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How to enable cuda support for tensor flow on Mac OS X (Updated on April:2016 Tensorflow 0.8)

These instructions will explain how to install tensorflow on mac with cuda enabled GPU suport. I assume you know what tensorflow is and why you would want to have a deep learning framework running on your computer.

Prerequisites

Make sure to update your homebrew formulas

brew update

Coreutils for Macosx.

brew install coreutils swig

Cuda Libraries for macosx. You can install cuda from homebrew using cask.

brew cask install cuda

Make sure that the installed cuda version is 7.5 you can check the version with

brew cask info cuda
cuda: 7.5.20
Nvidia CUDA
https://developer.nvidia.com/cuda-zone
Not installed
https://github.com/caskroom/homebrew-cask/blob/master/Casks/cuda.rb
No Artifact Info

If you don't see 7.5 make sure to upgrade your brew formulas:

brew update
brew upgrade cuda

You need NVIDIA's Cuda Neural Network library libCudnn. You have to register and download it from the website: https://developer.nvidia.com/cudnn.

(Note: from version 0.8 Tensorflow supports cuDNN v5, version 0.7 and 0.7.1 support v4)

Download the file cudnn-7.5-osx-x64-v5.0-rc.tgz

Once downloaded you need to manually copy the files over the /usr/local/cuda/ directory

tar xzvf ~/Downloads/cudnn-7.5-osx-x64-v5.0-rc.tgz
sudo mv -v cuda/lib/libcudnn* /usr/local/cuda/lib
sudo mv -v cuda/include/cudnn.h /usr/local/cuda/include

add in your ~/.bash_profile the reference to /usr/local/cuda/lib. You will need it to run the python scripts.

export DYLD_LIBRARY_PATH=`/usr/local/cuda/lib`:$DYLD_LIBRARY_PATH

Now let's make sure that we are able to compile cuda programs. If you have the latest Xcode Installed (7.3 as the time of this post) nvcc will not work and will give an error like:

nvcc fatal   : The version ('70300') of the host compiler ('Apple clang') is not supported

In order to fix this you need to:

  1. download Xcode 7.2 from the apple developer website
  2. create a new directory /Applications/XCode7.2/
  3. copy the entire XCode.App inside /Applications/XCode7.2
  4. run sudo xcode-select -s /Applications/XCode7.2/Xcode.app/

You should be able to compile the deviceQuery utility found inside the cuda sdk repository. Let's compile the deviceQuery utility to figure out the CUDA_CAPABILITY supported by our graphics card.

cd /usr/local/cuda/samples
sudo make -C 1_Utilities/deviceQuery

And now we run it:

cd /usr/local/cuda/samples/
./bin/x86_64/darwin/release/deviceQuery

The output will look like:

./bin/x86_64/darwin/release/deviceQuery Starting...

 CUDA Device Query (Runtime API) version (CUDART static linking)

Detected 1 CUDA Capable device(s)

Device 0: "GeForce GT 650M"
  CUDA Driver Version / Runtime Version          7.5 / 7.5
  CUDA Capability Major/Minor version number:    3.0
  Total amount of global memory:                 1024 MBytes (1073414144 bytes)
  ( 2) Multiprocessors, (192) CUDA Cores/MP:     384 CUDA Cores
  GPU Max Clock rate:                            900 MHz (0.90 GHz)
  Memory Clock rate:                             2508 Mhz
  Memory Bus Width:                              128-bit
  L2 Cache Size:                                 262144 bytes
  Maximum Texture Dimension Size (x,y,z)         1D=(65536), 2D=(65536, 65536), 3D=(4096, 4096, 4096)
  Maximum Layered 1D Texture Size, (num) layers  1D=(16384), 2048 layers
  Maximum Layered 2D Texture Size, (num) layers  2D=(16384, 16384), 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 1 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
  Device PCI Domain ID / Bus ID / location ID:   0 / 1 / 0
  Compute Mode:
     < Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) >

deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 7.5, CUDA Runtime Version = 7.5, NumDevs = 1, Device0 = GeForce GT 650M
Result = PASS

Here you can confirm that the driver is set to 7.5 and you can find also the cuda capability of your GPU, CUDA Capability Major/Minor version number: 3.0 in my case, so we can set this property when we configure tensorflow.

Install bazel

Use homebrew:

brew install bazel

or install it manually from source:

git clone https://github.com/bazelbuild/bazel.git
cd bazel
git checkout tags/0.2.1
./compile.sh
sudo cp output/bazel /usr/local/bin

Make sure you have the right version of bazel, at least 0.2.1

$ bazel version
Build label: 0.2.1-homebrew
Build target: bazel-out/local_darwin-fastbuild/bin/src/main/java/com/google/devtools/build/lib/bazel/BazelServer_deploy.jar
Build time: Fri Apr 1 00:35:17 2016 (1459470917)
Build timestamp: 1459470917
Build timestamp as int: 1459470917

Checkout tensorflow

As of end of April 2016 the build system is merged in the main development line!

git clone --recurse-submodules https://github.com/tensorflow/tensorflow
cd tensorflow
git checkout master

Then we need to configure it.

I use Anaconda for the python distribution. Notice that you need to set the right TF_CUDA_COMPUTE_CAPABILITES value from the previous deviceQuery operation.

PYTHON_BIN_PATH=$HOME/anaconda/bin/python CUDA_TOOLKIT_PATH="/usr/local/cuda" CUDNN_INSTALL_PATH="/usr/local/cuda" TF_UNOFFICIAL_SETTING=1 TF_NEED_CUDA=1 TF_CUDA_COMPUTE_CAPABILITIES="3.0" TF_CUDNN_VERSION="5" TF_CUDA_VERSION="7.5" TF_CUDA_VERSION_TOOLKIT=7.5 ./configure

Now we are ready to build tensorflow pip package. This may take a while.

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
pip install --upgrade /tmp/tensorflow_pkg/tensorflow-0.8.0rc0-py2-none-any.whl

if you are using anaconda like me you want to add `--ignore-installed``

pip install --upgrade --ignore-installed /tmp/tensorflow_pkg/tensorflow-0.8.0rc0-py2-none-any.whl 

now move to another directory and run a test script:

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)

You should see now the output from the sample program. If not check the Caveats section.

deeplearning$ python test_install.py
I tensorflow/stream_executor/dso_loader.cc:107] successfully opened CUDA library libcublas.7.5.dylib locally
I tensorflow/stream_executor/dso_loader.cc:107] successfully opened CUDA library libcudnn.6.5.dylib locally
I tensorflow/stream_executor/dso_loader.cc:107] successfully opened CUDA library libcufft.7.5.dylib locally
I tensorflow/stream_executor/dso_loader.cc:107] successfully opened CUDA library libcuda.dylib locally
I tensorflow/stream_executor/dso_loader.cc:107] 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:103] Found device 0 with properties:
name: GeForce GT 650M
major: 3 minor: 0 memoryClockRate (GHz) 0.9
pciBusID 0000:01:00.0
Total memory: 1023.69MiB
Free memory: 19.40MiB
I tensorflow/core/common_runtime/gpu/gpu_init.cc:127] DMA: 0
I tensorflow/core/common_runtime/gpu/gpu_init.cc:137] 0:   Y
I tensorflow/core/common_runtime/gpu/gpu_device.cc:703] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GT 650M, pci bus id: 0000:01:00.0)
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:42] Allocating 19.40MiB bytes.
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:52] GPU 0 memory begins at 0x700a80000 extends to 0x701de6000
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:66] Creating bin of max chunk size 1.0KiB
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:66] Creating bin of max chunk size 2.0KiB
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:66] Creating bin of max chunk size 4.0KiB
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:66] Creating bin of max chunk size 8.0KiB
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:66] Creating bin of max chunk size 16.0KiB
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:66] Creating bin of max chunk size 32.0KiB
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:66] Creating bin of max chunk size 64.0KiB
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:66] Creating bin of max chunk size 128.0KiB
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:66] Creating bin of max chunk size 256.0KiB
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:66] Creating bin of max chunk size 512.0KiB
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:66] Creating bin of max chunk size 1.00MiB
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:66] Creating bin of max chunk size 2.00MiB
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:66] Creating bin of max chunk size 4.00MiB
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:66] Creating bin of max chunk size 8.00MiB
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:66] Creating bin of max chunk size 16.00MiB
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:66] Creating bin of max chunk size 32.00MiB
Device mapping:
/job:localhost/replica:0/task:0/gpu:0 -> device: 0, name: GeForce GT 650M, pci bus id: 0000:01:00.0
I tensorflow/core/common_runtime/direct_session.cc:137] Device mapping:
/job:localhost/replica:0/task:0/gpu:0 -> device: 0, name: GeForce GT 650M, pci bus id: 0000:01:00.0

b: /job:localhost/replica:0/task:0/gpu:0
I tensorflow/core/common_runtime/simple_placer.cc:304] 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:304] a: /job:localhost/replica:0/task:0/gpu:0
MatMul: /job:localhost/replica:0/task:0/gpu:0
I tensorflow/core/common_runtime/simple_placer.cc:304] MatMul: /job:localhost/replica:0/task:0/gpu:0
[[ 22.  28.]
 [ 49.  64.]]

You can read more about using GPUs, in tensorflow in the official GPU article.

Caveats

If you run into this error:

ImportError: No module named core.framework.graph_pb2

you are running the script from the same tensorflow directory and python is using the local directory as the module. Change directory see Stackoverflow Question.

If you get this error:

: Library not loaded: @rpath/libcudart.7.5.dylib
  Referenced from: ~/anaconda/envs/tensorflow/lib/python2.7/site-packages/tensorflow/python/_pywrap_tensorflow.so
  Reason: image not found

Is because python is not able to find the cuda library. make sure to set the environment variable.

export DYLD_LIBRARY_PATH=/usr/local/cuda/lib:$DYLD_LIBRARY_PATH

If you see

Ignoring gpu device (device: 0, name: GeForce GT 650M, pci bus id: 0000:01:00.0) with Cuda compute capability 3.0. The minimum required Cuda capability is 3.5.

recompile the library and use the right compute capability (3.0).

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"]: 3.0
Setting up Cuda include
Setting up Cuda lib
Setting up Cuda bin
Setting up Cuda nvvm
Configuration finished

Note that is is still a pull request. So is not officially supported.

I hope that with this tutorial more OSX developers can try the patch and report any errors and confirm that would be a good patch to merge in the main repository.

Stay hungry and have fun.

Hey Mistobaan, thanks a lot for this writeup. I'm encountering an error that isn't documented in the caveats. Maybe you have some insight into what is going wrong? I've created an issue here: tensorflow/tensorflow#875. Thanks!

dland commented Jun 11, 2016

Thanks for the writeup! In case other people run into the same trouble as I did:

deviceQuery Starting...

 CUDA Device Query (Runtime API) version (CUDART static linking)

cudaGetDeviceCount returned 35
-> CUDA driver version is insufficient for CUDA runtime version
Result = FAIL

It probably means you're SOL. I had a closer look at the hardware on my Mac and it says

 Chipset Model: Intel Iris Graphics 6100
  Type: GPU

... and it doesn't support CUDA (e.g. see http://stackoverflow.com/questions/8193242/can-i-run-cuda-on-intel). So now I'm going to look into OpenCL instead.

While running the test script, I get the following error:

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:102] Couldn't open CUDA library libcuda.1.dylib. LD_LIBRARY_PATH: :/usr/local/cuda/lib
I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:160] hostname: sfocfjgn32.ads.autodesk.com
I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:185] libcuda reported version is: Not found: was unable to find libcuda.so DSO loaded into this program
Segmentation fault: 11

What could be the reason for this?

Thanks in advance!!

hbfs commented Jul 26, 2016

https://gist.github.com/Mistobaan/dd32287eeb6859c6668d#gistcomment-1828799 @ady477 , create a symlink for libcuda.1.dylib pointing to libcuda.dylib in /usr/local/cuda/lib

$ cd /usr/local/cuda/lib
$ ln -s libcuda.dylib libcuda.1.dylib

Working great - thanks! I ran into this issue: tensorflow/tensorflow#2040 so I had to run:
cp -r bazel-bin/tensorflow/tools/pip_package/build_pip_package.runfiles/__main__/* bazel-bin/tensorflow/tools/pip_package/build_pip_package.runfiles/ before running bazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/tensorflow_pkg

Hi I get the following issue could not create cudnn handle: CUDNN_STATUS_NOT_INITIALIZED when trying to run a tf script. Not sure what I have missed.

matt@Matthews-MacBook-Pro:~/$ py3 main.py
I tensorflow/stream_executor/dso_loader.cc:108] successfully opened CUDA library libcublas.dylib locally
I tensorflow/stream_executor/dso_loader.cc:108] successfully opened CUDA library libcudnn.dylib locally
I tensorflow/stream_executor/dso_loader.cc:108] successfully opened CUDA library libcufft.dylib locally
I tensorflow/stream_executor/dso_loader.cc:108] successfully opened CUDA library libcuda.1.dylib locally
I tensorflow/stream_executor/dso_loader.cc:108] successfully opened CUDA library libcurand.dylib locally
number of elements at final reshape = %d. 61440
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: 1.28GiB
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:838] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GT 750M, pci bus id: 0000:01:00.0)
E tensorflow/stream_executor/cuda/cuda_dnn.cc:354] could not create cudnn handle: CUDNN_STATUS_NOT_INITIALIZED
E tensorflow/stream_executor/cuda/cuda_dnn.cc:361] error retrieving driver version: Invalid argument: expected %d.%d or %d.%d.%d form for driver version; got ""
E tensorflow/stream_executor/cuda/cuda_dnn.cc:321] could not destroy cudnn handle: CUDNN_STATUS_BAD_PARAM
F tensorflow/core/kernels/conv_ops.cc:457] Check failed: stream->parent()->GetConvolveAlgorithms(&algorithms)
Abort trap: 6

I tensorflow/stream_executor/dso_loader.cc:116] successfully opened CUDA library libcublas.dylib locally
I tensorflow/stream_executor/dso_loader.cc:116] successfully opened CUDA library libcudnn.dylib locally
I tensorflow/stream_executor/dso_loader.cc:116] successfully opened CUDA library libcufft.dylib locally
I tensorflow/stream_executor/dso_loader.cc:116] successfully opened CUDA library libcuda.1.dylib locally
I tensorflow/stream_executor/dso_loader.cc:116] successfully opened CUDA library libcurand.dylib locally
E tensorflow/stream_executor/cuda/cuda_driver.cc:496] failed call to cuInit: CUDA_ERROR_NOT_INITIALIZED
I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:153] retrieving CUDA diagnostic information for host: localhost
I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:160] hostname: localhost
I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:185] libcuda reported version is: Not found: was unable to find libcuda.so DSO loaded into this program
I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:189] kernel reported version is: Invalid argument: expected %d.%d or %d.%d.%d form for driver version; got ""

how can I fix this?

jerrykur commented Nov 2, 2016

I am sure this a dumb question, but does this only support NVidia GPUed MacBooks. Mine is a 2015 and has the AMD GPU.

markstrefford commented Nov 4, 2016

As Cuda 8 is out now, brew cask install cuda installs that and not v7.5. You can get 7.5 from here:
https://developer.nvidia.com/cuda-toolkit-archive
On my Mac (running OSX Sierra), the libraries are installed here /Developer/NVIDIA/CUDA-7.5

markstrefford commented Nov 7, 2016

If you get a segmentation fault 11 when you perform import tensorflow as tf here's a link to the fix:
tensorflow/tensorflow#2278

can anyone succeed on osx 10.12.1 and cuda8.0

I was able to install on osx 10.12.1 and cuda8.0. See my post here

danilogr commented Dec 1, 2016

@wanghaisheng

For osx 10.12.1 and cuda 8.0:

DNN 5.0 for Cuda 8.0

Instead of downloading cuDNN 5.0 for Cuda 7.5, download it for Cuda 8.0 (cudnn-8.0-osx-x64-v5.0-ga-tgz from https://developer.nvidia.com/cudnn). Don't forget to unpack it and copy its files to your Cuda folder (in the gist above /usr/local/cuda).

I had to dynamically link libcudnn.5.dylib to libcudnn.5

sudo  ln -s /usr/local/cuda/lib/libcudnn.5.dylib /usr/local/cuda/lib/libcudnn.5

Building TensorFlow

Don't forget to checkout the correct tag (currently 0.12)

git checkout tags/0.12.0-rc0

Now you can configure it

CUDA_TOOLKIT_PATH="/usr/local/cuda" CUDNN_INSTALL_PATH="/usr/local/cuda" TF_UNOFFICIAL_SETTING=1 TF_NEED_CUDA=1 TF_CUDA_COMPUTE_CAPABILITIES="3.0" TF_CUDNN_VERSION="5" TF_CUDA_VERSION="8.0" TF_CUDA_VERSION_TOOLKIT=8.0 ./configure

or, if you need to specify Anaconda's Python path:

PYTHON_BIN_PATH=$HOME/anaconda/bin/python CUDA_TOOLKIT_PATH="/usr/local/cuda" CUDNN_INSTALL_PATH="/usr/local/cuda" TF_UNOFFICIAL_SETTING=1 TF_NEED_CUDA=1 TF_CUDA_COMPUTE_CAPABILITIES="3.0" TF_CUDNN_VERSION="5" TF_CUDA_VERSION="8.0" TF_CUDA_VERSION_TOOLKIT=8.0 ./configure

Time to build tensorflow:

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
 pip install --upgrade /tmp/tensorflow_pkg/tensorflow-0.12.0rc0-cp35-cp35m-macosx_10_6_x86_64.whl

or, if using Anaconda

pip install --upgrade --ignore-installed /tmp/tensorflow_pkg/tensorflow-0.12.0rc0-cp35-cp35m-macosx_10_6_x86_64.whl

After that, voala!

>>> import tensorflow as tf
I tensorflow/stream_executor/dso_loader.cc:128] successfully opened CUDA library libcublas.8.0.dylib locally
I tensorflow/stream_executor/dso_loader.cc:128] successfully opened CUDA library libcudnn.5.dylib locally
I tensorflow/stream_executor/dso_loader.cc:128] successfully opened CUDA library libcufft.8.0.dylib locally
I tensorflow/stream_executor/dso_loader.cc:128] successfully opened CUDA library libcuda.1.dylib locally
I tensorflow/stream_executor/dso_loader.cc:128] successfully opened CUDA library libcurand.8.0.dylib locally

Troubleshooting

If bazel build exits with the following error:
Library not loaded: .8.0.dylib

Then follow the instructions here: tensorflow/tensorflow#4187
Hint: Remember to execute bazel build with the flag --verbose_failure and look for Referenced from:

Referenced from: /private/var/tmp/_bazel_danilogr/0c5be10000016b79ce9fc0818f000dea/execroot/tensorflow/bazel-out/host/bin/tensorflow/python/gen_script_ops_py_wrappers_cc

then copy the path after Referenced from: all the way to /execroot/tensorflow. That's the directory where gen

# Copy only until `tensorflow`: `/private/var/tmp/_bazel_USER/0c5be10000016b79ce9fc0818f000dea/execroot/tensorflow/`
cd /private/var/tmp/_bazel_USER/0c5be10000016b79ce9fc0818f000dea/execroot/tensorflow/
cd external/bazel_tools/tools/genrule/

Being in that directory, follow the instructions available in the link above

anyone getting python crashed with just these and some code 138 error at the end. You need to create a symlink for libcuda.1

Last Seen-
Using TensorFlow backend.
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcublas.8.0.dylib locally
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcudnn.5.dylib locally
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcufft.8.0.dylib locally

Repair code in terminal-
ln -s libcuda.dylib libcuda.1.dylib

After code you will see
Using TensorFlow backend.
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcublas.8.0.dylib locally
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcudnn.5.dylib locally
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcufft.8.0.dylib locally
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcuda.1.dylib locally
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcurand.8.0.dylib locally

It took me a whole day to fix this. If someone needs help, this will help them.

I was able to install it as a binary version on my MacBook 2013 late.
Is there a difference in performance between getting the source code and building it?

tslater commented Aug 13, 2017

Has anyone gotten this to work with TensorFlow 1.2 yet?

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