Upgrade pipelines to SDK v2

In SDK v2, "pipelines" are consolidated into jobs.

A job has a type. Most jobs are command jobs that run a command, like python main.py. What runs in a job is agnostic to any programming language, so you can run bash scripts, invoke python interpreters, run a bunch of curl commands, or anything else.

A pipeline is another type of job, which defines child jobs that may have input/output relationships, forming a directed acyclic graph (DAG).

To upgrade, you'll need to change your code for defining and submitting the pipelines to SDK v2. What you run within the child job doesn't need to be upgraded to SDK v2. However, it's recommended to remove any code specific to Azure Machine Learning from your model training scripts. This separation allows for an easier transition between local and cloud and is considered best practice for mature MLOps. In practice, this means removing azureml.* lines of code. Model logging and tracking code should be replaced with MLflow. For more information, see how to use MLflow in v2.

This article gives a comparison of scenario(s) in SDK v1 and SDK v2. In the following examples, we'll build three steps (train, score and evaluate) into a dummy pipeline job. This demonstrates how to build pipeline jobs using SDK v1 and SDK v2, and how to consume data and transfer data between steps.

Run a pipeline

• SDK v1

  # import required libraries
  import os
  import azureml.core
  from azureml.core import (
      Workspace,
      Dataset,
      Datastore,
      ComputeTarget,
      Experiment,
      ScriptRunConfig,
  )
  from azureml.pipeline.steps import PythonScriptStep
  from azureml.pipeline.core import Pipeline
  
  # check core SDK version number
  print("Azure Machine Learning SDK Version: ", azureml.core.VERSION)
  
  # load workspace
  workspace = Workspace.from_config()
  print(
      "Workspace name: " + workspace.name,
      "Azure region: " + workspace.location,
      "Subscription id: " + workspace.subscription_id,
      "Resource group: " + workspace.resource_group,
      sep="\n",
  )
  
  # create an ML experiment
  experiment = Experiment(workspace=workspace, name="train_score_eval_pipeline")
  
  # create a directory
  script_folder = "./src"
  
  # create compute
  from azureml.core.compute import ComputeTarget, AmlCompute
  from azureml.core.compute_target import ComputeTargetException
  
  # Choose a name for your CPU cluster
  amlcompute_cluster_name = "cpu-cluster"
  
  # Verify that cluster does not exist already
  try:
      aml_compute = ComputeTarget(workspace=workspace, name=amlcompute_cluster_name)
      print('Found existing cluster, use it.')
  except ComputeTargetException:
      compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_DS12_V2',
                                                             max_nodes=4)
      aml_compute = ComputeTarget.create(ws, amlcompute_cluster_name, compute_config)
  
  aml_compute.wait_for_completion(show_output=True)
  
  # define data set
  data_urls = ["wasbs://demo@dprepdata.blob.core.chinacloudapi.cn/Titanic.csv"]
  input_ds = Dataset.File.from_files(data_urls)
  
  # define steps in pipeline
  from azureml.data import OutputFileDatasetConfig
  model_output = OutputFileDatasetConfig('model_output')
  train_step = PythonScriptStep(
      name="train step",
      script_name="train.py",
      arguments=['--training_data', input_ds.as_named_input('training_data').as_mount() ,'--max_epocs', 5, '--learning_rate', 0.1,'--model_output', model_output],
      source_directory=script_folder,
      compute_target=aml_compute,
      allow_reuse=True,
  )
  
  score_output = OutputFileDatasetConfig('score_output')
  score_step = PythonScriptStep(
      name="score step",
      script_name="score.py",
      arguments=['--model_input',model_output.as_input('model_input'), '--test_data', input_ds.as_named_input('test_data').as_mount(), '--score_output', score_output],
      source_directory=script_folder,
      compute_target=aml_compute,
      allow_reuse=True,
  )
  
  eval_output = OutputFileDatasetConfig('eval_output')
  eval_step = PythonScriptStep(
      name="eval step",
      script_name="eval.py",
      arguments=['--scoring_result',score_output.as_input('scoring_result'), '--eval_output', eval_output],
      source_directory=script_folder,
      compute_target=aml_compute,
      allow_reuse=True,
  )
  
  # built pipeline
  from azureml.pipeline.core import Pipeline
  
  pipeline_steps = [train_step, score_step, eval_step]
  
  pipeline = Pipeline(workspace = workspace, steps=pipeline_steps)
  print("Pipeline is built.")
  
  pipeline_run = experiment.submit(pipeline, regenerate_outputs=False)
  
  print("Pipeline submitted for execution.")
  
  

• SDK v2. Full sample link

  # import required libraries
  from azure.identity import DefaultAzureCredential, InteractiveBrowserCredential
  
  from azure.ai.ml import MLClient, Input
  from azure.ai.ml.dsl import pipeline
  
  try:
      credential = DefaultAzureCredential()
      # Check if given credential can get token successfully.
      credential.get_token("https://management.chinacloudapi.cn/.default")
  except Exception as ex:
      # Fall back to InteractiveBrowserCredential in case DefaultAzureCredential not work
      credential = InteractiveBrowserCredential()
  
  # Get a handle to workspace
  ml_client = MLClient.from_config(credential=credential)
  
  # Retrieve an already attached Azure Machine Learning Compute.
  cluster_name = "cpu-cluster"
  print(ml_client.compute.get(cluster_name))
  
  # Import components that are defined with Python function
  with open("src/components.py") as fin:
      print(fin.read())
  
  # You need to install mldesigner package to use command_component decorator.
  # Option 1: install directly
  # !pip install mldesigner
  
  # Option 2: install as an extra dependency of azure-ai-ml
  # !pip install azure-ai-ml[designer]
  
  # import the components as functions
  from src.components import train_model, score_data, eval_model
  
  cluster_name = "cpu-cluster"
  # define a pipeline with component
  @pipeline(default_compute=cluster_name)
  def pipeline_with_python_function_components(input_data, test_data, learning_rate):
      """E2E dummy train-score-eval pipeline with components defined via Python function components"""
  
      # Call component obj as function: apply given inputs & parameters to create a node in pipeline
      train_with_sample_data = train_model(
          training_data=input_data, max_epochs=5, learning_rate=learning_rate
      )
  
      score_with_sample_data = score_data(
          model_input=train_with_sample_data.outputs.model_output, test_data=test_data
      )
  
      eval_with_sample_data = eval_model(
          scoring_result=score_with_sample_data.outputs.score_output
      )
  
      # Return: pipeline outputs
      return {
          "eval_output": eval_with_sample_data.outputs.eval_output,
          "model_output": train_with_sample_data.outputs.model_output,
      }
  
  
  pipeline_job = pipeline_with_python_function_components(
      input_data=Input(
          path="wasbs://demo@dprepdata.blob.core.chinacloudapi.cn/Titanic.csv", type="uri_file"
      ),
      test_data=Input(
          path="wasbs://demo@dprepdata.blob.core.chinacloudapi.cn/Titanic.csv", type="uri_file"
      ),
      learning_rate=0.1,
  )
  
  # submit job to workspace
  pipeline_job = ml_client.jobs.create_or_update(
      pipeline_job, experiment_name="train_score_eval_pipeline"
  )
  

Mapping of key functionality in SDK v1 and SDK v2

Step and job/component type mapping

Related documents

For more information, see the documentation here:

• steps in SDK v1
• Create and run machine learning pipelines using components with the Azure Machine Learning SDK v2
• Build a simple ML pipeline for image classification (SDK v1)
• OutputDatasetConfig
• mldesigner