# Pipelines steps Pipelines Steps Pipelines are composed of steps. These steps define the actions that the pipeline takes and the relationships between steps using properties. The following page describes the types of steps, their properties, and the relationships between them. **Topics** + [ # Add a step ](build-and-manage-steps-types.md) + [ # Add integration ](build-and-manage-steps-integration.md) + [ ## Step properties ](#build-and-manage-properties) + [ ## Step parallelism ](#build-and-manage-parallelism) + [ ## Data dependency between steps ](#build-and-manage-data-dependency) + [ ## Custom dependency between steps ](#build-and-manage-custom-dependency) + [ ## Custom images in a step ](#build-and-manage-images) # Add a step The following describes the requirements of each step type and provides an example implementation of the step, as well as how to add the step to a Pipelines. These are not working implementations because they don't provide the resource and inputs needed. For a tutorial that implements these steps, see [Pipelines actions](pipelines-build.md). **Note** You can also create a step from your local machine learning code by converting it to a Pipelines step with the `@step` decorator. For more information, see [@step decorator](#step-type-custom). Amazon SageMaker Pipelines support the following step types: + [Execute code](#step-type-executecode) [Processing](#step-type-processing) + [Training](#step-type-training) + [Tuning](#step-type-tuning) + [AutoML](#step-type-automl) + [`Model`](#step-type-model) + [`Create model`](#step-type-create-model) + [`Register model`](#step-type-register-model) + [`Deploy model (endpoint)`](#step-type-deploy-model-endpoint) + [Transform](#step-type-transform) + [Condition](#step-type-condition) + [`Callback`](#step-type-callback) + [Lambda](#step-type-lambda) + [`ClarifyCheck`](#step-type-clarify-check) + [`QualityCheck`](#step-type-quality-check) + [EMR](#step-type-emr) + [Notebook Job](#step-type-notebook-job) + [Fail](#step-type-fail) ## @step decorator If you want to orchestrate a custom ML job that leverages advanced SageMaker AI features or other AWS services in the drag-and-drop Pipelines UI, use the [Execute code step](#step-type-executecode). 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 AI pipeline step by annotating it with the `@step` decorator. 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 AI pipeline steps. For more details about how to create a step with `@step` decorator, see [Lift-and-shift Python code with the @step decorator](pipelines-step-decorator.md). ## Execute code step Execute code In the Pipelines drag-and-drop UI, you can use an **Execute code** step to run your own code as a pipeline step. You can upload a Python function, script, or notebook to be executed as part of your pipeline. You should use this step if you want to orchestrate a custom ML job that leverages advanced SageMaker AI features or other AWS services. The **Execute Code** step uploads files to your default Amazon S3 bucket for Amazon SageMaker AI. This bucket might not have the required Cross-Origin Resource Sharing (CORS) permissions set. To learn more about configuring CORS permissions, see [CORS Requirement for Input Image Data](sms-cors-update.md). The **Execute Code** step uses an Amazon SageMaker training job to run your code. Ensure that your IAM role has the `sagemaker:DescribeTrainingJob` and `sagemaker:CreateTrainingJob` API permissions. To learn more about all the required permissions for Amazon SageMaker AI and how to set them up, see [Amazon SageMaker AI API Permissions: Actions, Permissions, and Resources Reference](api-permissions-reference.md). To add an execute code step to a pipeline using the Pipeline Designer, do the following: 1. Open the Amazon SageMaker Studio console by following the instructions in [Launch Amazon SageMaker Studio](studio-updated-launch.md). 1. In the left navigation pane, select **Pipelines**. 1. Choose **Create**. 1. Choose **Blank**. 1. In the left sidebar, choose **Execute code** and drag it to the canvas. 1. In the canvas, choose the **Execute code** step you added. 1. In the right sidebar, complete the forms in the **Setting** and **Details** tabs. 1. You can upload a single file to execute or upload a compressed folder containing multiple artifacts. 1. For single file uploads, you can provide optional parameters for notebooks, python functions, or scripts. 1. When providing Python functions, a handler must be provided in the format `file.py:` 1. For compressed folder uploads, relative paths to your code must be provided, and you can optionally provide paths to a `requirements.txt` file or initialization script inside the compressed folder. 1. If the canvas includes any step that immediately precedes the **Execute code** step you added, click and drag the cursor from the step to the **Execute code** step to create an edge. 1. If the canvas includes any step that immediately succeeds the **Execute code** step you added, click and drag the cursor from the **Execute code** step to the step to create an edge. Outputs from **Execute code** steps can be referenced for Python functions. ## Processing step Processing Use a processing step to create a processing job for data processing. For more information on processing jobs, see [Process Data and Evaluate Models](https://docs.aws.amazon.com/sagemaker/latest/dg/processing-job.html). ------ #### [ Pipeline Designer ] To add a processing step to a pipeline using the Pipeline Designer, do the following: 1. Open the Amazon SageMaker Studio console by following the instructions in [Launch Amazon SageMaker Studio](studio-updated-launch.md). 1. In the left navigation pane, select **Pipelines**. 1. Choose **Create**. 1. In the left sidebar, choose **Process data** and drag it to the canvas. 1. In the canvas, choose the **Process data** step you added. 1. In the right sidebar, complete the forms in the **Setting** and **Details** tabs. For information about the fields in these tabs, see [ sagemaker.workflow.steps.ProcessingStep](https://sagemaker.readthedocs.io/en/stable/workflows/pipelines/sagemaker.workflow.pipelines.html#sagemaker.workflow.steps.ProcessingStep). 1. If the canvas includes any step that immediately precedes the **Process data** step you added, click and drag the cursor from the step to the **Process data** step to create an edge. 1. If the canvas includes any step that immediately succeeds the **Process data** step you added, click and drag the cursor from the **Process data** step to the step to create an edge. ------ #### [ SageMaker Python SDK ] 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=, instance_type='ml.m5.xlarge', instance_count=1) ``` ``` from sagemaker.processing import ProcessingInput, ProcessingOutput from sagemaker.workflow.steps import ProcessingStep inputs = [ ProcessingInput(source=, 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=, instance_type='ml.m5.xlarge', instance_count=1, sagemaker_session=pipeline_session, ) step_args = pyspark_processor.run( inputs=[ProcessingInput(source=, 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](https://sagemaker.readthedocs.io/en/stable/workflows/pipelines/sagemaker.workflow.pipelines.html#sagemaker.workflow.steps.ProcessingStep) documentation. For an in-depth example, see the [Orchestrate Jobs to Train and Evaluate Models with Amazon SageMaker Pipelines](https://github.com/aws/amazon-sagemaker-examples/blob/62de6a1fca74c7e70089d77e36f1356033adbe5f/sagemaker-pipelines/tabular/abalone_build_train_deploy/sagemaker-pipelines-preprocess-train-evaluate-batch-transform.ipynb) example notebook. The *Define a Processing Step for Feature Engineering* section includes more information. ------ ## Training step Training 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 AI](https://docs.aws.amazon.com/sagemaker/latest/dg/how-it-works-training.html). A training step requires an estimator, as well as training and validation data inputs. ------ #### [ Pipeline Designer ] To add a training step to a pipeline using the Pipeline Designer, do the following: 1. Open the Amazon SageMaker Studio console by following the instructions in [Launch Amazon SageMaker Studio](studio-updated-launch.md). 1. In the left navigation pane, select **Pipelines**. 1. Choose **Create**. 1. Choose **Blank**. 1. In the left sidebar, choose **Train model** and drag it to the canvas. 1. In the canvas, choose the **Train model** step you added. 1. In the right sidebar, complete the forms in the **Setting** and **Details** tabs. For information about the fields in these tabs, see [ sagemaker.workflow.steps.TrainingStep](https://sagemaker.readthedocs.io/en/stable/workflows/pipelines/sagemaker.workflow.pipelines.html#sagemaker.workflow.steps.TrainingStep). 1. If the canvas includes any step that immediately precedes the **Train model** step you added, click and drag the cursor from the step to the **Train model** step to create an edge. 1. If the canvas includes any step that immediately succeeds the **Train model** step you added, click and drag the cursor from the **Train model** step to the step to create an edge. ------ #### [ SageMaker Python SDK ] The following example shows how to create a `TrainingStep` definition. For more information about training step requirements, see the [sagemaker.workflow.steps.TrainingStep](https://sagemaker.readthedocs.io/en/stable/workflows/pipelines/sagemaker.workflow.pipelines.html#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 Tuning 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, with each job producing a model version. For more information on hyperparameter tuning, see [Automatic model tuning with SageMaker AI](automatic-model-tuning.md). The tuning job is associated with the SageMaker AI experiment for the pipeline, with the training jobs created as trials. For more information, see [Experiments Integration](pipelines-experiments.md). A tuning step requires a [HyperparameterTuner](https://sagemaker.readthedocs.io/en/stable/api/training/tuner.html) 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](automatic-model-tuning-warm-start.md). You use the [get\$1top\$1model\$1s3\$1uri](https://sagemaker.readthedocs.io/en/stable/workflows/pipelines/sagemaker.workflow.pipelines.html#sagemaker.workflow.steps.TuningStep.get_top_model_s3_uri) method of the [sagemaker.workflow.steps.TuningStep](https://sagemaker.readthedocs.io/en/stable/workflows/pipelines/sagemaker.workflow.pipelines.html#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 AI pipeline, see [sagemaker-pipelines-tuning-step.ipynb](https://github.com/aws/amazon-sagemaker-examples/blob/main/sagemaker-pipelines/tabular/tuning-step/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 Amazon SageMaker Studio Classic](studio-tasks-update-studio.md). 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://amzn-s3-demo-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](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_HyperParameterTuningJobObjective.html). 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](https://sagemaker.readthedocs.io/en/stable/workflows/pipelines/sagemaker.workflow.pipelines.html#sagemaker.workflow.steps.TuningStep) documentation. ## Fine-tuning step Fine-tuning Fine-tuning trains a pretrained foundation model from Amazon SageMaker JumpStart on a new dataset. This process, also known as transfer learning, can produce accurate models with smaller datasets and less training time. When you fine-tune a model, you can use the default dataset or choose your own data. To learn more about fine-tuning a foundation model from JumpStart, see [Fine-Tune a Model](jumpstart-fine-tune.md). The fine-tuning step uses an Amazon SageMaker training job to customize your model. Ensure that your IAM role has the `sagemaker:DescribeTrainingJob` and `sagemaker:CreateTrainingJob` API permissions to execute the fine-tuning job in your pipeline. To learn more about the required permissions for Amazon SageMaker AI and how to set them up, see [Amazon SageMaker AI API Permissions: Actions, Permissions, and Resources Reference](api-permissions-reference.md). To add a **Fine-tune model** step to your pipeline using the drag-and-drop editor, follow these steps: 1. Open the Studio console by following the instructions in [Launch Amazon SageMaker Studio](studio-updated-launch.md). 1. In the left navigation pane, select **Pipelines**. 1. Choose **Create**. 1. Choose **Blank**. 1. In the left sidebar, choose **Fine-tune model** and drag it to the canvas. 1. In the canvas, choose the **Fine-tune model** step you added. 1. In the right sidebar, complete the forms in the **Setting** and **Details** tabs. 1. If the canvas includes any step that immediately precedes the **Fine-tune model** step you added, click and drag the cursor from the step to the **Fine-tune model** step to create an edge. 1. If the canvas includes any step that immediately succeeds the **Fine-tune model** step you added, click and drag the cursor from the **Fine-tune model** step to the step to create an edge. ## AutoML step AutoML Use the [AutoML](https://sagemaker.readthedocs.io/en/stable/api/training/automl.html) 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](https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-automate-model-development.html). **Note** Currently, the AutoML step supports only [ensembling training mode](https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-model-support-validation.html). 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="", target_attribute_name="my_target_attribute_name", mode="ENSEMBLING", sagemaker_session=pipeline_session) input_training = AutoMLInput( inputs="s3://amzn-s3-demo-bucket/my-training-data", target_attribute_name="my_target_attribute_name", channel_type="training", ) input_validation = AutoMLInput( inputs="s3://amzn-s3-demo-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. Get the model with the best objective metric from the AutoML job using the `get_best_auto_ml_model` method as follows. You must also use an IAM `role` to access model artifacts. ``` best_model = step_automl.get_best_auto_ml_model(role=) ``` For more information, see the [AutoML](https://sagemaker.readthedocs.io/en/stable/workflows/pipelines/sagemaker.workflow.pipelines.html#sagemaker.workflow.automl_step.AutoMLStep) step in the SageMaker Python SDK. ## Model step `Model` Use a `ModelStep` to create or register a SageMaker AI model. For more information on `ModelStep` requirements, see the [sagemaker.workflow.model\$1step.ModelStep](https://sagemaker.readthedocs.io/en/stable/workflows/pipelines/sagemaker.workflow.pipelines.html#sagemaker.workflow.model_step.ModelStep) documentation. ### Create a model You can use a `ModelStep` to create a SageMaker AI model. A `ModelStep` requires model artifacts and information about the SageMaker AI instance type that you need to use to create the model. For more information about SageMaker AI models, see [Train a Model with Amazon SageMaker AI](https://docs.aws.amazon.com/sagemaker/latest/dg/how-it-works-training.html). 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 [Model Registration Deployment with Model Registry](model-registry.md). 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, ) ``` ## Create model step `Create model` You use a Create model step to create a SageMaker AI model. For more information on SageMaker AI models, see [Train a Model with Amazon SageMaker](how-it-works-training.md). A create model step requires model artifacts and information about the SageMaker AI instance type that you need to use to create the model. The following examples show how to create a Create model step definition. For more information about Create model step requirements, see the [sagemaker.workflow.steps.CreateModelStep](https://sagemaker.readthedocs.io/en/stable/workflows/pipelines/sagemaker.workflow.pipelines.html#sagemaker.workflow.steps.CreateModelStep) documentation. ------ #### [ Pipeline Designer ] To add a create model step to your pipeline, do the following: 1. Open the Studio console by following the instructions in [Launch Amazon SageMaker Studio](studio-updated-launch.md). 1. In the left navigation pane, select **Pipelines**. 1. Choose **Create**. 1. Choose **Blank**. 1. In the left sidebar, choose **Create model** and drag it to the canvas. 1. In the canvas, choose the **Create model** step you added. 1. In the right sidebar, complete the forms in the **Setting** and **Details** tabs. For information about the fields in these tabs, see [ sagemaker.workflow.steps.CreateModelStep](https://sagemaker.readthedocs.io/en/stable/workflows/pipelines/sagemaker.workflow.pipelines.html#sagemaker.workflow.steps.CreateModelStep). 1. If the canvas includes any step that immediately precedes the **Create model** step you added, click and drag the cursor from the step to the **Create model** step to create an edge. 1. If the canvas includes any step that immediately succeeds the **Create model** step you added, click and drag the cursor from the **Create model** step to the step to create an edge. ------ #### [ SageMaker Python SDK ] **Important** We recommend using [Model step](#step-type-model) to create models as of v2.90.0 of the SageMaker AI Python SDK. `CreateModelStep` will continue to work in previous versions of the SageMaker Python SDK, but is no longer actively supported. ``` from sagemaker.workflow.steps import CreateModelStep step_create_model = CreateModelStep( name="AbaloneCreateModel", model=best_model, inputs=inputs ) ``` ------ ## Register model step `Register model` The Register model step registers a model into the SageMaker Model Registry. ------ #### [ Pipeline Designer ] To register a model from a pipeline using the Pipeline Designer, do the following: 1. Open the Amazon SageMaker Studio console by following the instructions in [Launch Amazon SageMaker Studio](studio-updated-launch.md). 1. In the left navigation pane, select **Pipelines**. 1. Choose **Create**. 1. Choose **Blank**. 1. In the left sidebar, choose **Register model** and drag it to the canvas. 1. In the canvas, choose the **Register model** step you added. 1. In the right sidebar, complete the forms in the **Setting** and **Details** tabs. For information about the fields in these tabs, see [ sagemaker.workflow.step\$1collections.RegisterModel](https://sagemaker.readthedocs.io/en/stable/workflows/pipelines/sagemaker.workflow.pipelines.html#sagemaker.workflow.step_collections.RegisterModel). 1. If the canvas includes any step that immediately precedes the **Register model** step you added, click and drag the cursor from the step to the **Register model** step to create an edge. 1. If the canvas includes any step that immediately succeeds the **Register model** step you added, click and drag the cursor from the **Register model** step to the step to create an edge. ------ #### [ SageMaker Python SDK ] **Important** We recommend using [Model step](#step-type-model) to register models as of v2.90.0 of the SageMaker AI 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](https://sagemaker.readthedocs.io/en/stable/api/inference/model.html) or a [sagemaker.pipeline.PipelineModel](https://sagemaker.readthedocs.io/en/stable/api/inference/pipeline.html#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 [Model Registration Deployment with Model Registry](model-registry.md). For more information on `RegisterModel` step requirements, see the [sagemaker.workflow.step\$1collections.RegisterModel](https://sagemaker.readthedocs.io/en/stable/workflows/pipelines/sagemaker.workflow.pipelines.html#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 ) ``` ------ ## Deploy model (endpoint) step `Deploy model (endpoint)` In the Pipeline Designer, use the Deploy model (endpoint) step to deploy your model to an endpoint. You can create a new endpoint or use an existing endpoint. Real-time inference is ideal for inference workloads where you have real-time, interactive, low latency requirements. You can deploy your model to SageMaker AI Hosting services and get a real-time endpoint that can be used for inference. These endpoints are fully managed and support auto-scaling. To learn more about real-time inference in SageMaker AI, see [Real-time inference](realtime-endpoints.md). Before adding a deploy model step to your pipeline, make sure that your IAM role has the following permissions: + `sagemaker:CreateModel` + `sagemaker:CreateEndpointConfig` + `sagemaker:CreateEndpoint` + `sagemaker:UpdateEndpoint` + `sagemaker:DescribeModel` + `sagemaker:DescribeEndpointConfig` + `sagemaker:DescribeEndpoint` To learn more about all the required permissions for SageMaker AI and how to set them up, see [Amazon SageMaker AI API Permissions: Actions, Permissions, and Resources Reference](api-permissions-reference.md). To add a model deployment step to your Pipeline in the drag-and-drop editor, complete the following steps: 1. Open the Studio console by following the instructions in [Launch Amazon SageMaker Studio](studio-updated-launch.md). 1. In the left navigation pane, select **Pipelines**. 1. Choose **Create**. 1. Choose **Blank**. 1. In the left sidebar, choose **Deploy model (endpoint)** and drag it to the canvas. 1. In the canvas, choose the **Deploy model (endpoint)** step you added. 1. In the right sidebar, complete the forms in the **Setting** and **Details** tabs. 1. If the canvas includes any step that immediately precedes the **Deploy model (endpoint)** step you added, click and drag the cursor from the step to the **Deploy model (endpoint)** step to create an edge. 1. If the canvas includes any step that immediately succeeds the **Deploy model (endpoint)** step you added, click and drag the cursor from the **Deploy model (endpoint)** step to the step to create an edge. ## Transform step Transform You use a transform step for batch transformation to run inference on an entire dataset. For more information about batch transformation, see [Batch transforms with inference pipelines](inference-pipeline-batch.md). 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](https://sagemaker.readthedocs.io/en/stable/workflows/pipelines/sagemaker.workflow.pipelines.html#sagemaker.workflow.steps.TransformStep) documentation. ------ #### [ Pipeline Designer ] To add a batch transform step to your pipeline using the drag-and-drop visual editor, do the following: 1. Open the Studio console by following the instructions in [Launch Amazon SageMaker Studio](studio-updated-launch.md). 1. In the left navigation pane, select **Pipelines**. 1. Choose **Create**. 1. Choose **Blank**. 1. In the left sidebar, choose **Deploy model (batch transform)** and drag it to the canvas. 1. In the canvas, choose the **Deploy model (batch transform)** step you added. 1. In the right sidebar, complete the forms in the **Setting** and **Details** tabs. For information about the fields in these tabs, see [ sagemaker.workflow.steps.TransformStep](https://sagemaker.readthedocs.io/en/stable/workflows/pipelines/sagemaker.workflow.pipelines.html#sagemaker.workflow.steps.TransformStep). 1. If the canvas includes any step that immediately precedes the **Deploy model (batch transform)** step you added, click and drag the cursor from the step to the **Deploy model (batch transform)** step to create an edge. 1. If the canvas includes any step that immediately succeeds the **Deploy model (batch transform)** step you added, click and drag the cursor from the **Deploy model (batch transform)** step to the step to create an edge. ------ #### [ SageMaker Python SDK ] ``` 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://amzn-s3-demo-bucket/my-data"), ) ``` ------ ## Condition step Condition 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`. + A list of steps to run if the condition evaluates to `false`. ------ #### [ Pipeline Designer ] To add a condition step to a pipeline using the Pipeline Designer, do the following: 1. Open the Amazon SageMaker Studio console by following the instructions in [Launch Amazon SageMaker Studio](studio-updated-launch.md). 1. In the left navigation pane, select **Pipelines**. 1. Choose **Create**. 1. Choose **Blank**. 1. In the left sidebar, choose **Condition** and drag it to the canvas. 1. In the canvas, choose the **Condition** step you added. 1. In the right sidebar, complete the forms in the **Setting** and **Details** tabs. For information about the fields in these tabs, see [ sagemaker.workflow.condition\$1step.ConditionStep](https://sagemaker.readthedocs.io/en/stable/workflows/pipelines/sagemaker.workflow.pipelines.html#sagemaker.workflow.condition_step.ConditionStep). 1. If the canvas includes any step that immediately precedes the **Condition** step you added, click and drag the cursor from the step to the **Condition** step to create an edge. 1. If the canvas includes any step that immediately succeeds the **Condition** step you added, click and drag the cursor from the **Condition** step to the step to create an edge. ------ #### [ SageMaker Python SDK ] The following example shows how to create a `ConditionStep` definition. **Limitations** + 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 from sagemaker.workflow.functions import 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\$1step.ConditionStep](https://sagemaker.readthedocs.io/en/stable/workflows/pipelines/sagemaker.workflow.pipelines.html#conditionstep) API reference. For more information on supported conditions, see *[Amazon SageMaker Pipelines - Conditions](https://sagemaker.readthedocs.io/en/stable/amazon_sagemaker_model_building_pipeline.html#conditions)* in the SageMaker AI Python SDK documentation. ------ ## Callback step `Callback` Use a `Callback` step to add additional processes and AWS services into your workflow that aren't directly provided by Amazon SageMaker Pipelines. When a `Callback` step runs, the following procedure occurs: + Pipelines sends a message to a customer-specified Amazon Simple Queue Service (Amazon SQS) queue. The message contains a Pipelines–generated token and a customer-supplied list of input parameters. After sending the message, 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 Pipelines–generated token: + [SendPipelineExecutionStepSuccess](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_SendPipelineExecutionStepSuccess.html), along with a list of output parameters + [SendPipelineExecutionStepFailure](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_SendPipelineExecutionStepFailure.html), along with a failure reason + The API call causes Pipelines to either continue the pipeline process or fail the process. For more information on `Callback` step requirements, see the [sagemaker.workflow.callback\$1step.CallbackStep](https://sagemaker.readthedocs.io/en/stable/workflows/pipelines/sagemaker.workflow.pipelines.html#sagemaker.workflow.callback_step.CallbackStep) documentation. For a complete solution, see [Extend SageMaker Pipelines to include custom steps using callback steps](https://aws.amazon.com/blogs/machine-learning/extend-amazon-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 Amazon SageMaker Studio Classic](studio-tasks-update-studio.md). The following sample shows 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 AI 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, SageMaker AI throws a non-retryable client error. **Stopping behavior** A pipeline process doesn't stop while a `Callback` step is running. When you call [StopPipelineExecution](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_StopPipelineExecution.html) on a pipeline process with a running `Callback` step, Pipelines sends an Amazon SQS message to the 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. Then add a call to `SendPipelineExecutionStepSuccess` or `SendPipelineExecutionStepFailure`. Only when Pipelines receives one of these calls does it stop the pipeline process. ## Lambda step Lambda You use a Lambda step to run an AWS Lambda function. You can run an existing Lambda function, or SageMaker AI can create and run a new Lambda function. If you choose to use an existing Lambda function, it must be in the same AWS Region as the SageMaker AI pipeline. For a notebook that shows how to use a Lambda step in a SageMaker AI pipeline, see [sagemaker-pipelines-lambda-step.ipynb](https://github.com/aws/amazon-sagemaker-examples/blob/main/sagemaker-pipelines/tabular/lambda-step/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 Amazon SageMaker Studio Classic](studio-tasks-update-studio.md). SageMaker AI provides the [sagemaker.lambda\$1helper.Lambda](https://sagemaker.readthedocs.io/en/stable/api/utility/lambda_helper.html) 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\$1step.LambdaStep](https://sagemaker.readthedocs.io/en/stable/workflows/pipelines/sagemaker.workflow.pipelines.html#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 stop the process while the Lambda function is running, the pipeline waits for the function to finish or until the timeout is hit. This depends on whichever occurs first. The process 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 `ClarifyCheck` 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](https://docs.aws.amazon.com/sagemaker/latest/dg/pipelines-quality-clarify-baseline-lifecycle.html#pipelines-quality-clarify-baseline-calculations) with the `model.register()` method and pass the output of that method to [Model step](#step-type-model) using `[step\$1args](https://sagemaker.readthedocs.io/en/stable/amazon_sagemaker_model_building_pipeline.html#model-step)`. These baselines for drift check can be used by Amazon SageMaker Model Monitor for your model endpoints. As a result, you don’t need to do a [baseline](https://docs.aws.amazon.com/sagemaker/latest/dg/model-monitor-create-baseline.html) suggestion separately. The `ClarifyCheck` step can also pull baselines for drift check from the model registry. The `ClarifyCheck` step uses the SageMaker Clarify prebuilt container. This container provides a range of model monitoring capabilities, including constraint suggestion and constraint validation against a given baseline. For more information, see [Prebuilt SageMaker Clarify Containers](clarify-processing-job-configure-container.md). ### Configuring the ClarifyCheck step Configuring `ClarifyCheck` 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 To do this, set 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 AI Clarify prebuilt container and requires dedicated [configurations for the check and the processing job](https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-configure-processing-jobs.html). `ClarifyCheckConfig` and `CheckJobConfig` are helper functions for these configurations. These helper functions 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](clarify-processing-job-run.md). ### Controlling step behaviors for drift check Configuring `ClarifyCheck` 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 Pipelines](pipelines-quality-clarify-baseline-lifecycle.md). ### Working with baselines Baselines You can optionally specify the `model_package_group_name` to locate the existing baseline. Then, 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](https://sagemaker.readthedocs.io/en/stable/workflows/pipelines/sagemaker.workflow.pipelines.html#sagemaker.workflow.clarify_check_step.ClarifyCheckStep) in the *Amazon SageMaker AI SageMaker AI SDK for Python*. For an Amazon SageMaker Studio Classic notebook that shows how to use `ClarifyCheck` step in Pipelines, see [sagemaker-pipeline-model-monitor-clarify-steps.ipynb](https://github.com/aws/amazon-sagemaker-examples/blob/main/sagemaker-pipelines/tabular/model-monitor-clarify-pipelines/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 `QualityCheck` Use the `QualityCheck` step to conduct [baseline suggestions](https://docs.aws.amazon.com/sagemaker/latest/dg/model-monitor-create-baseline.html) and drift checks against a previous baseline for data quality or model quality in a pipeline. You can then generate and [register your baselines](https://docs.aws.amazon.com/sagemaker/latest/dg/pipelines-quality-clarify-baseline-lifecycle.html#pipelines-quality-clarify-baseline-calculations) with the `model.register()` method and pass the output of that method to [Model step](#step-type-model) using `[step\$1args](https://sagemaker.readthedocs.io/en/stable/amazon_sagemaker_model_building_pipeline.html#model-step)`. ] 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 AI Model Monitor prebuilt container. This container 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](model-monitor-pre-built-container.md). ### Configuring the QualityCheck step Configuring `QualityCheck` You can configure the `QualityCheck` step to run 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. These helper functions 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](model-monitor-create-baseline.md) and [Create a model quality baseline](model-monitor-model-quality-baseline.md). ### Controlling step behaviors for drift check Configuring `QualityCheck` 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 Pipelines](pipelines-quality-clarify-baseline-lifecycle.md). ### Working with baselines Baselines You can specify a previous baseline directly through the `supplied_baseline_statistics` and `supplied_baseline_constraints` parameters. You can also 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 following, the `QualityCheck` step uses the baseline specified by `supplied_baseline_constraints` and `supplied_baseline_statistics` on the check type of the `QualityCheck` step. + `model_package_group_name` + `supplied_baseline_constraints` + `supplied_baseline_statistics` For more information on using the `QualityCheck` step requirements, see the [sagemaker.workflow.steps.QualityCheckStep](https://sagemaker.readthedocs.io/en/stable/workflows/pipelines/sagemaker.workflow.pipelines.html#sagemaker.workflow.quality_check_step.QualityCheckStep) in the *Amazon SageMaker AI SageMaker AI SDK for Python*. For an Amazon SageMaker Studio Classic notebook that shows how to use `QualityCheck` step in Pipelines, see [sagemaker-pipeline-model-monitor-clarify-steps.ipynb](https://github.com/aws/amazon-sagemaker-examples/blob/main/sagemaker-pipelines/tabular/model-monitor-clarify-pipelines/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 EMR Use the Amazon SageMaker Pipelines [EMR](https://docs.aws.amazon.com/emr/latest/ManagementGuide/emr-overview.html) step to: + Process [Amazon EMR steps](https://docs.aws.amazon.com/emr/latest/ManagementGuide/emr-work-with-steps.html) on a running Amazon EMR cluster. + Have the pipeline create and manage an Amazon EMR cluster for you. For more information about Amazon EMR, see [Getting started with Amazon EMR](https://docs.aws.amazon.com/emr/latest/ManagementGuide/emr-gs.html). The EMR step requires that `EMRStepConfig` include the location of the JAR file 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. You can also pass the cluster configuration to run the EMR step 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. Attach the [AWS managed policy: `AmazonSageMakerPipelinesIntegrations`](https://docs.aws.amazon.com/sagemaker/latest/dg/security-iam-awsmanpol-pipelines.html#security-iam-awsmanpol-AmazonSageMakerPipelinesIntegrations) to your pipeline role, or ensure that the role includes the permissions in that policy. 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` `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](pipelines-retry-policy.md). 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, 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. **Add an Amazon EMR step to your pipeline** To add an EMR step to your pipeline, do the following: + Open the Studio console by following the instructions in [Launch Amazon SageMaker Studio](https://docs.aws.amazon.com/sagemaker/latest/dg/studio-updated-launch.html). + In the left navigation pane, select **Pipelines**. + Choose **Create**. + Choose **Blank**. + In the left sidebar, choose **Process data** and drag it to the canvas. + In the canvas, choose the **Process data** step you added. + In the right sidebar, under mode, choose **EMR (managed)**. + In the right sidebar, complete the forms in the **Setting and Details** tabs. For information about the fields in these tabs, see [sagemaker.workflow.fail\$1step.EMRstep](https://sagemaker.readthedocs.io/en/stable/workflows/pipelines/sagemaker.workflow.pipelines.html#sagemaker.workflow.emr_step.EMRStep). **Launch a new job on a running Amazon EMR cluster** To launch a new job on a running Amazon EMR cluster, 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 [ Pipelines EMR Step With Running EMR Cluster](https://github.com/aws/amazon-sagemaker-examples/blob/main/sagemaker-pipelines/tabular/emr-step/sagemaker-pipelines-emr-step-with-running-emr-cluster.ipynb). **Launch a new job on a new Amazon EMR cluster** To launch a new job on a new cluster that `EMRStep` creates for you, provide your cluster configuration as a dictionary. The dictionary must have the same structure as a [RunJobFlow](https://docs.aws.amazon.com/emr/latest/APIReference/API_RunJobFlow.html) 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](https://docs.aws.amazon.com/emr/latest/APIReference/API_RunJobFlow.html). The following example passes a cluster configuration to an EMR step definition. This 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](https://docs.aws.amazon.com/emr/latest/ManagementGuide/emr-master-core-task-nodes.html). ``` 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 [ Pipelines EMR Step With Cluster Lifecycle Management](https://github.com/aws/amazon-sagemaker-examples/blob/main/sagemaker-pipelines/tabular/emr-step/sagemaker-pipelines-emr-step-with-cluster-lifecycle-management.ipynb). ## EMR serverless step EMR serverless step To add an EMR serverless step to your pipeline, do the following: + Open the Studio console by following the instructions in [Launch Amazon SageMaker Studio](https://docs.aws.amazon.com/sagemaker/latest/dg/studio-updated-launch.html). + In the left navigation pane, select **Pipelines**. + Choose **Create**. + Choose **Blank**. + In the left sidebar, choose **Process data** and drag it to the canvas. + In the canvas, choose the **Process data** step you added. + In the right sidebar, under mode, choose **EMR (serverless)**. + In the right sidebar, complete the forms in the **Setting and Details** tabs. ## Notebook job step Notebook Job Use a `NotebookJobStep` to run your SageMaker Notebook Job non-interactively as a pipeline step. If you build your pipeline in the Pipelines drag-and-drop UI, use the [Execute code step](#step-type-executecode) to run your notebook. For more information about SageMaker Notebook Jobs, see [SageMaker Notebook Jobs](notebook-auto-run.md). 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](https://sagemaker.readthedocs.io/en/stable/workflows/pipelines/sagemaker.workflow.pipelines.html#sagemaker.workflow.notebook_job_step.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. As a result, track the execution status in the Studio Classic UI notebook job dashboard by including specific tags with the `tags` argument. For more details about tags to include, see [View your notebook jobs in the Studio UI dashboard](create-notebook-auto-run-sdk.md#create-notebook-auto-run-dash). 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 AI Python SDK notebook jobs](notebook-auto-run-constraints.md#notebook-auto-run-constraints-image-sdk). ## Fail step Fail Use a Fail step to stop an Amazon SageMaker Pipelines execution when a desired condition or state is not achieved. The Fail step also allows you to enter a custom error message, indicating the cause of the pipeline's execution failure. **Note** When a Fail step and other pipeline steps execute at the same time, the pipeline does not terminate until all concurrent steps are completed. ### Limitations for using Fail step Fail step limitations + You cannot add a Fail step to the `DependsOn` list of other steps. For more information, see [Custom dependency between steps](build-and-manage-steps.md#build-and-manage-custom-dependency). + Other steps cannot reference the Fail step. It is *always* the last step in a pipeline's execution. + You cannot retry a pipeline execution ending with a Fail step. You can create the Fail step error message in the form of a static text string. Alternatively, you can also use [Pipeline Parameters](https://docs.aws.amazon.com/sagemaker/latest/dg/build-and-manage-parameters.html), a [Join](https://sagemaker.readthedocs.io/en/stable/workflows/pipelines/sagemaker.workflow.pipelines.html?highlight=Join#sagemaker.workflow.functions.Join) operation, or other [step properties](https://docs.aws.amazon.com/sagemaker/latest/dg/build-and-manage-steps.html#build-and-manage-properties) to create a more informative error message if you use the SDK. ------ #### [ Pipeline Designer ] To add a Fail step to your pipeline, do the following: 1. Open the Studio console by following the instructions in [Launch Amazon SageMaker Studio](studio-updated-launch.md). 1. In the left navigation pane, select **Pipelines**. 1. Choose **Create**. 1. Choose **Blank**. 1. In the left sidebar, choose **Fail** and drag it to the canvas. 1. In the canvas, choose the **Fail** step you added. 1. In the right sidebar, complete the forms in the **Setting** and **Details** tabs. For information about the fields in these tabs, see [ sagemaker.workflow.fail\$1step.FailStep](https://sagemaker.readthedocs.io/en/stable/workflows/pipelines/sagemaker.workflow.pipelines.html#sagemaker.workflow.fail_step.FailStep). 1. If the canvas includes any step that immediately precedes the **Fail** step you added, click and drag the cursor from the step to the **Fail** step to create an edge. 1. If the canvas includes any step that immediately succeeds the **Fail** step you added, click and drag the cursor from the **Fail** step to the step to create an edge. ------ #### [ SageMaker Python SDK ] **Example** 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] ), ) ``` ------ # Add integration MLflow integration allows you to use MLflow with pipelines to select a tracking server or serverless application, choose an experiment, and log metrics. ## Key concepts **Default app creation** - A default MLflow application will be created when you enter the pipeline visual editor. **Integrations panel** - A new integrations panel includes MLflow, which you can select and configure. **Update app and experiment** - The option to override selected application and experiment during the pipeline execution. ## How it works + Go to **Pipeline Visual Editor** + Choose **Integration** on the toolbar + Choose **MLflow** + Configure the MLflow app and experiment ## Example screenshots Integrations side panel ![\[The to do description.\]](http://docs.aws.amazon.com/sagemaker/latest/dg/images/screenshot-pipeline-1.png) MLflow configuration ![\[The to do description.\]](http://docs.aws.amazon.com/sagemaker/latest/dg/images/screenshot-pipeline-2.png) How to override experiment during pipeline execution ![\[The to do description.\]](http://docs.aws.amazon.com/sagemaker/latest/dg/images/screenshot-pipeline-3.png) ## Step properties Use the `properties` attribute to add data dependencies between steps in the pipeline. Pipelines use these data dependencies 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 Pipelines step matches the object returned by a `Describe` call for the corresponding SageMaker AI job type. For each job type, the `Describe` call returns the following response object: + `ProcessingStep` – [DescribeProcessingJob](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_DescribeProcessingJob.html) + `TrainingStep` – [DescribeTrainingJob](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_DescribeTrainingJob.html) + `TransformStep` – [DescribeTransformJob](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_DescribeTransformJob.html) To check which properties are referrable for each step type during data dependency creation, see *[Data Dependency - Property Reference](https://sagemaker.readthedocs.io/en/stable/amazon_sagemaker_model_building_pipeline.html#data-dependency-property-reference)* in the [Amazon SageMaker Python SDK](https://sagemaker.readthedocs.io/en/stable). ## Step parallelism When a step does not depend on any other step, it runs 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. This means you can append as many ** instances as needed to reach the desired nested property in the file. For more information on JsonPath notation, see the [JsonPath repo](https://github.com/json-path/JsonPath). ``` .properties.. ``` 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](https://sagemaker.readthedocs.io/en/stable/amazon_sagemaker_model_building_pipeline.html#data-dependency-property-reference)* in the [Amazon SageMaker Python SDK](https://sagemaker.readthedocs.io/en/stable). ## Custom dependency between steps When you specify a data dependency, Pipelines provides the data connection between the steps. Alternatively, one step can access the data from a previous step without directly using Pipelines. In this case, you can create a custom dependency that tells 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']} ] } ``` 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 ``` ## Custom images in a step You can use any of the available SageMaker AI [Deep Learning Container images](https://github.com/aws/deep-learning-containers/blob/master/available_images.md) 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 Studio Classic, you must create your image using another method before using it with 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 AI, see [Using Docker Containers with SageMaker AI](https://docs.aws.amazon.com/sagemaker/latest/dg/docker-containers.html).