Edit

Share via

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

Important

Azure Machine Learning SDK v1 was deprecated on March 31, 2025. Support ends on June 30, 2026. Migrate to SDK v2 before that date. For more information, see What is Azure Machine Learning CLI and Python SDK v2?

  • SDK v1: The following example shows an AutoML classification task. For the complete code, see the examples repo.

    # Imports
import logging

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

# 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: 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

Functionality in SDK v1 Rough mapping in SDK v2
AutoMLConfig automl.classification() / automl.regression() / automl.forecasting()
experiment.submit(automl_config) ml_client.jobs.create_or_update(job)
remote_run.get_portal_url() returned_job.services["Studio"].endpoint
AutoMLConfig(enable_early_stopping=True) job.set_limits(enable_early_termination=True)
AutoMLConfig(blocked_models=[...]) job.set_training(blocked_training_algorithms=[...])
AutoMLConfig(n_cross_validations=N) automl.classification(n_cross_validations=N)

Next steps

For more information, see:

  • Last updated on