2019/05/08 - Amazon SageMaker Service - 8 updated api methods
Changes Workteams now supports notification configurations. Neo now supports Jetson Nano as a target device and NumberOfHumanWorkersPerDataObject is now included in the ListLabelingJobsForWorkteam response.
{'OutputConfig': {'TargetDevice': {'jetson_nano', 'lambda'}}}
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'|'MXNET'|'ONNX'|'PYTORCH'|'XGBOOST'
},
OutputConfig={
'S3OutputLocation': 'string',
'TargetDevice': 'lambda'|'ml_m4'|'ml_m5'|'ml_c4'|'ml_c5'|'ml_p2'|'ml_p3'|'jetson_tx1'|'jetson_tx2'|'jetson_nano'|'rasp3b'|'deeplens'|'rk3399'|'rk3288'
},
StoppingCondition={
'MaxRuntimeInSeconds': 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]}
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 path where you want Amazon SageMaker to store the model artifacts. For example, s3://bucket-name/key-name-prefix.
TargetDevice (string) -- [REQUIRED]
Identifies the device that you want to run your model on after it has been compiled. For example: ml_c5.
dict
[REQUIRED]
The duration allowed for model compilation.
MaxRuntimeInSeconds (integer) --
The maximum length of time, in seconds, that the training job can run. If model training does not complete during this time, Amazon SageMaker ends the job. If value is not specified, default value is 1 day. Maximum value is 28 days.
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.
{'NotificationConfiguration': {'NotificationTopicArn': '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',
MemberDefinitions=[
{
'CognitoMemberDefinition': {
'UserPool': 'string',
'UserGroup': 'string',
'ClientId': '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.
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.
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
(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': {'TargetDevice': {'jetson_nano', 'lambda'}}}
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
},
'CreationTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'FailureReason': 'string',
'ModelArtifacts': {
'S3ModelArtifacts': 'string'
},
'RoleArn': 'string',
'InputConfig': {
'S3Uri': 'string',
'DataInputConfig': 'string',
'Framework': 'TENSORFLOW'|'MXNET'|'ONNX'|'PYTORCH'|'XGBOOST'
},
'OutputConfig': {
'S3OutputLocation': 'string',
'TargetDevice': 'lambda'|'ml_m4'|'ml_m5'|'ml_c4'|'ml_c5'|'ml_p2'|'ml_p3'|'jetson_tx1'|'jetson_tx2'|'jetson_nano'|'rasp3b'|'deeplens'|'rk3399'|'rk3288'
}
}
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) --
The duration allowed for model compilation.
MaxRuntimeInSeconds (integer) --
The maximum length of time, in seconds, that the training job can run. If model training does not complete during this time, Amazon SageMaker ends the job. If value is not specified, default value is 1 day. Maximum value is 28 days.
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]}
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 path where you want Amazon SageMaker to store the model artifacts. For example, s3://bucket-name/key-name-prefix.
TargetDevice (string) --
Identifies the device that you want to run your model on after it has been compiled. For example: ml_c5.
{'Workteam': {'NotificationConfiguration': {'NotificationTopicArn': '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'
}
},
],
'WorkteamArn': '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.
WorkteamArn (string) --
The Amazon Resource Name (ARN) that identifies the work team.
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': {'jetson_nano',
'lambda'}}}
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'|'jetson_tx1'|'jetson_tx2'|'jetson_nano'|'rasp3b'|'deeplens'|'rk3399'|'rk3288',
'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 compilation 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.
{'LabelingJobSummaryList': {'NumberOfHumanWorkersPerDataObject': 'integer'}}
Gets a list of labeling jobs assigned to a specified work team.
See also: AWS API Documentation
Request Syntax
client.list_labeling_jobs_for_workteam(
WorkteamArn='string',
MaxResults=123,
NextToken='string',
CreationTimeAfter=datetime(2015, 1, 1),
CreationTimeBefore=datetime(2015, 1, 1),
JobReferenceCodeContains='string',
SortBy='CreationTime',
SortOrder='Ascending'|'Descending'
)
string
[REQUIRED]
The Amazon Resource Name (ARN) of the work team for which you want to see labeling jobs for.
integer
The maximum number of labeling jobs to return in each page of the response.
string
If the result of the previous ListLabelingJobsForWorkteam request was truncated, the response includes a NextToken . To retrieve the next set of labeling jobs, use the token in the next request.
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).
string
A filter the limits jobs to only the ones whose job reference code 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 .
dict
Response Syntax
{
'LabelingJobSummaryList': [
{
'LabelingJobName': 'string',
'JobReferenceCode': 'string',
'WorkRequesterAccountId': 'string',
'CreationTime': datetime(2015, 1, 1),
'LabelCounters': {
'HumanLabeled': 123,
'PendingHuman': 123,
'Total': 123
},
'NumberOfHumanWorkersPerDataObject': 123
},
],
'NextToken': 'string'
}
Response Structure
(dict) --
LabelingJobSummaryList (list) --
An array of LabelingJobSummary objects, each describing a labeling job.
(dict) --
Provides summary information for a work team.
LabelingJobName (string) --
The name of the labeling job that the work team is assigned to.
JobReferenceCode (string) --
A unique identifier for a labeling job. You can use this to refer to a specific labeling job.
WorkRequesterAccountId (string) --
CreationTime (datetime) --
The date and time that the labeling job was created.
LabelCounters (dict) --
Provides information about the progress of a labeling job.
HumanLabeled (integer) --
The total number of data objects labeled by a human worker.
PendingHuman (integer) --
The total number of data objects that need to be labeled by a human worker.
Total (integer) --
The total number of tasks in the labeling job.
NumberOfHumanWorkersPerDataObject (integer) --
The configured number of workers per data object.
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': {'NotificationConfiguration': {'NotificationTopicArn': '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'
}
},
],
'WorkteamArn': '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.
WorkteamArn (string) --
The Amazon Resource Name (ARN) that identifies the work team.
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.
{'NotificationConfiguration': {'NotificationTopicArn': 'string'}}
Response {'Workteam': {'NotificationConfiguration': {'NotificationTopicArn': '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'
}
},
],
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.
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'
}
},
],
'WorkteamArn': '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.
WorkteamArn (string) --
The Amazon Resource Name (ARN) that identifies the work team.
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.