tensorflow Mac OS gpu支持 [英] tensorflow Mac OS gpu support

问题描述

根据

https://www.tensorflow.org/install/install_mac 注意:从版本开始1.2，TensorFlow不再在Mac OS X上提供GPU支持. 不再提供对OS X的GPU支持.

https://www.tensorflow.org/install/install_mac Note: As of version 1.2, TensorFlow no longer provides GPU support on Mac OS X. GPU support for OS X is no longer provided.

但是，我想通过雷电3运行带有1080 ti的akitio节点之类的e-gpu设置.

However, I would want to run an e-gpu setup like akitio node with a 1080 ti via thunderbolt 3.

要使此设置正常工作，需要执行哪些步骤? 到目前为止，我知道

What steps are required to get this setup to work? So far I know that

• 禁用SIP
• 运行自动化e-gpu脚本 https://github.com/goalque/automate-eGPU

是必需的.要使CUDA/tensorflow正常工作还需要什么?

are required. What else is needed to get CUDA / tensorflow to work?

推荐答案

我在

I wrote a little tutorial on compiling TensorFlow 1.2 with GPU support on macOS. I think it's customary to copy relevant parts to SO, so here it goes:

1. 如果您以前没有使用过TensorFlow-GPU设置，建议您先使用TensorFlow 1.0或1.1进行所有设置，然后仍然可以执行pip install tensorflow-gpu.一旦完成该工作，如果您正在编译TensorFlow，CUDA设置也将起作用.如果您有外部GPU，请使用YellowPillow的答案(或从源代码安装TensorFlow "，但显然可以用git checkout r1.0代替git checkout r1.2. 在执行./configure时，请注意Python库路径:有时提示不正确.在大多数情况下，我选择了默认选项，但以下选项除外:Python库路径，CUDA支持和计算能力.不要将Clang用作CUDA编译器:这将导致您出现错误"crosstool配置不一致；没有为CPU"darwin"找到与"local_darwin"相对应的工具链.".使用/usr/bin/gcc作为编译器实际上将使用macOS/XCode随附的Clang.以下是我的完整配置.
2. TensorFlow 1.2需要一个名为OpenMP的C库，该库在当前的Apple Clang中不可用.它应该可以在多CPU机器上加快多线程TensorFlow的运行，但是如果没有它，它也会进行编译.我们可以尝试使用gcc 4(我没有管理过)构建TensorFlow，或者从构建文件中删除包含OpenMP的行.就我而言，我注释了tensorflow/third_party/gpus/cuda/BUILD.tpl的第98行，该行又是contained linkopts = [-lgomp](但是该行的位置可能明显改变了).有些人 zmuldefs出现了问题，但我认为这是早期版本的问题;感谢udnaan指出可以注释掉这些行.
3. 在使用最新的bazel 0.5.3进行构建时遇到了一些问题，因此我恢复为使用已经安装的0.4.5.但是在github问题中的一些讨论提到bazel 0.5.2也没有问题.
4. 现在使用bazel进行构建，并按照官方安装指南的说明完成安装.在我的3.2 GHz iMac上，此过程耗时约37分钟.

1. If you haven’t used a TensorFlow-GPU set-up before, I suggest first setting everything up with TensorFlow 1.0 or 1.1, where you can still do pip install tensorflow-gpu. Once you get that working, the CUDA set-up would also work if you’re compiling TensorFlow. If you have an external GPU, YellowPillow's answer (or mine) might help you get things set up.
2. Follow the official tutorial "Installing TensorFlow from Sources", but obviously substitute git checkout r1.0 with git checkout r1.2. When doing ./configure, pay attention to the Python library path: it sometimes suggests an incorrect one. I chose the default options in most cases, except for: Python library path, CUDA support and compute capacity. Don’t use Clang as the CUDA compiler: this will lead you to an error "Inconsistent crosstool configuration; no toolchain corresponding to 'local_darwin' found for cpu 'darwin'.". Using /usr/bin/gcc as your compiler will actually use Clang that comes with macOS / XCode. Below is my full configuration.
3. TensorFlow 1.2 expects a C library called OpenMP, which is not available in the current Apple Clang. It should speed up multithreaded TensorFlow on multi-CPU machines, but it will also compile without it. We could try to build TensorFlow with gcc 4 (which I didn’t manage), or simply remove the line that includes OpenMP from the build file. In my case I commented out line 98 of tensorflow/third_party/gpus/cuda/BUILD.tpl, which contained linkopts = ["-lgomp"] (but the location of the line might obviously change). Some people had issues with zmuldefs, but I assume that was with earlier versions; thanks to udnaan for pointing out that it’s OK to comment out these lines.
4. I had some problems building with the latest bazel 0.5.3, so I reverted to using 0.4.5 that I already had installed. But some discussion in a github issue mentioned bazel 0.5.2 also didn’t have the problem.
5. Now build with bazel and finish the installation as instructed by the official install guide. On my 3.2 GHz iMac this took about 37 minutes.

使用python库路径:/Users/m/code/3rd/conda/envs/p3gpu/lib/python3.6/site-packages

Using python library path: /Users/m/code/3rd/conda/envs/p3gpu/lib/python3.6/site-packages

您是否希望在MKL支持下构建TensorFlow? [y/N] N

Do you wish to build TensorFlow with MKL support? [y/N] N

TensorFlow不会启用MKL支持

No MKL support will be enabled for TensorFlow

请指定在指定bazel选项"--config = opt"时在编译过程中使用的优化标志[默认为-march = native]:

Please specify optimization flags to use during compilation when bazel option "--config=opt" is specified [Default is -march=native]:

您是否希望使用Google Cloud Platform支持构建TensorFlow? [y/N]

Do you wish to build TensorFlow with Google Cloud Platform support? [y/N]

将不会为TensorFlow启用Google Cloud Platform支持

No Google Cloud Platform support will be enabled for TensorFlow

您是否希望使用Hadoop File System支持构建TensorFlow? [y/N]

Do you wish to build TensorFlow with Hadoop File System support? [y/N]

将不会为TensorFlow启用Hadoop文件系统支持

No Hadoop File System support will be enabled for TensorFlow

您是否希望使用XLA即时编译器(实验性)构建TensorFlow? [y/N]

Do you wish to build TensorFlow with the XLA just-in-time compiler (experimental)? [y/N]

不会为TensorFlow启用XLA支持

No XLA support will be enabled for TensorFlow

您想构建具有VERBS支持的TensorFlow吗? [y/N]

Do you wish to build TensorFlow with VERBS support? [y/N]

不会为TensorFlow启用VERBS支持

No VERBS support will be enabled for TensorFlow

您是否希望使用OpenCL支持构建TensorFlow? [y/N]

Do you wish to build TensorFlow with OpenCL support? [y/N]

不会为TensorFlow启用OpenCL支持

No OpenCL support will be enabled for TensorFlow

您是否希望在具有CUDA支持的情况下构建TensorFlow? [y/N] y

Do you wish to build TensorFlow with CUDA support? [y/N] y

将为TensorFlow启用CUDA支持

CUDA support will be enabled for TensorFlow

您要使用clang作为CUDA编译器吗? [y/N]

Do you want to use clang as CUDA compiler? [y/N]

nvcc将用作CUDA编译器

nvcc will be used as CUDA compiler

请指定您要使用的CUDA SDK版本，例如7.0. [留空以使用系统默认值]:

Please specify the CUDA SDK version you want to use, e.g. 7.0. [Leave empty to use system default]:

请指定CUDA工具包的安装位置.有关更多详细信息，请参考README.md. [默认值为/usr/local/cuda]:

Please specify the location where CUDA toolkit is installed. Refer to README.md for more details. [Default is /usr/local/cuda]:

请指定nvcc应使用哪个gcc作为主机编译器. [默认值为/usr/bin/gcc]:

Please specify which gcc should be used by nvcc as the host compiler. [Default is /usr/bin/gcc]:

请指定您要使用的cuDNN版本. [留空以使用系统默认值]:

Please specify the cuDNN version you want to use. [Leave empty to use system default]:

请指定cuDNN库的安装位置.有关更多详细信息，请参考README.md. [默认值为/usr/local/cuda]:

Please specify the location where cuDNN library is installed. Refer to README.md for more details. [Default is /usr/local/cuda]:

请指定要使用的逗号分隔的Cuda计算功能列表.

Please specify a list of comma-separated Cuda compute capabilities you want to build with.

您可以在以下位置找到设备的计算能力: https://developer.nvidia.com/cuda -gpus .

You can find the compute capability of your device at: https://developer.nvidia.com/cuda-gpus.

请注意，每种额外的计算功能都会显着增加构建时间和二进制文件大小.

Please note that each additional compute capability significantly increases your build time and binary size.

[默认值为:"3.5,5.2"]:6.1

[Default is: "3.5,5.2"]: 6.1

信息:开始清理(这可能需要一段时间).如果清理时间超过几分钟，请考虑使用--async.

INFO: Starting clean (this may take a while). Consider using --async if the clean takes more than several minutes.

配置完成

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