install tensorflow gpu version on macbook pro

install tensorflow gpu version on macbook pro

1. check if it has a nvidia gpu
2. build tensorflow binary
3. create python virtual environment
4. install tensorflow
5. test (on terminal)
6. pycharm configuration
7. test (on pycharm)

1.check if it has a nvidia gpu.

Menu / / About this Mac / Overview / System Report

https://support.apple.com/en-us/HT202053

2.build tensorflow binary

# home brew

/usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"

# bazel, swig

brew install bazel swig

# six, numpy, wheel

sudo easy_install -U six
sudo easy_install -U numpy
sudo easy_install wheel

# ipython

sudo easy_install ipython

# coreutils

brew install coreutils

# cuda toolkit

brew tap caskroom/cask
brew cask install cuda

# bash_profile

vi ~/.bash_profile

export CUDA_HOME=/usr/local/cuda
export DYLD_LIBRARY_PATH="$DYLD_LIBRARY_PATH:$CUDA_HOME/lib"
export PATH="$CUDA_HOME/bin:$PATH"

# download cuDNN

Finally, you will also want to install the CUDA Deep Neural Network (cuDNN) library which currently requires an Accelerated Computing Developer Program account. Once you have it downloaded locally, you can unzip and move the header and libraries to your local CUDA Toolkit folder:

Download cuDNN v5.1 RC (June 16, 2016), for CUDA 7.5

• cuDNN v5.1 Library for OSX (cudnn-7.5-osx-x64-v5.1-rc.tgz)

# unzip & move

sudo mv include/cudnn.h /Developer/NVIDIA/CUDA-7.5/include/
sudo mv lib/libcudnn* /Developer/NVIDIA/CUDA-7.5/lib
sudo ln -s /Developer/NVIDIA/CUDA-7.5/lib/libcudnn* /usr/local/cuda/lib/

# download tensorflow source code

git clone --branch v0.9.0rc0 --single-branch https://github.com/tensorflow/tensorflow

# cd

cd tensorflow

# check cuda compute capabilities

https://developer.nvidia.com/cuda-gpus

# configuration

./configure

Please specify the location of python. [Default is /usr/bin/python]:
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 GPU support? [y/N] y
GPU support will be enabled for TensorFlow
Please specify which gcc nvcc should use as the host compiler. [Default is /usr/bin/gcc]:
Please specify the Cuda SDK version you want to use, e.g. 7.0. [Leave empty to use system default]: 7.5
Please specify the location where CUDA 7.5 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 use system default]: 5
Please specify the location where cuDNN 5 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. 3.0
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.

Setting up Cuda include
Setting up Cuda lib
Setting up Cuda bin
Setting up Cuda nvvm
Setting up CUPTI include
Setting up CUPTI lib64
Configuration finished

# build

bazel build -c opt --config=cuda //tensorflow/tools/pip_package:build_pip_package
bazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/tensorflow_pkg

3.create python virtual environment

sudo easy_install pip

sudo pip install --upgrade virtualenv

virtualenv --system-site-packages ~/tensorflow_gpu

source ~/tensorflow_gpu/bin/activate

4.install tensorflow

# install
sudo pip install /tmp/tensorflow_pkg/tensorflow-0.9.0-py2-none-any.whl

5.test (on terminal)

# create a test program

vi test.py

import tensorflow as tf # Creates a graph. a = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[2, 3], name='a') b = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[3, 2], name='b') c = tf.matmul(a, b) # Creates a session with log_device_placement set to True. sess = tf.Session(config=tf.ConfigProto(log_device_placement=True)) # Runs the op. print sess.run(c)

# run test.py

python test.py

I tensorflow/stream_executor/dso_loader.cc:108] successfully opened CUDA library libcublas.7.5.dylib locally I tensorflow/stream_executor/dso_loader.cc:108] successfully opened CUDA library libcudnn.5.dylib locally I tensorflow/stream_executor/dso_loader.cc:108] successfully opened CUDA library libcufft.7.5.dylib locally I tensorflow/stream_executor/dso_loader.cc:108] successfully opened CUDA library libcuda.dylib locally I tensorflow/stream_executor/dso_loader.cc:108] successfully opened CUDA library libcurand.7.5.dylib locally I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:892] OS X does not support NUMA - returning NUMA node zero I tensorflow/core/common_runtime/gpu/gpu_init.cc:102] Found device 0 with properties: name: GeForce GT 750M major: 3 minor: 0 memoryClockRate (GHz) 0.9255 pciBusID 0000:01:00.0 Total memory: 2.00GiB Free memory: 1.74GiB I tensorflow/core/common_runtime/gpu/gpu_init.cc:126] DMA: 0 I tensorflow/core/common_runtime/gpu/gpu_init.cc:136] 0:   Y I tensorflow/core/common_runtime/gpu/gpu_device.cc:806] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GT 750M, pci bus id: 0000:01:00.0) Device mapping: /job:localhost/replica:0/task:0/gpu:0 -> device: 0, name: GeForce GT 750M, pci bus id: 0000:01:00.0 I tensorflow/core/common_runtime/direct_session.cc:175] Device mapping: /job:localhost/replica:0/task:0/gpu:0 -> device: 0, name: GeForce GT 750M, pci bus id: 0000:01:00.0 MatMul: /job:localhost/replica:0/task:0/gpu:0 I tensorflow/core/common_runtime/simple_placer.cc:818] MatMul: /job:localhost/replica:0/task:0/gpu:0 b: /job:localhost/replica:0/task:0/gpu:0 I tensorflow/core/common_runtime/simple_placer.cc:818] b: /job:localhost/replica:0/task:0/gpu:0 a: /job:localhost/replica:0/task:0/gpu:0 I tensorflow/core/common_runtime/simple_placer.cc:818] a: /job:localhost/replica:0/task:0/gpu:0 [[ 22.  28.] [ 49.  64.]]

6.pycharm configuration

# pycharm

https://www.jetbrains.com/pycharm/

# add virtual environment

menu / pycharm / preferences / / /

# select python interpreter

~/tensorflow_gpu/bin/python

# set environment variables

menu / run / edit configurations /

# set environment variables

Name: DYLD_LIBRARY_PATH

Value: /usr/local/cuda/lib

7.test (on pycharm)