What would happen if the "timeout" parameter is not set in SparkTrials?

If the "timeout" parameter is not set in SparkTrials, the trials would run without any duration restrictions. This means that every trial can continue to run until it either completes, fails, or is manually terminated, depending on the resources available and the execution environment.

The lack of a timeout allows for flexibility in running longer computations, which can be particularly useful in scenarios where more time-intensive tasks are being performed, such as hyperparameter tuning or complex model training. This approach can lead to more thorough exploration of the solution space, but it also means that inefficient trials could consume excessive resources or delay the overall experimentation process.

While the default behavior allows infinite runtime, it is still a good practice to consider setting a timeout to ensure that the system remains efficient and that resources are effectively managed.