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Execution

These options control how the pipeline is executed but should not affect what outputs get produced.

n_jobs module-attribute

Python
n_jobs = 1

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

parallel_backend module-attribute

Python
parallel_backend = '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 = 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 = 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 = '10G'

The maximum amount of RAM per Dask worker.

log_level module-attribute

Python
log_level = 'info'

Set the pipeline logging verbosity.

mne_log_level module-attribute

Python
mne_log_level = 'error'

Set the MNE-Python logging verbosity.

on_error module-attribute

Python
on_error = 'abort'

Whether to abort processing as soon as an error occurs, continue with all other processing steps for as long as possible, or drop you into a debugger in case of an error.

Info

Enabling debug mode deactivates parallel processing.

memory_location module-attribute

Python
memory_location = True

If not None (or False), caching will be enabled and the cache files will be stored in the given directory. The default (True) will use a "_cache" subdirectory (name configurable via the memory_subdir variable) in the BIDS derivative root of the dataset.

memory_subdir module-attribute

Python
memory_subdir = '_cache'

The caching directory name to use if memory_location is True.

memory_file_method module-attribute

Python
memory_file_method = 'mtime'

The method to use for cache invalidation (i.e., detecting changes). Using the "modified time" reported by the filesystem ('mtime', default) is very fast but requires that the filesystem supports proper mtime reporting. Using file hashes ('hash') is slower and requires reading all input files but should work on any filesystem.

memory_verbose module-attribute

Python
memory_verbose = 0

The verbosity to use when using memory. The default (0) does not print, while 1 will print the function calls that will be cached. See the documentation for the joblib.Memory class for more information.

config_validation module-attribute

Python
config_validation = 'raise'

How strictly to validate the configuration. Errors are always raised for invalid entries (e.g., not providing ch_types). This setting controls how to handle possibly or likely incorrect entries, such as likely misspellings (e.g., providing session instead of sessions).