title: Easy way to use parallel options of scikit-learn functions on HPC
body: <p>In many functions from scikit-learn implemented user-friendly parallelization. For example  in 
  <code>sklearn.cross_validation.cross_val_score</code> you just pass desired number of computational jobs in <code>n_jobs</code> argument. And for PC with multi-core processor it will work very nice. But if I want use such option in high performance cluster (with installed OpenMPI package and using SLURM for resource management) ? As I know <code>sklearn</code> uses <code>joblib</code> for parallelization, which uses <code>multiprocessing</code>. And, as I know (from this, for example, <a href="https://stackoverflow.com/questions/25772289/python-multiprocessing-within-mpi">Python multiprocessing within mpi</a>) Python programs parallelized with <code>multiprocessing</code> easy to scale oh whole MPI architecture with <code>mpirun</code> utility. Can I spread computation of <code>sklearn</code> functions on several computational nodes just using <code>mpirun</code> and <code>n_jobs</code> argument?    </p>