To parallelize the hyperparameter tuning process using Hyperopt and SparkTrials, you would first define the search space for the hyperparameters and the objective function that evaluates the model's performance. Then, you would create a SparkTrials object and pass it to the hyperopt.fmin function along with the search space, objective function, and the number of parallel trials. The hyperopt.fmin function would then distribute the trials sequentially across the available Spark workers, without any parallel execution.

To parallelize the hyperparameter tuning process using Hyperopt and SparkTrials, you would first define the search space for the hyperparameters and the objective function that evaluates the model's performance. Then, you would create a SparkTrials object and pass it to the hyperopt.fmin function along with the search space, objective function, and the number of sequential trials. The hyperopt.fmin function would then distribute the trials across the available Spark workers, allowing for parallel execution of the hyperparameter tuning process.

To parallelize the hyperparameter tuning process using Hyperopt and SparkTrials, you would first define the search space for the hyperparameters and the objective function that evaluates the model's performance. Then, you would create a SparkTrials object and pass it to the hyperopt.fmin function along with the search space, objective function, and the number of parallel trials. The hyperopt.fmin function would then distribute the trials across the available Spark workers, but the SparkTrials object would not enable parallelization.