SageMakerCreateTransformJobProps

class aws_cdk.aws_stepfunctions_tasks.SageMakerCreateTransformJobProps(*, comment=None, heartbeat=None, input_path=None, integration_pattern=None, output_path=None, result_path=None, timeout=None, model_name, transform_input, transform_job_name, transform_output, batch_strategy=None, environment=None, max_concurrent_transforms=None, max_payload=None, role=None, tags=None, transform_resources=None)

Bases: aws_cdk.aws_stepfunctions.TaskStateBaseProps

(experimental) Properties for creating an Amazon SageMaker training job task.

Parameters

  • comment (Optional[str]) – An optional description for this state. Default: - No comment

  • heartbeat (Optional[Duration]) – Timeout for the heartbeat. Default: - None

  • input_path (Optional[str]) – JSONPath expression to select part of the state to be the input to this state. May also be the special value JsonPath.DISCARD, which will cause the effective input to be the empty object {}. Default: - The entire task input (JSON path ‘$’)

  • integration_pattern (Optional[IntegrationPattern]) – AWS Step Functions integrates with services directly in the Amazon States Language. You can control these AWS services using service integration patterns Default: IntegrationPattern.REQUEST_RESPONSE

  • output_path (Optional[str]) – JSONPath expression to select select a portion of the state output to pass to the next state. May also be the special value JsonPath.DISCARD, which will cause the effective output to be the empty object {}. Default: - The entire JSON node determined by the state input, the task result, and resultPath is passed to the next state (JSON path ‘$’)

  • result_path (Optional[str]) – JSONPath expression to indicate where to inject the state’s output. May also be the special value JsonPath.DISCARD, which will cause the state’s input to become its output. Default: - Replaces the entire input with the result (JSON path ‘$’)

  • timeout (Optional[Duration]) – Timeout for the state machine. Default: - None

  • model_name (str) – (experimental) Name of the model that you want to use for the transform job.

  • transform_input (TransformInput) – (experimental) Dataset to be transformed and the Amazon S3 location where it is stored.

  • transform_job_name (str) – (experimental) Training Job Name.

  • transform_output (TransformOutput) – (experimental) S3 location where you want Amazon SageMaker to save the results from the transform job.

  • batch_strategy (Optional[BatchStrategy]) – (experimental) Number of records to include in a mini-batch for an HTTP inference request. Default: - No batch strategy

  • environment (Optional[Mapping[str, str]]) – (experimental) Environment variables to set in the Docker container. Default: - No environment variables

  • max_concurrent_transforms (Union[int, float, None]) – (experimental) Maximum number of parallel requests that can be sent to each instance in a transform job. Default: - Amazon 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.

  • max_payload (Optional[Size]) – (experimental) Maximum allowed size of the payload, in MB. Default: 6

  • role (Optional[IRole]) – (experimental) Role for the Training Job. Default: - A role is created with AmazonSageMakerFullAccess managed policy

  • tags (Optional[Mapping[str, str]]) – (experimental) Tags to be applied to the train job. Default: - No tags

  • transform_resources (Optional[TransformResources]) – (experimental) ML compute instances for the transform job. Default: - 1 instance of type M4.XLarge

Stability

experimental

Attributes

batch_strategy

(experimental) Number of records to include in a mini-batch for an HTTP inference request.

Default

  • No batch strategy

Stability

experimental

Return type

Optional[BatchStrategy]

comment

An optional description for this state.

Default

  • No comment

Return type

Optional[str]

environment

(experimental) Environment variables to set in the Docker container.

Default

  • No environment variables

Stability

experimental

Return type

Optional[Mapping[str, str]]

heartbeat

Timeout for the heartbeat.

Default

  • None

Return type

Optional[Duration]

input_path

JSONPath expression to select part of the state to be the input to this state.

May also be the special value JsonPath.DISCARD, which will cause the effective input to be the empty object {}.

Default

  • The entire task input (JSON path ‘$’)

Return type

Optional[str]

integration_pattern

AWS Step Functions integrates with services directly in the Amazon States Language.

You can control these AWS services using service integration patterns

Default

IntegrationPattern.REQUEST_RESPONSE

See

https://docs.aws.amazon.com/step-functions/latest/dg/connect-to-resource.html#connect-wait-token

Return type

Optional[IntegrationPattern]

max_concurrent_transforms

(experimental) Maximum number of parallel requests that can be sent to each instance in a transform job.

Default

  • Amazon 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.

Stability

experimental

Return type

Union[int, float, None]

max_payload

(experimental) Maximum allowed size of the payload, in MB.

Default

6

Stability

experimental

Return type

Optional[Size]

model_name

(experimental) Name of the model that you want to use for the transform job.

Stability

experimental

Return type

str

output_path

JSONPath expression to select select a portion of the state output to pass to the next state.

May also be the special value JsonPath.DISCARD, which will cause the effective output to be the empty object {}.

Default

  • The entire JSON node determined by the state input, the task result,

and resultPath is passed to the next state (JSON path ‘$’)

Return type

Optional[str]

result_path

JSONPath expression to indicate where to inject the state’s output.

May also be the special value JsonPath.DISCARD, which will cause the state’s input to become its output.

Default

  • Replaces the entire input with the result (JSON path ‘$’)

Return type

Optional[str]

role

(experimental) Role for the Training Job.

Default

  • A role is created with AmazonSageMakerFullAccess managed policy

Stability

experimental

Return type

Optional[IRole]

tags

(experimental) Tags to be applied to the train job.

Default

  • No tags

Stability

experimental

Return type

Optional[Mapping[str, str]]

timeout

Timeout for the state machine.

Default

  • None

Return type

Optional[Duration]

transform_input

(experimental) Dataset to be transformed and the Amazon S3 location where it is stored.

Stability

experimental

Return type

TransformInput

transform_job_name

(experimental) Training Job Name.

Stability

experimental

Return type

str

transform_output

(experimental) S3 location where you want Amazon SageMaker to save the results from the transform job.

Stability

experimental

Return type

TransformOutput

transform_resources

(experimental) ML compute instances for the transform job.

Default

  • 1 instance of type M4.XLarge

Stability

experimental

Return type

Optional[TransformResources]