Category : multiprocessing

I’m able to run a python script which is using multiprocessing module (also tried with concurrent.futures module but the same result) in my windows 10 machine, python 3.8.4 environment from command line. The same script if I try to run in command line on another machine with Win 7, python 3.7.4 environment, it stops with ..

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I’m able to run a python script which is using multiprocessing module (also tried with concurrent.futures module but the same result) in my windows 10 machine, python 3.8.4 environment from command line. The same script if I try to run in command line on another machine with Win 7, python 3.7.4 environment, it stops with ..

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Can anyone help me speeding up this this loop with Ray or Multiprocessing. Trying to get as much help as possible so any tip or advice is welcome. Thanks, M. n_train = 180 n_records = len(X) pred = [] for i in range(n_train, n_records): train_model = MultiOutputClassifier(DecisionTreeClassifier(), n_jobs=-1).fit(X[1:i], y[1:i]) predict_model = train_model.predict(X[i:i+1]) pred.append(predict_model) print(predict_model) Source: ..

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I have to download a list of files from a slow ftp server, each downloading lasts around 40s. For each of this files I have to process to tasks: ‘A’ lasts around 30-40s ‘a’ lasts less than 10s Each time a download is finished I’m launching processes ‘A’ and ‘a’ in parallel using multiprocessing library ..

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In an effort to make fitting multiple models more efficient, I have been trying to use all available CPU’s and/or parallelize the process. I found out that quite some sklearn functions support the n_jobs argument which allows for the use of all CPU cores. This is not available for all models and functions, especially when ..

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If my function to parallelize has many output variables, I can gather all the answers with p.join()? p=mp.Process(target=calculate,args=(a,b,c,)) p.start() # Launch fraction_calculate() processes.append(p) for p in processes: p.join() I understand that if there is only output this works but if there are more, how? Source: Python..

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