加载TensorFlow后派生一个python进程 [英] forking a python process after loading TensorFlow

问题描述

tf.Session()是不安全的，这意味着在TensorFlow加载到内存中时分叉一个进程后系统的行为是未知的.

tf.Session() is not fork safe which means that the behavior of the system after forking a process while TensorFlow is loaded into the memory is unknown.

在多个进程之间共享多个设备(在一台计算机上)是否有解决办法?

is there any work around for sharing multiple devices (on a single machine), between multiple processes?

推荐答案

在多个进程之间共享TensorFlow运行时的标准方法是使用

The standard way to share a TensorFlow runtime between multiple processes is to use the distributed TensorFlow support, which also works on a single machine.

在一个过程中，您可以通过运行以下代码来启动服务器:

In one process, you can start a server by running the following code:

import tensorflow as tf
server = tf.train.Server.create_local_server()
print server.target  # for other processes to connect
server.join()

默认情况下，此过程将拥有计算机上的所有设备.

This process will own all of the devices on the machine, by default.

在其他过程中，您可以创建连接到服务器的tf.Session对象:

In the other processes, you can create tf.Session objects that connect to the server:

sess = tf.Session("grpc://localhost:...")  # Use value of `server.target`.

这些会话可以像进程内会话一样使用.

These sessions can be used just like in-process sessions.

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