What does the "parallelism" parameter control in SparkTrials?

The "parallelism" parameter in SparkTrials controls the number of trials that can be executed concurrently during a hyperparameter tuning process. This means that when you are optimizing a model, you can specify how many different configurations of hyperparameters you want to test at the same time. By setting the parallelism level, you can take advantage of the distributed computing capabilities of Apache Spark, leading to more efficient utilization of resources and potentially faster optimization results.

Choosing the right level of parallelism is crucial because it can help balance the workload and ensure that the computing resources are effectively employed. If the level is set too low, it may take longer to reach an optimal solution as fewer trials are run simultaneously. Conversely, if it is set too high, it may lead to resource contention and diminished returns due to exceeding the available computational capacity.

The other options listed do not accurately describe the function of the parallelism parameter. For instance, duration of trials, session configuration, or maximum iterations pertain to different aspects of the SparkTrials operations and are not governed by the parallelism setting.