sklearn.utils.joblib.parallel_backend

sklearn.utils.joblib.parallel_backend(backend, n_jobs=-1, **backend_params)[source]

Change the default backend used by Parallel inside a with block.

If backend is a string it must match a previously registered implementation using the register_parallel_backend function.

Alternatively backend can be passed directly as an instance.

By default all available workers will be used (n_jobs=-1) unless the caller passes an explicit value for the n_jobs parameter.

This is an alternative to passing a backend='backend_name' argument to the Parallel class constructor. It is particularly useful when calling into library code that uses joblib internally but does not expose the backend argument in its own API.

>>> from operator import neg
>>> with parallel_backend('threading'):
...     print(Parallel()(delayed(neg)(i + 1) for i in range(5)))
...
[-1, -2, -3, -4, -5]

Warning: this function is experimental and subject to change in a future version of joblib.

New in version 0.10.