joblib

from joblib import Parallel, delayed def func(v): temp.append(v) return temp = [] Parallel(n_jobs=4)(delayed(func)(v) for v in range(10)) print temp I want to make shared memory variable. But the value of temp is empty []. How can I do it? For other method, I tried pickle.dump and load. But there is a lock problem. Please give me advice! from joblib import Parallel, delayed def func(v): return v temp = Parallel(n_jobs=4)(delayed(func)(v) for v in range(10)) print temp delayed collects the output returned by func in a list and returns it on completion. 来源： https://stackoverflow.com/questions

The joblib docs contain the following warning: Under Windows, it is important to protect the main loop of code to avoid recursive spawning of subprocesses when using joblib.Parallel. In other words, you should be writing code like this: import .... def function1(...): ... def function2(...): ... ... if __name__ == '__main__': # do stuff with imports and functions defined about ... No code should run outside of the “if __name__ == ‘__main__’” blocks, only imports and definitions. Initially, I assumed this was just to prevent against the occasional odd case where a function passed to joblib

As suggested in this answer , I tried to use joblib to train multiple scikit-learn models in parallel. import joblib import numpy from sklearn import tree, linear_model classifierParams = { "Decision Tree": (tree.DecisionTreeClassifier, {}),'' "Logistic Regression" : (linear_model.LogisticRegression, {}) } XTrain = numpy.array([[1,2,3],[4,5,6]]) yTrain = numpy.array([0, 1]) def trainModel(name, clazz, params, XTrain, yTrain): print("training ", name) model = clazz(**params) model.fit(XTrain, yTrain) return model joblib.Parallel(n_jobs=4)(joblib.delayed(trainModel)(name, clazz, params, XTrain,

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? This seems

I have a TfidfVectorizer that vectorizes collection of articles followed by feature selection. vectroizer = TfidfVectorizer() X_train = vectroizer.fit_transform(corpus) selector = SelectKBest(chi2, k = 5000 ) X_train_sel = selector.fit_transform(X_train, y_train) Now, I want to store this and use it in other programs. I don't want to re-run the TfidfVectorizer() and the feature selector on the training dataset. How do I do that? I know how to make a model persistent using joblib but I wonder if this is the same as making a model persistent. You can simply use the built in pickle lib: pickle