Databricks Machine Learning Associate Question 318
Select 3You are using Hyperopt with SparkTrials to optimize the hyperparameters of a Spark ML model on a Databricks cluster. Which of the following steps are required to ensure the hyperparameter tuning runs in parallel across multiple workers?
- A
Use SparkTrials instead of Trials while initializing Hyperopt.
- B
Set the max_evals parameter in Hyperopt's fmin() function to a value greater than 1.
- C
Ensure the Databricks cluster is configured with multiple worker nodes.
- D
Set the parallelism parameter in the fmin() function explicitly to control the degree of parallelism.
- E
Use the Hyperopt Spark estimator instead of standard Spark ML estimators.
Show answer and explanation
Correct answers: A, B, C
Explanation
To parallelize hyperparameter tuning using Hyperopt and SparkTrials in a Databricks environment, you must use SparkTrials (to enable distributed execution), set max_evals to a value greater than 1 (to perform multiple evaluations), and ensure that your Databricks cluster is configured with multiple worker nodes (to enable Spark's parallelism). The parallelism parameter in fmin() is handled automatically by SparkTrials, and there is no need for a specialized estimator beyond standard Spark ML estimators.
- A. Correct.
Using SparkTrials instead of Trials is necessary for distributed execution of Hyperopt's optimization process across the cluster.
- B. Correct.
Setting max_evals to a value greater than 1 ensures that multiple evaluations of the objective function are performed, enabling parallel execution.
- C. Correct.
A Databricks cluster with multiple worker nodes is required to distribute the workload and achieve parallelism.
- D. Incorrect.
The parallelism parameter in fmin() is not explicitly set when using SparkTrials, as the parallelism is managed by Spark based on the cluster's resources.
- E. Incorrect.
There is no special 'Hyperopt Spark estimator'; Hyperopt works with standard Spark ML estimators by optimizing their hyperparameters.