SageMaker

This module provides classes to build steps that integrate with Amazon SageMaker.

class stepfunctions.steps.sagemaker.TrainingStep(state_id, estimator, job_name, data=None, hyperparameters=None, mini_batch_size=None, experiment_config=None, wait_for_completion=True, tags=None, **kwargs)

Bases: stepfunctions.steps.states.Task

Creates a Task State to execute a SageMaker Training Job. The TrainingStep will also create a model by default, and the model shares the same name as the training job.

Parameters:
  • state_id (str) – State name whose length must be less than or equal to 128 unicode characters. State names must be unique within the scope of the whole state machine.
  • estimator (sagemaker.estimator.EstimatorBase) – The estimator for the training step. Can be a BYO estimator, Framework estimator or Amazon built-in algorithm estimator.
  • job_name (str or Placeholder) – Specify a training job name, this is required for the training job to run. We recommend to use ExecutionInput placeholder collection to pass the value dynamically in each execution.
  • data –

    Information about the training data. Please refer to the fit() method of the associated estimator, as this can take any of the following forms:

    • (str) - The S3 location where training data is saved.
    • (dict[str, str] or dict[str, sagemaker.inputs.TrainingInput]) - If using multiple
      channels for training data, you can specify a dict mapping channel names to strings or TrainingInput() objects.
    • (sagemaker.inputs.TrainingInput) - Channel configuration for S3 data sources that can
      provide additional information about the training dataset. See sagemaker.inputs.TrainingInput() for full details.
    • (sagemaker.amazon.amazon_estimator.RecordSet) - A collection of
      Amazon Record objects serialized and stored in S3. For use with an estimator for an Amazon algorithm.
    • (list[sagemaker.amazon.amazon_estimator.RecordSet]) - A list of
      sagemaker.amazon.amazon_estimator.RecordSet objects, where each instance is a different channel of training data.
  • hyperparameters (dict, optional) – Specify the hyper parameters for the training. (Default: None)
  • mini_batch_size (int) – Specify this argument only when estimator is a built-in estimator of an Amazon algorithm. For other estimators, batch size should be specified in the estimator.
  • experiment_config (dict, optional) – Specify the experiment config for the training. (Default: None)
  • wait_for_completion (bool, optional) – Boolean value set to True if the Task state should wait for the training job to complete before proceeding to the next step in the workflow. Set to False if the Task state should submit the training job and proceed to the next step. (default: True)
  • tags (list[dict], optional) – List to tags to associate with the resource.
get_expected_model(model_name=None)

Build Sagemaker model representation of the expected trained model from the Training step. This can be passed to the ModelStep to save the trained model in Sagemaker. :param model_name: Specify a model name. If not provided, training job name will be used as the model name. :type model_name: str, optional

Returns:Sagemaker model representation of the expected trained model.
Return type:sagemaker.model.Model
class stepfunctions.steps.sagemaker.TransformStep(state_id, transformer, job_name, model_name, data, data_type='S3Prefix', content_type=None, compression_type=None, split_type=None, experiment_config=None, wait_for_completion=True, tags=None, input_filter=None, output_filter=None, join_source=None, **kwargs)

Bases: stepfunctions.steps.states.Task

Creates a Task State to execute a SageMaker Transform Job.

Parameters:
  • state_id (str) – State name whose length must be less than or equal to 128 unicode characters. State names must be unique within the scope of the whole state machine.
  • transformer (sagemaker.transformer.Transformer) – The SageMaker transformer to use in the TransformStep.
  • job_name (str or Placeholder) – Specify a transform job name. We recommend to use ExecutionInput placeholder collection to pass the value dynamically in each execution.
  • model_name (str or Placeholder) – Specify a model name for the transform job to use. We recommend to use ExecutionInput placeholder collection to pass the value dynamically in each execution.
  • data (str) – Input data location in S3.
  • data_type (str) –

    What the S3 location defines (default: ‘S3Prefix’). Valid values:

    • ’S3Prefix’ - the S3 URI defines a key name prefix. All objects with this prefix will
      be used as inputs for the transform job.
    • ’ManifestFile’ - the S3 URI points to a single manifest file listing each S3 object
      to use as an input for the transform job.
  • content_type (str) – MIME type of the input data (default: None).
  • compression_type (str) – Compression type of the input data, if compressed (default: None). Valid values: ‘Gzip’, None.
  • split_type (str) – The record delimiter for the input object (default: ‘None’). Valid values: ‘None’, ‘Line’, ‘RecordIO’, and ‘TFRecord’.
  • experiment_config (dict, optional) – Specify the experiment config for the transform. (Default: None)
  • wait_for_completion (bool, optional) – Boolean value set to True if the Task state should wait for the transform job to complete before proceeding to the next step in the workflow. Set to False if the Task state should submit the transform job and proceed to the next step. (default: True)
  • tags (list[dict], optional) –

    List to tags to associate with the resource.

  • input_filter (str) – A JSONPath to select a portion of the input to pass to the algorithm container for inference. If you omit the field, it gets the value ‘$’, representing the entire input. For CSV data, each row is taken as a JSON array, so only index-based JSONPaths can be applied, e.g. $[0], $[1:]. CSV data should follow the RFC format. See Supported JSONPath Operators for a table of supported JSONPath operators. For more information, see the SageMaker API documentation for CreateTransformJob. Some examples: “$[1:]”, “$.features” (default: None).
  • output_filter (str) – A JSONPath to select a portion of the joined/original output to return as the output. For more information, see the SageMaker API documentation for CreateTransformJob. Some examples: “$[1:]”, “$.prediction” (default: None).
  • join_source (str) – The source of data to be joined to the transform output. It can be set to ‘Input’ meaning the entire input record will be joined to the inference result. You can use OutputFilter to select the useful portion before uploading to S3. (default: None). Valid values: Input, None.
class stepfunctions.steps.sagemaker.TuningStep(state_id, tuner, job_name, data, wait_for_completion=True, tags=None, **kwargs)

Bases: stepfunctions.steps.states.Task

Creates a Task State to execute a SageMaker HyperParameterTuning Job.

Parameters:
  • state_id (str) – State name whose length must be less than or equal to 128 unicode characters. State names must be unique within the scope of the whole state machine.
  • tuner (sagemaker.tuner.HyperparameterTuner) – The tuner to use in the TuningStep.
  • job_name (str or Placeholder) – Specify a tuning job name. We recommend to use ExecutionInput placeholder collection to pass the value dynamically in each execution.
  • data –

    Information about the training data. Please refer to the fit() method of the associated estimator in the tuner, as this can take any of the following forms:

    • (str) - The S3 location where training data is saved.
    • (dict[str, str] or dict[str, sagemaker.inputs.TrainingInput]) - If using multiple
      channels for training data, you can specify a dict mapping channel names to strings or TrainingInput() objects.
    • (sagemaker.inputs.TrainingInput) - Channel configuration for S3 data sources that can
      provide additional information about the training dataset. See sagemaker.inputs.TrainingInput() for full details.
    • (sagemaker.amazon.amazon_estimator.RecordSet) - A collection of
      Amazon Record objects serialized and stored in S3. For use with an estimator for an Amazon algorithm.
    • (list[sagemaker.amazon.amazon_estimator.RecordSet]) - A list of
      sagemaker.amazon.amazon_estimator.RecordSet objects, where each instance is a different channel of training data.
  • wait_for_completion (bool, optional) – Boolean value set to True if the Task state should wait for the tuning job to complete before proceeding to the next step in the workflow. Set to False if the Task state should submit the tuning job and proceed to the next step. (default: True)
  • tags (list[dict], optional) –

    List to tags to associate with the resource.
class stepfunctions.steps.sagemaker.ModelStep(state_id, model, model_name=None, instance_type=None, tags=None, **kwargs)

Bases: stepfunctions.steps.states.Task

Creates a Task State to create a model in SageMaker.

Parameters:
  • state_id (str) – State name whose length must be less than or equal to 128 unicode characters. State names must be unique within the scope of the whole state machine.
  • model (sagemaker.model.Model) – The SageMaker model to use in the ModelStep. If TrainingStep was used to train the model and saving the model is the next step in the workflow, the output of TrainingStep.get_expected_model() can be passed here.
  • model_name (str or Placeholder, optional) – Specify a model name, this is required for creating the model. We recommend to use ExecutionInput placeholder collection to pass the value dynamically in each execution.
  • instance_type (str, optional) – The EC2 instance type to deploy this Model to. For example, ‘ml.p2.xlarge’.
  • tags (list[dict], optional) –

    List to tags to associate with the resource.
class stepfunctions.steps.sagemaker.EndpointConfigStep(state_id, endpoint_config_name, model_name, initial_instance_count, instance_type, variant_name='AllTraffic', data_capture_config=None, tags=None, **kwargs)

Bases: stepfunctions.steps.states.Task

Creates a Task State to create an endpoint configuration in SageMaker.

Parameters:
  • state_id (str) – State name whose length must be less than or equal to 128 unicode characters. State names must be unique within the scope of the whole state machine.
  • endpoint_config_name (str or Placeholder) – The name of the endpoint configuration to create. We recommend to use ExecutionInput placeholder collection to pass the value dynamically in each execution.
  • model_name (str or Placeholder) – The name of the SageMaker model to attach to the endpoint configuration. We recommend to use ExecutionInput placeholder collection to pass the value dynamically in each execution.
  • initial_instance_count (int or Placeholder) – The initial number of instances to run in the Endpoint created from this Model.
  • instance_type (str or Placeholder) – The EC2 instance type to deploy this Model to. For example, ‘ml.p2.xlarge’.
  • variant_name (str, optional) – The name of the production variant.
  • data_capture_config (sagemaker.model_monitor.DataCaptureConfig, optional) – Specifies configuration related to Endpoint data capture for use with Amazon SageMaker Model Monitoring. Default: None.
  • tags (list[dict], optional) –

    List to tags to associate with the resource.
class stepfunctions.steps.sagemaker.EndpointStep(state_id, endpoint_name, endpoint_config_name, tags=None, update=False, **kwargs)

Bases: stepfunctions.steps.states.Task

Creates a Task State to create or update an endpoint in SageMaker.

Parameters:
  • state_id (str) – State name whose length must be less than or equal to 128 unicode characters. State names must be unique within the scope of the whole state machine.
  • endpoint_name (str or Placeholder) – The name of the endpoint to create. We recommend to use ExecutionInput placeholder collection to pass the value dynamically in each execution.
  • endpoint_config_name (str or Placeholder) – The name of the endpoint configuration to use for the endpoint. We recommend to use ExecutionInput placeholder collection to pass the value dynamically in each execution.
  • tags (list[dict], optional) –

    List to tags to associate with the resource.

  • update (bool, optional) – Boolean flag set to True if endpoint must to be updated. Set to False if new endpoint must be created. (default: False)
  • tags –

    List to tags to associate with the resource.
class stepfunctions.steps.sagemaker.ProcessingStep(state_id, processor, job_name, inputs=None, outputs=None, experiment_config=None, container_arguments=None, container_entrypoint=None, kms_key_id=None, wait_for_completion=True, tags=None, **kwargs)

Bases: stepfunctions.steps.states.Task

Creates a Task State to execute a SageMaker Processing Job.

Parameters:
  • state_id (str) – State name whose length must be less than or equal to 128 unicode characters. State names must be unique within the scope of the whole state machine.
  • processor (sagemaker.processing.Processor) – The processor for the processing step.
  • job_name (str or Placeholder) – Specify a processing job name, this is required for the processing job to run. We recommend to use ExecutionInput placeholder collection to pass the value dynamically in each execution.
  • inputs (list[ProcessingInput]) – Input files for the processing job. These must be provided as ProcessingInput objects (default: None).
  • outputs (list[ProcessingOutput]) – Outputs for the processing job. These can be specified as either path strings or ProcessingOutput objects (default: None).
  • experiment_config (dict, optional) – Specify the experiment config for the processing. (Default: None)
  • container_arguments ([str]) – The arguments for a container used to run a processing job.
  • container_entrypoint ([str]) – The entrypoint for a container used to run a processing job.
  • kms_key_id (str) – 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.
  • wait_for_completion (bool, optional) – Boolean value set to True if the Task state should wait for the processing job to complete before proceeding to the next step in the workflow. Set to False if the Task state should submit the processing job and proceed to the next step. (default: True)
  • tags (list[dict], optional) –

    List to tags to associate with the resource.