將本機執行升級至 SDK v2

本機執行在 V1 和 V2 中都很類似。 在任一版本中設定計算目標時，請使用 「local」 字串。

本文提供 SDK v1 和 SDK v2 中案例的比較。

提交本機執行

• SDK v1

  from azureml.core import Workspace, Experiment, Environment, ScriptRunConfig
  
  # connect to the workspace
  ws = Workspace.from_config()
  
  # define and configure the experiment
  experiment = Experiment(workspace=ws, name='day1-experiment-train')
  config = ScriptRunConfig(source_directory='./src',
                              script='train.py',
                              compute_target='local')
  
  # set up pytorch environment
  env = Environment.from_conda_specification(
      name='pytorch-env',
      file_path='pytorch-env.yml')
  config.run_config.environment = env
  
  run = experiment.submit(config)
  
  aml_url = run.get_portal_url()
  print(aml_url)
  

• SDK v2

  #import required libraries
  from azure.ai.ml import MLClient, command
  from azure.ai.ml.entities import Environment
  from azure.identity import DefaultAzureCredential
  
  #connect to the workspace
  ml_client = MLClient.from_config(DefaultAzureCredential())
  
  # set up pytorch environment
  env = Environment(
      image='mcr.microsoft.com/azureml/openmpi3.1.2-ubuntu18.04',
      conda_file='pytorch-env.yml',
      name='pytorch-env'
  )
  
  # define the command
  command_job = command(
      code='./src',
      command='train.py',
      environment=env,
      compute='local',
  )
  
  returned_job = ml_client.jobs.create_or_update(command_job)
  returned_job
  

SDK v1 和 SDK v2 中的主要功能對應

SDK v1 中的功能	SDK v2 中的粗略對應
experiment.submit	MLCLient.jobs.create_or_update

下一步

• 使用 Azure Machine Learning 將模型定型