Databricks Machine Learning Associate Question 311
Single answerYou are training a machine learning model on a single-node Databricks cluster. To improve the model's performance, you need to tune several hyperparameters efficiently. Which solution would allow you to parallelize hyperparameter tuning across multiple trials while leveraging the available resources on the single-node cluster?
- A
Use Hyperopt with SparkTrials to distribute trials across a cluster of nodes.
- B
Use Hyperopt with a single-node executor to parallelize trials across available CPU cores.
- C
Manually run individual hyperparameter tuning trials sequentially on the single-node cluster.
- D
Use a grid search approach to exhaustively search through a fixed set of hyperparameter combinations.
Show answer and explanation
Correct answer: B
Explanation
Hyperopt is a framework for efficient hyperparameter optimization. In a single-node cluster, Hyperopt can parallelize hyperparameter tuning by leveraging all available CPU cores, enabling multiple trials to run simultaneously. This makes it an ideal solution for tuning models on a single-node setup. While SparkTrials extends Hyperopt to distributed clusters, it is unnecessary for single-node configurations.
- A. Incorrect.
Hyperopt with SparkTrials is designed for distributed hyperparameter tuning across multiple nodes in a cluster, which is not applicable for a single-node setup.
- B. Correct.
Hyperopt can leverage all available CPU cores on a single-node cluster by using its default trial runner. This allows for parallel execution of hyperparameter tuning trials, making it an efficient solution in this scenario.
- C. Incorrect.
Manually running tuning trials sequentially is inefficient and does not leverage the parallel processing capabilities of the single-node cluster.
- D. Incorrect.
Grid search is computationally expensive and does not inherently parallelize across available resources, making it a suboptimal choice for single-node setups with multiple CPU cores.