如何在一个内核上运行Tensorflow？ [英] How can I run Tensorflow on one single core?

问题描述

我在集群上使用Tensorflow，我想告诉Tensorflow仅在一个核心上运行（即使有更多可用的核心）。

I'm using Tensorflow on a cluster and I want to tell Tensorflow to run only on one single core (even though there are more available).

有人吗？知道这是否可能吗？

Does someone know if this is possible?

推荐答案

要在一个CPU线程上运行Tensorflow，我使用：

To run Tensorflow on one single CPU thread, I use:

session_conf = tf.ConfigProto(
      intra_op_parallelism_threads=1,
      inter_op_parallelism_threads=1)
sess = tf.Session(config=session_conf)

device_count 限制使用的CPU，而不是内核或线程的数量。

device_count limits the number of CPUs being used, not the number of cores or threads.

tensorflow / tensorflow / core / protobuf / config.proto 说：

message ConfigProto {
  // Map from device type name (e.g., "CPU" or "GPU" ) to maximum
  // number of devices of that type to use.  If a particular device
  // type is not found in the map, the system picks an appropriate
  // number.
  map<string, int32> device_count = 1;

---

在Linux上，您可以运行 sudo dmidecode -t 4 | egrep -i Designation | Intel | core | thread 查看您有多少个CPU /内核/线程，例如以下有2个CPU，每个都有8个内核，每个都有2个线程，总共2 * 8 * 2 = 32个线程：

---

On Linux you can run sudo dmidecode -t 4 | egrep -i "Designation|Intel|core|thread" to see how many CPUs/cores/threads you have, e.g. the following has 2 CPUs, each of them has 8 cores, each of them has 2 threads, which gives a total of 2*8*2=32 threads:

fra@s:~$ sudo dmidecode -t 4 | egrep -i "Designation|Intel|core|thread"
    Socket Designation: CPU1
    Manufacturer: Intel
            HTT (Multi-threading)
    Version: Intel(R) Xeon(R) CPU E5-2667 v4 @ 3.20GHz
    Core Count: 8
    Core Enabled: 8
    Thread Count: 16
            Multi-Core
            Hardware Thread
    Socket Designation: CPU2
    Manufacturer: Intel
            HTT (Multi-threading)
    Version: Intel(R) Xeon(R) CPU E5-2667 v4 @ 3.20GHz
    Core Count: 8
    Core Enabled: 8
    Thread Count: 16
            Multi-Core
            Hardware Thread

在Tensorflow 0.12.1和1.0.0以及Ubuntu 14.04.5 LTS x64和Ubuntu 16.04 LTS x64上进行了测试。

Tested with Tensorflow 0.12.1 and 1.0.0 with Ubuntu 14.04.5 LTS x64 and Ubuntu 16.04 LTS x64.

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