如下所示：

 from multiprocessing import Processimport os  def training_function(...): import keras # 此處需要在子進程中 ... if __name__ == '__main__': p = Process(target=training_function, args=(...,)) p.start()

原文地址：https://stackoverflow.com/questions/42504669/keras-tensorflow-and-multiprocessing-in-python

1、DO NOT LOAD KERAS TO YOUR MAIN ENVIRONMENT. If you want to load Keras / Theano / TensorFlow do it only in the function environment. E.g. don't do this:

import keras def training_function(...): ...

but do the following:

def training_function(...): import keras ...

Run work connected with each model in a separate process: I'm usually creating workers which are making the job (like e.g. training, tuning, scoring) and I'm running them in separate processes. What is nice about it that whole memory used by this process is completely freedwhen your process is done. This helps you with loads of memory problems which you usually come across when you are using multiprocessing or even running multiple models in one process. So this looks e.g. like this:

def _training_worker(train_params): import keras model = obtain_model(train_params) model.fit(train_params) send_message_to_main_process(...) def train_new_model(train_params): training_process = multiprocessing.Process(target=_training_worker, args = train_params) training_process.start() get_message_from_training_process(...) training_process.join()

Different approach is simply preparing different scripts for different model actions. But this may cause memory errors especially when your models are memory consuming. NOTE that due to this reason it's better to make your execution strictly sequential.

以上這篇keras tensorflow 實現在python下多進程運行就是小編分享給大家的全部內容了，希望能給大家一個參考，也希望大家多多支持武林站長站。