Parallelization

These options control parallel processing (e.g., multiple subjects at once),

n_jobs module-attribute

Python

n_jobs: int = 1

Specifies how many subjects you want to process in parallel. If 1, disables parallel processing.

parallel_backend module-attribute

Python

parallel_backend: Literal['loky', 'dask'] = 'loky'

Specifies which backend to use for parallel job execution. loky is the default backend used by joblib. dask requires Dask to be installed. Ignored if n_jobs is set to 1.

dask_open_dashboard module-attribute

Python

dask_open_dashboard: bool = False

Whether to open the Dask dashboard in the default webbrowser automatically. Ignored if parallel_backend is not 'dask'.

dask_temp_dir module-attribute

Python

dask_temp_dir: PathLike | None = None

The temporary directory to use by Dask. Dask places lock-files in this directory, and also uses it to "spill" RAM contents to disk if the amount of free memory in the system hits a critical low. It is recommended to point this to a location on a fast, local disk (i.e., not a network-attached storage) to ensure good performance. The directory needs to be writable and will be created if it does not exist.

If None, will use .dask-worker-space inside of deriv_root.

dask_worker_memory_limit module-attribute

Python

dask_worker_memory_limit: str = '10G'

The maximum amount of RAM per Dask worker.