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如下所示：
from multiprocessing import Process
import os
def training_function(...):
import keras # 此处需要在子进程中
...
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.
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