Upgrade AutoML to SDK v2

In SDK v2, "experiments" and "runs" are consolidated into jobs.

In SDK v1, AutoML was primarily configured and run using the AutoMLConfig class. In SDK v2, this class has been converted to an AutoML job. Although there are some differences in the configuration options, by and large, naming & functionality has been preserved in V2.

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

Submit AutoML run

• SDK v1: Below is a sample AutoML classification task. For the entire code, check out our examples repo.

  # Imports
  
  import azureml.core
  from azureml.core.experiment import Experiment
  from azureml.core.workspace import Workspace
  from azureml.core.dataset import Dataset
  from azureml.train.automl import AutoMLConfig
  from azureml.train.automl.run import AutoMLRun   
  
  # Load tabular dataset
  data = "<url_to_data>"
  dataset = Dataset.Tabular.from_delimited_files(data)
  training_data, validation_data = dataset.random_split(percentage=0.8, seed=223)
  label_column_name = "Class"
  
  # Configure Auto ML settings
  automl_settings = {
      "n_cross_validations": 3,
      "primary_metric": "average_precision_score_weighted",
      "enable_early_stopping": True,
      "max_concurrent_iterations": 2,  
      "experiment_timeout_hours": 0.25,  
      "verbosity": logging.INFO,
  }
  
  # Put together an AutoML job constructor
  automl_config = AutoMLConfig(
      task="classification",
      debug_log="automl_errors.log",
      compute_target=compute_target,
      training_data=training_data,
      label_column_name=label_column_name,
      **automl_settings,
  )
  
  # Submit run
  remote_run = experiment.submit(automl_config, show_output=False)
  azureml_url = remote_run.get_portal_url()
  print(azureml_url)
  

• SDK v2: Below is a sample AutoML classification task. For the entire code, check out our examples repo.

  # Imports
  from azure.ai.ml import automl, Input, MLClient
  
  from azure.ai.ml.constants import AssetTypes
  from azure.ai.ml.automl import (
      classification,
      ClassificationPrimaryMetrics,
      ClassificationModels,
  )
  
  
  # 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