Keras + Tensorflow 和 Python 中的多处理 [英] Keras + Tensorflow and Multiprocessing in Python

问题描述

我使用 Keras 和 Tensorflow 作为后端.

I'm using Keras with Tensorflow as backend.

我试图在我的主进程中保存一个模型，然后在另一个进程中加载​​/运行(即调用 model.predict).

I am trying to save a model in my main process and then load/run (i.e. call model.predict) within another process.

我目前只是尝试使用文档中的幼稚方法来保存/加载模型:https://keras.io/getting-started/faq/#how-can-i-save-a-keras-model.
所以基本上:

I'm currently just trying the naive approach from the docs to save/load the model: https://keras.io/getting-started/faq/#how-can-i-save-a-keras-model.
So basically:

1. model.save() 在主进程中
2. model = load_model() 在子进程中
3. model.predict() 在子进程中

1. model.save() in main process
2. model = load_model() in child process
3. model.predict() in child process

然而，它只是挂在 load_model 调用上.

However, it simply hangs on the load_model call.

四处搜索我发现这个潜在相关的答案表明 Keras 只能在一个进程中使用:将多处理与 theano 结合使用，但我不确定这是否属实(似乎找不到太多关于此的信息).

Searching around I've discovered this potentially related answer suggesting that Keras can only be utilized in one process: using multiprocessing with theano but am unsure if this is true (can't seem to find much on this).

有没有办法实现我的目标?非常感谢高级描述或简短示例.

Is there a way to accomplish my goal? A high level description or short example is greatly appreciated.

注意:我尝试过将图形传递给进程的方法，但失败了，因为张量流图形似乎不可选择(相关的 SO 帖子在这里:Tensorflow:将会话传递给 python 多进程).如果确实有一种方法可以将张量流图/模型传递给子进程，那么我也对此持开放态度.

Note: I've attempted approaches along the lines of passing a graph to the process but failed since it seems tensorflow graphs aren't pickable (related SO post for that here: Tensorflow: Passing a session to a python multiprocess). If there is indeed a way to pass the tensorflow graph/model to the child process then I am open to that as well.

谢谢！

推荐答案

根据我的经验 - 问题在于将 Keras 加载到一个进程，然后在 keras 已加载到您的主环境中.但是对于某些应用程序(例如训练混合 Keras 模型)，最好在一个过程中拥有所有这些东西.所以我的建议是以下(有点麻烦 - 但对我有用)方法:

From my experience - the problem lies in loading Keras to one process and then spawning a new process when the keras has been loaded to your main environment. But for some applications (like e.g. training a mixture of Kerasmodels) it's simply better to have all of this things in one process. So what I advise is the following (a little bit cumbersome - but working for me) approach:

1. 请勿将 KERAS 加载到您的主要环境.如果你想加载 Keras/Theano/TensorFlow 只在函数环境中做.例如.不要这样做:

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(...):
    ...

但请执行以下操作:

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

在单独的流程中运行与每个模型相关的工作:我通常会创建工作人员来完成工作(例如训练、调整、评分)并且我正在运行它们在不同的进程中.当你的进程完成时，这个进程使用的整个内存被完全释放有什么好处.这可以帮助您解决在使用多处理甚至在一个进程中运行多个模型时通常会遇到的大量内存问题.所以这看起来例如像这样:

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 freed when 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()

不同的方法只是为不同的模型动作准备不同的脚本.但这可能会导致内存错误，尤其是当您的模型消耗内存时.注意，由于这个原因，最好严格按顺序执行.

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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