Python scikit learn n_jobs

Question

This is not a real issue, but I\'d like to understand:

• running sklearn from Anaconda distrib on a Win7 4 cores 8 GB system
• fitting a KMeans model on a 200

Answer 1

• what is the point of using n-jobs (and joblib) if the the library uses all cores anyway?

It does not, if you specify n_jobs to -1, it will use all cores. If it is set to 1 or 2, it will use one or two cores only (test done scikit-learn 0.20.3 under Linux).

Answer 2

You should either use n_jobs or joblib, don't use both simultaneously.

Answer 3

The documentation says:

This parameter is used to specify how many concurrent processes or threads should be used for routines that are parallelized with joblib.

n_jobs is an integer, specifying the maximum number of concurrently running workers. If 1 is given, no joblib parallelism is used at all, which is useful for debugging. If set to -1, all CPUs are used. For n_jobs below -1, (n_cpus + 1 + n_jobs) are used. For example with n_jobs=-2, all CPUs but one are used.

n_jobs is None by default, which means unset; it will generally be interpreted as n_jobs=1, unless the current joblib.Parallel backend context specifies otherwise.

For more details on the use of joblib and its interactions with scikit-learn, please refer to our parallelism notes.