams. If you add a user group to a private work team, all workers in that user group are added to the work team.
Groups (list) --
A list of comma seperated strings that identifies user groups in your OIDC IdP. Each user group is made up of a group of private workers.
(string) --
WorkteamArn (string) --
The Amazon Resource Name (ARN) that identifies the work team.
WorkforceArn (string) --
The Amazon Resource Name (ARN) of the workforce.
ProductListingIds (list) --
The Amazon Marketplace identifier for a vendor's work team.
(string) --
Description (string) --
A description of the work team.
SubDomain (string) --
The URI of the labeling job's user interface. Workers open this URI to start labeling your data objects.
CreateDate (datetime) --
The date and time that the work team was created (timestamp).
LastUpdatedDate (datetime) --
The date and time that the work team was last updated (timestamp).
NotificationConfiguration (dict) --
Configures SNS notifications of available or expiring work items for work teams.
NotificationTopicArn (string) --
The ARN for the SNS topic to which notifications should be published.
NextToken (string) --
If the response is truncated, Amazon SageMaker returns this token. To retrieve the next set of work teams, use it in the subsequent request.
{'Results': {'TrialComponent': {'SourceDetail': {'TransformJob': {'AutoMLJobArn': 'string',
'BatchStrategy': 'MultiRecord '
'| '
'SingleRecord',
'CreationTime': 'timestamp',
'DataProcessing': {'InputFilter': 'string',
'JoinSource': 'Input '
'| '
'None',
'OutputFilter': 'string'},
'Environment': {'string': 'string'},
'ExperimentConfig': {'ExperimentName': 'string',
'TrialComponentDisplayName': 'string',
'TrialName': 'string'},
'FailureReason': 'string',
'LabelingJobArn': 'string',
'MaxConcurrentTransforms': 'integer',
'MaxPayloadInMB': 'integer',
'ModelClientConfig': {'InvocationsMaxRetries': 'integer',
'InvocationsTimeoutInSeconds': 'integer'},
'ModelName': 'string',
'Tags': [{'Key': 'string',
'Value': 'string'}],
'TransformEndTime': 'timestamp',
'TransformInput': {'CompressionType': 'None '
'| '
'Gzip',
'ContentType': 'string',
'DataSource': {'S3DataSource': {'S3DataType': 'ManifestFile '
'| '
'S3Prefix '
'| '
'AugmentedManifestFile',
'S3Uri': 'string'}},
'SplitType': 'None '
'| '
'Line '
'| '
'RecordIO '
'| '
'TFRecord'},
'TransformJobArn': 'string',
'TransformJobName': 'string',
'TransformJobStatus': 'InProgress '
'| '
'Completed '
'| '
'Failed '
'| '
'Stopping '
'| '
'Stopped',
'TransformOutput': {'Accept': 'string',
'AssembleWith': 'None '
'| '
'Line',
'KmsKeyId': 'string',
'S3OutputPath': 'string'},
'TransformResources': {'InstanceCount': 'integer',
'InstanceType': 'ml.m4.xlarge '
'| '
'ml.m4.2xlarge '
'| '
'ml.m4.4xlarge '
'| '
'ml.m4.10xlarge '
'| '
'ml.m4.16xlarge '
'| '
'ml.c4.xlarge '
'| '
'ml.c4.2xlarge '
'| '
'ml.c4.4xlarge '
'| '
'ml.c4.8xlarge '
'| '
'ml.p2.xlarge '
'| '
'ml.p2.8xlarge '
'| '
'ml.p2.16xlarge '
'| '
'ml.p3.2xlarge '
'| '
'ml.p3.8xlarge '
'| '
'ml.p3.16xlarge '
'| '
'ml.c5.xlarge '
'| '
'ml.c5.2xlarge '
'| '
'ml.c5.4xlarge '
'| '
'ml.c5.9xlarge '
'| '
'ml.c5.18xlarge '
'| '
'ml.m5.large '
'| '
'ml.m5.xlarge '
'| '
'ml.m5.2xlarge '
'| '
'ml.m5.4xlarge '
'| '
'ml.m5.12xlarge '
'| '
'ml.m5.24xlarge',
'VolumeKmsKeyId': 'string'},
'TransformStartTime': 'timestamp'}}}}}
Finds Amazon SageMaker resources that match a search query. Matching resources are returned as a list of SearchRecord objects in the response. You can sort the search results by any resource property in a ascending or descending order.
You can query against the following value types: numeric, text, Boolean, and timestamp.
See also: AWS API Documentation
Request Syntax
client.search(
Resource='TrainingJob'|'Experiment'|'ExperimentTrial'|'ExperimentTrialComponent',
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
)
string
[REQUIRED]
The name of the Amazon 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, Amazon 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, Amazon 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.
dict
Response Syntax
{
'Results': [
{
'TrainingJob': {
'TrainingJobName': 'string',
'TrainingJobArn': 'string',
'TuningJobArn': 'string',
'LabelingJobArn': 'string',
'AutoMLJobArn': 'string',
'ModelArtifacts': {
'S3ModelArtifacts': 'string'
},
'TrainingJobStatus': 'InProgress'|'Completed'|'Failed'|'Stopping'|'Stopped',
'SecondaryStatus': 'Starting'|'LaunchingMLInstances'|'PreparingTrainingStack'|'Downloading'|'DownloadingTrainingImage'|'Training'|'Uploading'|'Stopping'|'Stopped'|'MaxRuntimeExceeded'|'Completed'|'Failed'|'Interrupted'|'MaxWaitTimeExceeded',
'FailureReason': 'string',
'HyperParameters': {
'string': 'string'
},
'AlgorithmSpecification': {
'TrainingImage': 'string',
'AlgorithmName': 'string',
'TrainingInputMode': 'Pipe'|'File',
'MetricDefinitions': [
{
'Name': 'string',
'Regex': 'string'
},
],
'EnableSageMakerMetricsTimeSeries': True|False
},
'RoleArn': 'string',
'InputDataConfig': [
{
'ChannelName': 'string',
'DataSource': {
'S3DataSource': {
'S3DataType': 'ManifestFile'|'S3Prefix'|'AugmentedManifestFile',
'S3Uri': 'string',
'S3DataDistributionType': 'FullyReplicated'|'ShardedByS3Key',
'AttributeNames': [
'string',
]
},
'FileSystemDataSource': {
'FileSystemId': 'string',
'FileSystemAccessMode': 'rw'|'ro',
'FileSystemType': 'EFS'|'FSxLustre',
'DirectoryPath': 'string'
}
},
'ContentType': 'string',
'CompressionType': 'None'|'Gzip',
'RecordWrapperType': 'None'|'RecordIO',
'InputMode': 'Pipe'|'File',
'ShuffleConfig': {
'Seed': 123
}
},
],
'OutputDataConfig': {
'KmsKeyId': 'string',
'S3OutputPath': 'string'
},
'ResourceConfig': {
'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'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.p3dn.24xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5n.xlarge'|'ml.c5n.2xlarge'|'ml.c5n.4xlarge'|'ml.c5n.9xlarge'|'ml.c5n.18xlarge',
'InstanceCount': 123,
'VolumeSizeInGB': 123,
'VolumeKmsKeyId': 'string'
},
'VpcConfig': {
'SecurityGroupIds': [
'string',
],
'Subnets': [
'string',
]
},
'StoppingCondition': {
'MaxRuntimeInSeconds': 123,
'MaxWaitTimeInSeconds': 123
},
'CreationTime': datetime(2015, 1, 1),
'TrainingStartTime': datetime(2015, 1, 1),
'TrainingEndTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'SecondaryStatusTransitions': [
{
'Status': 'Starting'|'LaunchingMLInstances'|'PreparingTrainingStack'|'Downloading'|'DownloadingTrainingImage'|'Training'|'Uploading'|'Stopping'|'Stopped'|'MaxRuntimeExceeded'|'Completed'|'Failed'|'Interrupted'|'MaxWaitTimeExceeded',
'StartTime': datetime(2015, 1, 1),
'EndTime': datetime(2015, 1, 1),
'StatusMessage': 'string'
},
],
'FinalMetricDataList': [
{
'MetricName': 'string',
'Value': ...,
'Timestamp': datetime(2015, 1, 1)
},
],
'EnableNetworkIsolation': True|False,
'EnableInterContainerTrafficEncryption': True|False,
'EnableManagedSpotTraining': True|False,
'CheckpointConfig': {
'S3Uri': 'string',
'LocalPath': 'string'
},
'TrainingTimeInSeconds': 123,
'BillableTimeInSeconds': 123,
'DebugHookConfig': {
'LocalPath': 'string',
'S3OutputPath': 'string',
'HookParameters': {
'string': 'string'
},
'CollectionConfigurations': [
{
'CollectionName': 'string',
'CollectionParameters': {
'string': 'string'
}
},
]
},
'ExperimentConfig': {
'ExperimentName': 'string',
'TrialName': 'string',
'TrialComponentDisplayName': 'string'
},
'DebugRuleConfigurations': [
{
'RuleConfigurationName': 'string',
'LocalPath': 'string',
'S3OutputPath': 'string',
'RuleEvaluatorImage': 'string',
'InstanceType': 'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge',
'VolumeSizeInGB': 123,
'RuleParameters': {
'string': 'string'
}
},
],
'TensorBoardOutputConfig': {
'LocalPath': 'string',
'S3OutputPath': 'string'
},
'DebugRuleEvaluationStatuses': [
{
'RuleConfigurationName': 'string',
'RuleEvaluationJobArn': 'string',
'RuleEvaluationStatus': 'InProgress'|'NoIssuesFound'|'IssuesFound'|'Error'|'Stopping'|'Stopped',
'StatusDetails': 'string',
'LastModifiedTime': datetime(2015, 1, 1)
},
],
'Tags': [
{
'Key': 'string',
'Value': 'string'
},
]
},
'Experiment': {
'ExperimentName': 'string',
'ExperimentArn': 'string',
'DisplayName': 'string',
'Source': {
'SourceArn': 'string',
'SourceType': 'string'
},
'Description': 'string',
'CreationTime': datetime(2015, 1, 1),
'CreatedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string'
},
'LastModifiedTime': datetime(2015, 1, 1),
'LastModifiedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string'
},
'Tags': [
{
'Key': 'string',
'Value': 'string'
},
]
},
'Trial': {
'TrialName': 'string',
'TrialArn': 'string',
'DisplayName': 'string',
'ExperimentName': 'string',
'Source': {
'SourceArn': 'string',
'SourceType': 'string'
},
'CreationTime': datetime(2015, 1, 1),
'CreatedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string'
},
'LastModifiedTime': datetime(2015, 1, 1),
'LastModifiedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string'
},
'Tags': [
{
'Key': 'string',
'Value': 'string'
},
],
'TrialComponentSummaries': [
{
'TrialComponentName': 'string',
'TrialComponentArn': 'string',
'TrialComponentSource': {
'SourceArn': 'string',
'SourceType': 'string'
},
'CreationTime': datetime(2015, 1, 1),
'CreatedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string'
}
},
]
},
'TrialComponent': {
'TrialComponentName': 'string',
'DisplayName': 'string',
'TrialComponentArn': 'string',
'Source': {
'SourceArn': 'string',
'SourceType': 'string'
},
'Status': {
'PrimaryStatus': 'InProgress'|'Completed'|'Failed'|'Stopping'|'Stopped',
'Message': 'string'
},
'StartTime': datetime(2015, 1, 1),
'EndTime': datetime(2015, 1, 1),
'CreationTime': datetime(2015, 1, 1),
'CreatedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string'
},
'LastModifiedTime': datetime(2015, 1, 1),
'LastModifiedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string'
},
'Parameters': {
'string': {
'StringValue': 'string',
'NumberValue': 123.0
}
},
'InputArtifacts': {
'string': {
'MediaType': 'string',
'Value': 'string'
}
},
'OutputArtifacts': {
'string': {
'MediaType': 'string',
'Value': 'string'
}
},
'Metrics': [
{
'MetricName': 'string',
'SourceArn': 'string',
'TimeStamp': datetime(2015, 1, 1),
'Max': 123.0,
'Min': 123.0,
'Last': 123.0,
'Count': 123,
'Avg': 123.0,
'StdDev': 123.0
},
],
'SourceDetail': {
'SourceArn': 'string',
'TrainingJob': {
'TrainingJobName': 'string',
'TrainingJobArn': 'string',
'TuningJobArn': 'string',
'LabelingJobArn': 'string',
'AutoMLJobArn': 'string',
'ModelArtifacts': {
'S3ModelArtifacts': 'string'
},
'TrainingJobStatus': 'InProgress'|'Completed'|'Failed'|'Stopping'|'Stopped',
'SecondaryStatus': 'Starting'|'LaunchingMLInstances'|'PreparingTrainingStack'|'Downloading'|'DownloadingTrainingImage'|'Training'|'Uploading'|'Stopping'|'Stopped'|'MaxRuntimeExceeded'|'Completed'|'Failed'|'Interrupted'|'MaxWaitTimeExceeded',
'FailureReason': 'string',
'HyperParameters': {
'string': 'string'
},
'AlgorithmSpecification': {
'TrainingImage': 'string',
'AlgorithmName': 'string',
'TrainingInputMode': 'Pipe'|'File',
'MetricDefinitions': [
{
'Name': 'string',
'Regex': 'string'
},
],
'EnableSageMakerMetricsTimeSeries': True|False
},
'RoleArn': 'string',
'InputDataConfig': [
{
'ChannelName': 'string',
'DataSource': {
'S3DataSource': {
'S3DataType': 'ManifestFile'|'S3Prefix'|'AugmentedManifestFile',
'S3Uri': 'string',
'S3DataDistributionType': 'FullyReplicated'|'ShardedByS3Key',
'AttributeNames': [
'string',
]
},
'FileSystemDataSource': {
'FileSystemId': 'string',
'FileSystemAccessMode': 'rw'|'ro',
'FileSystemType': 'EFS'|'FSxLustre',
'DirectoryPath': 'string'
}
},
'ContentType': 'string',
'CompressionType': 'None'|'Gzip',
'RecordWrapperType': 'None'|'RecordIO',
'InputMode': 'Pipe'|'File',
'ShuffleConfig': {
'Seed': 123
}
},
],
'OutputDataConfig': {
'KmsKeyId': 'string',
'S3OutputPath': 'string'
},
'ResourceConfig': {
'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'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.p3dn.24xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5n.xlarge'|'ml.c5n.2xlarge'|'ml.c5n.4xlarge'|'ml.c5n.9xlarge'|'ml.c5n.18xlarge',
'InstanceCount': 123,
'VolumeSizeInGB': 123,
'VolumeKmsKeyId': 'string'
},
'VpcConfig': {
'SecurityGroupIds': [
'string',
],
'Subnets': [
'string',
]
},
'StoppingCondition': {
'MaxRuntimeInSeconds': 123,
'MaxWaitTimeInSeconds': 123
},
'CreationTime': datetime(2015, 1, 1),
'TrainingStartTime': datetime(2015, 1, 1),
'TrainingEndTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'SecondaryStatusTransitions': [
{
'Status': 'Starting'|'LaunchingMLInstances'|'PreparingTrainingStack'|'Downloading'|'DownloadingTrainingImage'|'Training'|'Uploading'|'Stopping'|'Stopped'|'MaxRuntimeExceeded'|'Completed'|'Failed'|'Interrupted'|'MaxWaitTimeExceeded',
'StartTime': datetime(2015, 1, 1),
'EndTime': datetime(2015, 1, 1),
'StatusMessage': 'string'
},
],
'FinalMetricDataList': [
{
'MetricName': 'string',
'Value': ...,
'Timestamp': datetime(2015, 1, 1)
},
],
'EnableNetworkIsolation': True|False,
'EnableInterContainerTrafficEncryption': True|False,
'EnableManagedSpotTraining': True|False,
'CheckpointConfig': {
'S3Uri': 'string',
'LocalPath': 'string'
},
'TrainingTimeInSeconds': 123,
'BillableTimeInSeconds': 123,
'DebugHookConfig': {
'LocalPath': 'string',
'S3OutputPath': 'string',
'HookParameters': {
'string': 'string'
},
'CollectionConfigurations': [
{
'CollectionName': 'string',
'CollectionParameters': {
'string': 'string'
}
},
]
},
'ExperimentConfig': {
'ExperimentName': 'string',
'TrialName': 'string',
'TrialComponentDisplayName': 'string'
},
'DebugRuleConfigurations': [
{
'RuleConfigurationName': 'string',
'LocalPath': 'string',
'S3OutputPath': 'string',
'RuleEvaluatorImage': 'string',
'InstanceType': 'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge',
'VolumeSizeInGB': 123,
'RuleParameters': {
'string': 'string'
}
},
],
'TensorBoardOutputConfig': {
'LocalPath': 'string',
'S3OutputPath': 'string'
},
'DebugRuleEvaluationStatuses': [
{
'RuleConfigurationName': 'string',
'RuleEvaluationJobArn': 'string',
'RuleEvaluationStatus': 'InProgress'|'NoIssuesFound'|'IssuesFound'|'Error'|'Stopping'|'Stopped',
'StatusDetails': 'string',
'LastModifiedTime': datetime(2015, 1, 1)
},
],
'Tags': [
{
'Key': 'string',
'Value': 'string'
},
]
},
'ProcessingJob': {
'ProcessingInputs': [
{
'InputName': 'string',
'S3Input': {
'S3Uri': 'string',
'LocalPath': 'string',
'S3DataType': 'ManifestFile'|'S3Prefix',
'S3InputMode': 'Pipe'|'File',
'S3DataDistributionType': 'FullyReplicated'|'ShardedByS3Key',
'S3CompressionType': 'None'|'Gzip'
}
},
],
'ProcessingOutputConfig': {
'Outputs': [
{
'OutputName': 'string',
'S3Output': {
'S3Uri': 'string',
'LocalPath': 'string',
'S3UploadMode': 'Continuous'|'EndOfJob'
}
},
],
'KmsKeyId': 'string'
},
'ProcessingJobName': 'string',
'ProcessingResources': {
'ClusterConfig': {
'InstanceCount': 123,
'InstanceType': 'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge',
'VolumeSizeInGB': 123,
'VolumeKmsKeyId': 'string'
}
},
'StoppingCondition': {
'MaxRuntimeInSeconds': 123
},
'AppSpecification': {
'ImageUri': 'string',
'ContainerEntrypoint': [
'string',
],
'ContainerArguments': [
'string',
]
},
'Environment': {
'string': 'string'
},
'NetworkConfig': {
'EnableInterContainerTrafficEncryption': True|False,
'EnableNetworkIsolation': True|False,
'VpcConfig': {
'SecurityGroupIds': [
'string',
],
'Subnets': [
'string',
]
}
},
'RoleArn': 'string',
'ExperimentConfig': {
'ExperimentName': 'string',
'TrialName': 'string',
'TrialComponentDisplayName': 'string'
},
'ProcessingJobArn': 'string',
'ProcessingJobStatus': 'InProgress'|'Completed'|'Failed'|'Stopping'|'Stopped',
'ExitMessage': 'string',
'FailureReason': 'string',
'ProcessingEndTime': datetime(2015, 1, 1),
'ProcessingStartTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'CreationTime': datetime(2015, 1, 1),
'MonitoringScheduleArn': 'string',
'AutoMLJobArn': 'string',
'TrainingJobArn': 'string',
'Tags': [
{
'Key': 'string',
'Value': 'string'
},
]
},
'TransformJob': {
'TransformJobName': 'string',
'TransformJobArn': 'string',
'TransformJobStatus': 'InProgress'|'Completed'|'Failed'|'Stopping'|'Stopped',
'FailureReason': 'string',
'ModelName': 'string',
'MaxConcurrentTransforms': 123,
'ModelClientConfig': {
'InvocationsTimeoutInSeconds': 123,
'InvocationsMaxRetries': 123
},
'MaxPayloadInMB': 123,
'BatchStrategy': 'MultiRecord'|'SingleRecord',
'Environment': {
'string': 'string'
},
'TransformInput': {
'DataSource': {
'S3DataSource': {
'S3DataType': 'ManifestFile'|'S3Prefix'|'AugmentedManifestFile',
'S3Uri': 'string'
}
},
'ContentType': 'string',
'CompressionType': 'None'|'Gzip',
'SplitType': 'None'|'Line'|'RecordIO'|'TFRecord'
},
'TransformOutput': {
'S3OutputPath': 'string',
'Accept': 'string',
'AssembleWith': 'None'|'Line',
'KmsKeyId': 'string'
},
'TransformResources': {
'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge',
'InstanceCount': 123,
'VolumeKmsKeyId': 'string'
},
'CreationTime': datetime(2015, 1, 1),
'TransformStartTime': datetime(2015, 1, 1),
'TransformEndTime': datetime(2015, 1, 1),
'LabelingJobArn': 'string',
'AutoMLJobArn': 'string',
'DataProcessing': {
'InputFilter': 'string',
'OutputFilter': 'string',
'JoinSource': 'Input'|'None'
},
'ExperimentConfig': {
'ExperimentName': 'string',
'TrialName': 'string',
'TrialComponentDisplayName': 'string'
},
'Tags': [
{
'Key': 'string',
'Value': 'string'
},
]
}
},
'Tags': [
{
'Key': 'string',
'Value': 'string'
},
],
'Parents': [
{
'TrialName': 'string',
'ExperimentName': 'string'
},
]
}
},
],
'NextToken': 'string'
}
Response Structure
(dict) --
Results (list) --
A list of SearchRecord objects.
(dict) --
A single resource returned as part of the Search API response.
TrainingJob (dict) --
The properties of a training job.
TrainingJobName (string) --
The name of the training job.
TrainingJobArn (string) --
The Amazon Resource Name (ARN) of the training job.
TuningJobArn (string) --
The Amazon Resource Name (ARN) of the associated hyperparameter tuning job if the training job was launched by a hyperparameter tuning job.
LabelingJobArn (string) --
The Amazon Resource Name (ARN) of the labeling job.
AutoMLJobArn (string) --
The Amazon Resource Name (ARN) of the job.
ModelArtifacts (dict) --
Information about the Amazon S3 location that is configured for storing model artifacts.
S3ModelArtifacts (string) --
The path of the S3 object that contains the model artifacts. For example, s3://bucket-name/keynameprefix/model.tar.gz.
TrainingJobStatus (string) --
The status of the training job.
Training job statuses are:
InProgress - The training is in progress.
Completed - The training job has completed.
Failed - The training job has failed. To see the reason for the failure, see the FailureReason field in the response to a DescribeTrainingJobResponse call.
Stopping - The training job is stopping.
Stopped - The training job has stopped.
For more detailed information, see SecondaryStatus.
SecondaryStatus (string) --
Provides detailed information about the state of the training job. For detailed information about the secondary status of the training job, see StatusMessage under SecondaryStatusTransition.
Amazon SageMaker provides primary statuses and secondary statuses that apply to each of them:
InProgress
Starting - Starting the training job.
Downloading - An optional stage for algorithms that support File training input mode. It indicates that data is being downloaded to the ML storage volumes.
Training - Training is in progress.
Uploading - Training is complete and the model artifacts are being uploaded to the S3 location.
Completed
Completed - The training job has completed.
Failed
Failed - The training job has failed. The reason for the failure is returned in the FailureReason field of DescribeTrainingJobResponse.
Stopped
MaxRuntimeExceeded - The job stopped because it exceeded the maximum allowed runtime.
Stopped - The training job has stopped.
Stopping
Stopping - Stopping the training job.
We no longer support the following secondary statuses:
LaunchingMLInstances
PreparingTrainingStack
DownloadingTrainingImage
FailureReason (string) --
If the training job failed, the reason it failed.
HyperParameters (dict) --
Algorithm-specific parameters.
(string) --
(string) --
AlgorithmSpecification (dict) --
Information about the algorithm used for training, and algorithm metadata.
TrainingImage (string) --
The registry path of the Docker image that contains the training algorithm. For information about docker registry paths for built-in algorithms, see Algorithms Provided by Amazon SageMaker: Common Parameters. Amazon SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker.
AlgorithmName (string) --
The name of the algorithm resource to use for the training job. This must be an algorithm resource that you created or subscribe to on AWS Marketplace. If you specify a value for this parameter, you can't specify a value for TrainingImage.
TrainingInputMode (string) --
The input mode that the algorithm supports. For the input modes that Amazon SageMaker algorithms support, see Algorithms. If an algorithm supports the File input mode, Amazon SageMaker downloads the training data from S3 to the provisioned ML storage Volume, and mounts the directory to docker volume for training container. If an algorithm supports the Pipe input mode, Amazon SageMaker streams data directly from S3 to the container.
In File mode, make sure you provision ML storage volume with sufficient capacity to accommodate the data download from S3. In addition to the training data, the ML storage volume also stores the output model. The algorithm container use ML storage volume to also store intermediate information, if any.
For distributed algorithms using File mode, training data is distributed uniformly, and your training duration is predictable if the input data objects size is approximately same. Amazon SageMaker does not split the files any further for model training. If the object sizes are skewed, training won't be optimal as the data distribution is also skewed where one host in a training cluster is overloaded, thus becoming bottleneck in training.
MetricDefinitions (list) --
A list of metric definition objects. Each object specifies the metric name and regular expressions used to parse algorithm logs. Amazon SageMaker publishes each metric to Amazon CloudWatch.
(dict) --
Specifies a metric that the training algorithm writes to stderr or stdout . Amazon SageMakerhyperparameter tuning captures all defined metrics. You specify one metric that a hyperparameter tuning job uses as its objective metric to choose the best training job.
Name (string) --
The name of the metric.
Regex (string) --
A regular expression that searches the output of a training job and gets the value of the metric. For more information about using regular expressions to define metrics, see Defining Objective Metrics.
EnableSageMakerMetricsTimeSeries (boolean) --
To generate and save time-series metrics during training, set to true. The default is false and time-series metrics aren't generated except in the following cases:
You use one of the Amazon SageMaker built-in algorithms
You use one of the following Prebuilt Amazon SageMaker Docker Images:
Tensorflow (version >= 1.15)
MXNet (version >= 1.6)
PyTorch (version >= 1.3)
You specify at least one MetricDefinition
RoleArn (string) --
The AWS Identity and Access Management (IAM) role configured for the training job.
InputDataConfig (list) --
An array of Channel objects that describes each data input channel.
(dict) --
A channel is a named input source that training algorithms can consume.
ChannelName (string) --
The name of the channel.
DataSource (dict) --
The location of the channel data.
S3DataSource (dict) --
The S3 location of the data source that is associated with a channel.
S3DataType (string) --
If you choose S3Prefix, S3Uri identifies a key name prefix. Amazon SageMaker uses all objects that match the specified key name prefix for model training.
If you choose ManifestFile, S3Uri identifies an object that is a manifest file containing a list of object keys that you want Amazon SageMaker to use for model training.
If you choose AugmentedManifestFile, S3Uri identifies an object that is an augmented manifest file in JSON lines format. This file contains the data you want to use for model training. AugmentedManifestFile can only be used if the Channel's input mode is Pipe.
S3Uri (string) --
Depending on the value specified for the S3DataType, identifies either a key name prefix or a manifest. For example:
A key name prefix might look like this: s3://bucketname/exampleprefix
A manifest might look like this: s3://bucketname/example.manifest A manifest is an S3 object which is a JSON file consisting of an array of elements. The first element is a prefix which is followed by one or more suffixes. SageMaker appends the suffix elements to the prefix to get a full set of S3Uri. Note that the prefix must be a valid non-empty S3Uri that precludes users from specifying a manifest whose individual S3Uri is sourced from different S3 buckets. The following code example shows a valid manifest format: [ {"prefix": "s3://customer_bucket/some/prefix/"}, "relative/path/to/custdata-1", "relative/path/custdata-2", ... "relative/path/custdata-N" ] This JSON is equivalent to the following S3Uri list: s3://customer_bucket/some/prefix/relative/path/to/custdata-1 s3://customer_bucket/some/prefix/relative/path/custdata-2 ... s3://customer_bucket/some/prefix/relative/path/custdata-N The complete set of S3Uri in this manifest is the input data for the channel for this data source. The object that each S3Uri points to must be readable by the IAM role that Amazon SageMaker uses to perform tasks on your behalf.
S3DataDistributionType (string) --
If you want Amazon SageMaker to replicate the entire dataset on each ML compute instance that is launched for model training, specify FullyReplicated.
If you want Amazon SageMaker to replicate a subset of data on each ML compute instance that is launched for model training, specify ShardedByS3Key. If there are n ML compute instances launched for a training job, each instance gets approximately 1/n of the number of S3 objects. In this case, model training on each machine uses only the subset of training data.
Don't choose more ML compute instances for training than available S3 objects. If you do, some nodes won't get any data and you will pay for nodes that aren't getting any training data. This applies in both File and Pipe modes. Keep this in mind when developing algorithms.
In distributed training, where you use multiple ML compute EC2 instances, you might choose ShardedByS3Key. If the algorithm requires copying training data to the ML storage volume (when TrainingInputMode is set to File), this copies 1/n of the number of objects.
AttributeNames (list) --
A list of one or more attribute names to use that are found in a specified augmented manifest file.
(string) --
FileSystemDataSource (dict) --
The file system that is associated with a channel.
FileSystemId (string) --
The file system id.
FileSystemAccessMode (string) --
The access mode of the mount of the directory associated with the channel. A directory can be mounted either in ro (read-only) or rw (read-write) mode.
FileSystemType (string) --
The file system type.
DirectoryPath (string) --
The full path to the directory to associate with the channel.
ContentType (string) --
The MIME type of the data.
CompressionType (string) --
If training data is compressed, the compression type. The default value is None. CompressionType is used only in Pipe input mode. In File mode, leave this field unset or set it to None.
RecordWrapperType (string) --
Specify RecordIO as the value when input data is in raw format but the training algorithm requires the RecordIO format. In this case, Amazon SageMaker wraps each individual S3 object in a RecordIO record. If the input data is already in RecordIO format, you don't need to set this attribute. For more information, see Create a Dataset Using RecordIO.
In File mode, leave this field unset or set it to None.
InputMode (string) --
(Optional) The input mode to use for the data channel in a training job. If you don't set a value for InputMode, Amazon SageMaker uses the value set for TrainingInputMode. Use this parameter to override the TrainingInputMode setting in a AlgorithmSpecification request when you have a channel that needs a different input mode from the training job's general setting. To download the data from Amazon Simple Storage Service (Amazon S3) to the provisioned ML storage volume, and mount the directory to a Docker volume, use File input mode. To stream data directly from Amazon S3 to the container, choose Pipe input mode.
To use a model for incremental training, choose File input model.
ShuffleConfig (dict) --
A configuration for a shuffle option for input data in a channel. If you use S3Prefix for S3DataType, this shuffles the results of the S3 key prefix matches. If you use ManifestFile, the order of the S3 object references in the ManifestFile is shuffled. If you use AugmentedManifestFile, the order of the JSON lines in the AugmentedManifestFile is shuffled. The shuffling order is determined using the Seed value.
For Pipe input mode, shuffling is done at the start of every epoch. With large datasets this ensures that the order of the training data is different for each epoch, it helps reduce bias and possible overfitting. In a multi-node training job when ShuffleConfig is combined with S3DataDistributionType of ShardedByS3Key, the data is shuffled across nodes so that the content sent to a particular node on the first epoch might be sent to a different node on the second epoch.
Seed (integer) --
Determines the shuffling order in ShuffleConfig value.
OutputDataConfig (dict) --
The S3 path where model artifacts that you configured when creating the job are stored. Amazon SageMaker creates subfolders for model artifacts.
KmsKeyId (string) --
The AWS Key Management Service (AWS KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The KmsKeyId can be any of the following formats:
// KMS Key ID "1234abcd-12ab-34cd-56ef-1234567890ab"
// Amazon Resource Name (ARN) of a KMS Key "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
// KMS Key Alias "alias/ExampleAlias"
// Amazon Resource Name (ARN) of a KMS Key Alias "arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias"
If you use a KMS key ID or an alias of your master key, the Amazon SageMaker execution role must include permissions to call kms:Encrypt. If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account. Amazon SageMaker uses server-side encryption with KMS-managed keys for OutputDataConfig. If you use a bucket policy with an s3:PutObject permission that only allows objects with server-side encryption, set the condition key of s3:x-amz-server-side-encryption to "aws:kms". For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.
The KMS key policy must grant permission to the IAM role that you specify in your CreateTrainingJob, CreateTransformJob, or CreateHyperParameterTuningJob requests. For more information, see Using Key Policies in AWS KMS in the AWS Key Management Service Developer Guide.
S3OutputPath (string) --
Identifies the S3 path where you want Amazon SageMaker to store the model artifacts. For example, s3://bucket-name/key-name-prefix.
ResourceConfig (dict) --
Resources, including ML compute instances and ML storage volumes, that are configured for model training.
InstanceType (string) --
The ML compute instance type.
InstanceCount (integer) --
The number of ML compute instances to use. For distributed training, provide a value greater than 1.
VolumeSizeInGB (integer) --
The size of the ML storage volume that you want to provision.
ML storage volumes store model artifacts and incremental states. Training algorithms might also use the ML storage volume for scratch space. If you want to store the training data in the ML storage volume, choose File as the TrainingInputMode in the algorithm specification.
You must specify sufficient ML storage for your scenario.
VolumeKmsKeyId (string) --
The AWS KMS key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the training job.
The VolumeKmsKeyId can be in any of the following formats:
// KMS Key ID "1234abcd-12ab-34cd-56ef-1234567890ab"
// Amazon Resource Name (ARN) of a KMS Key "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
VpcConfig (dict) --
A VpcConfig object that specifies the VPC that this training job has access to. For more information, see Protect Training Jobs 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) --
StoppingCondition (dict) --
Specifies a limit to how long a model training job can run. When the job reaches the time limit, Amazon SageMaker ends the training job. Use this API to cap model training costs.
To stop a job, Amazon 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.
MaxRuntimeInSeconds (integer) --
The maximum length of time, in seconds, that the training or compilation job can run. If job does not complete during this time, Amazon SageMaker ends the job. If value is not specified, default value is 1 day. The maximum value is 28 days.
MaxWaitTimeInSeconds (integer) --
The maximum length of time, in seconds, how long you are willing to wait for a managed spot training job to complete. It is the amount of time spent waiting for Spot capacity plus the amount of time the training job runs. It must be equal to or greater than MaxRuntimeInSeconds.
CreationTime (datetime) --
A timestamp that indicates when the training job was created.
TrainingStartTime (datetime) --
Indicates the time when the training job starts on training instances. You are billed for the time interval between this time and the value of TrainingEndTime. The start time in CloudWatch Logs might be later than this time. The difference is due to the time it takes to download the training data and to the size of the training container.
TrainingEndTime (datetime) --
Indicates the time when the training job ends on training instances. You are billed for the time interval between the value of TrainingStartTime and this time. For successful jobs and stopped jobs, this is the time after model artifacts are uploaded. For failed jobs, this is the time when Amazon SageMaker detects a job failure.
LastModifiedTime (datetime) --
A timestamp that indicates when the status of the training job was last modified.
SecondaryStatusTransitions (list) --
A history of all of the secondary statuses that the training job has transitioned through.
(dict) --
An array element of DescribeTrainingJobResponse$SecondaryStatusTransitions. It provides additional details about a status that the training job has transitioned through. A training job can be in one of several states, for example, starting, downloading, training, or uploading. Within each state, there are a number of intermediate states. For example, within the starting state, Amazon SageMaker could be starting the training job or launching the ML instances. These transitional states are referred to as the job's secondary status.
Status (string) --
Contains a secondary status information from a training job.
Status might be one of the following secondary statuses:
InProgress
Starting - Starting the training job.
Downloading - An optional stage for algorithms that support File training input mode. It indicates that data is being downloaded to the ML storage volumes.
Training - Training is in progress.
Uploading - Training is complete and the model artifacts are being uploaded to the S3 location.
Completed
Completed - The training job has completed.
Failed
Failed - The training job has failed. The reason for the failure is returned in the FailureReason field of DescribeTrainingJobResponse.
Stopped
MaxRuntimeExceeded - The job stopped because it exceeded the maximum allowed runtime.
Stopped - The training job has stopped.
Stopping
Stopping - Stopping the training job.
We no longer support the following secondary statuses:
LaunchingMLInstances
PreparingTrainingStack
DownloadingTrainingImage
StartTime (datetime) --
A timestamp that shows when the training job transitioned to the current secondary status state.
EndTime (datetime) --
A timestamp that shows when the training job transitioned out of this secondary status state into another secondary status state or when the training job has ended.
StatusMessage (string) --
A detailed description of the progress within a secondary status.
Amazon SageMaker provides secondary statuses and status messages that apply to each of them:
Starting
Starting the training job.
Launching requested ML instances.
Insufficient capacity error from EC2 while launching instances, retrying!
Launched instance was unhealthy, replacing it!
Preparing the instances for training.
Training
Downloading the training image.
Training image download completed. Training in progress.
To have an overview of your training job's progress, view TrainingJobStatus and SecondaryStatus in DescribeTrainingJob, and StatusMessage together. For example, at the start of a training job, you might see the following:
TrainingJobStatus - InProgress
SecondaryStatus - Training
StatusMessage - Downloading the training image
FinalMetricDataList (list) --
A list of final metric values that are set when the training job completes. Used only if the training job was configured to use metrics.
(dict) --
The name, value, and date and time of a metric that was emitted to Amazon CloudWatch.
MetricName (string) --
The name of the metric.
Value (float) --
The value of the metric.
Timestamp (datetime) --
The date and time that the algorithm emitted the metric.
EnableNetworkIsolation (boolean) --
If the TrainingJob was created with network isolation, the value is set to true. If network isolation is enabled, nodes can't communicate beyond the VPC they run in.
EnableInterContainerTrafficEncryption (boolean) --
To encrypt all communications between ML compute instances in distributed training, choose True. Encryption provides greater security for distributed training, but training might take longer. How long it takes depends on the amount of communication between compute instances, especially if you use a deep learning algorithm in distributed training.
EnableManagedSpotTraining (boolean) --
When true, enables managed spot training using Amazon EC2 Spot instances to run training jobs instead of on-demand instances. For more information, see Managed Spot Training.
CheckpointConfig (dict) --
Contains information about the output location for managed spot training checkpoint data.
S3Uri (string) --
Identifies the S3 path where you want Amazon SageMaker to store checkpoints. For example, s3://bucket-name/key-name-prefix.
LocalPath (string) --
(Optional) The local directory where checkpoints are written. The default directory is /opt/ml/checkpoints/.
TrainingTimeInSeconds (integer) --
The training time in seconds.
BillableTimeInSeconds (integer) --
The billable time in seconds.
DebugHookConfig (dict) --
Configuration information for the debug hook parameters, collection configuration, and storage paths.
LocalPath (string) --
Path to local storage location for tensors. Defaults to /opt/ml/output/tensors/.
S3OutputPath (string) --
Path to Amazon S3 storage location for tensors.
HookParameters (dict) --
Configuration information for the debug hook parameters.
(string) --
(string) --
CollectionConfigurations (list) --
Configuration information for tensor collections.
(dict) --
Configuration information for tensor collections.
CollectionName (string) --
The name of the tensor collection. The name must be unique relative to other rule configuration names.
CollectionParameters (dict) --
Parameter values for the tensor collection. The allowed parameters are "name", "include_regex", "reduction_config", "save_config", "tensor_names", and "save_histogram".
(string) --
(string) --
ExperimentConfig (dict) --
Associates a SageMaker job as a trial component with an experiment and trial. Specified when you call the following APIs:
CreateProcessingJob
CreateTrainingJob
CreateTransformJob
ExperimentName (string) --
The name of an existing experiment to associate the trial component with.
TrialName (string) --
The name of an existing trial to associate the trial component with. If not specified, a new trial is created.
TrialComponentDisplayName (string) --
The display name for the trial component. If this key isn't specified, the display name is the trial component name.
DebugRuleConfigurations (list) --
Information about the debug rule configuration.
(dict) --
Configuration information for debugging rules.
RuleConfigurationName (string) --
The name of the rule configuration. It must be unique relative to other rule configuration names.
LocalPath (string) --
Path to local storage location for output of rules. Defaults to /opt/ml/processing/output/rule/.
S3OutputPath (string) --
Path to Amazon S3 storage location for rules.
RuleEvaluatorImage (string) --
The Amazon Elastic Container (ECR) Image for the managed rule evaluation.
InstanceType (string) --
The instance type to deploy for a training job.
VolumeSizeInGB (integer) --
The size, in GB, of the ML storage volume attached to the processing instance.
RuleParameters (dict) --
Runtime configuration for rule container.
(string) --
(string) --
TensorBoardOutputConfig (dict) --
Configuration of storage locations for TensorBoard output.
LocalPath (string) --
Path to local storage location for tensorBoard output. Defaults to /opt/ml/output/tensorboard.
S3OutputPath (string) --
Path to Amazon S3 storage location for TensorBoard output.
DebugRuleEvaluationStatuses (list) --
Information about the evaluation status of the rules for the training job.
(dict) --
Information about the status of the rule evaluation.
RuleConfigurationName (string) --
The name of the rule configuration
RuleEvaluationJobArn (string) --
The Amazon Resource Name (ARN) of the rule evaluation job.
RuleEvaluationStatus (string) --
Status of the rule evaluation.
StatusDetails (string) --
Details from the rule evaluation.
LastModifiedTime (datetime) --
Timestamp when the rule evaluation status was last modified.
Tags (list) --
An array of key-value pairs. For more information, see Using Cost Allocation Tags in the AWS Billing and Cost Management User Guide.
(dict) --
Describes a tag.
Key (string) --
The tag key.
Value (string) --
The tag value.
Experiment (dict) --
The properties of an experiment.
ExperimentName (string) --
The name of the experiment.
ExperimentArn (string) --
The Amazon Resource Name (ARN) of the experiment.
DisplayName (string) --
The name of the experiment as displayed. If DisplayName isn't specified, ExperimentName is displayed.
Source (dict) --
The source of the experiment.
SourceArn (string) --
The Amazon Resource Name (ARN) of the source.
SourceType (string) --
The source type.
Description (string) --
The description of the experiment.
CreationTime (datetime) --
When the experiment was created.
CreatedBy (dict) --
Information about the user who created or modified an experiment, trial, or trial component.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
LastModifiedTime (datetime) --
When the experiment was last modified.
LastModifiedBy (dict) --
Information about the user who created or modified an experiment, trial, or trial component.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
Tags (list) --
The list of tags that are associated with the experiment. You can use Search API to search on the tags.
(dict) --
Describes a tag.
Key (string) --
The tag key.
Value (string) --
The tag value.
Trial (dict) --
The properties of a trial.
TrialName (string) --
The name of the trial.
TrialArn (string) --
The Amazon Resource Name (ARN) of the trial.
DisplayName (string) --
The name of the trial as displayed. If DisplayName isn't specified, TrialName is displayed.
ExperimentName (string) --
The name of the experiment the trial is part of.
Source (dict) --
The source of the trial.
SourceArn (string) --
The Amazon Resource Name (ARN) of the source.
SourceType (string) --
The source job type.
CreationTime (datetime) --
When the trial was created.
CreatedBy (dict) --
Information about the user who created or modified an experiment, trial, or trial component.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
LastModifiedTime (datetime) --
Who last modified the trial.
LastModifiedBy (dict) --
Information about the user who created or modified an experiment, trial, or trial component.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
Tags (list) --
The list of tags that are associated with the trial. You can use Search API to search on the tags.
(dict) --
Describes a tag.
Key (string) --
The tag key.
Value (string) --
The tag value.
TrialComponentSummaries (list) --
A list of the components associated with the trial. For each component, a summary of the component's properties is included.
(dict) --
A short summary of a trial component.
TrialComponentName (string) --
The name of the trial component.
TrialComponentArn (string) --
The Amazon Resource Name (ARN) of the trial component.
TrialComponentSource (dict) --
The Amazon Resource Name (ARN) and job type of the source of a trial component.
SourceArn (string) --
The source ARN.
SourceType (string) --
The source job type.
CreationTime (datetime) --
When the component was created.
CreatedBy (dict) --
Information about the user who created or modified an experiment, trial, or trial component.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
TrialComponent (dict) --
The properties of a trial component.
TrialComponentName (string) --
The name of the trial component.
DisplayName (string) --
The name of the component as displayed. If DisplayName isn't specified, TrialComponentName is displayed.
TrialComponentArn (string) --
The Amazon Resource Name (ARN) of the trial component.
Source (dict) --
The Amazon Resource Name (ARN) and job type of the source of the component.
SourceArn (string) --
The source ARN.
SourceType (string) --
The source job type.
Status (dict) --
The status of the trial component.
PrimaryStatus (string) --
The status of the trial component.
Message (string) --
If the component failed, a message describing why.
StartTime (datetime) --
When the component started.
EndTime (datetime) --
When the component ended.
CreationTime (datetime) --
When the component was created.
CreatedBy (dict) --
Information about the user who created or modified an experiment, trial, or trial component.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
LastModifiedTime (datetime) --
When the component was last modified.
LastModifiedBy (dict) --
Information about the user who created or modified an experiment, trial, or trial component.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
Parameters (dict) --
The hyperparameters of the component.
(string) --
(dict) --
The value of a hyperparameter. Only one of NumberValue or StringValue can be specified.
This object is specified in the CreateTrialComponent request.
StringValue (string) --
The string value of a categorical hyperparameter. If you specify a value for this parameter, you can't specify the NumberValue parameter.
NumberValue (float) --
The numeric value of a numeric hyperparameter. If you specify a value for this parameter, you can't specify the StringValue parameter.
InputArtifacts (dict) --
The input artifacts of the component.
(string) --
(dict) --
Represents an input or output artifact of a trial component. You specify TrialComponentArtifact as part of the InputArtifacts and OutputArtifacts parameters in the CreateTrialComponent request.
Examples of input artifacts are datasets, algorithms, hyperparameters, source code, and instance types. Examples of output artifacts are metrics, snapshots, logs, and images.
MediaType (string) --
The media type of the artifact, which indicates the type of data in the artifact file. The media type consists of a type and a subtype concatenated with a slash (/) character, for example, text/csv, image/jpeg, and s3/uri. The type specifies the category of the media. The subtype specifies the kind of data.
Value (string) --
The location of the artifact.
OutputArtifacts (dict) --
The output artifacts of the component.
(string) --
(dict) --
Represents an input or output artifact of a trial component. You specify TrialComponentArtifact as part of the InputArtifacts and OutputArtifacts parameters in the CreateTrialComponent request.
Examples of input artifacts are datasets, algorithms, hyperparameters, source code, and instance types. Examples of output artifacts are metrics, snapshots, logs, and images.
MediaType (string) --
The media type of the artifact, which indicates the type of data in the artifact file. The media type consists of a type and a subtype concatenated with a slash (/) character, for example, text/csv, image/jpeg, and s3/uri. The type specifies the category of the media. The subtype specifies the kind of data.
Value (string) --
The location of the artifact.
Metrics (list) --
The metrics for the component.
(dict) --
A summary of the metrics of a trial component.
MetricName (string) --
The name of the metric.
SourceArn (string) --
The Amazon Resource Name (ARN) of the source.
TimeStamp (datetime) --
When the metric was last updated.
Max (float) --
The maximum value of the metric.
Min (float) --
The minimum value of the metric.
Last (float) --
The most recent value of the metric.
Count (integer) --
The number of samples used to generate the metric.
Avg (float) --
The average value of the metric.
StdDev (float) --
The standard deviation of the metric.
SourceDetail (dict) --
Details of the source of the component.
SourceArn (string) --
The Amazon Resource Name (ARN) of the source.
TrainingJob (dict) --
Information about a training job that's the source of a trial component.
TrainingJobName (string) --
The name of the training job.
TrainingJobArn (string) --
The Amazon Resource Name (ARN) of the training job.
TuningJobArn (string) --
The Amazon Resource Name (ARN) of the associated hyperparameter tuning job if the training job was launched by a hyperparameter tuning job.
LabelingJobArn (string) --
The Amazon Resource Name (ARN) of the labeling job.
AutoMLJobArn (string) --
The Amazon Resource Name (ARN) of the job.
ModelArtifacts (dict) --
Information about the Amazon S3 location that is configured for storing model artifacts.
S3ModelArtifacts (string) --
The path of the S3 object that contains the model artifacts. For example, s3://bucket-name/keynameprefix/model.tar.gz.
TrainingJobStatus (string) --
The status of the training job.
Training job statuses are:
InProgress - The training is in progress.
Completed - The training job has completed.
Failed - The training job has failed. To see the reason for the failure, see the FailureReason field in the response to a DescribeTrainingJobResponse call.
Stopping - The training job is stopping.
Stopped - The training job has stopped.
For more detailed information, see SecondaryStatus.
SecondaryStatus (string) --
Provides detailed information about the state of the training job. For detailed information about the secondary status of the training job, see StatusMessage under SecondaryStatusTransition.
Amazon SageMaker provides primary statuses and secondary statuses that apply to each of them:
InProgress
Starting - Starting the training job.
Downloading - An optional stage for algorithms that support File training input mode. It indicates that data is being downloaded to the ML storage volumes.
Training - Training is in progress.
Uploading - Training is complete and the model artifacts are being uploaded to the S3 location.
Completed
Completed - The training job has completed.
Failed
Failed - The training job has failed. The reason for the failure is returned in the FailureReason field of DescribeTrainingJobResponse.
Stopped
MaxRuntimeExceeded - The job stopped because it exceeded the maximum allowed runtime.
Stopped - The training job has stopped.
Stopping
Stopping - Stopping the training job.
We no longer support the following secondary statuses:
LaunchingMLInstances
PreparingTrainingStack
DownloadingTrainingImage
FailureReason (string) --
If the training job failed, the reason it failed.
HyperParameters (dict) --
Algorithm-specific parameters.
(string) --
(string) --
AlgorithmSpecification (dict) --
Information about the algorithm used for training, and algorithm metadata.
TrainingImage (string) --
The registry path of the Docker image that contains the training algorithm. For information about docker registry paths for built-in algorithms, see Algorithms Provided by Amazon SageMaker: Common Parameters. Amazon SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker.
AlgorithmName (string) --
The name of the algorithm resource to use for the training job. This must be an algorithm resource that you created or subscribe to on AWS Marketplace. If you specify a value for this parameter, you can't specify a value for TrainingImage.
TrainingInputMode (string) --
The input mode that the algorithm supports. For the input modes that Amazon SageMaker algorithms support, see Algorithms. If an algorithm supports the File input mode, Amazon SageMaker downloads the training data from S3 to the provisioned ML storage Volume, and mounts the directory to docker volume for training container. If an algorithm supports the Pipe input mode, Amazon SageMaker streams data directly from S3 to the container.
In File mode, make sure you provision ML storage volume with sufficient capacity to accommodate the data download from S3. In addition to the training data, the ML storage volume also stores the output model. The algorithm container use ML storage volume to also store intermediate information, if any.
For distributed algorithms using File mode, training data is distributed uniformly, and your training duration is predictable if the input data objects size is approximately same. Amazon SageMaker does not split the files any further for model training. If the object sizes are skewed, training won't be optimal as the data distribution is also skewed where one host in a training cluster is overloaded, thus becoming bottleneck in training.
MetricDefinitions (list) --
A list of metric definition objects. Each object specifies the metric name and regular expressions used to parse algorithm logs. Amazon SageMaker publishes each metric to Amazon CloudWatch.
(dict) --
Specifies a metric that the training algorithm writes to stderr or stdout . Amazon SageMakerhyperparameter tuning captures all defined metrics. You specify one metric that a hyperparameter tuning job uses as its objective metric to choose the best training job.
Name (string) --
The name of the metric.
Regex (string) --
A regular expression that searches the output of a training job and gets the value of the metric. For more information about using regular expressions to define metrics, see Defining Objective Metrics.
EnableSageMakerMetricsTimeSeries (boolean) --
To generate and save time-series metrics during training, set to true. The default is false and time-series metrics aren't generated except in the following cases:
You use one of the Amazon SageMaker built-in algorithms
You use one of the following Prebuilt Amazon SageMaker Docker Images:
Tensorflow (version >= 1.15)
MXNet (version >= 1.6)
PyTorch (version >= 1.3)
You specify at least one MetricDefinition
RoleArn (string) --
The AWS Identity and Access Management (IAM) role configured for the training job.
InputDataConfig (list) --
An array of Channel objects that describes each data input channel.
(dict) --
A channel is a named input source that training algorithms can consume.
ChannelName (string) --
The name of the channel.
DataSource (dict) --
The location of the channel data.
S3DataSource (dict) --
The S3 location of the data source that is associated with a channel.
S3DataType (string) --
If you choose S3Prefix, S3Uri identifies a key name prefix. Amazon SageMaker uses all objects that match the specified key name prefix for model training.
If you choose ManifestFile, S3Uri identifies an object that is a manifest file containing a list of object keys that you want Amazon SageMaker to use for model training.
If you choose AugmentedManifestFile, S3Uri identifies an object that is an augmented manifest file in JSON lines format. This file contains the data you want to use for model training. AugmentedManifestFile can only be used if the Channel's input mode is Pipe.
S3Uri (string) --
Depending on the value specified for the S3DataType, identifies either a key name prefix or a manifest. For example:
A key name prefix might look like this: s3://bucketname/exampleprefix
A manifest might look like this: s3://bucketname/example.manifest A manifest is an S3 object which is a JSON file consisting of an array of elements. The first element is a prefix which is followed by one or more suffixes. SageMaker appends the suffix elements to the prefix to get a full set of S3Uri. Note that the prefix must be a valid non-empty S3Uri that precludes users from specifying a manifest whose individual S3Uri is sourced from different S3 buckets. The following code example shows a valid manifest format: [ {"prefix": "s3://customer_bucket/some/prefix/"}, "relative/path/to/custdata-1", "relative/path/custdata-2", ... "relative/path/custdata-N" ] This JSON is equivalent to the following S3Uri list: s3://customer_bucket/some/prefix/relative/path/to/custdata-1 s3://customer_bucket/some/prefix/relative/path/custdata-2 ... s3://customer_bucket/some/prefix/relative/path/custdata-N The complete set of S3Uri in this manifest is the input data for the channel for this data source. The object that each S3Uri points to must be readable by the IAM role that Amazon SageMaker uses to perform tasks on your behalf.
S3DataDistributionType (string) --
If you want Amazon SageMaker to replicate the entire dataset on each ML compute instance that is launched for model training, specify FullyReplicated.
If you want Amazon SageMaker to replicate a subset of data on each ML compute instance that is launched for model training, specify ShardedByS3Key. If there are n ML compute instances launched for a training job, each instance gets approximately 1/n of the number of S3 objects. In this case, model training on each machine uses only the subset of training data.
Don't choose more ML compute instances for training than available S3 objects. If you do, some nodes won't get any data and you will pay for nodes that aren't getting any training data. This applies in both File and Pipe modes. Keep this in mind when developing algorithms.
In distributed training, where you use multiple ML compute EC2 instances, you might choose ShardedByS3Key. If the algorithm requires copying training data to the ML storage volume (when TrainingInputMode is set to File), this copies 1/n of the number of objects.
AttributeNames (list) --
A list of one or more attribute names to use that are found in a specified augmented manifest file.
(string) --
FileSystemDataSource (dict) --
The file system that is associated with a channel.
FileSystemId (string) --
The file system id.
FileSystemAccessMode (string) --
The access mode of the mount of the directory associated with the channel. A directory can be mounted either in ro (read-only) or rw (read-write) mode.
FileSystemType (string) --
The file system type.
DirectoryPath (string) --
The full path to the directory to associate with the channel.
ContentType (string) --
The MIME type of the data.
CompressionType (string) --
If training data is compressed, the compression type. The default value is None. CompressionType is used only in Pipe input mode. In File mode, leave this field unset or set it to None.
RecordWrapperType (string) --
Specify RecordIO as the value when input data is in raw format but the training algorithm requires the RecordIO format. In this case, Amazon SageMaker wraps each individual S3 object in a RecordIO record. If the input data is already in RecordIO format, you don't need to set this attribute. For more information, see Create a Dataset Using RecordIO.
In File mode, leave this field unset or set it to None.
InputMode (string) --
(Optional) The input mode to use for the data channel in a training job. If you don't set a value for InputMode, Amazon SageMaker uses the value set for TrainingInputMode. Use this parameter to override the TrainingInputMode setting in a AlgorithmSpecification request when you have a channel that needs a different input mode from the training job's general setting. To download the data from Amazon Simple Storage Service (Amazon S3) to the provisioned ML storage volume, and mount the directory to a Docker volume, use File input mode. To stream data directly from Amazon S3 to the container, choose Pipe input mode.
To use a model for incremental training, choose File input model.
ShuffleConfig (dict) --
A configuration for a shuffle option for input data in a channel. If you use S3Prefix for S3DataType, this shuffles the results of the S3 key prefix matches. If you use ManifestFile, the order of the S3 object references in the ManifestFile is shuffled. If you use AugmentedManifestFile, the order of the JSON lines in the AugmentedManifestFile is shuffled. The shuffling order is determined using the Seed value.
For Pipe input mode, shuffling is done at the start of every epoch. With large datasets this ensures that the order of the training data is different for each epoch, it helps reduce bias and possible overfitting. In a multi-node training job when ShuffleConfig is combined with S3DataDistributionType of ShardedByS3Key, the data is shuffled across nodes so that the content sent to a particular node on the first epoch might be sent to a different node on the second epoch.
Seed (integer) --
Determines the shuffling order in ShuffleConfig value.
OutputDataConfig (dict) --
The S3 path where model artifacts that you configured when creating the job are stored. Amazon SageMaker creates subfolders for model artifacts.
KmsKeyId (string) --
The AWS Key Management Service (AWS KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The KmsKeyId can be any of the following formats:
// KMS Key ID "1234abcd-12ab-34cd-56ef-1234567890ab"
// Amazon Resource Name (ARN) of a KMS Key "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
// KMS Key Alias "alias/ExampleAlias"
// Amazon Resource Name (ARN) of a KMS Key Alias "arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias"
If you use a KMS key ID or an alias of your master key, the Amazon SageMaker execution role must include permissions to call kms:Encrypt. If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account. Amazon SageMaker uses server-side encryption with KMS-managed keys for OutputDataConfig. If you use a bucket policy with an s3:PutObject permission that only allows objects with server-side encryption, set the condition key of s3:x-amz-server-side-encryption to "aws:kms". For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.
The KMS key policy must grant permission to the IAM role that you specify in your CreateTrainingJob, CreateTransformJob, or CreateHyperParameterTuningJob requests. For more information, see Using Key Policies in AWS KMS in the AWS Key Management Service Developer Guide.
S3OutputPath (string) --
Identifies the S3 path where you want Amazon SageMaker to store the model artifacts. For example, s3://bucket-name/key-name-prefix.
ResourceConfig (dict) --
Resources, including ML compute instances and ML storage volumes, that are configured for model training.
InstanceType (string) --
The ML compute instance type.
InstanceCount (integer) --
The number of ML compute instances to use. For distributed training, provide a value greater than 1.
VolumeSizeInGB (integer) --
The size of the ML storage volume that you want to provision.
ML storage volumes store model artifacts and incremental states. Training algorithms might also use the ML storage volume for scratch space. If you want to store the training data in the ML storage volume, choose File as the TrainingInputMode in the algorithm specification.
You must specify sufficient ML storage for your scenario.
VolumeKmsKeyId (string) --
The AWS KMS key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the training job.
The VolumeKmsKeyId can be in any of the following formats:
// KMS Key ID "1234abcd-12ab-34cd-56ef-1234567890ab"
// Amazon Resource Name (ARN) of a KMS Key "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
VpcConfig (dict) --
A VpcConfig object that specifies the VPC that this training job has access to. For more information, see Protect Training Jobs 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) --
StoppingCondition (dict) --
Specifies a limit to how long a model training job can run. When the job reaches the time limit, Amazon SageMaker ends the training job. Use this API to cap model training costs.
To stop a job, Amazon 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.
MaxRuntimeInSeconds (integer) --
The maximum length of time, in seconds, that the training or compilation job can run. If job does not complete during this time, Amazon SageMaker ends the job. If value is not specified, default value is 1 day. The maximum value is 28 days.
MaxWaitTimeInSeconds (integer) --
The maximum length of time, in seconds, how long you are willing to wait for a managed spot training job to complete. It is the amount of time spent waiting for Spot capacity plus the amount of time the training job runs. It must be equal to or greater than MaxRuntimeInSeconds.
CreationTime (datetime) --
A timestamp that indicates when the training job was created.
TrainingStartTime (datetime) --
Indicates the time when the training job starts on training instances. You are billed for the time interval between this time and the value of TrainingEndTime. The start time in CloudWatch Logs might be later than this time. The difference is due to the time it takes to download the training data and to the size of the training container.
TrainingEndTime (datetime) --
Indicates the time when the training job ends on training instances. You are billed for the time interval between the value of TrainingStartTime and this time. For successful jobs and stopped jobs, this is the time after model artifacts are uploaded. For failed jobs, this is the time when Amazon SageMaker detects a job failure.
LastModifiedTime (datetime) --
A timestamp that indicates when the status of the training job was last modified.
SecondaryStatusTransitions (list) --
A history of all of the secondary statuses that the training job has transitioned through.
(dict) --
An array element of DescribeTrainingJobResponse$SecondaryStatusTransitions. It provides additional details about a status that the training job has transitioned through. A training job can be in one of several states, for example, starting, downloading, training, or uploading. Within each state, there are a number of intermediate states. For example, within the starting state, Amazon SageMaker could be starting the training job or launching the ML instances. These transitional states are referred to as the job's secondary status.
Status (string) --
Contains a secondary status information from a training job.
Status might be one of the following secondary statuses:
InProgress
Starting - Starting the training job.
Downloading - An optional stage for algorithms that support File training input mode. It indicates that data is being downloaded to the ML storage volumes.
Training - Training is in progress.
Uploading - Training is complete and the model artifacts are being uploaded to the S3 location.
Completed
Completed - The training job has completed.
Failed
Failed - The training job has failed. The reason for the failure is returned in the FailureReason field of DescribeTrainingJobResponse.
Stopped
MaxRuntimeExceeded - The job stopped because it exceeded the maximum allowed runtime.
Stopped - The training job has stopped.
Stopping
Stopping - Stopping the training job.
We no longer support the following secondary statuses:
LaunchingMLInstances
PreparingTrainingStack
DownloadingTrainingImage
StartTime (datetime) --
A timestamp that shows when the training job transitioned to the current secondary status state.
EndTime (datetime) --
A timestamp that shows when the training job transitioned out of this secondary status state into another secondary status state or when the training job has ended.
StatusMessage (string) --
A detailed description of the progress within a secondary status.
Amazon SageMaker provides secondary statuses and status messages that apply to each of them:
Starting
Starting the training job.
Launching requested ML instances.
Insufficient capacity error from EC2 while launching instances, retrying!
Launched instance was unhealthy, replacing it!
Preparing the instances for training.
Training
Downloading the training image.
Training image download completed. Training in progress.
To have an overview of your training job's progress, view TrainingJobStatus and SecondaryStatus in DescribeTrainingJob, and StatusMessage together. For example, at the start of a training job, you might see the following:
TrainingJobStatus - InProgress
SecondaryStatus - Training
StatusMessage - Downloading the training image
FinalMetricDataList (list) --
A list of final metric values that are set when the training job completes. Used only if the training job was configured to use metrics.
(dict) --
The name, value, and date and time of a metric that was emitted to Amazon CloudWatch.
MetricName (string) --
The name of the metric.
Value (float) --
The value of the metric.
Timestamp (datetime) --
The date and time that the algorithm emitted the metric.
EnableNetworkIsolation (boolean) --
If the TrainingJob was created with network isolation, the value is set to true. If network isolation is enabled, nodes can't communicate beyond the VPC they run in.
EnableInterContainerTrafficEncryption (boolean) --
To encrypt all communications between ML compute instances in distributed training, choose True. Encryption provides greater security for distributed training, but training might take longer. How long it takes depends on the amount of communication between compute instances, especially if you use a deep learning algorithm in distributed training.
EnableManagedSpotTraining (boolean) --
When true, enables managed spot training using Amazon EC2 Spot instances to run training jobs instead of on-demand instances. For more information, see Managed Spot Training.
CheckpointConfig (dict) --
Contains information about the output location for managed spot training checkpoint data.
S3Uri (string) --
Identifies the S3 path where you want Amazon SageMaker to store checkpoints. For example, s3://bucket-name/key-name-prefix.
LocalPath (string) --
(Optional) The local directory where checkpoints are written. The default directory is /opt/ml/checkpoints/.
TrainingTimeInSeconds (integer) --
The training time in seconds.
BillableTimeInSeconds (integer) --
The billable time in seconds.
DebugHookConfig (dict) --
Configuration information for the debug hook parameters, collection configuration, and storage paths.
LocalPath (string) --
Path to local storage location for tensors. Defaults to /opt/ml/output/tensors/.
S3OutputPath (string) --
Path to Amazon S3 storage location for tensors.
HookParameters (dict) --
Configuration information for the debug hook parameters.
(string) --
(string) --
CollectionConfigurations (list) --
Configuration information for tensor collections.
(dict) --
Configuration information for tensor collections.
CollectionName (string) --
The name of the tensor collection. The name must be unique relative to other rule configuration names.
CollectionParameters (dict) --
Parameter values for the tensor collection. The allowed parameters are "name", "include_regex", "reduction_config", "save_config", "tensor_names", and "save_histogram".
(string) --
(string) --
ExperimentConfig (dict) --
Associates a SageMaker job as a trial component with an experiment and trial. Specified when you call the following APIs:
CreateProcessingJob
CreateTrainingJob
CreateTransformJob
ExperimentName (string) --
The name of an existing experiment to associate the trial component with.
TrialName (string) --
The name of an existing trial to associate the trial component with. If not specified, a new trial is created.
TrialComponentDisplayName (string) --
The display name for the trial component. If this key isn't specified, the display name is the trial component name.
DebugRuleConfigurations (list) --
Information about the debug rule configuration.
(dict) --
Configuration information for debugging rules.
RuleConfigurationName (string) --
The name of the rule configuration. It must be unique relative to other rule configuration names.
LocalPath (string) --
Path to local storage location for output of rules. Defaults to /opt/ml/processing/output/rule/.
S3OutputPath (string) --
Path to Amazon S3 storage location for rules.
RuleEvaluatorImage (string) --
The Amazon Elastic Container (ECR) Image for the managed rule evaluation.
InstanceType (string) --
The instance type to deploy for a training job.
VolumeSizeInGB (integer) --
The size, in GB, of the ML storage volume attached to the processing instance.
RuleParameters (dict) --
Runtime configuration for rule container.
(string) --
(string) --
TensorBoardOutputConfig (dict) --
Configuration of storage locations for TensorBoard output.
LocalPath (string) --
Path to local storage location for tensorBoard output. Defaults to /opt/ml/output/tensorboard.
S3OutputPath (string) --
Path to Amazon S3 storage location for TensorBoard output.
DebugRuleEvaluationStatuses (list) --
Information about the evaluation status of the rules for the training job.
(dict) --
Information about the status of the rule evaluation.
RuleConfigurationName (string) --
The name of the rule configuration
RuleEvaluationJobArn (string) --
The Amazon Resource Name (ARN) of the rule evaluation job.
RuleEvaluationStatus (string) --
Status of the rule evaluation.
StatusDetails (string) --
Details from the rule evaluation.
LastModifiedTime (datetime) --
Timestamp when the rule evaluation status was last modified.
Tags (list) --
An array of key-value pairs. For more information, see Using Cost Allocation Tags in the AWS Billing and Cost Management User Guide.
(dict) --
Describes a tag.
Key (string) --
The tag key.
Value (string) --
The tag value.
ProcessingJob (dict) --
Information about a processing job that's the source of a trial component.
ProcessingInputs (list) --
For each input, data is downloaded from S3 into the processing container before the processing job begins running if "S3InputMode" is set to File.
(dict) --
The inputs for a processing job.
InputName (string) --
The name of the inputs for the processing job.
S3Input (dict) --
The S3 inputs for the processing job.
S3Uri (string) --
The URI for the Amazon S3 storage where you want Amazon SageMaker to download the artifacts needed to run a processing job.
LocalPath (string) --
The local path to the Amazon S3 bucket where you want Amazon SageMaker to download the inputs to run a processing job. LocalPath is an absolute path to the input data.
S3DataType (string) --
Whether you use an S3Prefix or a ManifestFile for the data type. If you choose S3Prefix, S3Uri identifies a key name prefix. Amazon SageMaker uses all objects with the specified key name prefix for the processing job. If you choose ManifestFile, S3Uri identifies an object that is a manifest file containing a list of object keys that you want Amazon SageMaker to use for the processing job.
S3InputMode (string) --
Whether to use File or Pipe input mode. In File mode, Amazon SageMaker copies the data from the input source onto the local Amazon Elastic Block Store (Amazon EBS) volumes before starting your training algorithm. This is the most commonly used input mode. In Pipe mode, Amazon SageMaker streams input data from the source directly to your algorithm without using the EBS volume.
S3DataDistributionType (string) --
Whether the data stored in Amazon S3 is FullyReplicated or ShardedByS3Key.
S3CompressionType (string) --
Whether to use Gzip compression for Amazon S3 storage.
ProcessingOutputConfig (dict) --
The output configuration for the processing job.
Outputs (list) --
Output configuration information for a processing job.
(dict) --
Describes the results of a processing job.
OutputName (string) --
The name for the processing job output.
S3Output (dict) --
Configuration for processing job outputs in Amazon S3.
S3Uri (string) --
A URI that identifies the Amazon S3 bucket where you want Amazon SageMaker to save the results of a processing job.
LocalPath (string) --
The local path to the Amazon S3 bucket where you want Amazon SageMaker to save the results of an processing job. LocalPath is an absolute path to the input data.
S3UploadMode (string) --
Whether to upload the results of the processing job continuously or after the job completes.
KmsKeyId (string) --
The AWS Key Management Service (AWS KMS) key that Amazon SageMaker uses to encrypt the processing job output. KmsKeyId can be an ID of a KMS key, ARN of a KMS key, alias of a KMS key, or alias of a KMS key. The KmsKeyId is applied to all outputs.
ProcessingJobName (string) --
The name of the processing job.
ProcessingResources (dict) --
Identifies the resources, ML compute instances, and ML storage volumes to deploy for a processing job. In distributed training, you specify more than one instance.
ClusterConfig (dict) --
The configuration for the resources in a cluster used to run the processing job.
InstanceCount (integer) --
The number of ML compute instances to use in the processing job. For distributed processing jobs, specify a value greater than 1. The default value is 1.
InstanceType (string) --
The ML compute instance type for the processing job.
VolumeSizeInGB (integer) --
The size of the ML storage volume in gigabytes that you want to provision. You must specify sufficient ML storage for your scenario.
VolumeKmsKeyId (string) --
The AWS Key Management Service (AWS KMS) key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the processing job.
StoppingCondition (dict) --
Specifies a time limit for how long the processing job is allowed to run.
MaxRuntimeInSeconds (integer) --
Specifies the maximum runtime in seconds.
AppSpecification (dict) --
Configuration to run a processing job in a specified container image.
ImageUri (string) --
The container image to be run by the processing job.
ContainerEntrypoint (list) --
The entrypoint for a container used to run a processing job.
(string) --
ContainerArguments (list) --
The arguments for a container used to run a processing job.
(string) --
Environment (dict) --
Sets the environment variables in the Docker container.
(string) --
(string) --
NetworkConfig (dict) --
Networking options for a job, such as network traffic encryption between containers, whether to allow inbound and outbound network calls to and from containers, and the VPC subnets and security groups to use for VPC-enabled jobs.
EnableInterContainerTrafficEncryption (boolean) --
Whether to encrypt all communications between distributed processing jobs. Choose True to encrypt communications. Encryption provides greater security for distributed processing jobs, but the processing might take longer.
EnableNetworkIsolation (boolean) --
Whether to allow inbound and outbound network calls to and from the containers used for the processing job.
VpcConfig (dict) --
Specifies a VPC that your training jobs and hosted models have access to. Control access to and from your training and model containers by configuring the VPC. For more information, see Protect Endpoints by Using an Amazon Virtual Private Cloud and Protect Training Jobs 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) --
RoleArn (string) --
The ARN of the role used to create the processing job.
ExperimentConfig (dict) --
Associates a SageMaker job as a trial component with an experiment and trial. Specified when you call the following APIs:
CreateProcessingJob
CreateTrainingJob
CreateTransformJob
ExperimentName (string) --
The name of an existing experiment to associate the trial component with.
TrialName (string) --
The name of an existing trial to associate the trial component with. If not specified, a new trial is created.
TrialComponentDisplayName (string) --
The display name for the trial component. If this key isn't specified, the display name is the trial component name.
ProcessingJobArn (string) --
The ARN of the processing job.
ProcessingJobStatus (string) --
The status of the processing job.
ExitMessage (string) --
A string, up to one KB in size, that contains metadata from the processing container when the processing job exits.
FailureReason (string) --
A string, up to one KB in size, that contains the reason a processing job failed, if it failed.
ProcessingEndTime (datetime) --
The time that the processing job ended.
ProcessingStartTime (datetime) --
The time that the processing job started.
LastModifiedTime (datetime) --
The time the processing job was last modified.
CreationTime (datetime) --
The time the processing job was created.
MonitoringScheduleArn (string) --
The ARN of a monitoring schedule for an endpoint associated with this processing job.
AutoMLJobArn (string) --
The Amazon Resource Name (ARN) of the AutoML job associated with this processing job.
TrainingJobArn (string) --
The ARN of the training job associated with this processing job.
Tags (list) --
An array of key-value pairs. For more information, see Using Cost Allocation Tags in the AWS Billing and Cost Management User Guide.
(dict) --
Describes a tag.
Key (string) --
The tag key.
Value (string) --
The tag value.
TransformJob (dict) --
Information about a transform job that's the source of the trial component.
TransformJobName (string) --
The name of the transform job.
TransformJobArn (string) --
The Amazon Resource Name (ARN) of the transform job.
TransformJobStatus (string) --
The status of the transform job.
Transform job statuses are:
InProgress - The job is in progress.
Completed - The job has completed.
Failed - The transform job has failed. To see the reason for the failure, see the FailureReason field in the response to a DescribeTransformJob call.
Stopping - The transform job is stopping.
Stopped - The transform job has stopped.
FailureReason (string) --
If the transform job failed, the reason it failed.
ModelName (string) --
The name of the model associated with the transform job.
MaxConcurrentTransforms (integer) --
The maximum number of parallel requests that can be sent to each instance in a transform job. If MaxConcurrentTransforms is set to 0 or left unset, SageMaker checks the optional execution-parameters to determine the settings for your chosen algorithm. If the execution-parameters endpoint is not enabled, the default value is 1. For built-in algorithms, you don't need to set a value for MaxConcurrentTransforms.
ModelClientConfig (dict) --
Configures the timeout and maximum number of retries for processing a transform job invocation.
InvocationsTimeoutInSeconds (integer) --
The timeout value in seconds for an invocation request.
InvocationsMaxRetries (integer) --
The maximum number of retries when invocation requests are failing.
MaxPayloadInMB (integer) --
The maximum allowed size of the payload, in MB. A payload is the data portion of a record (without metadata). The value in MaxPayloadInMB must be greater than, or equal to, the size of a single record. To estimate the size of a record in MB, divide the size of your dataset by the number of records. To ensure that the records fit within the maximum payload size, we recommend using a slightly larger value. The default value is 6 MB. For cases where the payload might be arbitrarily large and is transmitted using HTTP chunked encoding, set the value to 0. This feature works only in supported algorithms. Currently, SageMaker built-in algorithms do not support HTTP chunked encoding.
BatchStrategy (string) --
Specifies the number of records to include in a mini-batch for an HTTP inference request. A record is a single unit of input data that inference can be made on. For example, a single line in a CSV file is a record.
Environment (dict) --
The environment variables to set in the Docker container. We support up to 16 key and values entries in the map.
(string) --
(string) --
TransformInput (dict) --
Describes the input source of a transform job and the way the transform job consumes it.
DataSource (dict) --
Describes the location of the channel data, which is, the S3 location of the input data that the model can consume.
S3DataSource (dict) --
The S3 location of the data source that is associated with a channel.
S3DataType (string) --
If you choose S3Prefix, S3Uri identifies a key name prefix. Amazon SageMaker uses all objects with the specified key name prefix for batch transform.
If you choose ManifestFile, S3Uri identifies an object that is a manifest file containing a list of object keys that you want Amazon SageMaker to use for batch transform.
The following values are compatible: ManifestFile, S3Prefix
The following value is not compatible: AugmentedManifestFile
S3Uri (string) --
Depending on the value specified for the S3DataType, identifies either a key name prefix or a manifest. For example:
A key name prefix might look like this: s3://bucketname/exampleprefix.
A manifest might look like this: s3://bucketname/example.manifest The manifest is an S3 object which is a JSON file with the following format: [ {"prefix": "s3://customer_bucket/some/prefix/"}, "relative/path/to/custdata-1", "relative/path/custdata-2", ... "relative/path/custdata-N" ] The preceding JSON matches the following S3Uris: s3://customer_bucket/some/prefix/relative/path/to/custdata-1 s3://customer_bucket/some/prefix/relative/path/custdata-2 ... s3://customer_bucket/some/prefix/relative/path/custdata-N The complete set of S3Uris in this manifest constitutes the input data for the channel for this datasource. The object that each S3Uris points to must be readable by the IAM role that Amazon SageMaker uses to perform tasks on your behalf.
ContentType (string) --
The multipurpose internet mail extension (MIME) type of the data. Amazon SageMaker uses the MIME type with each http call to transfer data to the transform job.
CompressionType (string) --
If your transform data is compressed, specify the compression type. Amazon SageMaker automatically decompresses the data for the transform job accordingly. The default value is None.
SplitType (string) --
The method to use to split the transform job's data files into smaller batches. Splitting is necessary when the total size of each object is too large to fit in a single request. You can also use data splitting to improve performance by processing multiple concurrent mini-batches. The default value for SplitType is None, which indicates that input data files are not split, and request payloads contain the entire contents of an input object. Set the value of this parameter to Line to split records on a newline character boundary. SplitType also supports a number of record-oriented binary data formats.
When splitting is enabled, the size of a mini-batch depends on the values of the BatchStrategy and MaxPayloadInMB parameters. When the value of BatchStrategy is MultiRecord, Amazon SageMaker sends the maximum number of records in each request, up to the MaxPayloadInMB limit. If the value of BatchStrategy is SingleRecord, Amazon SageMaker sends individual records in each request.
TransformOutput (dict) --
Describes the results of a transform job.
S3OutputPath (string) --
The Amazon S3 path where you want Amazon SageMaker to store the results of the transform job. For example, s3://bucket-name/key-name-prefix.
For every S3 object used as input for the transform job, batch transform stores the transformed data with an . out suffix in a corresponding subfolder in the location in the output prefix. For example, for the input data stored at s3://bucket-name/input-name-prefix/dataset01/data.csv, batch transform stores the transformed data at s3://bucket-name/output-name-prefix/input-name-prefix/data.csv.out. Batch transform doesn't upload partially processed objects. For an input S3 object that contains multiple records, it creates an . out file only if the transform job succeeds on the entire file. When the input contains multiple S3 objects, the batch transform job processes the listed S3 objects and uploads only the output for successfully processed objects. If any object fails in the transform job batch transform marks the job as failed to prompt investigation.
Accept (string) --
The MIME type used to specify the output data. Amazon SageMaker uses the MIME type with each http call to transfer data from the transform job.
AssembleWith (string) --
Defines how to assemble the results of the transform job as a single S3 object. Choose a format that is most convenient to you. To concatenate the results in binary format, specify None. To add a newline character at the end of every transformed record, specify Line.
KmsKeyId (string) --
The AWS Key Management Service (AWS KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The KmsKeyId can be any of the following formats:
Key ID: 1234abcd-12ab-34cd-56ef-1234567890ab
Key ARN: arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab
Alias name: alias/ExampleAlias
Alias name ARN: arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias
If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.
The KMS key policy must grant permission to the IAM role that you specify in your CreateModel request. For more information, see Using Key Policies in AWS KMS in the AWS Key Management Service Developer Guide.
TransformResources (dict) --
Describes the resources, including ML instance types and ML instance count, to use for transform job.
InstanceType (string) --
The ML compute instance type for the transform job. If you are using built-in algorithms to transform moderately sized datasets, we recommend using ml.m4.xlarge or ml.m5.large instance types.
InstanceCount (integer) --
The number of ML compute instances to use in the transform job. For distributed transform jobs, specify a value greater than 1. The default value is 1.
VolumeKmsKeyId (string) --
The AWS Key Management Service (AWS KMS) key that Amazon SageMaker uses to encrypt model data on the storage volume attached to the ML compute instance(s) that run the batch transform job. The VolumeKmsKeyId can be any of the following formats:
Key ID: 1234abcd-12ab-34cd-56ef-1234567890ab
Key ARN: arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab
Alias name: alias/ExampleAlias
Alias name ARN: arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias
CreationTime (datetime) --
A timestamp that shows when the transform Job was created.
TransformStartTime (datetime) --
Indicates when the transform job starts on ML instances. You are billed for the time interval between this time and the value of TransformEndTime.
TransformEndTime (datetime) --
Indicates when the transform job has been completed, or has stopped or failed. You are billed for the time interval between this time and the value of TransformStartTime.
LabelingJobArn (string) --
The Amazon Resource Name (ARN) of the labeling job that created the transform job.
AutoMLJobArn (string) --
The Amazon Resource Name (ARN) of the AutoML job that created the transform job.
DataProcessing (dict) --
The data structure used to specify the data to be used for inference in a batch transform job and to associate the data that is relevant to the prediction results in the output. The input filter provided allows you to exclude input data that is not needed for inference in a batch transform job. The output filter provided allows you to include input data relevant to interpreting the predictions in the output from the job. For more information, see Associate Prediction Results with their Corresponding Input Records.
InputFilter (string) --
A JSONPath expression used to select a portion of the input data to pass to the algorithm. Use the InputFilter parameter to exclude fields, such as an ID column, from the input. If you want Amazon SageMaker to pass the entire input dataset to the algorithm, accept the default value $.
Examples: "$", "$[1:]", "$.features"
OutputFilter (string) --
A JSONPath expression used to select a portion of the joined dataset to save in the output file for a batch transform job. If you want Amazon SageMaker to store the entire input dataset in the output file, leave the default value, $. If you specify indexes that aren't within the dimension size of the joined dataset, you get an error.
Examples: "$", "$[0,5:]", "$['id','SageMakerOutput']"
JoinSource (string) --
Specifies the source of the data to join with the transformed data. The valid values are None and Input. The default value is None, which specifies not to join the input with the transformed data. If you want the batch transform job to join the original input data with the transformed data, set JoinSource to Input.
For JSON or JSONLines objects, such as a JSON array, Amazon SageMaker adds the transformed data to the input JSON object in an attribute called SageMakerOutput. The joined result for JSON must be a key-value pair object. If the input is not a key-value pair object, Amazon SageMaker creates a new JSON file. In the new JSON file, and the input data is stored under the SageMakerInput key and the results are stored in SageMakerOutput.
For CSV files, Amazon SageMaker combines the transformed data with the input data at the end of the input data and stores it in the output file. The joined data has the joined input data followed by the transformed data and the output is a CSV file.
ExperimentConfig (dict) --
Associates a SageMaker job as a trial component with an experiment and trial. Specified when you call the following APIs:
CreateProcessingJob
CreateTrainingJob
CreateTransformJob
ExperimentName (string) --
The name of an existing experiment to associate the trial component with.
TrialName (string) --
The name of an existing trial to associate the trial component with. If not specified, a new trial is created.
TrialComponentDisplayName (string) --
The display name for the trial component. If this key isn't specified, the display name is the trial component name.
Tags (list) --
A list of tags associated with the transform job.
(dict) --
Describes a tag.
Key (string) --
The tag key.
Value (string) --
The tag value.
Tags (list) --
The list of tags that are associated with the component. You can use Search API to search on the tags.
(dict) --
Describes a tag.
Key (string) --
The tag key.
Value (string) --
The tag value.
Parents (list) --
An array of the parents of the component. A parent is a trial the component is associated with and the experiment the trial is part of. A component might not have any parents.
(dict) --
The trial that a trial component is associated with and the experiment the trial is part of. A component might not be associated with a trial. A component can be associated with multiple trials.
TrialName (string) --
The name of the trial.
ExperimentName (string) --
The name of the experiment.
NextToken (string) --
If the result of the previous Search request was truncated, the response includes a NextToken. To retrieve the next set of results, use the token in the next request.
{'OidcConfig': {'AuthorizationEndpoint': 'string',
'ClientId': 'string',
'ClientSecret': 'string',
'Issuer': 'string',
'JwksUri': 'string',
'LogoutEndpoint': 'string',
'TokenEndpoint': 'string',
'UserInfoEndpoint': 'string'}}
Response {'Workforce': {'CognitoConfig': {'ClientId': 'string', 'UserPool': 'string'},
'CreateDate': 'timestamp',
'OidcConfig': {'AuthorizationEndpoint': 'string',
'ClientId': 'string',
'Issuer': 'string',
'JwksUri': 'string',
'LogoutEndpoint': 'string',
'TokenEndpoint': 'string',
'UserInfoEndpoint': 'string'},
'SubDomain': 'string'}}
Restricts access to tasks assigned to workers in the specified workforce to those within specific ranges of IP addresses. You specify allowed IP addresses by creating a list of up to ten CIDRs.
By default, a workforce isn't restricted to specific IP addresses. If you specify a range of IP addresses, workers who attempt to access tasks using any IP address outside the specified range are denied access and get a Not Found error message on the worker portal. After restricting access with this operation, you can see the allowed IP values for a private workforce with the operation.
See also: AWS API Documentation
Request Syntax
client.update_workforce(
WorkforceName='string',
SourceIpConfig={
'Cidrs': [
'string',
]
},
OidcConfig={
'ClientId': 'string',
'ClientSecret': 'string',
'Issuer': 'string',
'AuthorizationEndpoint': 'string',
'TokenEndpoint': 'string',
'UserInfoEndpoint': 'string',
'LogoutEndpoint': 'string',
'JwksUri': 'string'
}
)
string
[REQUIRED]
The name of the private workforce whose access you want to restrict. WorkforceName is automatically set to default when a workforce is created and cannot be modified.
dict
A list of one to ten worker IP address ranges ( CIDRs) that can be used to access tasks assigned to this workforce.
Maximum: Ten CIDR values
Cidrs (list) -- [REQUIRED]
A list of one to ten Classless Inter-Domain Routing (CIDR) values.
Maximum: Ten CIDR values
(string) --
dict
Use this parameter to update your OIDC Identity Provider (IdP) configuration for a workforce made using your own IdP.
ClientId (string) -- [REQUIRED]
The OIDC IdP client ID used to configure your private workforce.
ClientSecret (string) -- [REQUIRED]
The OIDC IdP client secret used to configure your private workforce.
Issuer (string) -- [REQUIRED]
The OIDC IdP issuer used to configure your private workforce.
AuthorizationEndpoint (string) -- [REQUIRED]
The OIDC IdP authorization endpoint used to configure your private workforce.
TokenEndpoint (string) -- [REQUIRED]
The OIDC IdP token endpoint used to configure your private workforce.
UserInfoEndpoint (string) -- [REQUIRED]
The OIDC IdP user information endpoint used to configure your private workforce.
LogoutEndpoint (string) -- [REQUIRED]
The OIDC IdP logout endpoint used to configure your private workforce.
JwksUri (string) -- [REQUIRED]
The OIDC IdP JSON Web Key Set (Jwks) URI used to configure your private workforce.
dict
Response Syntax
{
'Workforce': {
'WorkforceName': 'string',
'WorkforceArn': 'string',
'LastUpdatedDate': datetime(2015, 1, 1),
'SourceIpConfig': {
'Cidrs': [
'string',
]
},
'SubDomain': 'string',
'CognitoConfig': {
'UserPool': 'string',
'ClientId': 'string'
},
'OidcConfig': {
'ClientId': 'string',
'Issuer': 'string',
'AuthorizationEndpoint': 'string',
'TokenEndpoint': 'string',
'UserInfoEndpoint': 'string',
'LogoutEndpoint': 'string',
'JwksUri': 'string'
},
'CreateDate': datetime(2015, 1, 1)
}
}
Response Structure
(dict) --
Workforce (dict) --
A single private workforce, which is automatically created when you create your first private work team. You can create one private work force in each AWS Region. By default, any workforce-related API operation used in a specific region will apply to the workforce created in that region. To learn how to create a private workforce, see Create a Private Workforce.
WorkforceName (string) --
The name of the private workforce.
WorkforceArn (string) --
The Amazon Resource Name (ARN) of the private workforce.
LastUpdatedDate (datetime) --
The most recent date that was used to successfully add one or more IP address ranges ( CIDRs) to a private workforce's allow list.
SourceIpConfig (dict) --
A list of one to ten IP address ranges ( CIDRs) to be added to the workforce allow list.
Cidrs (list) --
A list of one to ten Classless Inter-Domain Routing (CIDR) values.
Maximum: Ten CIDR values
(string) --
SubDomain (string) --
The subdomain for your OIDC Identity Provider.
CognitoConfig (dict) --
The configuration of an Amazon Cognito workforce. A single Cognito workforce is created using and corresponds to a single Amazon Cognito user pool.
UserPool (string) --
A user pool is a user directory in Amazon Cognito. With a user pool, your users can sign in to your web or mobile app through Amazon Cognito. Your users can also sign in through social identity providers like Google, Facebook, Amazon, or Apple, and through SAML identity providers.
ClientId (string) --
The client ID for your Amazon Cognito user pool.
OidcConfig (dict) --
The configuration of an OIDC Identity Provider (IdP) private workforce.
ClientId (string) --
The OIDC IdP client ID used to configure your private workforce.
Issuer (string) --
The OIDC IdP issuer used to configure your private workforce.
AuthorizationEndpoint (string) --
The OIDC IdP authorization endpoint used to configure your private workforce.
TokenEndpoint (string) --
The OIDC IdP token endpoint used to configure your private workforce.
UserInfoEndpoint (string) --
The OIDC IdP user information endpoint used to configure your private workforce.
LogoutEndpoint (string) --
The OIDC IdP logout endpoint used to configure your private workforce.
JwksUri (string) --
The OIDC IdP JSON Web Key Set (Jwks) URI used to configure your private workforce.
CreateDate (datetime) --
The date that the workforce is created.
{'MemberDefinitions': {'OidcMemberDefinition': {'Groups': ['string']}}}
Response {'Workteam': {'MemberDefinitions': {'OidcMemberDefinition': {'Groups': ['string']}},
'WorkforceArn': 'string'}}
Updates an existing work team with new member definitions or description.
See also: AWS API Documentation
Request Syntax
client.update_workteam(
WorkteamName='string',
MemberDefinitions=[
{
'CognitoMemberDefinition': {
'UserPool': 'string',
'UserGroup': 'string',
'ClientId': 'string'
},
'OidcMemberDefinition': {
'Groups': [
'string',
]
}
},
],
Description='string',
NotificationConfiguration={
'NotificationTopicArn': 'string'
}
)
string
[REQUIRED]
The name of the work team to update.
list
A list of MemberDefinition objects that contain the updated work team members.
(dict) --
Defines the Amazon Cognito user group that is part of a work team.
CognitoMemberDefinition (dict) --
The Amazon Cognito user group that is part of the work team.
UserPool (string) -- [REQUIRED]
An identifier for a user pool. The user pool must be in the same region as the service that you are calling.
UserGroup (string) -- [REQUIRED]
An identifier for a user group.
ClientId (string) -- [REQUIRED]
An identifier for an application client. You must create the app client ID using Amazon Cognito.
OidcMemberDefinition (dict) --
A list user groups that exist in your OIDC Identity Provider (IdP). One to ten groups can be used to create a single private work team. When you add a user group to the list of Groups, you can add that user group to one or more private work teams. If you add a user group to a private work team, all workers in that user group are added to the work team.
Groups (list) -- [REQUIRED]
A list of comma seperated strings that identifies user groups in your OIDC IdP. Each user group is made up of a group of private workers.
(string) --
string
An updated description for the work team.
dict
Configures SNS topic notifications for available or expiring work items
NotificationTopicArn (string) --
The ARN for the SNS topic to which notifications should be published.
dict
Response Syntax
{
'Workteam': {
'WorkteamName': 'string',
'MemberDefinitions': [
{
'CognitoMemberDefinition': {
'UserPool': 'string',
'UserGroup': 'string',
'ClientId': 'string'
},
'OidcMemberDefinition': {
'Groups': [
'string',
]
}
},
],
'WorkteamArn': 'string',
'WorkforceArn': 'string',
'ProductListingIds': [
'string',
],
'Description': 'string',
'SubDomain': 'string',
'CreateDate': datetime(2015, 1, 1),
'LastUpdatedDate': datetime(2015, 1, 1),
'NotificationConfiguration': {
'NotificationTopicArn': 'string'
}
}
}
Response Structure
(dict) --
Workteam (dict) --
A Workteam object that describes the updated work team.
WorkteamName (string) --
The name of the work team.
MemberDefinitions (list) --
The Amazon Cognito user groups that make up the work team.
(dict) --
Defines the Amazon Cognito user group that is part of a work team.
CognitoMemberDefinition (dict) --
The Amazon Cognito user group that is part of the work team.
UserPool (string) --
An identifier for a user pool. The user pool must be in the same region as the service that you are calling.
UserGroup (string) --
An identifier for a user group.
ClientId (string) --
An identifier for an application client. You must create the app client ID using Amazon Cognito.
OidcMemberDefinition (dict) --
A list user groups that exist in your OIDC Identity Provider (IdP). One to ten groups can be used to create a single private work team. When you add a user group to the list of Groups, you can add that user group to one or more private work teams. If you add a user group to a private work team, all workers in that user group are added to the work team.
Groups (list) --
A list of comma seperated strings that identifies user groups in your OIDC IdP. Each user group is made up of a group of private workers.
(string) --
WorkteamArn (string) --
The Amazon Resource Name (ARN) that identifies the work team.
WorkforceArn (string) --
The Amazon Resource Name (ARN) of the workforce.
ProductListingIds (list) --
The Amazon Marketplace identifier for a vendor's work team.
(string) --
Description (string) --
A description of the work team.
SubDomain (string) --
The URI of the labeling job's user interface. Workers open this URI to start labeling your data objects.
CreateDate (datetime) --
The date and time that the work team was created (timestamp).
LastUpdatedDate (datetime) --
The date and time that the work team was last updated (timestamp).
NotificationConfiguration (dict) --
Configures SNS notifications of available or expiring work items for work teams.
NotificationTopicArn (string) --
The ARN for the SNS topic to which notifications should be published.