# Define a pipeline To orchestrate your workflows with Amazon SageMaker Pipelines, you must generate a directed acyclic graph (DAG) in the form of a JSON pipeline definition. The DAG specifies the different steps involved in your ML process, such as data preprocessing, model training, model evaluation, and model deployment, as well as the dependencies and flow of data between these steps. The following topic shows you how to generate a pipeline definition. You can generate your JSON pipeline definition using either the SageMaker Python SDK or the visual drag-and-drop Pipeline Designer feature in Amazon SageMaker Studio. The following image is a representation of the pipeline DAG that you create in this tutorial: ![\[Screenshot of the visual drag-and-drop interface for Pipelines in Studio.\]](http://docs.aws.amazon.com/sagemaker/latest/dg/images/pipelines/pipelines-studio-overview.png) The pipeline that you define in the following sections solves a regression problem to determine the age of an abalone based on its physical measurements. For a runnable Jupyter notebook that includes the content in this tutorial, see [Orchestrating Jobs with Amazon SageMaker Model Building Pipelines](https://sagemaker-examples.readthedocs.io/en/latest/sagemaker-pipelines/tabular/abalone_build_train_deploy/sagemaker-pipelines-preprocess-train-evaluate-batch-transform.html). **Note** You can reference the model location as a property of the training step, as shown in the end-to-end example [CustomerChurn pipeline](https://github.com/aws-samples/customer-churn-sagemaker-pipelines-sample/blob/main/pipelines/customerchurn/pipeline.py) in Github. **Topics** ## Define a pipeline (Pipeline Designer) The following walkthrough guides you through the steps to create a barebones pipeline using the drag-and-drop Pipeline Designer. If you need to pause or end your Pipeline editing session in the visual designer at any time, click on the **Export** option. This allows you to download the current definition of your Pipeline to your local environment. Later, when you want to resume the Pipeline editing process, you can import the same JSON definition file into the visual designer. ### Create a Processing step To create a data processing job step, 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 **Process data** and drag it to the canvas. 1. In the canvas, choose the **Process data** step you added. 1. To add an input dataset, choose **Add** under **Data (input)** in the right sidebar and select a dataset. 1. To add a location to save output datasets, choose **Add** under **Data (output)** in the right sidebar and navigate to the destination. 1. Complete the remaining fields in the right sidebar. 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). ### Create a Training step To set up a model training step, do the following: 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. To add an input dataset, choose **Add** under **Data (input)** in the right sidebar and select a dataset. 1. To choose a location to save your model artifacts, enter an Amazon S3 URI in the **Location (S3 URI)** field, or choose **Browse S3** to navigate to the destination location. 1. Complete the remaining fields in the right sidebar. 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. Click and drag the cursor from the **Process data** step you added in the previous section to the **Train model** step to create an edge connecting the two steps. ### Create a model package with a Register model step To create a model package with a model registration step, do the following: 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. To select a model to register, choose **Add** under **Model (input)**. 1. Choose **Create a model group** to add your model to a new model group. 1. Complete the remaining fields in the right sidebar. 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. Click and drag the cursor from the **Train model** step you added in the previous section to the **Register model** step to create an edge connecting the two steps. ### Deploy the model to an endpoint with a Deploy model (endpoint) step To deploy your model using a model deployment step, do the following: 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. To choose a model to deploy, choose **Add** under **Model (input)**. 1. Choose the **Create endpoint** radio button to create a new endpoint. 1. Enter a **Name** and **Description** for your endpoint. 1. Click and drag the cursor from the **Register model** step you added in the previous section to the **Deploy model (endpoint)** step to create an edge connecting the two steps. 1. Complete the remaining fields in the right sidebar. ### Define the Pipeline parameters You can configure a set of Pipeline parameters whose values can be updated for every execution. To define the pipeline parameters and set the default values, click on the gear icon at the bottom of the visual designer. ### Save Pipeline After you have entered all the required information to create your pipeline, click on **Save** at the bottom of the visual designer. This validates your pipeline for any potential errors at runtime and notifies you. The **Save** operation won't succeed until you address all errors flagged by the automated validations checks. If you want to resume editing at a later point, you can save your in-progress pipeline as a JSON definition in your local environment. You can export your Pipeline as a JSON definition file by clicking on the **Export** button at the bottom of the visual designer. Later, to resume updating your Pipeline, upload that JSON definition file by clicking on the **Import** button. ## Define a pipeline (SageMaker Python SDK) ### Prerequisites To run the following tutorial, complete the following: + Set up your notebook instance as outlined in [Create a notebook instance](https://docs.aws.amazon.com/sagemaker/latest/dg/howitworks-create-ws.html). This gives your role permissions to read and write to Amazon S3, and create training, batch transform, and processing jobs in SageMaker AI. + Grant your notebook permissions to get and pass its own role as shown in [Modifying a role permissions policy](https://docs.aws.amazon.com/IAM/latest/UserGuide/roles-managingrole-editing-console.html#roles-modify_permissions-policy). Add the following JSON snippet to attach this policy to your role. Replace `` with the ARN used to create your notebook instance. ------ #### [ JSON ] **** ``` { "Version":"2012-10-17", "Statement": [ { "Effect": "Allow", "Action": [ "iam:GetRole", "iam:PassRole" ], "Resource": "arn:aws:iam::111122223333:role/role-name" } ] } ``` ------ + Trust the SageMaker AI service principal by following the steps in [Modifying a role trust policy](https://docs.aws.amazon.com/IAM/latest/UserGuide/roles-managingrole-editing-cli.html#roles-managingrole_edit-trust-policy-cli). Add the following statement fragment to the trust relationship of your role: ``` { "Sid": "", "Effect": "Allow", "Principal": { "Service": "sagemaker.amazonaws.com" }, "Action": "sts:AssumeRole" } ``` #### Set up your environment Create a new SageMaker AI session using the following code block. This returns the role ARN for the session. This role ARN should be the execution role ARN that you set up as a prerequisite. ``` import boto3 import sagemaker import sagemaker.session from sagemaker.workflow.pipeline_context import PipelineSession region = boto3.Session().region_name sagemaker_session = sagemaker.session.Session() role = sagemaker.get_execution_role() default_bucket = sagemaker_session.default_bucket() pipeline_session = PipelineSession() model_package_group_name = f"AbaloneModelPackageGroupName" ``` ### Create a pipeline **Important** Custom IAM policies that allow Amazon SageMaker Studio or Amazon SageMaker Studio Classic to create Amazon SageMaker resources must also grant permissions to add tags to those resources. The permission to add tags to resources is required because Studio and Studio Classic automatically tag any resources they create. If an IAM policy allows Studio and Studio Classic to create resources but does not allow tagging, "AccessDenied" errors can occur when trying to create resources. For more information, see [Provide permissions for tagging SageMaker AI resources](security_iam_id-based-policy-examples.md#grant-tagging-permissions). [AWS managed policies for Amazon SageMaker AI](security-iam-awsmanpol.md) that give permissions to create SageMaker resources already include permissions to add tags while creating those resources. Run the following steps from your SageMaker AI notebook instance to create a pipeline that includes steps for: + preprocessing + training + evaluation + conditional evaluation + model registration **Note** You can use [ExecutionVariables](https://sagemaker.readthedocs.io/en/stable/workflows/pipelines/sagemaker.workflow.pipelines.html#execution-variables) and the [ Join](https://sagemaker.readthedocs.io/en/stable/workflows/pipelines/sagemaker.workflow.pipelines.html#execution-variables) function to specify your output location. `ExecutionVariables` is resolved at runtime. For instance, `ExecutionVariables.PIPELINE_EXECUTION_ID` is resolved to the ID of the current execution, which can be used as a unique identifier across different runs. #### Step 1: Download the dataset This notebook uses the UCI Machine Learning Abalone Dataset. The dataset contains the following features: + `length` – The longest shell measurement of the abalone. + `diameter` – The diameter of the abalone perpendicular to its length. + `height` – The height of the abalone with meat in the shell. + `whole_weight` – The weight of the whole abalone. + `shucked_weight` – The weight of the meat removed from the abalone. + `viscera_weight` – The weight of the abalone viscera after bleeding. + `shell_weight` – The weight of the abalone shell after meat removal and drying. + `sex` – The sex of the abalone. One of 'M', 'F', or 'I', where 'I' is an infant abalone. + `rings` – The number of rings in the abalone shell. The number of rings in the abalone shell is a good approximation for its age using the formula `age=rings + 1.5`. However, getting this number is a time-consuming task. You must cut the shell through the cone, stain the section, and count the number of rings through a microscope. However, the other physical measurements are easier to get. This notebook uses the dataset to build a predictive model of the variable rings using the other physical measurements. **To download the dataset** 1. Download the dataset into your account's default Amazon S3 bucket. ``` !mkdir -p data local_path = "data/abalone-dataset.csv" s3 = boto3.resource("s3") s3.Bucket(f"sagemaker-servicecatalog-seedcode-{region}").download_file( "dataset/abalone-dataset.csv", local_path ) base_uri = f"s3://{default_bucket}/abalone" input_data_uri = sagemaker.s3.S3Uploader.upload( local_path=local_path, desired_s3_uri=base_uri, ) print(input_data_uri) ``` 1. Download a second dataset for batch transformation after your model is created. ``` local_path = "data/abalone-dataset-batch.csv" s3 = boto3.resource("s3") s3.Bucket(f"sagemaker-servicecatalog-seedcode-{region}").download_file( "dataset/abalone-dataset-batch", local_path ) base_uri = f"s3://{default_bucket}/abalone" batch_data_uri = sagemaker.s3.S3Uploader.upload( local_path=local_path, desired_s3_uri=base_uri, ) print(batch_data_uri) ``` #### Step 2: Define pipeline parameters This code block defines the following parameters for your pipeline: + `processing_instance_count` – The instance count of the processing job. + `input_data` – The Amazon S3 location of the input data. + `batch_data` – The Amazon S3 location of the input data for batch transformation. + `model_approval_status` – The approval status to register the trained model with for CI/CD. For more information, see [MLOps Automation With SageMaker Projects](sagemaker-projects.md). ``` from sagemaker.workflow.parameters import ( ParameterInteger, ParameterString, ) processing_instance_count = ParameterInteger( name="ProcessingInstanceCount", default_value=1 ) model_approval_status = ParameterString( name="ModelApprovalStatus", default_value="PendingManualApproval" ) input_data = ParameterString( name="InputData", default_value=input_data_uri, ) batch_data = ParameterString( name="BatchData", default_value=batch_data_uri, ) ``` #### Step 3: Define a processing step for feature engineering This section shows how to create a processing step to prepare the data from the dataset for training. **To create a processing step** 1. Create a directory for the processing script. ``` !mkdir -p abalone ``` 1. Create a file in the `/abalone` directory named `preprocessing.py` with the following content. This preprocessing script is passed in to the processing step for running on the input data. The training step then uses the preprocessed training features and labels to train a model. The evaluation step uses the trained model and preprocessed test features and labels to evaluate the model. The script uses `scikit-learn` to do the following: + Fill in missing `sex` categorical data and encode it so it's suitable for training. + Scale and normalize all numerical fields except for `rings` and `sex`. + Split the data into training, test, and validation datasets. ``` %%writefile abalone/preprocessing.py import argparse import os import requests import tempfile import numpy as np import pandas as pd from sklearn.compose import ColumnTransformer from sklearn.impute import SimpleImputer from sklearn.pipeline import Pipeline from sklearn.preprocessing import StandardScaler, OneHotEncoder # Because this is a headerless CSV file, specify the column names here. feature_columns_names = [ "sex", "length", "diameter", "height", "whole_weight", "shucked_weight", "viscera_weight", "shell_weight", ] label_column = "rings" feature_columns_dtype = { "sex": str, "length": np.float64, "diameter": np.float64, "height": np.float64, "whole_weight": np.float64, "shucked_weight": np.float64, "viscera_weight": np.float64, "shell_weight": np.float64 } label_column_dtype = {"rings": np.float64} def merge_two_dicts(x, y): z = x.copy() z.update(y) return z if __name__ == "__main__": base_dir = "/opt/ml/processing" df = pd.read_csv( f"{base_dir}/input/abalone-dataset.csv", header=None, names=feature_columns_names + [label_column], dtype=merge_two_dicts(feature_columns_dtype, label_column_dtype) ) numeric_features = list(feature_columns_names) numeric_features.remove("sex") numeric_transformer = Pipeline( steps=[ ("imputer", SimpleImputer(strategy="median")), ("scaler", StandardScaler()) ] ) categorical_features = ["sex"] categorical_transformer = Pipeline( steps=[ ("imputer", SimpleImputer(strategy="constant", fill_value="missing")), ("onehot", OneHotEncoder(handle_unknown="ignore")) ] ) preprocess = ColumnTransformer( transformers=[ ("num", numeric_transformer, numeric_features), ("cat", categorical_transformer, categorical_features) ] ) y = df.pop("rings") X_pre = preprocess.fit_transform(df) y_pre = y.to_numpy().reshape(len(y), 1) X = np.concatenate((y_pre, X_pre), axis=1) np.random.shuffle(X) train, validation, test = np.split(X, [int(.7*len(X)), int(.85*len(X))]) pd.DataFrame(train).to_csv(f"{base_dir}/train/train.csv", header=False, index=False) pd.DataFrame(validation).to_csv(f"{base_dir}/validation/validation.csv", header=False, index=False) pd.DataFrame(test).to_csv(f"{base_dir}/test/test.csv", header=False, index=False) ``` 1. Create an instance of an `SKLearnProcessor` to pass in to the processing step. ``` from sagemaker.sklearn.processing import SKLearnProcessor framework_version = "0.23-1" sklearn_processor = SKLearnProcessor( framework_version=framework_version, instance_type="ml.m5.xlarge", instance_count=processing_instance_count, base_job_name="sklearn-abalone-process", sagemaker_session=pipeline_session, role=role, ) ``` 1. Create a processing step. This step takes in the `SKLearnProcessor`, the input and output channels, and the `preprocessing.py` script that you created. This is very similar to a processor instance's `run` method in the SageMaker AI Python SDK. The `input_data` parameter passed into `ProcessingStep` is the input data of the step itself. This input data is used by the processor instance when it runs. Note the  `"train`, `"validation`, and `"test"` named channels specified in the output configuration for the processing job. Step `Properties` such as these can be used in subsequent steps and resolve to their runtime values at runtime. ``` from sagemaker.processing import ProcessingInput, ProcessingOutput from sagemaker.workflow.steps import ProcessingStep processor_args = sklearn_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="abalone/preprocessing.py", ) step_process = ProcessingStep( name="AbaloneProcess", step_args=processor_args ) ``` #### Step 4: Define a training step This section shows how to use the SageMaker AI [XGBoost Algorithm](https://docs.aws.amazon.com/sagemaker/latest/dg/xgboost.html) to train a model on the training data output from the processing steps. **To define a training step** 1. Specify the model path where you want to save the models from training. ``` model_path = f"s3://{default_bucket}/AbaloneTrain" ``` 1. Configure an estimator for the XGBoost algorithm and the input dataset. The training instance type is passed into the estimator. A typical training script: + loads data from the input channels + configures training with hyperparameters + trains a model + saves a model to `model_dir` so that it can be hosted later SageMaker AI uploads the model to Amazon S3 in the form of a `model.tar.gz` at the end of the training job. ``` from sagemaker.estimator import Estimator image_uri = sagemaker.image_uris.retrieve( framework="xgboost", region=region, version="1.0-1", py_version="py3", instance_type="ml.m5.xlarge" ) xgb_train = Estimator( image_uri=image_uri, instance_type="ml.m5.xlarge", instance_count=1, output_path=model_path, sagemaker_session=pipeline_session, role=role, ) xgb_train.set_hyperparameters( objective="reg:linear", num_round=50, max_depth=5, eta=0.2, gamma=4, min_child_weight=6, subsample=0.7, silent=0 ) ``` 1. Create a `TrainingStep` using the estimator instance and properties of the `ProcessingStep`. Pass in the `S3Uri` of the `"train"` and `"validation"` output channel to the `TrainingStep`.  ``` from sagemaker.inputs import TrainingInput from sagemaker.workflow.steps import TrainingStep train_args = xgb_train.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="AbaloneTrain", step_args = train_args ) ``` #### Step 5: Define a processing step for model evaluation This section shows how to create a processing step to evaluate the accuracy of the model. The result of this model evaluation is used in the condition step to determine which run path to take. **To define a processing step for model evaluation** 1. Create a file in the `/abalone` directory named `evaluation.py`. This script is used in a processing step to perform model evaluation. It takes a trained model and the test dataset as input, then produces a JSON file containing classification evaluation metrics. ``` %%writefile abalone/evaluation.py import json import pathlib import pickle import tarfile import joblib import numpy as np import pandas as pd import xgboost from sklearn.metrics import mean_squared_error if __name__ == "__main__": model_path = f"/opt/ml/processing/model/model.tar.gz" with tarfile.open(model_path) as tar: tar.extractall(path=".") model = pickle.load(open("xgboost-model", "rb")) test_path = "/opt/ml/processing/test/test.csv" df = pd.read_csv(test_path, header=None) y_test = df.iloc[:, 0].to_numpy() df.drop(df.columns[0], axis=1, inplace=True) X_test = xgboost.DMatrix(df.values) predictions = model.predict(X_test) mse = mean_squared_error(y_test, predictions) std = np.std(y_test - predictions) report_dict = { "regression_metrics": { "mse": { "value": mse, "standard_deviation": std }, }, } output_dir = "/opt/ml/processing/evaluation" pathlib.Path(output_dir).mkdir(parents=True, exist_ok=True) evaluation_path = f"{output_dir}/evaluation.json" with open(evaluation_path, "w") as f: f.write(json.dumps(report_dict)) ``` 1. Create an instance of a `ScriptProcessor` that is used to create a `ProcessingStep`. ``` from sagemaker.processing import ScriptProcessor script_eval = ScriptProcessor( image_uri=image_uri, command=["python3"], instance_type="ml.m5.xlarge", instance_count=1, base_job_name="script-abalone-eval", sagemaker_session=pipeline_session, role=role, ) ``` 1. Create a `ProcessingStep` using the processor instance, the input and output channels, and the  `evaluation.py` script. Pass in: + the `S3ModelArtifacts` property from the `step_train` training step + the `S3Uri` of the `"test"` output channel of the `step_process` processing step This is very similar to a processor instance's `run` method in the SageMaker AI Python SDK.  ``` from sagemaker.workflow.properties import PropertyFile evaluation_report = PropertyFile( name="EvaluationReport", output_name="evaluation", path="evaluation.json" ) eval_args = script_eval.run( inputs=[ ProcessingInput( source=step_train.properties.ModelArtifacts.S3ModelArtifacts, destination="/opt/ml/processing/model" ), ProcessingInput( source=step_process.properties.ProcessingOutputConfig.Outputs[ "test" ].S3Output.S3Uri, destination="/opt/ml/processing/test" ) ], outputs=[ ProcessingOutput(output_name="evaluation", source="/opt/ml/processing/evaluation"), ], code="abalone/evaluation.py", ) step_eval = ProcessingStep( name="AbaloneEval", step_args=eval_args, property_files=[evaluation_report], ) ``` #### Step 6: Define a CreateModelStep for batch transformation **Important** We recommend using [Model step](build-and-manage-steps-types.md#step-type-model) 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. This section shows how to create a SageMaker AI model from the output of the training step. This model is used for batch transformation on a new dataset. This step is passed into the condition step and only runs if the condition step evaluates to `true`. **To define a CreateModelStep for batch transformation** 1. Create a SageMaker AI model. Pass in the `S3ModelArtifacts` property from the `step_train` training step. ``` from sagemaker.model import Model model = Model( image_uri=image_uri, model_data=step_train.properties.ModelArtifacts.S3ModelArtifacts, sagemaker_session=pipeline_session, role=role, ) ``` 1. Define the model input for your SageMaker AI model. ``` from sagemaker.inputs import CreateModelInput inputs = CreateModelInput( instance_type="ml.m5.large", accelerator_type="ml.eia1.medium", ) ``` 1. Create your `CreateModelStep` using the `CreateModelInput` and SageMaker AI model instance you defined. ``` from sagemaker.workflow.steps import CreateModelStep step_create_model = CreateModelStep( name="AbaloneCreateModel", model=model, inputs=inputs, ) ``` #### Step 7: Define a TransformStep to perform batch transformation This section shows how to create a `TransformStep` to perform batch transformation on a dataset after the model is trained. This step is passed into the condition step and only runs if the condition step evaluates to `true`. **To define a TransformStep to perform batch transformation** 1. Create a transformer instance with the appropriate compute instance type, instance count, and desired output Amazon S3 bucket URI. Pass in the `ModelName` property from the `step_create_model` `CreateModel` step. ``` from sagemaker.transformer import Transformer transformer = Transformer( model_name=step_create_model.properties.ModelName, instance_type="ml.m5.xlarge", instance_count=1, output_path=f"s3://{default_bucket}/AbaloneTransform" ) ``` 1. Create a `TransformStep` using the transformer instance you defined and the `batch_data` pipeline parameter. ``` from sagemaker.inputs import TransformInput from sagemaker.workflow.steps import TransformStep step_transform = TransformStep( name="AbaloneTransform", transformer=transformer, inputs=TransformInput(data=batch_data) ) ``` #### Step 8: Define a RegisterModel step to create a model package **Important** We recommend using [Model step](build-and-manage-steps-types.md#step-type-model) 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. This section shows how to create an instance of `RegisterModel`. The result of running `RegisterModel` in a pipeline is a model package. A model package is a reusable model artifacts abstraction that packages all ingredients necessary for inference. It consists of an inference specification that defines the inference image to use along with an optional model weights location. A model package group is a collection of model packages. You can use a `ModelPackageGroup` for Pipelines to add a new version and model package to the group for every pipeline run. For more information about model registry, see [Model Registration Deployment with Model Registry](model-registry.md). This step is passed into the condition step and only runs if the condition step evaluates to `true`. **To define a RegisterModel step to create a model package** + Construct a `RegisterModel` step using the estimator instance you used for the training step . Pass in the `S3ModelArtifacts` property from the `step_train` training step and specify a `ModelPackageGroup`. Pipelines creates this `ModelPackageGroup` for you. ``` from sagemaker.model_metrics import MetricsSource, ModelMetrics from sagemaker.workflow.step_collections import RegisterModel model_metrics = ModelMetrics( model_statistics=MetricsSource( s3_uri="{}/evaluation.json".format( step_eval.arguments["ProcessingOutputConfig"]["Outputs"][0]["S3Output"]["S3Uri"] ), content_type="application/json" ) ) 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 ) ``` #### Step 9: Define a condition step to verify model accuracy A `ConditionStep` allows Pipelines to support conditional running in your pipeline DAG based on the condition of step properties. In this case, you only want to register a model package if the accuracy of that model exceeds the required value. The accuracy of the model is determined by the model evaluation step. If the accuracy exceeds the required value, the pipeline also creates a SageMaker AI Model and runs batch transformation on a dataset. This section shows how to define the Condition step. **To define a condition step to verify model accuracy** 1. Define a `ConditionLessThanOrEqualTo` condition using the accuracy value found in the output of the model evaluation processing step, `step_eval`. Get this output using the property file you indexed in the processing step and the respective JSONPath of the mean squared error value, `"mse"`. ``` 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 ) ``` 1. Construct a `ConditionStep`. Pass the `ConditionEquals` condition in, then set the model package registration and batch transformation steps as the next steps if the condition passes. ``` step_cond = ConditionStep( name="AbaloneMSECond", conditions=[cond_lte], if_steps=[step_register, step_create_model, step_transform], else_steps=[], ) ``` #### Step 10: Create a pipeline Now that you’ve created all of the steps, combine them into a pipeline. **To create a pipeline** 1. Define the following for your pipeline: `name`, `parameters`, and `steps`. Names must be unique within an `(account, region)` pair. **Note** A step can only appear once in either the pipeline's step list or the if/else step lists of the condition step. It cannot appear in both. ``` from sagemaker.workflow.pipeline import Pipeline pipeline_name = f"AbalonePipeline" pipeline = Pipeline( name=pipeline_name, parameters=[ processing_instance_count, model_approval_status, input_data, batch_data, ], steps=[step_process, step_train, step_eval, step_cond], ) ``` 1. (Optional) Examine the JSON pipeline definition to ensure that it's well-formed. ``` import json json.loads(pipeline.definition()) ``` This pipeline definition is ready to submit to SageMaker AI. In the next tutorial, you submit this pipeline to SageMaker AI and start a run. ## Define a pipeline (JSON) You can also use [boto3](https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/sagemaker.html#SageMaker.Client.create_pipeline) or [CloudFormation](https://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-sagemaker-pipeline.html) to create a pipeline. Creating a pipeline requires a pipeline definition, which is a JSON object that defines each step of the pipeline. The SageMaker SDK offers a simple way to construct the pipeline definition, which you can use with any of the APIs previously mentioned to create the pipeline itself. Without using the SDK, users have to write the raw JSON definition to create the pipeline without any of the error checks provided by the SageMaker Python SDK. To see the schema for the pipeline JSON definition, see [ SageMaker AI Pipeline Definition JSON Schema](https://aws-sagemaker-mlops.github.io/sagemaker-model-building-pipeline-definition-JSON-schema/). The following code sample shows an example of a SageMaker AI pipeline definition JSON object: ``` {'Version': '2020-12-01', 'Metadata': {}, 'Parameters': [{'Name': 'ProcessingInstanceType', 'Type': 'String', 'DefaultValue': 'ml.m5.xlarge'}, {'Name': 'ProcessingInstanceCount', 'Type': 'Integer', 'DefaultValue': 1}, {'Name': 'TrainingInstanceType', 'Type': 'String', 'DefaultValue': 'ml.m5.xlarge'}, {'Name': 'ModelApprovalStatus', 'Type': 'String', 'DefaultValue': 'PendingManualApproval'}, {'Name': 'ProcessedData', 'Type': 'String', 'DefaultValue': 'S3_URL', {'Name': 'InputDataUrl', 'Type': 'String', 'DefaultValue': 'S3_URL', 'PipelineExperimentConfig': {'ExperimentName': {'Get': 'Execution.PipelineName'}, 'TrialName': {'Get': 'Execution.PipelineExecutionId'}}, 'Steps': [{'Name': 'ReadTrainDataFromFS', 'Type': 'Processing', 'Arguments': {'ProcessingResources': {'ClusterConfig': {'InstanceType': 'ml.m5.4xlarge', 'InstanceCount': 2, 'VolumeSizeInGB': 30}}, 'AppSpecification': {'ImageUri': 'IMAGE_URI', 'ContainerArguments': [....]}, 'RoleArn': 'ROLE', 'ProcessingInputs': [...], 'ProcessingOutputConfig': {'Outputs': [.....]}, 'StoppingCondition': {'MaxRuntimeInSeconds': 86400}}, 'CacheConfig': {'Enabled': True, 'ExpireAfter': '30d'}}, ... ... ... } ``` **Next step:** [Run a pipeline](run-pipeline.md)