In SparkTrials, which parameter would you adjust to change the number of concurrent trials related to computational resources?

In SparkTrials, the parameter that controls the number of concurrent trials is parallelism. This parameter determines how many trials can be run at the same time, which directly relates to the available computational resources in your environment. By adjusting this value, you can optimize resource utilization and potentially reduce the total time taken for hyperparameter tuning.

If you set a higher parallelism value, more trials are able to execute concurrently, assuming your system has sufficient resources (like CPU or memory) to handle that load. This can lead to faster convergence towards the best hyperparameters, especially in large-scale machine learning tasks where multiple configurations need to be evaluated.

The other parameters do not specifically govern concurrent trials. The max_trials parameter would limit the total number of trials, but it does not affect how many can run at the same time. The timeout_duration sets a time limit for how long a single trial can run but does not influence concurrency. Lastly, run_mode pertains to how the trials are executed (e.g., whether to run them in a synchronous or asynchronous manner), but again, it does not directly control the number of concurrent trials. Understanding how parallelism works in SparkTrials is key for efficient model tuning in distributed environments.