macOS: 10.13.3(17D102)
WebDriver: 387.10.10.10.25.156
CUDA Toolkit: 9.1.128
cuDNN: 7
NVDAEGPUSupport: 6
XCode: 8.2
Bazel: 0.15.2(>=0.15.0)
OpenMP: latest
Python: 3.6.5
TensorFlow: 1.11.0
https://qiita.com/74th/items/fc6ebb684c23f3655e7c
https://gist.github.com/smitshilu/53cf9ff0fd6cdb64cca69a7e2827ed0f
http://melonteam.com/posts/pei_zhi_tensorflow_gpu_ban_ben_tian_keng_lu/
https://tweakmind.com/tensorflow-1-5-macos-10-13-2/
https://egpu.io/forums/mac-setup/wip-nvidia-egpu-support-for-high-sierra/
https://github.com/vulgo/webdriver.sh
1 Remove/undo any Info.plist modifications (they aren’t needed anymore and might conflict).
2 Disable SIP
- Restart holding command + r
- Execute 'csrutil disable’
- Restart
3 Install WebDriver with webdriver.sh
git clone https://github.com/vulgo/webdriver.sh.git
cd webdriver.sh
sudo ./webdriver.sh -u https://us.download.nvidia.com/Mac/Quadro_Certified/387.10.10.10.25.156/WebDriver-387.10.10.10.25.156.pkg4 Enable SIP
- Restart holding command + r
- Execute 'csrutil enable --without kext’
- Restart
5 Install nvidia-egpu
- Download https://cdn.egpu.io/wp-content/uploads/wpforo/attachments/71/4376-NVDAEGPUSupport-v6.zip
- Unzip 4376-NVDAEGPUSupport-v6.zip
- Install package NVDAEGPUSupport-v6.pkg
6 Check eGPU status
- Reboot
- Attach the egpu
- Login
- Check whether the GPU is recognized
1 Download packages
#cudadriver_387.128_macos.dmg
http://us.download.nvidia.com/Mac/cuda_387/cudadriver_387.128_macos.dmg
#cuda_9.1.128_mac.dmg
https://developer.nvidia.com/cuda-toolkit-archive
#cudnn-9.1-osx-x64-v7-ga.tgz
https://developer.nvidia.com/rdp/cudnn-download
2 Install packages
-
Install cuda_9.1.128_mac.dmg with default options
-
Install cudadriver_387.128_macos.dmg with default options
-
Install cuDNN
tar -zxf cudnn-9.1-osx-x64-v7-ga.tgz
cd cuda
sudo cp -RPf include/* /Developer/NVIDIA/CUDA-9.1/include/
sudo cp -RPf lib/* /Developer/NVIDIA/CUDA-9.1/lib/
sudo ln -s /Developer/NVIDIA/CUDA-9.1/lib/libcudnn* /usr/local/cuda/lib/3 Add environment variables
vim ~/.zshrc
#if you use bash, this should be ~/.bash_profile
export CUDA_HOME=/usr/local/cuda
export DYLD_LIBRARY_PATH=$CUDA_HOME/lib:$CUDA_HOME/extras/CUPTI/lib
export LD_LIBRARY_PATH=$DYLD_LIBRARY_PATH
export PATH=$CUDA_HOME/bin:$PATH
source ~/.zshrc3 Verify CUDA works fine
cd /Developer/NVIDIA/CUDA-9.1/samples/1_Utilities/deviceQuery
sudo make
./deviceQuery1 Install homebrew
/usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"2 Install coreutils
brew install coreutils3 Install OpenMP
http://antonmenshov.com/2017/09/09/clang-openmp-setup-in-xcode/
https://iscinumpy.gitlab.io/post/omp-on-high-sierra/
brew install cliutils/apple/libomp4 Install bazel
brew install bazel
bazel version5 Install anaconda
brew install anaconda
vim ~/.zshrc
#If you use bash, this should be ~/.bash_profile
export PATH=/usr/local/anaconda3/bin:$PATH
source ~/.zshrc- Download https://download.developer.apple.com/Developer\_Tools/Xcode\_8.2/Xcode\_8.2.xip
- Extract it, and rename Xcode.app to Xcode8.2.app
- Drag Xcode8.2.app to Applications
sudo xcode-select -s /Applications/Xcode8.2.appYou need restore xcode configuration or remove Xcode8.2.app later, since it will break homebrew
sudo xcode-select -s /Applications/Xcode.app1 Add virtualenv and activate virtualenv
conda create --p egpu python=3.6
source activate egpu
pip install six numpy wheel mock
pip install keras_applications==1.0.5 --no-deps
pip install keras_preprocessing==1.0.3 --no-deps
pip install h5py==2.8.02 Clone TensorFlow code
git clone https://github.com/tensorflow/tensorflow.git -b v1.11.03 Modify the code, to make it compatible with macOS
cd tensorflow
sed -i -e "s/ __align__(sizeof(T))//g" tensorflow/core/kernels/concat_lib_gpu_impl.cu.cc
sed -i -e "s/ __align__(sizeof(T))//g" tensorflow/core/kernels/split_lib_gpu.cu.cc
sed -i -e "s/const Subgraph\:\:Identity empty_parent/Subgraph\:\:Identity empty_parent/g" tensorflow/core/grappler/graph_analyzer/graph_analyzer.cc
#disable nccl
sed -i -e "s/\"\/\/tensorflow\/contrib\/nccl/\#\"\/\/tensorflow\/contrib\/nccl/g" tensorflow/contrib/BUILD
sed -i -e "s/\"\/\/tensorflow\/contrib\/nccl/\#\"\/\/tensorflow\/contrib\/nccl/g" tensorflow/contrib/all_reduce/BUILD
sed -i -e "s/\"\/\/tensorflow\/contrib\/nccl/\#\"\/\/tensorflow\/contrib\/nccl/g" tensorflow/contrib/distribute/python/BUILD4 Compile the code
./configure
#Please specify the location of python.: Accept the default option
#Please input the desired Python library path to use.: Accept the default option
#Do you wish to build TensorFlow with Google Cloud Platform support? [Y/n]: n
#Do you wish to build TensorFlow with Hadoop File System support? [Y/n]: n
#Do you wish to build TensorFlow with Amazon AWS Platform support? [Y/n]: n
#Do you wish to build TensorFlow with Apache Kafka Platform support? [Y/n]: n
#Do you wish to build TensorFlow with XLA JIT support? [y/N]: n
#Do you wish to build TensorFlow with GDR support? [y/N]: n
#Do you wish to build TensorFlow with VERBS support? [y/N]: n
#Do you wish to build TensorFlow with nGraph support? [y/N]: n
#Do you wish to build TensorFlow with OpenCL SYCL support? [y/N]: n
#Do you wish to build TensorFlow with CUDA support? [y/N]: y
#Please specify the CUDA SDK version you want to use.: 9.1
#Please specify the location where CUDA 9.1 toolkit is installed.: Accept the default option
#Please specify the cuDNN version you want to use.: 7
#Please specify the location where cuDNN 7 library is installed.: Accept the default option
##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.(GTX10X0: 6.1, GTX9X0: 5.2)
#Please note that each additional compute capability significantly increases your build time and binary size.: 6.1
#Do you want to use clang as CUDA compiler? [y/N]: n
#Please specify which gcc should be used by nvcc as the host compiler. [Default is /usr/bin/gcc]: Accept the default option
#Do you wish to build TensorFlow with MPI support? [y/N]: n
#Please specify optimization flags to use during compilation when bazel option "--config=opt" is specified: Accept the default option
#Would you like to interactively configure ./WORKSPACE for Android builds? [y/N]: n
export CUDA_HOME=/usr/local/cuda
export DYLD_LIBRARY_PATH=/usr/local/cuda/lib:/usr/local/cuda/extras/CUPTI/lib
export LD_LIBRARY_PATH=$DYLD_LIBRARY_PATH
export PATH=$DYLD_LIBRARY_PATH:$PATH
#bazel clean --expunge
bazel build --config=cuda --config=opt --action_env PATH --action_env LD_LIBRARY_PATH --action_env DYLD_LIBRARY_PATH //tensorflow/tools/pip_package:build_pip_package5 Build the wheel and install
bazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/tensorflow_pkg
pip install /tmp/tensorflow_pkg/tensorflow-1.11.0-cp36-cp36m-macosx_10_9_x86_64.whl1 CUDA_ERROR_OUT_OF_MEMORY
https://stackoverflow.com/questions/39465503/cuda-error-out-of-memory-in-tensorflow
https://stackoverflow.com/questions/43467586/tensorflow-cuda-error-out-of-memory-always-happen
#TensorFlow
gpu_options = tf.GPUOptions(allow_growth=True)
with tf.Session(config=tf.ConfigProto(gpu_options=gpu_options)) as sess:
#Keras
import keras.backend as K
config = K.tf.ConfigProto()
config.gpu_options.allow_growth = True
session = K.tf.Session(config=config)2 PyCharm Library not loaded: @rpath/libcudnn.7.dylib
https://stackoverflow.com/questions/37933890/tensorflow-gpu-setup-error-with-cuda-on-pycharm
Add environment variables to python default configuration:
CUDA_HOME=/usr/local/cuda
DYLD_LIBRARY_PATH=/usr/local/cuda/lib:/usr/local/cuda/extras/CUPTI/lib
LD_LIBRARY_PATH=/usr/local/cuda/lib:/usr/local/cuda/extras/CUPTI/lib
Hi, thanks for the tutorial. However, I just can't bazel build successfully. It keeps showing
Any idea why? Also, do I need to plug in the gpu while I am building the file? I didn't attach my egpu during the building, is that a reason for causing the trouble?