Upgrade AutoML to SDK v2

In SDK v2, jobs consolidate "experiments" and "runs".

In SDK v1, you primarily configure and run AutoML by using the AutoMLConfig class. In SDK v2, this class is now an AutoML job. Although some configuration options differ, most naming and functionality is preserved in V2.

This article compares scenarios in SDK v1 and SDK v2.

Submit AutoML run

• SDK v2: The following example shows an AutoML classification task. For the complete code, see the examples repo.

  # Imports
  from azure.ai.ml import automl, Input, MLClient
  from azure.ai.ml.constants import AssetTypes
  
  # Create MLTables for training dataset
  # Note that AutoML Job can also take in tabular data
  my_training_data_input = Input(
      type=AssetTypes.MLTABLE, path="./data/training-mltable-folder"
  )
  
  # Create the AutoML classification job with the related factory-function.
  classification_job = automl.classification(
      compute="<compute_name>",
      experiment_name="<exp_name>",
      training_data=my_training_data_input,
      target_column_name="<name_of_target_column>",
      primary_metric="accuracy",
      n_cross_validations=5,
      enable_model_explainability=True,
      tags={"my_custom_tag": "My custom value"},
  )
  
  # Limits are all optional
  classification_job.set_limits(
      timeout_minutes=600,
      trial_timeout_minutes=20,
      max_trials=5,
      max_concurrent_trials = 4,
      max_cores_per_trial= 1,
      enable_early_termination=True,
  )
  
  # Training properties are optional
  classification_job.set_training(
      blocked_training_algorithms=["LogisticRegression"],
      enable_onnx_compatible_models=True,
  )
  
  # Submit the AutoML job
  returned_job = ml_client.jobs.create_or_update(classification_job)  
  returned_job
  

Mapping of key functionality in SDK v1 and SDK v2

Next steps

For more information, see:

• How to train an AutoML model with Python SDKv2