Mac Cuda Driver Version Is Insufficient For Cuda Runtime Version

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Nvcc — version “` nvcc: NVIDIA (R) Cuda compiler driver. Status: CUDA driver version is insufficient for CUDA runtime version.



Ubuntu 16.04, CUDA 8-CUDA driver version is insufficient for CUDA runtime version (3)

I've installed the latest nvidia drivers (375.26) manually, and installed CUDA using cuda_8.0.44_linux.run (skipping the driver install there, since the bundled drivers are older, 367 I think).

Running the deviceQuery in CUDA samples produces the following error however:

Version info:

$ nvcc --version

The anwer to similar problems has been updating the nvidia display drivers, though in my case this is already done. Does anyone have any ideas? Thanks.

I got that error on Ubuntu 16.04, because I was still using the open-source X.Org video driver. The error went away when I switched to the NVIDIA binary driver.

I found the driver settings by opening the System Settings, then clicking on Software & Updates. The video drivers are on the Additional Drivers tab.

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.

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Prerequisites

Make sure to update your homebrew formulas

Coreutils for Macosx.

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

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

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

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

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

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:

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.

And now we run it:

The output will look like:

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:

or install it manually from source:

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

Checkout tensorflow

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

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.

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

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

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now move to another directory and run a test script:

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

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

Caveats

If you run into this error:

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:

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

If you see

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

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.