Step Types Step Properties Step Parallelism Data Dependency Between Steps Custom Dependency Between Steps Use a Custom Image in a Step

Pipeline Steps

SageMaker Pipelines are composed of steps. These steps define the actions that the pipeline takes and the relationships between steps using properties.

Topics

Step Types
Step Properties
Step Parallelism
Data Dependency Between Steps
Custom Dependency Between Steps
Use a Custom Image in a Step

Step Types

The following describes the requirements of each step type and provides an example implementation of the step. These are not functional implementations because they don't provide the resource and inputs needed. For a tutorial that implements these steps, see Create and Manage SageMaker Pipelines.

Note

You can also create a step from your local machine learning code by converting it to a SageMaker Pipelines step with the @step decorator. For more information, see @step decorator.

Amazon SageMaker Model Building Pipelines support the following step types:

Processing
Training
Tuning
AutoML
Model
CreateModel
RegisterModel
Transform
Condition
Callback
Lambda
ClarifyCheck
QualityCheck
EMR
Notebook Job
Fail

@step decorator

You can create a step from local machine learning code using the @step decorator. After you test your code, you can convert the function to a SageMaker pipeline step by annotating it with the @step decorator. SageMaker Pipelines creates and runs a pipeline when you pass the output of the @step-decorated function as a step to your pipeline. You can also create a multi-step DAG pipeline that includes one or more @step-decorated functions as well as traditional SageMaker pipeline steps. For more details about how to create a step with @step decorator, see Lift-and-shift Python code with the @step decorator.

Processing Step

Use a processing step to create a processing job for data processing. For more information on processing jobs, see Process Data and Evaluate Models.

A processing step requires a processor, a Python script that defines the processing code, outputs for processing, and job arguments. The following example shows how to create a ProcessingStep definition.


from sagemaker.sklearn.processing import SKLearnProcessor

sklearn_processor = SKLearnProcessor(framework_version='1.0-1',
                                     role=<role>,
                                     instance_type='ml.m5.xlarge',
                                     instance_count=1)


from sagemaker.processing import ProcessingInput, ProcessingOutput
from sagemaker.workflow.steps import ProcessingStep

inputs = [
    ProcessingInput(source=<input_data>, destination="/opt/ml/processing/input"),
]

outputs = [
    ProcessingOutput(output_name="train", source="/opt/ml/processing/train"),
    ProcessingOutput(output_name="validation", source="/opt/ml/processing/validation"),
    ProcessingOutput(output_name="test", source="/opt/ml/processing/test")
]

step_process = ProcessingStep(
    name="AbaloneProcess",
    step_args = sklearn_processor.run(inputs=inputs, outputs=outputs,
        code="abalone/preprocessing.py")
)

Pass runtime parameters

The following example shows how to pass runtime parameters from a PySpark processor to a ProcessingStep.


from sagemaker.workflow.pipeline_context import PipelineSession
from sagemaker.spark.processing import PySparkProcessor
from sagemaker.processing import ProcessingInput, ProcessingOutput
from sagemaker.workflow.steps import ProcessingStep

pipeline_session = PipelineSession()

pyspark_processor = PySparkProcessor(
    framework_version='2.4',
    role=<role>,
    instance_type='ml.m5.xlarge',
    instance_count=1,
    sagemaker_session=pipeline_session,
)

step_args = pyspark_processor.run(
    inputs=[ProcessingInput(source=<input_data>, destination="/opt/ml/processing/input"),],
    outputs=[
        ProcessingOutput(output_name="train", source="/opt/ml/processing/train"),
        ProcessingOutput(output_name="validation", source="/opt/ml/processing/validation"),
        ProcessingOutput(output_name="test", source="/opt/ml/processing/test")
    ],
    code="preprocess.py",
    arguments=None,
)


step_process = ProcessingStep(
    name="AbaloneProcess",
    step_args=step_args,
)

For more information on processing step requirements, see the sagemaker.workflow.steps.ProcessingStep documentation. For an in-depth example, see Define a Processing Step for Feature Engineering in the Orchestrate Jobs to Train and Evaluate Models with Amazon SageMaker Pipelines example notebook.

Training Step

You use a training step to create a training job to train a model. For more information on training jobs, see Train a Model with Amazon SageMaker.

A training step requires an estimator, as well as training and validation data inputs. The following example shows how to create a TrainingStep definition. For more information on training step requirements, see the sagemaker.workflow.steps.TrainingStep documentation.


from sagemaker.workflow.pipeline_context import PipelineSession

from sagemaker.inputs import TrainingInput
from sagemaker.workflow.steps import TrainingStep

from sagemaker.xgboost.estimator import XGBoost

pipeline_session = PipelineSession()

xgb_estimator = XGBoost(..., sagemaker_session=pipeline_session)

step_args = xgb_estimator.fit(
    inputs={
        "train": TrainingInput(
            s3_data=step_process.properties.ProcessingOutputConfig.Outputs[
                "train"
            ].S3Output.S3Uri,
            content_type="text/csv"
        ),
        "validation": TrainingInput(
            s3_data=step_process.properties.ProcessingOutputConfig.Outputs[
                "validation"
            ].S3Output.S3Uri,
            content_type="text/csv"
        )
    }
)

step_train = TrainingStep(
    name="TrainAbaloneModel",
    step_args=step_args,
)

Tuning Step

You use a tuning step to create a hyperparameter tuning job, also known as hyperparameter optimization (HPO). A hyperparameter tuning job runs multiple training jobs, each one producing a model version. For more information on hyperparameter tuning, see Perform Automatic Model Tuning with SageMaker.

The tuning job is associated with the SageMaker experiment for the pipeline, with the training jobs created as trials. For more information, see Experiments Integration.

A tuning step requires a HyperparameterTuner and training inputs. You can retrain previous tuning jobs by specifying the warm_start_config parameter of the HyperparameterTuner. For more information on hyperparameter tuning and warm start, see Run a Warm Start Hyperparameter Tuning Job.

You use the get_top_model_s3_uri method of the sagemaker.workflow.steps.TuningStep class to get the model artifact from one of the top-performing model versions. For a notebook that shows how to use a tuning step in a SageMaker pipeline, see sagemaker-pipelines-tuning-step.ipynb.

Important

Tuning steps were introduced in Amazon SageMaker Python SDK v2.48.0 and Amazon SageMaker Studio Classic v3.8.0. You must update Studio Classic before you use a tuning step or the pipeline DAG doesn't display. To update Studio Classic, see Shut down and Update SageMaker Studio Classic.

The following example shows how to create a TuningStep definition.


from sagemaker.workflow.pipeline_context import PipelineSession

from sagemaker.tuner import HyperparameterTuner
from sagemaker.inputs import TrainingInput
from sagemaker.workflow.steps import TuningStep

tuner = HyperparameterTuner(..., sagemaker_session=PipelineSession())
    
step_tuning = TuningStep(
    name = "HPTuning",
    step_args = tuner.fit(inputs=TrainingInput(s3_data="s3://my-bucket/my-data"))
)

Get the best model version

The following example shows how to get the best model version from the tuning job using the get_top_model_s3_uri method. At most, the top 50 performing versions are available ranked according to HyperParameterTuningJobObjective. The top_k argument is an index into the versions, where top_k=0 is the best-performing version and top_k=49 is the worst-performing version.


best_model = Model(
    image_uri=image_uri,
    model_data=step_tuning.get_top_model_s3_uri(
        top_k=0,
        s3_bucket=sagemaker_session.default_bucket()
    ),
    ...
)

For more information on tuning step requirements, see the sagemaker.workflow.steps.TuningStep documentation.

AutoML Step

Use the AutoML API to create an AutoML job to automatically train a model. For more information on AutoML jobs, see Automate model development with Amazon SageMaker Autopilot.

Note

Currently, the AutoML step supports only ensembling training mode.

The following example shows how to create a definition using AutoMLStep.


from sagemaker.workflow.pipeline_context import PipelineSession
from sagemaker.workflow.automl_step import AutoMLStep

pipeline_session = PipelineSession()

auto_ml = AutoML(...,
    role="<role>",
    target_attribute_name="my_target_attribute_name",
    mode="ENSEMBLING",
    sagemaker_session=pipeline_session) 

input_training = AutoMLInput(
    inputs="s3://my-bucket/my-training-data",
    target_attribute_name="my_target_attribute_name",
    channel_type="training",
)
input_validation = AutoMLInput(
    inputs="s3://my-bucket/my-validation-data",
    target_attribute_name="my_target_attribute_name",
    channel_type="validation",
)

step_args = auto_ml.fit(
    inputs=[input_training, input_validation]
)

step_automl = AutoMLStep(
    name="AutoMLStep",
    step_args=step_args,
)

Get the best model version

The AutoML step automatically trains several model candidates. You can get the model with the best objective metric from the AutoML job using the get_best_auto_ml_model method and an IAM role to access model artifacts as follows.


best_model = step_automl.get_best_auto_ml_model(role=<role>)

For more information, see the AutoML step in the SageMaker Python SDK.

Model Step

Use a ModelStep to create or register a SageMaker model. For more information on ModelStep requirements, see the sagemaker.workflow.model_step.ModelStep documentation.

Create a model

You can use a ModelStep to create a SageMaker model. A ModelStep requires model artifacts and information about the SageMaker instance type that you need to use to create the model. For more information on SageMaker models, see Train a Model with Amazon SageMaker.

The following example shows how to create a ModelStep definition.


from sagemaker.workflow.pipeline_context import PipelineSession
from sagemaker.model import Model
from sagemaker.workflow.model_step import ModelStep

step_train = TrainingStep(...)
model = Model(
    image_uri=pytorch_estimator.training_image_uri(),
    model_data=step_train.properties.ModelArtifacts.S3ModelArtifacts,
    sagemaker_session=PipelineSession(),
    role=role,
)

step_model_create = ModelStep(
   name="MyModelCreationStep",
   step_args=model.create(instance_type="ml.m5.xlarge"),
)

Register a model

You can use a ModelStep to register a sagemaker.model.Model or a sagemaker.pipeline.PipelineModel with the Amazon SageMaker model registry. A PipelineModel represents an inference pipeline, which is a model composed of a linear sequence of containers that process inference requests. For more information about how to register a model, see Register and Deploy Models with Model Registry.

The following example shows how to create a ModelStep that registers a PipelineModel.


import time

from sagemaker.workflow.pipeline_context import PipelineSession
from sagemaker.sklearn import SKLearnModel
from sagemaker.xgboost import XGBoostModel

pipeline_session = PipelineSession()

code_location = 's3://{0}/{1}/code'.format(bucket_name, prefix)

sklearn_model = SKLearnModel(
   model_data=processing_step.properties.ProcessingOutputConfig.Outputs['model'].S3Output.S3Uri,
   entry_point='inference.py',
   source_dir='sklearn_source_dir/',
   code_location=code_location,
   framework_version='1.0-1',
   role=role,
   sagemaker_session=pipeline_session,
   py_version='py3'
)

xgboost_model = XGBoostModel(
   model_data=training_step.properties.ModelArtifacts.S3ModelArtifacts,
   entry_point='inference.py',
   source_dir='xgboost_source_dir/',
   code_location=code_location,
   framework_version='0.90-2',
   py_version='py3',
   sagemaker_session=pipeline_session,
   role=role
)

from sagemaker.workflow.model_step import ModelStep
from sagemaker import PipelineModel

pipeline_model = PipelineModel(
   models=[sklearn_model, xgboost_model],
   role=role,sagemaker_session=pipeline_session,
)

register_model_step_args = pipeline_model.register(
    content_types=["application/json"],
   response_types=["application/json"],
   inference_instances=["ml.t2.medium", "ml.m5.xlarge"],
   transform_instances=["ml.m5.xlarge"],
   model_package_group_name='sipgroup',
)

step_model_registration = ModelStep(
   name="AbaloneRegisterModel",
   step_args=register_model_step_args,
)

CreateModel Step

Important

We recommend using Model Step to create models as of v2.90.0 of the SageMaker Python SDK. CreateModelStep will continue to work in previous versions of the SageMaker Python SDK, but is no longer actively supported.

You use a CreateModel step to create a SageMaker model. For more information on SageMaker models, see Train a Model with Amazon SageMaker.

A create model step requires model artifacts and information about the SageMaker instance type that you need to use to create the model. The following example shows how to create a CreateModel step definition. For more information on CreateModel step requirements, see the sagemaker.workflow.steps.CreateModelStep documentation.


from sagemaker.workflow.steps import CreateModelStep

step_create_model = CreateModelStep(
    name="AbaloneCreateModel",
    model=best_model,
    inputs=inputs
)

RegisterModel Step

Important

We recommend using Model Step to register models as of v2.90.0 of the SageMaker Python SDK. RegisterModel will continue to work in previous versions of the SageMaker Python SDK, but is no longer actively supported.

You use a RegisterModel step to register a sagemaker.model.Model or a sagemaker.pipeline.PipelineModel with the Amazon SageMaker model registry. A PipelineModel represents an inference pipeline, which is a model composed of a linear sequence of containers that process inference requests.

For more information about how to register a model, see Register and Deploy Models with Model Registry. For more information on RegisterModel step requirements, see the sagemaker.workflow.step_collections.RegisterModel documentation.

The following example shows how to create a RegisterModel step that registers a PipelineModel.


import time
from sagemaker.sklearn import SKLearnModel
from sagemaker.xgboost import XGBoostModel

code_location = 's3://{0}/{1}/code'.format(bucket_name, prefix)

sklearn_model = SKLearnModel(model_data=processing_step.properties.ProcessingOutputConfig.Outputs['model'].S3Output.S3Uri,
 entry_point='inference.py',
 source_dir='sklearn_source_dir/',
 code_location=code_location,
 framework_version='1.0-1',
 role=role,
 sagemaker_session=sagemaker_session,
 py_version='py3')

xgboost_model = XGBoostModel(model_data=training_step.properties.ModelArtifacts.S3ModelArtifacts,
 entry_point='inference.py',
 source_dir='xgboost_source_dir/',
 code_location=code_location,
 framework_version='0.90-2',
 py_version='py3',
 sagemaker_session=sagemaker_session,
 role=role)

from sagemaker.workflow.step_collections import RegisterModel
from sagemaker import PipelineModel
pipeline_model = PipelineModel(models=[sklearn_model,xgboost_model],role=role,sagemaker_session=sagemaker_session)

step_register = RegisterModel(
 name="AbaloneRegisterModel",
 model=pipeline_model,
 content_types=["application/json"],
 response_types=["application/json"],
 inference_instances=["ml.t2.medium", "ml.m5.xlarge"],
 transform_instances=["ml.m5.xlarge"],
 model_package_group_name='sipgroup',
)

If model isn't provided, the register model step requires an estimator as shown in the following example.


from sagemaker.workflow.step_collections import RegisterModel

step_register = RegisterModel(
    name="AbaloneRegisterModel",
    estimator=xgb_train,
    model_data=step_train.properties.ModelArtifacts.S3ModelArtifacts,
    content_types=["text/csv"],
    response_types=["text/csv"],
    inference_instances=["ml.t2.medium", "ml.m5.xlarge"],
    transform_instances=["ml.m5.xlarge"],
    model_package_group_name=model_package_group_name,
    approval_status=model_approval_status,
    model_metrics=model_metrics
)

Transform Step

You use a transform step for batch transformation to run inference on an entire dataset. For more information about batch transformation, see Run Batch Transforms with Inference Pipelines.

A transform step requires a transformer and the data on which to run batch transformation. The following example shows how to create a Transform step definition. For more information on Transform step requirements, see the sagemaker.workflow.steps.TransformStep documentation.


from sagemaker.workflow.pipeline_context import PipelineSession

from sagemaker.transformer import Transformer
from sagemaker.inputs import TransformInput
from sagemaker.workflow.steps import TransformStep

transformer = Transformer(..., sagemaker_session=PipelineSession())

step_transform = TransformStep(
    name="AbaloneTransform",
    step_args=transformer.transform(data="s3://my-bucket/my-data"),
)

Condition Step

You use a condition step to evaluate the condition of step properties to assess which action should be taken next in the pipeline.

A condition step requires a list of conditions, a list of steps to run if the condition evaluates to true, and a list of steps to run if the condition evaluates to false. The following example shows how to create a ConditionStep definition.

Limitations

SageMaker Pipelines doesn't support the use of nested condition steps. You can't pass a condition step as the input for another condition step.
A condition step can't use identical steps in both branches. If you need the same step functionality in both branches, duplicate the step and give it a different name.


from sagemaker.workflow.conditions import ConditionLessThanOrEqualTo
from sagemaker.workflow.condition_step import (
    ConditionStep,
    JsonGet
)

cond_lte = ConditionLessThanOrEqualTo(
    left=JsonGet(
        step_name=step_eval.name,
        property_file=evaluation_report,
        json_path="regression_metrics.mse.value"
    ),
    right=6.0
)

step_cond = ConditionStep(
    name="AbaloneMSECond",
    conditions=[cond_lte],
    if_steps=[step_register, step_create_model, step_transform],
    else_steps=[]
)

For more information on ConditionStep requirements, see the sagemaker.workflow.condition_step.ConditionStep API reference. For more information on supported conditions, see Amazon SageMaker Model Building Pipelines - Conditions in the SageMaker Python SDK documentation.

Callback Step

You use a Callback step to incorporate additional processes and AWS services into your workflow that aren't directly provided by Amazon SageMaker Model Building Pipelines. When a Callback step runs, the following procedure occurs:

SageMaker Pipelines sends a message to a customer-specified Amazon Simple Queue Service (Amazon SQS) queue. The message contains a SageMaker Pipelines–generated token and a customer-supplied list of input parameters. After sending the message, SageMaker Pipelines waits for a response from the customer.
The customer retrieves the message from the Amazon SQS queue and starts their custom process.
When the process finishes, the customer calls one of the following APIs and submits the SageMaker Pipelines–generated token:
- SendPipelineExecutionStepSuccess, along with a list of output parameters
- SendPipelineExecutionStepFailure, along with a failure reason
The API call causes SageMaker Pipelines to either continue the pipeline process or fail the process.

For more information on Callback step requirements, see the sagemaker.workflow.callback_step.CallbackStep documentation. For a complete solution, see Extend SageMaker Pipelines to include custom steps using callback steps.

Important

Callback steps were introduced in Amazon SageMaker Python SDK v2.45.0 and Amazon SageMaker Studio Classic v3.6.2. You must update Studio Classic before you use a Callback step or the pipeline DAG doesn't display. To update Studio Classic, see Shut down and Update SageMaker Studio Classic.

The following sample demonstrates an implementation of the preceding procedure.


from sagemaker.workflow.callback_step import CallbackStep

step_callback = CallbackStep(
    name="MyCallbackStep",
    sqs_queue_url="https://sqs.us-east-2.amazonaws.com/012345678901/MyCallbackQueue",
    inputs={...},
    outputs=[...]
)

callback_handler_code = '
    import boto3
    import json

    def handler(event, context):
        sagemaker_client=boto3.client("sagemaker")

        for record in event["Records"]:
            payload=json.loads(record["body"])
            token=payload["token"]

            # Custom processing

            # Call SageMaker to complete the step
            sagemaker_client.send_pipeline_execution_step_success(
                CallbackToken=token,
                OutputParameters={...}
            )
'

Note

Output parameters for CallbackStep should not be nested. For example, if you use a nested dictionary as your output parameter, then the dictionary is treated as a single string (ex. {"output1": "{\"nested_output1\":\"my-output\"}"}). If you provide a nested value, then when you try to refer to a particular output parameter, a non-retryable client error is thrown.

Stopping behavior

A pipeline process doesn't stop while a Callback step is running.

When you call StopPipelineExecution on a pipeline process with a running Callback step, SageMaker Pipelines sends an additional Amazon SQS message to the specified SQS queue. The body of the SQS message contains a Status field, which is set to Stopping. The following shows an example SQS message body.


{
  "token": "26vcYbeWsZ",
  "pipelineExecutionArn": "arn:aws:sagemaker:us-east-2:012345678901:pipeline/callback-pipeline/execution/7pinimwddh3a",
  "arguments": {
    "number": 5,
    "stringArg": "some-arg",
    "inputData": "s3://sagemaker-us-west-2-012345678901/abalone/abalone-dataset.csv"
  },
  "status": "Stopping"
}

You should add logic to your Amazon SQS message consumer to take any needed action (for example, resource cleanup) upon receipt of the message, followed by a call to SendPipelineExecutionStepSuccess or SendPipelineExecutionStepFailure.

Only when SageMaker Pipelines receives one of these calls does it stop the pipeline process.

Lambda Step

You use a Lambda step to run an AWS Lambda function. You can run an existing Lambda function, or SageMaker can create and run a new Lambda function. For a notebook that shows how to use a Lambda step in a SageMaker pipeline, see sagemaker-pipelines-lambda-step.ipynb.

Important

Lambda steps were introduced in Amazon SageMaker Python SDK v2.51.0 and Amazon SageMaker Studio Classic v3.9.1. You must update Studio Classic before you use a Lambda step or the pipeline DAG doesn't display. To update Studio Classic, see Shut down and Update SageMaker Studio Classic.

SageMaker provides the sagemaker.lambda_helper.Lambda class to create, update, invoke, and delete Lambda functions. Lambda has the following signature.


Lambda(
    function_arn,       # Only required argument to invoke an existing Lambda function

    # The following arguments are required to create a Lambda function:
    function_name,
    execution_role_arn,
    zipped_code_dir,    # Specify either zipped_code_dir and s3_bucket, OR script
    s3_bucket,          # S3 bucket where zipped_code_dir is uploaded
    script,             # Path of Lambda function script
    handler,            # Lambda handler specified as "lambda_script.lambda_handler"
    timeout,            # Maximum time the Lambda function can run before the lambda step fails
    ...
)

The sagemaker.workflow.lambda_step.LambdaStep class has a lambda_func argument of type Lambda. To invoke an existing Lambda function, the only requirement is to supply the Amazon Resource Name (ARN) of the function to function_arn. If you don't supply a value for function_arn, you must specify handler and one of the following:

zipped_code_dir – The path of the zipped Lambda function

s3_bucket – Amazon S3 bucket where zipped_code_dir is to be uploaded
script – The path of the Lambda function script file

The following example shows how to create a Lambda step definition that invokes an existing Lambda function.


from sagemaker.workflow.lambda_step import LambdaStep
from sagemaker.lambda_helper import Lambda

step_lambda = LambdaStep(
    name="ProcessingLambda",
    lambda_func=Lambda(
        function_arn="arn:aws:lambda:us-west-2:012345678910:function:split-dataset-lambda"
    ),
    inputs={
        s3_bucket = s3_bucket,
        data_file = data_file
    },
    outputs=[
        "train_file", "test_file"
    ]
)

The following example shows how to create a Lambda step definition that creates and invokes a Lambda function using a Lambda function script.


from sagemaker.workflow.lambda_step import LambdaStep
from sagemaker.lambda_helper import Lambda

step_lambda = LambdaStep(
    name="ProcessingLambda",
    lambda_func=Lambda(
      function_name="split-dataset-lambda",
      execution_role_arn=execution_role_arn,
      script="lambda_script.py",
      handler="lambda_script.lambda_handler",
      ...
    ),
    inputs={
        s3_bucket = s3_bucket,
        data_file = data_file
    },
    outputs=[
        "train_file", "test_file"
    ]
)

Inputs and outputs

If your Lambda function has inputs or outputs, these must also be defined in your Lambda step.

Note

Input and output parameters should not be nested. For example, if you use a nested dictionary as your output parameter, then the dictionary is treated as a single string (ex. {"output1": "{\"nested_output1\":\"my-output\"}"}). If you provide a nested value and try to refer to it later, a non-retryable client error is thrown.

When defining the Lambda step, inputs must be a dictionary of key-value pairs. Each value of the inputs dictionary must be a primitive type (string, integer, or float). Nested objects are not supported. If left undefined, the inputs value defaults to None.

The outputs value must be a list of keys. These keys refer to a dictionary defined in the output of the Lambda function. Like inputs, these keys must be primitive types, and nested objects are not supported.

Timeout and stopping behavior

The Lambda class has a timeout argument that specifies the maximum time that the Lambda function can run. The default value is 120 seconds with a maximum value of 10 minutes. If the Lambda function is running when the timeout is met, the Lambda step fails; however, the Lambda function continues to run.

A pipeline process can't be stopped while a Lambda step is running because the Lambda function invoked by the Lambda step can't be stopped. If you attempt to stop the process while the Lambda function is running, the pipeline waits for the Lambda function to finish or until the timeout is hit, whichever occurs first, and then stops. If the Lambda function finishes, the pipeline process status is Stopped. If the timeout is hit the pipeline process status is Failed.

ClarifyCheck Step

You can use the ClarifyCheck step to conduct baseline drift checks against previous baselines for bias analysis and model explainability. You can then generate and register your baselines with the model.register() method and pass the output of that method to Model Step using step_args. These baselines for drift check can be used by Amazon SageMaker Model Monitor for your model endpoints so that you don’t need to do a baseline suggestion separately. The ClarifyCheck step can also pull baselines for drift check from the model registry. The ClarifyCheck step leverages the Amazon SageMaker Clarify prebuilt container that provides a range of model monitoring capabilities, including constraint suggestion and constraint validation against a given baseline. For more information, see Getting Started with a SageMaker Clarify Container.

Configuring the ClarifyCheck step

You can configure the ClarifyCheck step to conduct only one of the following check types each time it’s used in a pipeline.

Data bias check
Model bias check
Model explainability check

You do this by setting the clarify_check_config parameter with one of the following check type values:

DataBiasCheckConfig
ModelBiasCheckConfig
ModelExplainabilityCheckConfig

The ClarifyCheck step launches a processing job that runs the SageMaker Clarify prebuilt container and requires dedicated configurations for the check and the processing job. ClarifyCheckConfig and CheckJobConfig are helper functions for these configurations that are aligned with how the SageMaker Clarify processing job computes for checking model bias, data bias, or model explainability. For more information, see Run SageMaker Clarify Processing Jobs for Bias Analysis and Explainability.

Controlling step behaviors for drift check

The ClarifyCheck step requires the following two boolean flags to control its behavior:

skip_check: This parameter indicates if the drift check against the previous baseline is skipped or not. If it is set to False, the previous baseline of the configured check type must be available.
register_new_baseline: This parameter indicates if a newly calculated baseline can be accessed though step property BaselineUsedForDriftCheckConstraints. If it is set to False, the previous baseline of the configured check type also must be available. This can be accessed through the BaselineUsedForDriftCheckConstraints property.

For more information, see Baseline calculation, drift detection and lifecycle with ClarifyCheck and QualityCheck steps in Amazon SageMaker Model Building Pipelines.

Working with baselines

You can optionally specify the model_package_group_name to locate the existing baseline and the ClarifyCheck step pulls the DriftCheckBaselines on the latest approved model package in the model package group. Or, you can provide a previous baseline through the supplied_baseline_constraints parameter. If you specify both the model_package_group_name and the supplied_baseline_constraints, the ClarifyCheck step uses the baseline specified by the supplied_baseline_constraints parameter.

For more information on using the ClarifyCheck step requirements, see the sagemaker.workflow.steps.ClarifyCheckStep in the Amazon SageMaker SageMaker SDK for Python. For an Amazon SageMaker Studio Classic notebook that shows how to use ClarifyCheck step in SageMaker Pipelines, see sagemaker-pipeline-model-monitor-clarify-steps.ipynb.

Example Create a `ClarifyCheck` step for data bias check


from sagemaker.workflow.check_job_config import CheckJobConfig
from sagemaker.workflow.clarify_check_step import DataBiasCheckConfig, ClarifyCheckStep
from sagemaker.workflow.execution_variables import ExecutionVariables

check_job_config = CheckJobConfig(
    role=role,
    instance_count=1,
    instance_type="ml.c5.xlarge",
    volume_size_in_gb=120,
    sagemaker_session=sagemaker_session,
)

data_bias_data_config = DataConfig(
    s3_data_input_path=step_process.properties.ProcessingOutputConfig.Outputs["train"].S3Output.S3Uri,
    s3_output_path=Join(on='/', values=['s3:/', your_bucket, base_job_prefix, ExecutionVariables.PIPELINE_EXECUTION_ID, 'databiascheckstep']),
    label=0,
    dataset_type="text/csv",
    s3_analysis_config_output_path=data_bias_analysis_cfg_output_path,
)

data_bias_config = BiasConfig(
    label_values_or_threshold=[15.0], facet_name=[8], facet_values_or_threshold=[[0.5]]  
)

data_bias_check_config = DataBiasCheckConfig(
    data_config=data_bias_data_config,
    data_bias_config=data_bias_config,
)h

data_bias_check_step = ClarifyCheckStep(
    name="DataBiasCheckStep",
    clarify_check_config=data_bias_check_config,
    check_job_config=check_job_config,
    skip_check=False,
    register_new_baseline=False
   supplied_baseline_constraints="s3://sagemaker-us-west-2-111122223333/baseline/analysis.json",
    model_package_group_name="MyModelPackageGroup"
)

QualityCheck Step

You can use the QualityCheck step to conduct baseline suggestions and drift checks against a previous baseline for data quality or model quality in a pipeline. You can then generate and register your baselines with the model.register() method and pass the output of that method to Model Step using step_args. Model Monitor can use these baselines for drift check for your model endpoints so that you don’t need to do a baseline suggestion separately. The QualityCheck step can also pull baselines for drift check from the model registry. The QualityCheck step leverages the Amazon SageMaker Model Monitor prebuilt container, which has a range of model monitoring capabilities including constraint suggestion, statistics generation, and constraint validation against a baseline. For more information, see Amazon SageMaker Model Monitor prebuilt container.

Configuring the QualityCheck step

You can configure the QualityCheck step to conduct only one of the following check types each time it’s used in a pipeline.

Data quality check
Model quality check

You do this by setting the quality_check_config parameter with one of the following check type values:

DataQualityCheckConfig
ModelQualityCheckConfig

The QualityCheck step launches a processing job that runs the Model Monitor prebuilt container and requires dedicated configurations for the check and the processing job. The QualityCheckConfig and CheckJobConfig are helper functions for these configurations that are aligned with how Model Monitor creates a baseline for the model quality or data quality monitoring. For more information on the Model Monitor baseline suggestions, see Create a Baseline and Create a Model Quality Baseline.

Controlling step behaviors for drift check

The QualityCheck step requires the following two Boolean flags to control its behavior:

skip_check: This parameter indicates if the drift check against the previous baseline is skipped or not. If it is set to False, the previous baseline of the configured check type must be available.
register_new_baseline: This parameter indicates if a newly calculated baseline can be accessed through step properties BaselineUsedForDriftCheckConstraints and BaselineUsedForDriftCheckStatistics. If it is set to False, the previous baseline of the configured check type must also be available. These can be accessed through the BaselineUsedForDriftCheckConstraints and BaselineUsedForDriftCheckStatistics properties.

For more information, see Baseline calculation, drift detection and lifecycle with ClarifyCheck and QualityCheck steps in Amazon SageMaker Model Building Pipelines.

Working with baselines

You can specify a previous baseline directly through the supplied_baseline_statistics and supplied_baseline_constraints parameters, or you can simply specify the model_package_group_name and the QualityCheck step pulls the DriftCheckBaselines on the latest approved model package in the model package group. When you specify the model_package_group_name, the supplied_baseline_constraints, and supplied_baseline_statistics, the QualityCheck step uses the baseline specified by supplied_baseline_constraints and supplied_baseline_statistics on the check type of the QualityCheck step you are running.

For more information on using the QualityCheck step requirements, see the sagemaker.workflow.steps.QualityCheckStep in the Amazon SageMaker SageMaker SDK for Python. For an Amazon SageMaker Studio Classic notebook that shows how to use QualityCheck step in SageMaker Pipelines, see sagemaker-pipeline-model-monitor-clarify-steps.ipynb.

Example Create a `QualityCheck` step for data quality check


from sagemaker.workflow.check_job_config import CheckJobConfig
from sagemaker.workflow.quality_check_step import DataQualityCheckConfig, QualityCheckStep
from sagemaker.workflow.execution_variables import ExecutionVariables

check_job_config = CheckJobConfig(
    role=role,
    instance_count=1,
    instance_type="ml.c5.xlarge",
    volume_size_in_gb=120,
    sagemaker_session=sagemaker_session,
)

data_quality_check_config = DataQualityCheckConfig(
    baseline_dataset=step_process.properties.ProcessingOutputConfig.Outputs["train"].S3Output.S3Uri,
    dataset_format=DatasetFormat.csv(header=False, output_columns_position="START"),
    output_s3_uri=Join(on='/', values=['s3:/', your_bucket, base_job_prefix, ExecutionVariables.PIPELINE_EXECUTION_ID, 'dataqualitycheckstep'])
)

data_quality_check_step = QualityCheckStep(
    name="DataQualityCheckStep",
    skip_check=False,
    register_new_baseline=False,
    quality_check_config=data_quality_check_config,
    check_job_config=check_job_config,
    supplied_baseline_statistics="s3://sagemaker-us-west-2-555555555555/baseline/statistics.json",
    supplied_baseline_constraints="s3://sagemaker-us-west-2-555555555555/baseline/constraints.json",
    model_package_group_name="MyModelPackageGroup"
)

EMR Step

You can use the Amazon SageMaker Model Building Pipelines EMR step to process Amazon EMR steps on a running Amazon EMR cluster or have the pipeline create and manage an Amazon EMR cluster for you. For more information about Amazon EMR, see Getting started with Amazon EMR.

The EMR step requires that EMRStepConfig include the location of the JAR file to be used by the Amazon EMR cluster and any arguments to be passed. You also provide the Amazon EMR cluster ID if you want to run the step on a running EMR cluster, or the cluster configuration if you want the EMR step to run on a cluster that it creates, manages, and terminates for you. The following sections include examples and links to sample notebooks demonstrating both methods.

Note

EMR steps require that the role passed to your pipeline has additional permissions. You should attach the AWS managed policy: AmazonSageMakerPipelinesIntegrations to your pipeline role, or ensure that the role includes the permissions in that policy.
EMR step is not supported on EMR serverless, nor on Amazon EMR on EKS.
If you process an EMR step on a running cluster, you can only use a cluster that is in one of the following states: STARTING, BOOTSTRAPPING, RUNNING, or WAITING.
If you process EMR steps on a running cluster, you can have at most 256 EMR steps in a PENDING state on an EMR cluster. EMR steps submitted beyond this limit result in pipeline execution failure. You may consider using Retry Policy for Pipeline Steps.
You can specify either cluster ID or cluster configuration, but not both.
The EMR step relies on Amazon EventBridge to monitor changes in the EMR step or cluster state. If you process your Amazon EMR job on a running cluster, the EMR step uses the SageMakerPipelineExecutionEMRStepStatusUpdateRule rule to monitor EMR step state. If you process your job on a cluster that the EMR step creates for you, the step uses the SageMakerPipelineExecutionEMRClusterStatusRule rule to monitor changes in cluster state. If you see either of these EventBridge rules in your AWS account, do not delete them or else your EMR step may not complete.

Launch a new job on a running Amazon EMR cluster

If you want to launch a new job on a running Amazon EMR cluster, you pass the cluster ID as a string to the cluster_id argument of EMRStep. The following example demonstrates this procedure.


from sagemaker.workflow.emr_step import EMRStep, EMRStepConfig

emr_config = EMRStepConfig(
    jar="jar-location", # required, path to jar file used
    args=["--verbose", "--force"], # optional list of arguments to pass to the jar
    main_class="com.my.Main1", # optional main class, this can be omitted if jar above has a manifest 
    properties=[ # optional list of Java properties that are set when the step runs
    {
        "key": "mapred.tasktracker.map.tasks.maximum",
        "value": "2"
    },
    {
        "key": "mapreduce.map.sort.spill.percent",
        "value": "0.90"
   },
   {
       "key": "mapreduce.tasktracker.reduce.tasks.maximum",
       "value": "5"
    }
  ]
)

step_emr = EMRStep (
    name="EMRSampleStep", # required
    cluster_id="j-1ABCDEFG2HIJK", # include cluster_id to use a running cluster
    step_config=emr_config, # required
    display_name="My EMR Step",
    description="Pipeline step to execute EMR job"
)

For a sample notebook that guides you through a complete example, see SageMaker Pipelines EMR Step With Running EMR Cluster.

Launch a new job on a new Amazon EMR cluster

If you want to launch a new job on a new cluster that EMRStep creates for you, provide your cluster configuration as a dictionary with the same structure as a RunJobFlow request. However, do not include the following fields in your cluster configuration:

[Name]
[Steps]
[AutoTerminationPolicy]
[Instances][KeepJobFlowAliveWhenNoSteps]
[Instances][TerminationProtected]

All other RunJobFlow arguments are available for use in your cluster configuration. For details about the request syntax, see RunJobFlow.

The following example passes a cluster configuration to an EMR step definition, which prompts the step to launch a new job on a new EMR cluster. The EMR cluster configuration in this example includes specifications for primary and core EMR cluster nodes. For more information about Amazon EMR node types, see Understand node types: primary, core, and task nodes.


from sagemaker.workflow.emr_step import EMRStep, EMRStepConfig

emr_step_config = EMRStepConfig(
    jar="jar-location", # required, path to jar file used
    args=["--verbose", "--force"], # optional list of arguments to pass to the jar
    main_class="com.my.Main1", # optional main class, this can be omitted if jar above has a manifest 
    properties=[ # optional list of Java properties that are set when the step runs
    {
        "key": "mapred.tasktracker.map.tasks.maximum",
        "value": "2"
    },
    {
        "key": "mapreduce.map.sort.spill.percent",
        "value": "0.90"
   },
   {
       "key": "mapreduce.tasktracker.reduce.tasks.maximum",
       "value": "5"
    }
  ]
)

# include your cluster configuration as a dictionary
emr_cluster_config = {
    "Applications": [
        {
            "Name": "Spark", 
        }
    ],
    "Instances":{
        "InstanceGroups":[
            {
                "InstanceRole": "MASTER",
                "InstanceCount": 1,
                "InstanceType": "m5.2xlarge"
            },
            {
                "InstanceRole": "CORE",
                "InstanceCount": 2,
                "InstanceType": "m5.2xlarge"
            }
        ]
    },
    "BootstrapActions":[],
    "ReleaseLabel": "emr-6.6.0",
    "JobFlowRole": "job-flow-role",
    "ServiceRole": "service-role"
}

emr_step = EMRStep(
    name="emr-step",
    cluster_id=None,
    display_name="emr_step",
    description="MyEMRStepDescription",
    step_config=emr_step_config,
    cluster_config=emr_cluster_config
)

For a sample notebook that guides you through a complete example, see SageMaker Pipelines EMR Step With Cluster Lifecycle Management.

Notebook Job Step

Use a NotebookJobStep to run your SageMaker Notebook Job non-interactively as a pipeline step. For more information about SageMaker Notebook Jobs, see SageMaker Notebook Jobs.

A NotebookJobStep requires at minimum an input notebook, image URI and kernel name. For more information about Notebook Job step requirements and other parameters you can set to customize your step, see sagemaker.workflow.steps.NotebookJobStep.

The following example uses minimum arguments to define a NotebookJobStep.


from sagemaker.workflow.notebook_job_step import NotebookJobStep


notebook_job_step = NotebookJobStep(
    input_notebook=input_notebook,
    image_uri=image_uri,
    kernel_name=kernel_name
)

Your NotebookJobStep pipeline step is treated as a SageMaker notebook job, so you can track the execution status in the Studio Classic UI notebook job dashboard if you include specific tags with the tags argument. For more details about tags to include, see View your notebook jobs in the Studio UI dashboard.

Also, if you schedule your notebook job using the SageMaker Python SDK, you can only specify certain images to run your notebook job. For more information, see Image constraints for SageMaker Python SDK notebook jobs.

Fail Step

You use a FailStep to stop an Amazon SageMaker Model Building Pipelines execution when a desired condition or state is not achieved and to mark that pipeline's execution as failed. The FailStep also allows you to enter a custom error message, indicating the cause of the pipeline's execution failure.

Note

When a FailStep and other pipeline steps execute concurrently, the pipeline does not terminate until all concurrent steps are completed.

Limitations for using `FailStep`

You cannot add a FailStep to the DependsOn list of other steps. For more information, see Custom Dependency Between Steps.
Other steps cannot reference the FailStep. It is always the last step in a pipeline's execution.
You cannot retry a pipeline execution ending with a FailStep.

You can create the FailStep ErrorMessage in the form of a static text string. Alternatively, you can also use Pipeline Parameters a Join operation, or other step properties to create a more informative error message.

The following example code snippet uses a FailStep with an ErrorMessage configured with Pipeline Parameters and a Join operation.


from sagemaker.workflow.fail_step import FailStep
from sagemaker.workflow.functions import Join
from sagemaker.workflow.parameters import ParameterInteger

mse_threshold_param = ParameterInteger(name="MseThreshold", default_value=5)
step_fail = FailStep(
    name="AbaloneMSEFail",
    error_message=Join(
        on=" ", values=["Execution failed due to MSE >", mse_threshold_param]
    ),
)

Step Properties

The properties attribute is used to add data dependencies between steps in the pipeline. These data dependencies are then used by SageMaker Pipelines to construct the DAG from the pipeline definition. These properties can be referenced as placeholder values and are resolved at runtime.

The properties attribute of a SageMaker Pipelines step matches the object returned by a Describe call for the corresponding SageMaker job type. For each job type, the Describe call returns the following response object:

ProcessingStep – DescribeProcessingJob
TrainingStep – DescribeTrainingJob
TransformStep – DescribeTransformJob

To check which properties are referrable for each step type during data dependency creation, see Data Dependency - Property Reference in the Amazon SageMaker Python SDK.

Step Parallelism

When a step does not depend on any other step, it is run immediately upon pipeline execution. However, executing too many pipeline steps in parallel can quickly exhaust available resources. Control the number of concurrent steps for a pipeline execution with ParallelismConfiguration.

The following example uses ParallelismConfiguration to set the concurrent step limit to five.


pipeline.create(
    parallelism_config=ParallelismConfiguration(5),
)

Data Dependency Between Steps

You define the structure of your DAG by specifying the data relationships between steps. To create data dependencies between steps, pass the properties of one step as the input to another step in the pipeline. The step receiving the input isn't started until after the step providing the input finishes running.

A data dependency uses JsonPath notation in the following format. This format traverses the JSON property file, which means you can append as many <property> instances as needed to reach the desired nested property in the file. For more information on JsonPath notation, see the JsonPath repo.


<step_name>.properties.<property>.<property>

The following shows how to specify an Amazon S3 bucket using the ProcessingOutputConfig property of a processing step.


step_process.properties.ProcessingOutputConfig.Outputs["train_data"].S3Output.S3Uri

To create the data dependency, pass the bucket to a training step as follows.


from sagemaker.workflow.pipeline_context import PipelineSession

sklearn_train = SKLearn(..., sagemaker_session=PipelineSession())

step_train = TrainingStep(
    name="CensusTrain",
    step_args=sklearn_train.fit(inputs=TrainingInput(
        s3_data=step_process.properties.ProcessingOutputConfig.Outputs[
            "train_data"].S3Output.S3Uri
    ))
)

To check which properties are referrable for each step type during data dependency creation, see Data Dependency - Property Reference in the Amazon SageMaker Python SDK.

Custom Dependency Between Steps

When you specify a data dependency, SageMaker Pipelines provides the data connection between the steps. Alternatively, one step can access the data from a previous step without directly using SageMaker Pipelines. In this case, you can create a custom dependency that tells SageMaker Pipelines not to start a step until after another step has finished running. You create a custom dependency by specifying a step's DependsOn attribute.

As an example, the following defines a step C that starts only after both step A and step B finish running.


{
  'Steps': [
    {'Name':'A', ...},
    {'Name':'B', ...},
    {'Name':'C', 'DependsOn': ['A', 'B']}
  ]
}

SageMaker Pipelines throws a validation exception if the dependency would create a cyclic dependency.

The following example creates a training step that starts after a processing step finishes running.


processing_step = ProcessingStep(...)
training_step = TrainingStep(...)

training_step.add_depends_on([processing_step])

The following example creates a training step that doesn't start until two different processing steps finish running.


processing_step_1 = ProcessingStep(...)
processing_step_2 = ProcessingStep(...)

training_step = TrainingStep(...)

training_step.add_depends_on([processing_step_1, processing_step_2])

The following provides an alternate way to create the custom dependency.


training_step.add_depends_on([processing_step_1])
training_step.add_depends_on([processing_step_2])

The following example creates a training step that receives input from one processing step and waits for a different processing step to finish running.


processing_step_1 = ProcessingStep(...)
processing_step_2 = ProcessingStep(...)

training_step = TrainingStep(
    ...,
    inputs=TrainingInput(
        s3_data=processing_step_1.properties.ProcessingOutputConfig.Outputs[
            "train_data"
        ].S3Output.S3Uri
    )

training_step.add_depends_on([processing_step_2])

The following example shows how to retrieve a string list of the custom dependencies of a step.


custom_dependencies = training_step.depends_on

Use a Custom Image in a Step

You can use any of the available SageMaker Deep Learning Container images when you create a step in your pipeline.

You can also use your own container with pipeline steps. Because you can’t create an image from within Amazon SageMaker Studio Classic, you must create your image using another method before using it with Amazon SageMaker Model Building Pipelines.

To use your own container when creating the steps for your pipeline, include the image URI in the estimator definition. For more information on using your own container with SageMaker, see Using Docker Containers with SageMaker.

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Document Conventions

Pipeline Parameters

Lift-and-shift Python code with the @step decorator

Pipeline Steps

Topics

Step Types

Note

@step decorator

Processing Step

Training Step

Tuning Step

Important

AutoML Step

Note

Model Step

Create a model

Register a model

CreateModel Step

Important

RegisterModel Step

Important

Transform Step

Condition Step

Callback Step

Important

Note

Lambda Step

Important

Note

ClarifyCheck Step

Configuring the ClarifyCheck step

Controlling step behaviors for drift check

Working with baselines

Example Create a ClarifyCheck step for data bias check

QualityCheck Step

Configuring the QualityCheck step

Controlling step behaviors for drift check

Working with baselines

Example Create a QualityCheck step for data quality check

EMR Step

Note

Notebook Job Step

Fail Step

Note

Limitations for using FailStep

Step Properties

Step Parallelism

Data Dependency Between Steps

Custom Dependency Between Steps

Use a Custom Image in a Step

Example Create a `ClarifyCheck` step for data bias check

Example Create a `QualityCheck` step for data quality check

Limitations for using `FailStep`