migrate-to-v2-assets-model.md

Upgrade model management to SDK v2

This article gives a comparison of scenario(s) in SDK v1 and SDK v2.

Create model

• SDK v1

  import urllib.request
  from azureml.core.model import Model
  
  # Register model
  model = Model.register(ws, model_name="local-file-example", model_path="mlflow-model/model.pkl")

• SDK v2

  from azure.ai.ml.entities import Model
  from azure.ai.ml.constants import AssetTypes
  
  file_model = Model(
      path="mlflow-model/model.pkl",
      type=AssetTypes.CUSTOM_MODEL,
      name="local-file-example",
      description="Model created from local file."
  )
  ml_client.models.create_or_update(file_model)

Use model in an experiment/job

• SDK v1

  model = run.register_model(model_name='run-model-example',
                             model_path='outputs/model/')
  print(model.name, model.id, model.version, sep='\t')

• SDK v2

  from azure.ai.ml.entities import Model
  from azure.ai.ml.constants import AssetTypes
  
  run_model = Model(
      path="azureml://jobs/$RUN_ID/outputs/artifacts/paths/model/",
      name="run-model-example",
      description="Model created from run.",
      type=AssetTypes.CUSTOM_MODEL
  )
  
  ml_client.models.create_or_update(run_model)

Mapping of key functionality in SDK v1 and SDK v2

Functionality in SDK v1	Rough mapping in SDK v2
Model.register	ml_client.models.create_or_update
run.register_model	ml_client.models.create_or_update
Model.deploy	ml_client.begin_create_or_update(blue_deployment)

Next steps

For more information, see the documentation here:

• Create a model in v1
• Deploy a model in v1
• Create a model in v2
• Deploy a model in v2