Which parameter specifies the time limit for executing trials in SparkTrials?

The parameter that specifies the time limit for executing trials in SparkTrials is indeed the timeout parameter. This is critical in scenarios where machine learning models are being tuned via hyperparameter optimization, as it imposes a cap on how long any individual trial may run. By setting a timeout, you ensure that your workload will not exceed a certain duration, allowing for more efficient use of computational resources and preventing excessive latency in obtaining results. This is particularly useful in distributed computing environments such as Spark, where you may be running multiple trials simultaneously.

The other parameters listed, such as parallelism, max_iterations, and num_trials, address different aspects of the training and optimization process. While parallelism relates to the number of concurrent trials, and max_iterations defines the maximum number of iterations for optimization algorithms, they do not impose a time constraint on trial execution. Similarly, num_trials specifies how many trials should be conducted but doesn't limit their individual running time. Thus, the timeout parameter is specifically designed to control execution time within SparkTrials.