Scale hyperparameter optimization

Completed

One of the advantages of using Apache Spark in Azure Databricks is the ability to distribute processing tasks across multiple cluster nodes. When you use a Spark-aware machine learning library like MLlib, machine learning training can be scaled out to reduce the overall time the training process takes.

Optuna supports distributed hyperparameter optimization through integration with various backends, including Apache Spark. You can parallelize the optimization process across multiple nodes in your Databricks cluster using the optuna.integration.SparkOptimizer class.

The following example code shows how to use Optuna with Spark for distributed optimization:

from optuna.integration import SparkOptimizer
import mlflow

def objective(trial):
    # Define your hyperparameters
    x = trial.suggest_float("x", -10, 10)
    # ... your model training and evaluation ...
    return (x - 2) ** 2  # Example objective

# Create a SparkOptimizer
spark_optimizer = SparkOptimizer(study_name="distributed_study",
                               storage="sqlite:///example.db",
                               n_trials=100)

# Run optimization with MLflow tracking
with mlflow.start_run():
    study = spark_optimizer.run_study(objective, direction="minimize")
    
    print("Best param values: ", study.best_params)
    print("Best value: ", study.best_value)

Some of the key benefits of using Optuna with Spark in Azure Databricks include:

• Automatic distribution of trials across worker nodes
• Built-in fault tolerance and recovery
• Seamless integration with MLflow for experiment tracking
• Support for both distributed and nondistributed ML libraries

Tip

For more information about distributed optimization with Optuna, see the Optuna documentation on distributed optimization.