Pipelines

ConditionStep

class sagemaker.workflow.condition_step.ConditionStep(name, depends_on=None, display_name=None, description=None, conditions=None, if_steps=None, else_steps=None)

Conditional step for pipelines to support conditional branching in the execution of steps.

Construct a ConditionStep for pipelines to support conditional branching.

If all of the conditions in the condition list evaluate to True, the if_steps are marked as ready for execution. Otherwise, the else_steps are marked as ready for execution.

Parameters
  • name (str) – The name of the condition step.

  • depends_on (List[Union[str, Step, StepCollection]]) – The list of Step/StepCollection` names or Step instances or StepCollection instances that the current Step depends on.

  • display_name (str) – The display name of the condition step.

  • description (str) – The description of the condition step.

  • conditions (List[Condition]) – A list of sagemaker.workflow.conditions.Condition instances.

  • if_steps (List[Union[Step, StepCollection]]) – A list of sagemaker.workflow.steps.Step or sagemaker.workflow.step_collections.StepCollection instances that are marked as ready for execution if the list of conditions evaluates to True.

  • else_steps (List[Union[Step, StepCollection]]) – A list of sagemaker.workflow.steps.Step or sagemaker.workflow.step_collections.StepCollection instances that are marked as ready for execution if the list of conditions evaluates to False.

Deprecated since version sagemaker.workflow.condition_step.JsonGet.

Conditions

class sagemaker.workflow.conditions.ConditionTypeEnum(*args, value=<object object>, **kwargs)

Condition type enum.

class sagemaker.workflow.conditions.Condition(condition_type=NOTHING)

Abstract Condition entity.

Parameters

condition_type (sagemaker.workflow.conditions.ConditionTypeEnum) –

Return type

None

condition_type

The type of condition.

Type

ConditionTypeEnum

Method generated by attrs for class Condition.

class sagemaker.workflow.conditions.ConditionComparison(condition_type=NOTHING, left=None, right=None)

Generic comparison condition that can be used to derive specific condition comparisons.

Parameters
Return type

None

left

The execution variable, parameter, property, or Python primitive value to use in the comparison.

Type

Union[ConditionValueType, PrimitiveType]

right

The execution variable, parameter, property, or Python primitive value to compare to.

Type

Union[ConditionValueType, PrimitiveType]

Method generated by attrs for class ConditionComparison.

class sagemaker.workflow.conditions.ConditionEquals(left, right)

A condition for equality comparisons.

Construct A condition for equality comparisons.

Parameters
  • left (Union[ConditionValueType, PrimitiveType]) – The execution variable, parameter, property, or Python primitive value to use in the comparison.

  • right (Union[ConditionValueType, PrimitiveType]) – The execution variable, parameter, property, or Python primitive value to compare to.

class sagemaker.workflow.conditions.ConditionGreaterThan(left, right)

A condition for greater than comparisons.

Construct an instance of ConditionGreaterThan for greater than comparisons.

Parameters
  • left (Union[ConditionValueType, PrimitiveType]) – The execution variable, parameter, property, or Python primitive value to use in the comparison.

  • right (Union[ConditionValueType, PrimitiveType]) – The execution variable, parameter, property, or Python primitive value to compare to.

class sagemaker.workflow.conditions.ConditionGreaterThanOrEqualTo(left, right)

A condition for greater than or equal to comparisons.

Construct of ConditionGreaterThanOrEqualTo for greater than or equal to comparisons.

Parameters
  • left (Union[ConditionValueType, PrimitiveType]) – The execution variable, parameter, property, or Python primitive value to use in the comparison.

  • right (Union[ConditionValueType, PrimitiveType]) – The execution variable, parameter, property, or Python primitive value to compare to.

class sagemaker.workflow.conditions.ConditionLessThan(left, right)

A condition for less than comparisons.

Construct an instance of ConditionLessThan for less than comparisons.

Parameters
  • left (Union[ConditionValueType, PrimitiveType]) – The execution variable, parameter, property, or Python primitive value to use in the comparison.

  • right (Union[ConditionValueType, PrimitiveType]) – The execution variable, parameter, property, or Python primitive value to compare to.

class sagemaker.workflow.conditions.ConditionLessThanOrEqualTo(left, right)

A condition for less than or equal to comparisons.

Construct ConditionLessThanOrEqualTo for less than or equal to comparisons.

Parameters
  • left (Union[ConditionValueType, PrimitiveType]) – The execution variable, parameter, property, or Python primitive value to use in the comparison.

  • right (Union[ConditionValueType, PrimitiveType]) – The execution variable, parameter, property, or Python primitive value to compare to.

class sagemaker.workflow.conditions.ConditionIn(value, in_values)

A condition to check membership.

Construct a ConditionIn condition to check membership.

Parameters
  • value (Union[ConditionValueType, PrimitiveType]) – The execution variable, parameter, property or primitive value to check for membership.

  • in_values (List[Union[ConditionValueType, PrimitiveType]]) – The list of values to check for membership in.

class sagemaker.workflow.conditions.ConditionNot(expression)

A condition for negating another Condition.

Construct a ConditionNot condition for negating another Condition.

Parameters

expression (sagemaker.workflow.conditions.Condition) –

expression

A Condition to take the negation of.

Type

Condition

class sagemaker.workflow.conditions.ConditionOr(conditions=None)

A condition for taking the logical OR of a list of Condition instances.

Construct a ConditionOr condition.

Parameters

conditions (List[sagemaker.workflow.conditions.Condition]) –

conditions

A list of Condition instances to logically OR.

Type

List[Condition]

CheckJobConfig

class sagemaker.workflow.check_job_config.CheckJobConfig(role, instance_count=1, instance_type='ml.m5.xlarge', volume_size_in_gb=30, volume_kms_key=None, output_kms_key=None, max_runtime_in_seconds=None, base_job_name=None, sagemaker_session=None, env=None, tags=None, network_config=None)

Check job config for QualityCheckStep and ClarifyCheckStep.

Constructs a CheckJobConfig instance.

Parameters
  • role (str) – An AWS IAM role. The Amazon SageMaker jobs use this role.

  • instance_count (int) – The number of instances to run the jobs with (default: 1).

  • instance_type (str) – Type of EC2 instance to use for the job (default: ‘ml.m5.xlarge’).

  • volume_size_in_gb (int) – Size in GB of the EBS volume to use for storing data during processing (default: 30).

  • volume_kms_key (str) – A KMS key for the processing volume (default: None).

  • output_kms_key (str) – The KMS key id for the job’s outputs (default: None).

  • max_runtime_in_seconds (int) – Timeout in seconds. After this amount of time, Amazon SageMaker terminates the job regardless of its current status. Default: 3600 if not specified

  • base_job_name (str) – Prefix for the job name. If not specified, a default name is generated based on the training image name and current timestamp (default: None).

  • sagemaker_session (sagemaker.session.Session) – Session object which manages interactions with Amazon SageMaker APIs and any other AWS services needed (default: None). If not specified, one is created using the default AWS configuration chain.

  • env (dict) – Environment variables to be passed to the job (default: None).

  • tags ([dict]) – List of tags to be passed to the job (default: None).

  • network_config (sagemaker.network.NetworkConfig) – A NetworkConfig object that configures network isolation, encryption of inter-container traffic, security group IDs, and subnets (default: None).

Entities

class sagemaker.workflow.entities.Entity

Base object for workflow entities.

Entities must implement the to_request method.

class sagemaker.workflow.entities.DefaultEnumMeta(cls, bases, classdict, **kwds)

An EnumMeta which defaults to the first value in the Enum list.

class sagemaker.workflow.entities.Expression

Base object for expressions.

Expressions must implement the expr property.

class sagemaker.workflow.entities.PipelineVariable

Base object for pipeline variables

PipelineVariable subclasses must implement the expr property. Its subclasses include: Parameter, Properties, Join, JsonGet, ExecutionVariable.

Execution Variables

class sagemaker.workflow.execution_variables.ExecutionVariable(name)

Pipeline execution variables for workflow.

Create a pipeline execution variable.

Parameters

name (str) – The name of the execution variable.

class sagemaker.workflow.execution_variables.ExecutionVariables

Provide access to all available execution variables:

  • ExecutionVariables.START_DATETIME

  • ExecutionVariables.CURRENT_DATETIME

  • ExecutionVariables.PIPELINE_NAME

  • ExecutionVariables.PIPELINE_ARN

  • ExecutionVariables.PIPELINE_EXECUTION_ID

  • ExecutionVariables.PIPELINE_EXECUTION_ARN

  • ExecutionVariables.TRAINING_JOB_NAME

  • ExecutionVariables.PROCESSING_JOB_NAME

Functions

class sagemaker.workflow.functions.Join(on=NOTHING, values=NOTHING)

Join together properties.

Examples: Build a Amazon S3 Uri with bucket name parameter and pipeline execution Id and use it as training input:

bucket = ParameterString('bucket', default_value='my-bucket')

TrainingInput(
    s3_data=Join(on='/', ['s3:/', bucket, ExecutionVariables.PIPELINE_EXECUTION_ID]),
    content_type="text/csv")
Parameters
  • on (str) –

  • values (List) –

Return type

None

values

The primitive type values, parameters, step properties, expressions to join.

Type

List[Union[PrimitiveType, Parameter, Expression]]

on

The string to join the values on (Defaults to “”).

Type

str

Method generated by attrs for class Join.

class sagemaker.workflow.functions.JsonGet(step_name, property_file, json_path)

Get JSON properties from PropertyFiles.

Parameters
Return type

None

step_name

The step name from which to get the property file.

Type

str

property_file

Either a PropertyFile instance or the name of a property file.

Type

Union[PropertyFile, str]

json_path

The JSON path expression to the requested value.

Type

str

Method generated by attrs for class JsonGet.

Parameters

class sagemaker.workflow.parameters.ParameterTypeEnum(*args, value=<object object>, **kwargs)

Parameter type enum.

class sagemaker.workflow.parameters.Parameter(name=NOTHING, parameter_type=NOTHING, default_value=None)

Pipeline parameter for workflow.

Parameters
Return type

None

name

The name of the parameter.

Type

str

parameter_type

The type of the parameter.

Type

ParameterTypeEnum

default_value

The default value of the parameter.

Type

PrimitiveType

Method generated by attrs for class Parameter.

class sagemaker.workflow.parameters.ParameterString(name, default_value=None, enum_values=None)

String parameter for pipelines.

Create a pipeline string parameter.

Parameters
  • name (str) – The name of the parameter.

  • default_value (str) – The default value of the parameter. The default value could be overridden at start of an execution. If not set or it is set to None, a value must be provided at the start of the execution.

  • enum_values (List[str]) – Enum values for this parameter.

class sagemaker.workflow.parameters.ParameterInteger(name, default_value=None)

Integer parameter for pipelines.

Create a pipeline integer parameter.

Parameters
  • name (str) – The name of the parameter.

  • default_value (int) – The default value of the parameter. The default value could be overridden at start of an execution. If not set or it is set to None, a value must be provided at the start of the execution.

class sagemaker.workflow.parameters.ParameterFloat(name, default_value=None)

Float parameter for pipelines.

Create a pipeline float parameter.

Parameters
  • name (str) – The name of the parameter.

  • default_value (float) – The default value of the parameter. The default value could be overridden at start of an execution. If not set or it is set to None, a value must be provided at the start of the execution.

sagemaker.workflow.parameters.ParameterBoolean

Pipeline

class sagemaker.workflow.pipeline.Pipeline(name='', parameters=None, pipeline_experiment_config=<sagemaker.workflow.pipeline_experiment_config.PipelineExperimentConfig object>, steps=None, sagemaker_session=None)

Pipeline for workflow.

Initialize a Pipeline

Parameters
  • name (str) – The name of the pipeline.

  • parameters (Sequence[Parameter]) – The list of the parameters.

  • pipeline_experiment_config (Optional[PipelineExperimentConfig]) – If set, the workflow will attempt to create an experiment and trial before executing the steps. Creation will be skipped if an experiment or a trial with the same name already exists. By default, pipeline name is used as experiment name and execution id is used as the trial name. If set to None, no experiment or trial will be created automatically.

  • steps (Sequence[Union[Step, StepCollection]]) – The list of the non-conditional steps associated with the pipeline. Any steps that are within the if_steps or else_steps of a ConditionStep cannot be listed in the steps of a pipeline. Of particular note, the workflow service rejects any pipeline definitions that specify a step in the list of steps of a pipeline and that step in the if_steps or else_steps of any ConditionStep.

  • sagemaker_session (sagemaker.session.Session) – Session object that manages interactions with Amazon SageMaker APIs and any other AWS services needed. If not specified, the pipeline creates one using the default AWS configuration chain.

to_request()

Gets the request structure for workflow service calls.

Return type

Union[Dict[str, Any], List[Dict[str, Any]]]

create(role_arn, description=None, tags=None, parallelism_config=None)

Creates a Pipeline in the Pipelines service.

Parameters
  • role_arn (str) – The role arn that is assumed by the pipeline to create step artifacts.

  • description (str) – A description of the pipeline.

  • tags (List[Dict[str, str]]) – A list of {“Key”: “string”, “Value”: “string”} dicts as tags.

  • parallelism_config (Optional[ParallelismConfiguration]) – Parallelism configuration that is applied to each of the executions of the pipeline. It takes precedence over the parallelism configuration of the parent pipeline.

Returns

A response dict from the service.

Return type

Dict[str, Any]

describe()

Describes a Pipeline in the Workflow service.

Returns

Response dict from the service. See boto3 client documentation

Return type

Dict[str, Any]

update(role_arn, description=None, parallelism_config=None)

Updates a Pipeline in the Workflow service.

Parameters
  • role_arn (str) – The role arn that is assumed by pipelines to create step artifacts.

  • description (str) – A description of the pipeline.

  • parallelism_config (Optional[ParallelismConfiguration]) – Parallelism configuration that is applied to each of the executions of the pipeline. It takes precedence over the parallelism configuration of the parent pipeline.

Returns

A response dict from the service.

Return type

Dict[str, Any]

upsert(role_arn, description=None, tags=None, parallelism_config=None)

Creates a pipeline or updates it, if it already exists.

Parameters
  • role_arn (str) – The role arn that is assumed by workflow to create step artifacts.

  • description (str) – A description of the pipeline.

  • tags (List[Dict[str, str]]) – A list of {“Key”: “string”, “Value”: “string”} dicts as tags.

  • (Optional[Config for parallel steps, Parallelism configuration that (parallelism_config) – is applied to each of. the executions

  • parallelism_config (sagemaker.workflow.parallelism_config.ParallelismConfiguration) –

Returns

response dict from service

Return type

Dict[str, Any]

delete()

Deletes a Pipeline in the Workflow service.

Returns

A response dict from the service.

Return type

Dict[str, Any]

start(parameters=None, execution_display_name=None, execution_description=None, parallelism_config=None)

Starts a Pipeline execution in the Workflow service.

Parameters
  • parameters (Dict[str, Union[str, bool, int, float]]) – values to override pipeline parameters.

  • execution_display_name (str) – The display name of the pipeline execution.

  • execution_description (str) – A description of the execution.

  • parallelism_config (Optional[ParallelismConfiguration]) – Parallelism configuration that is applied to each of the executions of the pipeline. It takes precedence over the parallelism configuration of the parent pipeline.

Returns

A _PipelineExecution instance, if successful.

definition()

Converts a request structure to string representation for workflow service calls.

Return type

str

class sagemaker.workflow.pipeline._PipelineExecution(arn, sagemaker_session=NOTHING)

Internal class for encapsulating pipeline execution instances.

Parameters
Return type

None

arn

The arn of the pipeline execution.

Type

str

sagemaker_session

Session object which manages interactions with Amazon SageMaker APIs and any other AWS services needed. If not specified, the pipeline creates one using the default AWS configuration chain.

Type

sagemaker.session.Session

Method generated by attrs for class _PipelineExecution.

stop()

Stops a pipeline execution.

describe()

Describes a pipeline execution.

Returns

Information about the pipeline execution. See boto3 client describe_pipeline_execution.

list_steps()

Describes a pipeline execution’s steps.

Returns

Information about the steps of the pipeline execution. See boto3 client list_pipeline_execution_steps.

wait(delay=30, max_attempts=60)

Waits for a pipeline execution.

Parameters
  • delay (int) – The polling interval. (Defaults to 30 seconds)

  • max_attempts (int) – The maximum number of polling attempts. (Defaults to 60 polling attempts)

Pipeline Context

class sagemaker.workflow.pipeline_context.PipelineSession(boto_session=None, sagemaker_client=None, default_bucket=None, settings=<sagemaker.session_settings.SessionSettings object>)

Managing interactions with SageMaker APIs and AWS services needed under Pipeline Context

This class inherits the SageMaker session, it provides convenient methods for manipulating entities and resources that Amazon SageMaker uses, such as training jobs, endpoints, and input datasets in S3. When composing SageMaker Model-Building Pipeline, PipelineSession is recommended over regular SageMaker Session

Initialize a PipelineSession.

Parameters
  • boto_session (boto3.session.Session) – The underlying Boto3 session which AWS service calls are delegated to (default: None). If not provided, one is created with default AWS configuration chain.

  • sagemaker_client (boto3.SageMaker.Client) – Client which makes Amazon SageMaker service calls other than InvokeEndpoint (default: None). Estimators created using this Session use this client. If not provided, one will be created using this instance’s boto_session.

  • default_bucket (str) – The default Amazon S3 bucket to be used by this session. This will be created the next time an Amazon S3 bucket is needed (by calling default_bucket()). If not provided, a default bucket will be created based on the following format: “sagemaker-{region}-{aws-account-id}”. Example: “sagemaker-my-custom-bucket”.

  • settings (sagemaker.session_settings.SessionSettings) – Optional. Set of optional parameters to apply to the session.

property context

Hold contextual information useful to the session

init_model_step_arguments(model)

Create a _ModelStepArguments (if not exist) as pipeline context

Parameters

model (Model or PipelineModel) – A sagemaker.model.Model or sagemaker.pipeline.PipelineModel instance

class sagemaker.workflow.pipeline_context.LocalPipelineSession(boto_session=None, default_bucket=None, s3_endpoint_url=None, disable_local_code=False)

Managing a session that executes Sagemaker pipelines and jobs locally in a pipeline context.

This class inherits from the LocalSession and PipelineSession classes. When running Sagemaker pipelines locally, this class is preferred over LocalSession.

Initialize a LocalPipelineSession.

Parameters
  • boto_session (boto3.session.Session) – The underlying Boto3 session which AWS service calls are delegated to (default: None). If not provided, one is created with default AWS configuration chain.

  • default_bucket (str) – The default Amazon S3 bucket to be used by this session. This will be created the next time an Amazon S3 bucket is needed (by calling default_bucket()). If not provided, a default bucket will be created based on the following format: “sagemaker-{region}-{aws-account-id}”. Example: “sagemaker-my-custom-bucket”.

  • s3_endpoint_url (str) – Override the default endpoint URL for Amazon S3, if set (default: None).

  • disable_local_code (bool) – Set to True to override the default AWS configuration chain to disable the local.local_code setting, which may not be supported for some SDK features (default: False).

Parallelism Configuration

class sagemaker.workflow.parallelism_config.ParallelismConfiguration(max_parallel_execution_steps)

Parallelism config for SageMaker pipeline.

Create a ParallelismConfiguration

Parameters
  • int (max_parallel_execution_steps,) – max number of steps which could be parallelized

  • max_parallel_execution_steps (int) –

to_request()

Returns: the request structure.

Return type

Union[Dict[str, Any], List[Dict[str, Any]]]

Pipeline Experiment Config

class sagemaker.workflow.pipeline_experiment_config.PipelineExperimentConfig(experiment_name, trial_name)

Experiment config for SageMaker pipeline.

Create a PipelineExperimentConfig

Examples: Use pipeline name as the experiment name and pipeline execution id as the trial name:

PipelineExperimentConfig(
    ExecutionVariables.PIPELINE_NAME, ExecutionVariables.PIPELINE_EXECUTION_ID)

Use a customized experiment name and pipeline execution id as the trial name:

PipelineExperimentConfig(
    'MyExperiment', ExecutionVariables.PIPELINE_EXECUTION_ID)
Parameters
class sagemaker.workflow.pipeline_experiment_config.PipelineExperimentConfigProperty(name)

Reference to pipeline experiment config property.

Create a reference to pipeline experiment property.

Parameters

name (str) – The name of the pipeline experiment config property.

Properties

class sagemaker.workflow.properties.PropertiesMeta(*args, **kwargs)

Load an internal shapes attribute from the botocore service model

for sagemaker and emr service.

Loads up the shapes from the botocore service model.

class sagemaker.workflow.properties.Properties(step_name, path=None, shape_name=None, shape_names=None, service_name='sagemaker')

Properties for use in workflow expressions.

Create a Properties instance representing the given shape.

Parameters
  • step_name (str) – The name of the Step this Property belongs to.

  • path (str) – The relative path of this Property value.

  • shape_name (str) – The botocore service model shape name.

  • shape_names (str) – A List of the botocore service model shape name.

  • service_name (str) –

class sagemaker.workflow.properties.PropertiesList(step_name, path, shape_name=None, service_name='sagemaker')

PropertiesList for use in workflow expressions.

Create a PropertiesList instance representing the given shape.

Parameters
  • step_name (str) – The name of the Step this Property belongs to.

  • path (str) – The relative path of this Property value.

  • shape_name (str) – The botocore service model shape name.

  • service_name (str) – The botocore service name.

class sagemaker.workflow.properties.PropertyFile(name, output_name, path)

Provides a property file struct.

Parameters
  • name (str) –

  • output_name (str) –

  • path (str) –

Return type

None

name

The name of the property file for reference with JsonGet functions.

Type

str

output_name

The name of the processing job output channel.

Type

str

path

The path to the file at the output channel location.

Type

str

Method generated by attrs for class PropertyFile.

Step Collections

class sagemaker.workflow.step_collections.StepCollection(name, steps=NOTHING)

A wrapper of pipeline steps for workflow.

Parameters
Return type

None

name

The name of the StepCollection.

Type

str

steps

A list of steps.

Type

List[Step]

Method generated by attrs for class StepCollection.

class sagemaker.workflow.step_collections.RegisterModel(name, content_types, response_types, inference_instances=None, transform_instances=None, estimator=None, model_data=None, depends_on=None, repack_model_step_retry_policies=None, register_model_step_retry_policies=None, model_package_group_name=None, model_metrics=None, approval_status=None, image_uri=None, compile_model_family=None, display_name=None, description=None, tags=None, model=None, drift_check_baselines=None, customer_metadata_properties=None, domain=None, sample_payload_url=None, task=None, framework=None, framework_version=None, nearest_model_name=None, data_input_configuration=None, **kwargs)

Register Model step collection for workflow.

Construct steps _RepackModelStep and _RegisterModelStep based on the estimator.

Parameters
  • name (str) – The name of the training step.

  • estimator (sagemaker.estimator.EstimatorBase) – The estimator instance.

  • model_data – The S3 uri to the model data from training.

  • content_types (list) – The supported MIME types for the input data (default: None).

  • response_types (list) – The supported MIME types for the output data (default: None).

  • inference_instances (list) – A list of the instance types that are used to generate inferences in real-time (default: None).

  • transform_instances (list) – A list of the instance types on which a transformation job can be run or on which an endpoint can be deployed (default: None).

  • depends_on (List[Union[str, Step, StepCollection]]) – The list of Step/StepCollection names or Step instances or StepCollection instances that the first step in the collection depends on (default: None).

  • repack_model_step_retry_policies (List[RetryPolicy]) – The list of retry policies for the repack model step

  • register_model_step_retry_policies (List[RetryPolicy]) – The list of retry policies for register model step

  • model_package_group_name (str) – The Model Package Group name or Arn, exclusive to model_package_name, using model_package_group_name makes the Model Package versioned (default: None).

  • model_metrics (ModelMetrics) – ModelMetrics object (default: None).

  • approval_status (str) – Model Approval Status, values can be “Approved”, “Rejected”, or “PendingManualApproval” (default: “PendingManualApproval”).

  • image_uri (str) – The container image uri for Model Package, if not specified, Estimator’s training container image is used (default: None).

  • compile_model_family (str) – The instance family for the compiled model. If specified, a compiled model is used (default: None).

  • description (str) – Model Package description (default: None).

  • tags (List[dict[str, str]]) – The list of tags to attach to the model package group. Note that tags will only be applied to newly created model package groups; if the name of an existing group is passed to “model_package_group_name”, tags will not be applied.

  • model (object or Model) – A PipelineModel object that comprises a list of models which gets executed as a serial inference pipeline or a Model object.

  • drift_check_baselines (DriftCheckBaselines) – DriftCheckBaselines object (default: None).

  • customer_metadata_properties (dict[str, str]) – A dictionary of key-value paired metadata properties (default: None).

  • domain (str) – Domain values can be “COMPUTER_VISION”, “NATURAL_LANGUAGE_PROCESSING”, “MACHINE_LEARNING” (default: None).

  • sample_payload_url (str) – The S3 path where the sample payload is stored (default: None).

  • task (str) – Task values which are supported by Inference Recommender are “FILL_MASK”, “IMAGE_CLASSIFICATION”, “OBJECT_DETECTION”, “TEXT_GENERATION”, “IMAGE_SEGMENTATION”, “CLASSIFICATION”, “REGRESSION”, “OTHER” (default: None).

  • framework (str) – Machine learning framework of the model package container image (default: None).

  • framework_version (str) – Framework version of the Model Package Container Image (default: None).

  • nearest_model_name (str) – Name of a pre-trained machine learning benchmarked by Amazon SageMaker Inference Recommender (default: None).

  • data_input_configuration (str) – Input object for the model (default: None).

  • **kwargs – additional arguments to create_model.

class sagemaker.workflow.step_collections.EstimatorTransformer(name, estimator, model_data, model_inputs, instance_count, instance_type, transform_inputs, description=None, display_name=None, image_uri=None, predictor_cls=None, env=None, strategy=None, assemble_with=None, output_path=None, output_kms_key=None, accept=None, max_concurrent_transforms=None, max_payload=None, tags=None, volume_kms_key=None, depends_on=None, repack_model_step_retry_policies=None, model_step_retry_policies=None, transform_step_retry_policies=None, **kwargs)

Creates a Transformer step collection for workflow.

Construct steps required for a Transformer step collection:

An estimator-centric step collection. It models what happens in workflows when invoking the transform() method on an estimator instance: First, if custom model artifacts are required, a _RepackModelStep is included. Second, a CreateModelStep with the model data passed in from a training step or other training job output. Finally, a TransformerStep.

If repacking the model artifacts is not necessary, only the CreateModelStep and TransformerStep are in the step collection.

Parameters
  • name (str) – The name of the Transform Step.

  • estimator (sagemaker.estimator.EstimatorBase) – The estimator instance.

  • instance_count (int) – The number of EC2 instances to use.

  • instance_type (str) – The type of EC2 instance to use.

  • strategy (str) – The strategy used to decide how to batch records in a single request (default: None). Valid values: ‘MultiRecord’ and ‘SingleRecord’.

  • assemble_with (str) – How the output is assembled (default: None). Valid values: ‘Line’ or ‘None’.

  • output_path (str) – The S3 location for saving the transform result. If not specified, results are stored to a default bucket.

  • output_kms_key (str) – Optional. A KMS key ID for encrypting the transform output (default: None).

  • accept (str) – The accept header passed by the client to the inference endpoint. If it is supported by the endpoint, it will be the format of the batch transform output.

  • env (dict) – The Environment variables to be set for use during the transform job (default: None).

  • depends_on (List[Union[str, Step, StepCollection]]) – The list of Step/StepCollection names or Step instances or StepCollection instances that the first step in the collection depends on (default: None).

  • repack_model_step_retry_policies (List[RetryPolicy]) – The list of retry policies for the repack model step

  • model_step_retry_policies (List[RetryPolicy]) – The list of retry policies for model step

  • transform_step_retry_policies (List[RetryPolicy]) – The list of retry policies for transform step

  • description (str) –

  • display_name (str) –

class sagemaker.workflow.model_step.ModelStep(name, step_args, depends_on=None, retry_policies=None, display_name=None, description=None)

ModelStep for SageMaker Pipelines Workflows.

Constructs a ModelStep.

Parameters
  • name (str) – The name of the ModelStep. A name is required and must be unique within a pipeline.

  • step_args (_ModelStepArguments) –

    The arguments for the ModelStep definition, generated by invoking the register() or create() under the PipelineSession. Example:

    model = Model(sagemaker_session=PipelineSession())
    model_step = ModelStep(step_args=model.register())
    

  • depends_on (List[Union[str, Step, StepCollection]]) – A list of Step or StepCollection names or Step instances or StepCollection instances that it depends on. If a listed Step name does not exist, an error is returned (default: None).

  • retry_policies (List[RetryPolicy] or Dict[str, List[RetryPolicy]]) –

    The list of retry policies for the ModelStep (default: None).

    If a list of retry policies is provided, it would be applied to all steps in the ModelStep collection. Note: in this case, SageMakerJobStepRetryPolicy is not allowed, since create/register model step does not support it. Please find the example below:

    ModelStep(
        ...
        retry_policies=[
            StepRetryPolicy(...),
        ],
    )
    

    If a dict is provided, it can specify different retry policies for different types of steps in the ModelStep collection. Similarly, SageMakerJobStepRetryPolicy is not allowed for create/register model step. See examples below:

    ModelStep(
        ...
        retry_policies=dict(
           register_model_retry_policies=[
               StepRetryPolicy(...),
           ],
           repack_model_retry_policies=[
               SageMakerJobStepRetryPolicy(...),
           ],
        )
    )
    

    or

    ModelStep(
        ...
        retry_policies=dict(
           create_model_retry_policies=[
               StepRetryPolicy(...),
           ],
           repack_model_retry_policies=[
               SageMakerJobStepRetryPolicy(...),
           ],
        )
    )
    

  • display_name (str) – The display name of the ModelStep. The display name provides better UI readability. (default: None).

  • description (str) – The description of the ModelStep (default: None).

class sagemaker.workflow.monitor_batch_transform_step.MonitorBatchTransformStep(name, transform_step_args, monitor_configuration, check_job_configuration, monitor_before_transform=False, fail_on_violation=True, supplied_baseline_statistics=None, supplied_baseline_constraints=None, display_name=None, description=None)

Creates a Transformer step with Quality or Clarify check step

Used to monitor the inputs and outputs of the batch transform job.

Construct a step collection of TransformStep, QualityCheckStep or ClarifyCheckStep

Parameters
  • name (str) – The name of the MonitorBatchTransformStep. The corresponding transform step will be named {name}-transform; and the corresponding check step will be named {name}-monitoring

  • transform_step_args (_JobStepArguments) – the transform step transform arguments.

  • (Union[ (monitor_configuration) – sagemaker.workflow.quality_check_step.QualityCheckConfig, sagemaker.workflow.quality_check_step.ClarifyCheckConfig

  • ]) – the monitoring configuration used for run model monitoring.

  • check_job_configuration (sagemaker.workflow.check_job_config.CheckJobConfig) – the check job (processing job) cluster resource configuration.

  • monitor_before_transform (bool) – If to run data quality or model explainability monitoring type, a true value of this flag indicates running the check step before the transform job.

  • fail_on_violation (Union[bool, PipelineVariable]) – A opt-out flag to not to fail the check step when a violation is detected.

  • supplied_baseline_statistics (Union[str, PipelineVariable]) – The S3 path to the supplied statistics object representing the statistics JSON file which will be used for drift to check (default: None).

  • supplied_baseline_constraints (Union[str, PipelineVariable]) – The S3 path to the supplied constraints object representing the constraints JSON file which will be used for drift to check (default: None).

  • display_name (str) – The display name of the MonitorBatchTransformStep. The display name provides better UI readability. The corresponding transform step will be named {display_name}-transform; and the corresponding check step will be named {display_name}-monitoring (default: None).

  • description (str) – The description of the MonitorBatchTransformStep (default: None).

  • monitor_configuration (Union[sagemaker.workflow.quality_check_step.QualityCheckConfig, sagemaker.workflow.clarify_check_step.ClarifyCheckConfig]) –

Steps

class sagemaker.workflow.steps.StepTypeEnum(*args, value=<object object>, **kwargs)

Enum of Step types.

class sagemaker.workflow.steps.Step(name=NOTHING, display_name=None, description=None, step_type=NOTHING, depends_on=None)

Pipeline Step for SageMaker Pipelines Workflows.

Parameters
Return type

None

name

The name of the Step.

Type

str

display_name

The display name of the Step.

Type

str

description

The description of the Step.

Type

str

step_type

The type of the Step.

Type

StepTypeEnum

depends_on

The list of Step/StepCollection names or Step instances or StepCollection instances that the current Step depends on.

Type

List[Union[str, Step, StepCollection]]

Method generated by attrs for class Step.

class sagemaker.workflow.steps.TrainingStep(name, step_args=None, estimator=None, display_name=None, description=None, inputs=None, cache_config=None, depends_on=None, retry_policies=None)

TrainingStep for SageMaker Pipelines Workflows.

Construct a TrainingStep, given an EstimatorBase instance.

In addition to the EstimatorBase instance, the other arguments are those that are supplied to the fit method of the sagemaker.estimator.Estimator.

Parameters
  • name (str) – The name of the TrainingStep.

  • step_args (_JobStepArguments) – The arguments for the TrainingStep definition.

  • estimator (EstimatorBase) – A sagemaker.estimator.EstimatorBase instance.

  • display_name (str) – The display name of the TrainingStep.

  • description (str) – The description of the TrainingStep.

  • inputs (Union[str, dict, TrainingInput, FileSystemInput]) –

    Information about the training data. This can be one of three types:

    • (str) the S3 location where training data is saved, or a file:// path in local mode.

    • (dict[str, str] or dict[str, sagemaker.inputs.TrainingInput]) If using multiple channels for training data, you can specify a dictionary mapping channel names to strings or TrainingInput() objects.

    • (sagemaker.inputs.TrainingInput) - channel configuration for S3 data sources that can provide additional information as well as the path to the training dataset. See sagemaker.inputs.TrainingInput() for full details.

    • (sagemaker.inputs.FileSystemInput) - channel configuration for a file system data source that can provide additional information as well as the path to the training dataset.

  • cache_config (CacheConfig) – A sagemaker.workflow.steps.CacheConfig instance.

  • depends_on (List[Union[str, Step, StepCollection]]) – A list of Step/StepCollection names or Step instances or StepCollection instances that this TrainingStep depends on.

  • retry_policies (List[RetryPolicy]) – A list of retry policies.

class sagemaker.workflow.steps.TuningStep(name, step_args=None, tuner=None, display_name=None, description=None, inputs=None, job_arguments=None, cache_config=None, depends_on=None, retry_policies=None)

TuningStep for SageMaker Pipelines Workflows.

Construct a TuningStep, given a HyperparameterTuner instance.

In addition to the HyperparameterTuner instance, the other arguments are those that are supplied to the fit method of the sagemaker.tuner.HyperparameterTuner.

Parameters
  • name (str) – The name of the TuningStep.

  • step_args (_JobStepArguments) – The arguments for the TuningStep definition.

  • tuner (HyperparameterTuner) – A sagemaker.tuner.HyperparameterTuner instance.

  • display_name (str) – The display name of the TuningStep.

  • description (str) – The description of the TuningStep.

  • inputs

    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 dictionary 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.session.FileSystemInput) - channel configuration for

      a file system data source that can provide additional information as well as the path to the training dataset.

    • (sagemaker.amazon.amazon_estimator.RecordSet) - A collection of

      Amazon :class:~`Record` objects serialized and stored in S3. For use with an estimator for an Amazon algorithm.

    • (sagemaker.amazon.amazon_estimator.FileSystemRecordSet) -

      Amazon SageMaker channel configuration for a file system data source for Amazon algorithms.

    • (list[sagemaker.amazon.amazon_estimator.RecordSet]) - A list of

      :class:~`sagemaker.amazon.amazon_estimator.RecordSet` objects, where each instance is a different channel of training data.

    • (list[sagemaker.amazon.amazon_estimator.FileSystemRecordSet]) - A list of

      :class:~`sagemaker.amazon.amazon_estimator.FileSystemRecordSet` objects, where each instance is a different channel of training data.

  • job_arguments (List[str]) – A list of strings to be passed into the processing job. Defaults to None.

  • cache_config (CacheConfig) – A sagemaker.workflow.steps.CacheConfig instance.

  • depends_on (List[Union[str, Step, StepCollection]]) – A list of Step/StepCollection names or Step instances or StepCollection instances that this TuningStep depends on.

  • retry_policies (List[RetryPolicy]) – A list of retry policies.

sagemaker.workflow.steps.TuningStep.get_top_model_s3_uri(self, top_k, s3_bucket, prefix='')

Get the model artifact S3 URI from the top performing training jobs.

Parameters
  • top_k (int) – The index of the top performing training job tuning step stores up to 50 top performing training jobs. A valid top_k value is from 0 to 49. The best training job model is at index 0.

  • s3_bucket (str) – The S3 bucket to store the training job output artifact.

  • prefix (str) – The S3 key prefix to store the training job output artifact.

Return type

sagemaker.workflow.functions.Join

class sagemaker.workflow.steps.TransformStep(name, step_args=None, transformer=None, inputs=None, display_name=None, description=None, cache_config=None, depends_on=None, retry_policies=None)

TransformStep for SageMaker Pipelines Workflows.

Constructs a TransformStep, given a Transformer instance.

In addition to the Transformer instance, the other arguments are those that are supplied to the transform method of the sagemaker.transformer.Transformer.

Parameters
  • name (str) – The name of the TransformStep.

  • step_args (_JobStepArguments) – The arguments for the TransformStep definition.

  • transformer (Transformer) – A sagemaker.transformer.Transformer instance.

  • inputs (TransformInput) – A sagemaker.inputs.TransformInput instance.

  • cache_config (CacheConfig) – A sagemaker.workflow.steps.CacheConfig instance.

  • display_name (str) – The display name of the TransformStep.

  • description (str) – The description of the TransformStep.

  • depends_on (List[Union[str, Step, StepCollection]]) – A list of Step/StepCollection names or Step instances or StepCollection instances that this TransformStep depends on.

  • retry_policies (List[RetryPolicy]) – A list of retry policies.

class sagemaker.workflow.steps.ProcessingStep(name, step_args=None, processor=None, display_name=None, description=None, inputs=None, outputs=None, job_arguments=None, code=None, property_files=None, cache_config=None, depends_on=None, retry_policies=None, kms_key=None)

ProcessingStep for SageMaker Pipelines Workflows.

Construct a ProcessingStep, given a Processor instance.

In addition to the Processor instance, the other arguments are those that are supplied to the process method of the sagemaker.processing.Processor.

Parameters
  • name (str) – The name of the ProcessingStep.

  • step_args (_JobStepArguments) – The arguments for the ProcessingStep definition.

  • processor (Processor) – A sagemaker.processing.Processor instance.

  • display_name (str) – The display name of the ProcessingStep.

  • description (str) – The description of the ProcessingStep

  • inputs (List[ProcessingInput]) – A list of sagemaker.processing.ProcessorInput instances. Defaults to None.

  • outputs (List[ProcessingOutput]) – A list of sagemaker.processing.ProcessorOutput instances. Defaults to None.

  • job_arguments (List[str]) – A list of strings to be passed into the processing job. Defaults to None.

  • code (str) – This can be an S3 URI or a local path to a file with the framework script to run. Defaults to None.

  • property_files (List[PropertyFile]) – A list of property files that workflow looks for and resolves from the configured processing output list.

  • cache_config (CacheConfig) – A sagemaker.workflow.steps.CacheConfig instance.

  • depends_on (List[Union[str, Step, StepCollection]]) – A list of Step/StepCollection names or Step instances or StepCollection instances that this ProcessingStep depends on.

  • retry_policies (List[RetryPolicy]) – A list of retry policies.

  • kms_key (str) – The ARN of the KMS key that is used to encrypt the user code file. Defaults to None.

class sagemaker.workflow.steps.CreateModelStep(name, step_args=None, model=None, inputs=None, depends_on=None, retry_policies=None, display_name=None, description=None)

CreateModelStep for SageMaker Pipelines Workflows.

Construct a CreateModelStep, given an sagemaker.model.Model instance.

In addition to the Model instance, the other arguments are those that are supplied to the _create_sagemaker_model method of the sagemaker.model.Model._create_sagemaker_model.

Parameters
  • name (str) – The name of the CreateModelStep.

  • step_args (dict) – The arguments for the CreateModelStep definition (default: None).

  • model (Model or PipelineModel) – A sagemaker.model.Model or sagemaker.pipeline.PipelineModel instance (default: None).

  • inputs (CreateModelInput) – A sagemaker.inputs.CreateModelInput instance. (default: None).

  • depends_on (List[Union[str, Step, StepCollection]]) – A list of Step/StepCollection names or Step instances or StepCollection instances that this CreateModelStep depends on (default: None).

  • retry_policies (List[RetryPolicy]) – A list of retry policies (default: None).

  • display_name (str) – The display name of the CreateModelStep (default: None).

  • description (str) – The description of the CreateModelStep (default: None).

class sagemaker.workflow.callback_step.CallbackStep(name, sqs_queue_url, inputs, outputs, display_name=None, description=None, cache_config=None, depends_on=None)

Callback step for workflow.

Constructs a CallbackStep.

Parameters
  • name (str) – The name of the callback step.

  • sqs_queue_url (str) – An SQS queue URL for receiving callback messages.

  • inputs (dict) – Input arguments that will be provided in the SQS message body of callback messages.

  • outputs (List[CallbackOutput]) – Outputs that can be provided when completing a callback.

  • display_name (str) – The display name of the callback step.

  • description (str) – The description of the callback step.

  • cache_config (CacheConfig) – A sagemaker.workflow.steps.CacheConfig instance.

  • depends_on (List[Union[str, Step, StepCollection]]) – A list of Step/StepCollection names or Step instances or StepCollection instances that this CallbackStep depends on.

class sagemaker.workflow.steps.CacheConfig(enable_caching=False, expire_after=None)

Configuration class to enable caching in SageMaker Pipelines Workflows.

If caching is enabled, the pipeline attempts to find a previous execution of a Step that was called with the same arguments. Step caching only considers successful execution. If a successful previous execution is found, the pipeline propagates the values from the previous execution rather than recomputing the Step. When multiple successful executions exist within the timeout period, it uses the result for the most recent successful execution.

Parameters

enable_caching (bool) –

Return type

None

enable_caching

To enable Step caching. Defaults to False.

Type

bool

expire_after

If Step caching is enabled, a timeout also needs to defined. It defines how old a previous execution can be to be considered for reuse. Value should be an ISO 8601 duration string. Defaults to None.

Examples:

'p30d' # 30 days
'P4DT12H' # 4 days and 12 hours
'T12H' # 12 hours
Type

str

Method generated by attrs for class CacheConfig.

class sagemaker.workflow.lambda_step.LambdaStep(name, lambda_func, display_name=None, description=None, inputs=None, outputs=None, cache_config=None, depends_on=None)

Lambda step for workflow.

Constructs a LambdaStep.

Parameters
  • name (str) – The name of the lambda step.

  • display_name (str) – The display name of the Lambda step.

  • description (str) – The description of the Lambda step.

  • lambda_func (str) – An instance of sagemaker.lambda_helper.Lambda. If lambda arn is specified in the instance, LambdaStep just invokes the function, else lambda function will be created while creating the pipeline.

  • inputs (dict) – Input arguments that will be provided to the lambda function.

  • outputs (List[LambdaOutput]) – List of outputs from the lambda function.

  • cache_config (CacheConfig) – A sagemaker.workflow.steps.CacheConfig instance.

  • depends_on (List[Union[str, Step, StepCollection]]) – A list of Step/StepCollection names or Step instances or StepCollection instances that this LambdaStep depends on.

class sagemaker.workflow.quality_check_step.QualityCheckConfig(baseline_dataset, dataset_format, *, output_s3_uri=None, post_analytics_processor_script=None)

Quality Check Config.

Parameters
Return type

None

baseline_dataset

The path to the baseline_dataset file. This can be a local path or an S3 uri string

Type

str or PipelineVariable

dataset_format

The format of the baseline_dataset.

Type

dict

output_s3_uri

Desired S3 destination of the constraint_violations and statistics json files (default: None). If not specified an auto generated path will be used: “s3://<default_session_bucket>/model-monitor/baselining/<job_name>/results”

Type

str or PipelineVariable

post_analytics_processor_script

The path to the record post-analytics processor script (default: None). This can be a local path or an S3 uri string but CANNOT be any type of the PipelineVariable.

Type

str

Method generated by attrs for class QualityCheckConfig.

class sagemaker.workflow.quality_check_step.QualityCheckStep(name, quality_check_config, check_job_config, skip_check=False, fail_on_violation=True, register_new_baseline=False, model_package_group_name=None, supplied_baseline_statistics=None, supplied_baseline_constraints=None, display_name=None, description=None, cache_config=None, depends_on=None)

QualityCheck step for workflow.

Constructs a QualityCheckStep.

Parameters
  • name (str) – The name of the QualityCheckStep step.

  • quality_check_config (QualityCheckConfig) – A QualityCheckConfig instance.

  • check_job_config (CheckJobConfig) – A CheckJobConfig instance.

  • skip_check (bool or PipelineVariable) – Whether the check should be skipped (default: False).

  • fail_on_violation (bool or PipelineVariable) – Whether to fail the step if violation detected (default: True).

  • register_new_baseline (bool or PipelineVariable) – Whether the new baseline should be registered (default: False).

  • model_package_group_name (str or PipelineVariable) – The name of a registered model package group, among which the baseline will be fetched from the latest approved model (default: None).

  • supplied_baseline_statistics (str or PipelineVariable) – The S3 path to the supplied statistics object representing the statistics JSON file which will be used for drift to check (default: None).

  • supplied_baseline_constraints (str or PipelineVariable) – The S3 path to the supplied constraints object representing the constraints JSON file which will be used for drift to check (default: None).

  • display_name (str) – The display name of the QualityCheckStep step (default: None).

  • description (str) – The description of the QualityCheckStep step (default: None).

  • cache_config (CacheConfig) – A sagemaker.workflow.steps.CacheConfig instance (default: None).

  • depends_on (List[Union[str, Step, StepCollection]]) – A list of Step/StepCollection names or Step instances or StepCollection instances that this QualityCheckStep depends on (default: None).

class sagemaker.workflow.clarify_check_step.ClarifyCheckConfig(data_config, *, kms_key=None, monitoring_analysis_config_uri=None)

Clarify Check Config

Parameters
Return type

None

data_config

Config of the input/output data.

Type

DataConfig

kms_key

The ARN of the KMS key that is used to encrypt the user code file (default: None). This field CANNOT be any type of the PipelineVariable.

Type

str

monitoring_analysis_config_uri

(str): The uri of monitoring analysis config. This field does not take input. It will be generated once uploading the created analysis config file.

Method generated by attrs for class ClarifyCheckConfig.

class sagemaker.workflow.clarify_check_step.ClarifyCheckStep(name, clarify_check_config, check_job_config, skip_check=False, fail_on_violation=True, register_new_baseline=False, model_package_group_name=None, supplied_baseline_constraints=None, display_name=None, description=None, cache_config=None, depends_on=None)

ClarifyCheckStep step for workflow.

Constructs a ClarifyCheckStep.

Parameters
  • name (str) – The name of the ClarifyCheckStep step.

  • clarify_check_config (ClarifyCheckConfig) – A ClarifyCheckConfig instance.

  • check_job_config (CheckJobConfig) – A CheckJobConfig instance.

  • skip_check (bool or PipelineVariable) – Whether the check should be skipped (default: False).

  • fail_on_violation (bool or PipelineVariable) – Whether to fail the step if violation detected (default: True).

  • register_new_baseline (bool or PipelineVariable) – Whether the new baseline should be registered (default: False).

  • model_package_group_name (str or PipelineVariable) – The name of a registered model package group, among which the baseline will be fetched from the latest approved model (default: None).

  • supplied_baseline_constraints (str or PipelineVariable) – The S3 path to the supplied constraints object representing the constraints JSON file which will be used for drift to check (default: None).

  • display_name (str) – The display name of the ClarifyCheckStep step (default: None).

  • description (str) – The description of the ClarifyCheckStep step (default: None).

  • cache_config (CacheConfig) – A sagemaker.workflow.steps.CacheConfig instance (default: None).

  • depends_on (List[Union[str, Step, StepCollection]]) – A list of Step/StepCollection names or Step instances or StepCollection instances that this ClarifyCheckStep depends on (default: None).

class sagemaker.workflow.fail_step.FailStep(name, error_message=None, display_name=None, description=None, depends_on=None)

FailStep for SageMaker Pipelines Workflows.

Constructs a FailStep.

Parameters
  • name (str) – The name of the FailStep. A name is required and must be unique within a pipeline.

  • error_message (str or PipelineVariable) – An error message defined by the user. Once the FailStep is reached, the execution fails and the error message is set as the failure reason (default: None).

  • display_name (str) – The display name of the FailStep. The display name provides better UI readability. (default: None).

  • description (str) – The description of the FailStep (default: None).

  • depends_on (List[Union[str, Step, StepCollection]]) – A list of Step/StepCollection names or Step instances or StepCollection instances that this FailStep depends on. If a listed Step name does not exist, an error is returned (default: None).

class sagemaker.workflow.automl_step.AutoMLStep(name, step_args, display_name=None, description=None, cache_config=None, depends_on=None, retry_policies=None)

AutoMLStep for SageMaker Pipelines Workflows.

Construct a AutoMLStep, given a AutoML instance.

In addition to the AutoML instance, the other arguments are those that are supplied to the fit method of the sagemaker.automl.automl.AutoML.

Parameters
  • name (str) – The name of the AutoMLStep.

  • step_args (_JobStepArguments) – The arguments for the AutoMLStep definition.

  • display_name (str) – The display name of the AutoMLStep.

  • description (str) – The description of the AutoMLStep.

  • cache_config (CacheConfig) – A sagemaker.workflow.steps.CacheConfig instance.

  • depends_on (List[Union[str, Step, StepCollection]]) – A list of Step/StepCollection names or Step instances or StepCollection instances that this AutoMLStep depends on.

  • retry_policies (List[RetryPolicy]) – A list of retry policies.