tensorflow Mac OS gpu support

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. 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.

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

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

No MKL support will be enabled for TensorFlow

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

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

No Google Cloud Platform support will be enabled for TensorFlow

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

No Hadoop File System support will be enabled for TensorFlow

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

No XLA support will be enabled for TensorFlow

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

No VERBS support will be enabled for TensorFlow

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

No OpenCL support will be enabled for TensorFlow

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

CUDA support will be enabled for TensorFlow

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

nvcc will be used as CUDA compiler

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

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

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

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

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

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

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

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

[Default is: “3.5,5.2”]: 6.1

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

Configuration finished