Parallelism¶
SMAC supports multiple workers natively via Dask. Just specify n_workers in the scenario and you are ready to go.
Note
Please keep in mind that additional workers are only used to evaluate trials. The main thread still orchestrates the optimization process, including training the surrogate model.
Warning
Using high number of workers when the target function evaluation is fast might be counterproductive due to the overhead of communcation. Consider using only one worker in this case.
Warning
When using multiple workers, SMAC is not reproducible anymore.
Running on a Cluster¶
You can also pass a custom dask client, e.g. to run on a slurm cluster. See our parallelism example.
Warning
On some clusters you cannot spawn new jobs when running a SLURMCluster inside a job instead of on the login node. No obvious errors might be raised but it can hang silently.
Warning
Sometimes you need to modify your launch command which can be done with
SLURMCluster.job_class.submit_command.
cluster.job_cls.submit_command = submit_command
cluster.job_cls.cancel_command = cancel_command