Databricks Machine Learning Associate Question 221
Select 3You are tasked with tuning the hyperparameters of a machine learning model using Hyperopt in Databricks. To speed up the tuning process, you decide to use SparkTrials. Which of the following steps must you take to properly parallelize the hyperparameter tuning process using SparkTrials?
- A
Set the
max_evalsparameter to define the number of hyperparameter combinations to evaluate. - B
Initialize SparkTrials and pass it to the
trialsargument of Hyperopt'sfminfunction. - C
Ensure that the objective function is serializable and can be distributed across workers.
- D
Manually distribute the hyperparameter search space across Spark executors.
- E
Use a Spark DataFrame to define the hyperparameter search space.
Show answer and explanation
Correct answers: A, B, C
Explanation
To parallelize hyperparameter tuning using Hyperopt and SparkTrials in Databricks, you need to configure the max_evals parameter to control the number of evaluations, ensure the objective function is serializable for distributed execution, and pass an instance of SparkTrials to the fmin function. SparkTrials handles the parallelization process, so manual distribution or the use of Spark DataFrames for the search space is unnecessary.
- A. Correct.
Correct. The
max_evalsparameter specifies the number of hyperparameter combinations to evaluate, which is critical when running parallelized hyperparameter tuning. - B. Correct.
Correct. When using SparkTrials, it needs to be explicitly passed to the
trialsargument of Hyperopt'sfminfunction to enable parallelization. - C. Correct.
Correct. The objective function must be serializable and able to run on distributed Spark workers for Hyperopt with SparkTrials to function properly.
- D. Incorrect.
Incorrect. SparkTrials automatically handles the distribution of the hyperparameter search space. Manual distribution is not required.
- E. Incorrect.
Incorrect. The hyperparameter search space is defined using Hyperopt's
hpmodule, not a Spark DataFrame.