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This is not a real issue, but I'd like to understand:
if __name__ == '__main__': line to my script) I see the script starting 4 processes with
10 threads each. Each process uses about 25% of the CPU (total:
100%). Seems to work as expectedMy question: what is the point of using n-jobs (and joblib) if the the library uses all cores anyway? Am I missing something? Is it a Windows-specific behaviour?
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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.
n_jobs: Specify the number of cores to use for key machine learning tasks.
Scikit-learn relies heavily on NumPy and SciPy, which internally call multi-threaded linear algebra routines implemented in libraries such as MKL, OpenBLAS or BLIS.
By default it does not use GPU, especially if it is running inside Docker, unless you use nvidia-docker and an image with a built-in support. Scikit-learn is not intended to be used as a deep-learning framework and it does not provide any GPU support.
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).
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