Databricks Machine Learning Associate Question 577
Select 2You have implemented a machine learning model on a single node using scikit-learn, and now you want to tune its hyperparameters using a grid search. Due to the large size of the hyperparameter space, the operation is computationally expensive. Which of the following approaches can you use in Databricks to parallelize this hyperparameter tuning process?
- A
Use Spark's MLlib to distribute the hyperparameter search across a cluster.
- B
Leverage scikit-learn's
jobliblibrary with a Databricks cluster to parallelize the computation. - C
Use Databricks notebooks to manually distribute hyperparameter search tasks to different executors.
- D
Implement hyperparameter tuning using the
Hyperoptlibrary with SparkTrials on a Databricks cluster. - E
Run the grid search sequentially on a single node as Databricks does not support parallelization of Python-based models.
Show answer and explanation
Correct answers: B, D
Explanation
To parallelize hyperparameter tuning for single-node models, you can either use scikit-learn's joblib library for parallel processing or leverage Databricks' integration with Hyperopt and SparkTrials for distributed hyperparameter optimization. These approaches ensure efficient utilization of the Databricks cluster's resources for faster tuning. Running the grid search sequentially or manually distributing tasks is inefficient, and using Spark's MLlib would unnecessarily complicate the tuning process for scikit-learn models.
- A. Incorrect.
Correct: scikit-learn's
joblibcan be used to parallelize grid search when running on a Databricks cluster, as joblib distributes tasks to worker nodes when properly configured. - B. Correct.
Incorrect: While Spark's MLlib is designed for distributed machine learning, it would require rewriting the model and tuning logic in MLlib's APIs, which isn't necessary for parallelizing scikit-learn models.
- C. Incorrect.
Incorrect: Manually distributing tasks in Databricks notebooks is not an efficient or scalable approach for hyperparameter tuning.
- D. Correct.
Correct: Hyperopt is a library that integrates with SparkTrials in Databricks, allowing distributed hyperparameter tuning across a cluster without requiring significant code changes.
- E. Incorrect.
Incorrect: Databricks supports parallelization of Python-based models, and running grid search sequentially would not leverage the computational power of the cluster.