gist for byai.io/howto-tensorflow-1-6-on-mac-with-gpu-acceleration/

clone_tensorflow.sh

$ cd ~/temp
$ git clone https://github.com/tensorflow/tensorflow
$ cd tensorflow
$ git checkout v1.6.0-rc1

compile_samples.sh

$ cp -R /Developer/NVIDIA/CUDA-9.1/samples ~/temp/cuda_samples
$ cd ~/temp/cuda_samples/
$ make -C 1_Utilities/deviceQuery

# execute sample
$ ~/temp/cuda_samples/bin/x86_64/darwin/release/deviceQuery

Detected 1 CUDA Capable device(s)

Device 0: "GeForce GTX 1060 6GB"
  CUDA Driver Version / Runtime Version          9.1 / 9.1
  CUDA Capability Major/Minor version number:    6.1
  Total amount of global memory:                 6144 MBytes (6442254336 bytes)
  (10) Multiprocessors, (128) CUDA Cores/MP:     1280 CUDA Cores
  GPU Max Clock rate:                            1709 MHz (1.71 GHz)
  Memory Clock rate:                             4004 Mhz
  Memory Bus Width:                              192-bit
  L2 Cache Size:                                 1572864 bytes
  Maximum Texture Dimension Size (x,y,z)         1D=(131072), 2D=(131072, 65536), 3D=(16384, 16384, 16384)
  Maximum Layered 1D Texture Size, (num) layers  1D=(32768), 2048 layers
  Maximum Layered 2D Texture Size, (num) layers  2D=(32768, 32768), 2048 layers
  Total amount of constant memory:               65536 bytes
  Total amount of shared memory per block:       49152 bytes
  Total number of registers available per block: 65536
  Warp size:                                     32
  Maximum number of threads per multiprocessor:  2048
  Maximum number of threads per block:           1024
  Max dimension size of a thread block (x,y,z): (1024, 1024, 64)
  Max dimension size of a grid size    (x,y,z): (2147483647, 65535, 65535)
  Maximum memory pitch:                          2147483647 bytes
  Texture alignment:                             512 bytes
  Concurrent copy and kernel execution:          Yes with 2 copy engine(s)
  Run time limit on kernels:                     Yes
  Integrated GPU sharing Host Memory:            No
  Support host page-locked memory mapping:       Yes
  Alignment requirement for Surfaces:            Yes
  Device has ECC support:                        Disabled
  Device supports Unified Addressing (UVA):      Yes
  Supports Cooperative Kernel Launch:            Yes
  Supports MultiDevice Co-op Kernel Launch:      No
  Device PCI Domain ID / Bus ID / location ID:   0 / 195 / 0
  Compute Mode:
     < Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) >

create_wheel_file.sh

$ bazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/tensorflow_pkg
$ cd ~
$ pip install /tmp/tensorflow_pkg/tensorflow-1.6.0rc0-cp36-cp36m-macosx_10_13_x86_64.whl

cuda_driver_check.sh

$ kextstat | grep -i cuda
164    0 0xffffff7f83c65000 0x2000     0x2000     com.nvidia.CUDA (1.1.0) 4329B052-6C8A-3900-8E83-744487AEDEF1 <4 1>

cuda_toolkit_installation.sh

$ vim ~/.bash_profile
    # add to .bash_profile
    export PATH=/usr/local/cuda/bin:/Developer/NVIDIA/CUDA-9.1/bin${PATH:+:${PATH}}
    export DYLD_LIBRARY_PATH=/usr/local/cuda/lib:/Developer/NVIDIA/CUDA-9.1/lib
$ source ~/.bash_profile

downgrading_clt.sh

$ sudo mv /Library/Developer/CommandLineTools /Library/Developer/CommandLineTools_backup
$ sudo xcode-select --switch /Library/Developer/CommandLineTools

install_cudnn.sh

$ tar -xzvf cudnn-9.1-osx-x64-v7-ga.tgz
$ sudo cp cuda/include/cudnn.h /usr/local/cuda/include
$ sudo cp cuda/lib/libcudnn* /usr/local/cuda/lib
$ sudo chmod a+r /usr/local/cuda/include/cudnn.h /usr/local/cuda/lib/libcudnn*

prepare_build.sh

$ ./configure

You have bazel 0.8.1 installed.
Please specify the location of python. [Default is /Users/user/.pyenv/versions/tensorflow-gpu/bin/python]: 


Found possible Python library paths:
  /Users/user/.pyenv/versions/tensorflow-gpu/lib/python3.6/site-packages
Please input the desired Python library path to use.  Default is [/Users/user/.pyenv/versions/tensorflow-gpu/lib/python3.6/site-packages]

Do you wish to build TensorFlow with Google Cloud Platform support? [Y/n]: n
No Google Cloud Platform support will be enabled for TensorFlow.

Do you wish to build TensorFlow with Hadoop File System support? [Y/n]: n
No Hadoop File System support will be enabled for TensorFlow.

Do you wish to build TensorFlow with Amazon S3 File System support? [Y/n]: n
No Amazon S3 File System support will be enabled for TensorFlow.

Do you wish to build TensorFlow with Apache Kafka Platform support? [y/N]: n
No Apache Kafka Platform support will be enabled for TensorFlow.

Do you wish to build TensorFlow with XLA JIT support? [y/N]: n
No XLA JIT support will be enabled for TensorFlow.

Do you wish to build TensorFlow with GDR support? [y/N]: n
No GDR support will be enabled for TensorFlow.

Do you wish to build TensorFlow with VERBS support? [y/N]: n
No VERBS support will be enabled for TensorFlow.

Do you wish to build TensorFlow with OpenCL SYCL support? [y/N]: n
No OpenCL SYCL 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.

Please specify the CUDA SDK version you want to use, e.g. 7.0. [Leave empty to default to CUDA 9.0]: 9.1


Please specify the location where CUDA 9.1 toolkit is installed. Refer to README.md for more details. [Default is /usr/local/cuda]: 


Please specify the cuDNN version you want to use. [Leave empty to default to cuDNN 7.0]: 


Please specify the location where cuDNN 7 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


Do you want to use clang as CUDA compiler? [y/N]: n
nvcc will be used as CUDA compiler.

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


Do you wish to build TensorFlow with MPI support? [y/N]: 
No MPI 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]: 


Would you like to interactively configure ./WORKSPACE for Android builds? [y/N]: 
Not configuring the WORKSPACE for Android builds.

Preconfigured Bazel build configs. You can use any of the below by adding "--config=<>" to your build command. See tools/bazel.rc for more details.
	--config=mkl         	# Build with MKL support.
	--config=monolithic  	# Config for mostly static monolithic build.
Configuration finished

python_and_pyenv.sh

$ brew update
$ brew install pyenv pyenv-virtualenv

    # add to bottom of `.bash_profile`
    if command -v pyenv 1>/dev/null 2>&1; then
      eval "$(pyenv init -)"
      eval "$(pyenv virtualenv-init -)"
    fi

$ source .bash_profile

# install Python
$ pyenv install 3.6.0

# create virtualenv
$ pyenv virtualenv 3.6.0 tensorflow-gpu
$ pyenv activate tensorflow-gpu

set_environment_variables.sh

$ export CUDA_HOME=/usr/local/cuda
# (of course USERNAME is your Mac Username)
$ export DYLD_LIBRARY_PATH=/Users/USERNAME/lib:/usr/local/cuda/lib:/usr/local/cuda/extras/CUPTI/lib 
$ export LD_LIBRARY_PATH=$DYLD_LIBRARY_PATH
$ export PATH=$DYLD_LIBRARY_PATH:$PATH

test_gpu_acceleration.sh

$ pip install git+git://github.com/Theano/Theano.git
$ pip install keras

$ cd ~/temp
$ git clone https://github.com/fchollet/keras.git
$ cd keras/examples

# Run in CPU mode
$ THEANO_FLAGS=mode=FAST_RUN python imdb_cnn.py

# Run in GPU mode
$ THEANO_FLAGS=mode=FAST_RUN,device=gpu,floatX=float32 python imdb_cnn.py

25000/25000 [==============================] - 15s 595us/step - loss: 0.4028 - acc: 0.8008 - val_loss: 0.3038 - val_acc: 0.8690
Epoch 2/2
25000/25000 [==============================] - 10s 387us/step - loss: 0.2298 - acc: 0.9072 - val_loss: 0.2858 - val_acc: 0.8817