问题:

What is the reason of such issue in joblib? 'Multiprocessing backed parallel loops cannot be nested below threads, setting n_jobs=1' What should I do to avoid such issue?

Actually I need to implement XMLRPC server which run heavy computation in background thread and report current progress through polling from UI client. It uses scikit-learn which are based on joblib.

P.S.: I've simply changed name of the thread to "MainThread" to avoid such warning and everything looks working good (run in parallel as expected without issues). What might be a problem in future for such workaround?

回答1:

This seems to be due to this issue in JobLib library. At the moment of writing this seems to be fixed but not released yet. As written in the question, a dirty fix would to rename the main thread back to MainThread:

threading.current_thread().name = 'MainThread'

回答2:

I had the same warning while doing predictions with sklearn within a thread, using a model I loaded and which was fitted with n_jobs > 1. It appears when you pickle a model it is saved with its parameters, including n_jobs.

To avoid the warning (and potential serialization cost), set n_jobs to 1 when pickling your models:

clf = joblib.load(model_filename).set_params(n_jobs=1)