Parallel computing

Many problems in machine learning require training several or many estimators. At the same time in applications single model training can take from minutes to hours.

In order to get things done faster we can use parallel computing.

Such meta-algorithms as:

• rep.metaml.folding.FoldingClassifier and rep.metaml.folding.FoldingRegressor
• rep.metaml.factory.ClassifiersFactory and rep.metaml.factory.RegressorsFactory
• rep.metaml.gridsearch.GridOptimalSearchCV

support parallel_profile option. The following options exist:

• parallel_profile=None to use single thread (default)
• parallel_profile='threads-3' to run in 3 threads on a single machine (you can use any, not necessarily 3)
• parallel_profile='my_ipython_cluster_name' to use IPython cluster.

More details about IPython cluster (ipyparallel) is available here.

Important remark: since algorithms inside REP interplays with each other, you can combine parallel computations. You can run GridOptimalSearchCV (using IPython cluster) to optimize FoldingClassifier (using threads) which runs over RandomForest (using again threads).

This way you can use available resources more effectively.