为什么 Tensorflow 创建这么多 CPU 线程 [英] Why Tensorflow creates so many CPU threads

问题描述

即使有

inter_op_parallelism_threads = 1
intra_op_parallelism_threads = 1

值设置，TensorFlow 1.5 进程不是单线程的.为什么?有没有办法完全禁用意外的线程生成?

values set, TensorFlow 1.5 process is not single-threaded. Why? Is there a way to completely disable unexpected thread spawning?

推荐答案

首先，TensorFlow 是一个多层次的软件栈，每一层都试图变得聪明，并引入了一些自己的工作线程:

First of all, TensorFlow is a multi-level software stack, and each layer tries to be smart and introduces some worker threads of its own:

• 一个线程由 Python 运行时创建
• NVIDIA CUDA 运行时创建了另外两个线程

接下来，有一些线程源于 TensorFlow 如何管理内部计算作业:

Next, there are threads originating from the way how TensorFlow administers internal compute jobs:

• 始终创建/加入线程以轮询作业完成(GRPC 引擎)

因此，即使所有选项都设置为 1，TensorFlow 也不能是单线程的.也许，这种设计旨在减少异步作业的延迟.然而，有一个缺点:多核计算库，如线性代数，使用静态对称核心线程映射最好地执行缓存密集型操作.TensorFlow 产生的悬空回调线程会一直扰乱这种对称性.

Thus, TensorFlow cannot be single-threaded, even with all options set to 1. Perhaps, this design is intended to reduce latencies for async jobs. Yet, there is a certain drawback: multicore compute libraries, such as linear algebra, do cache-intensive operations best with static symmetric core-thread mapping. And dangling callback threads produced by TensorFlow will disturb this symmetry all the time.

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