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Note : this question was initially asked on github, but it was asked to be here instead

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I'm having trouble running tensorflow on gpu, and it does not seems to be the usual cuda's configuration problem, because everything seems to indicate cuda is properly setup.

The main symptom: when running tensorflow, my gpu is not detected (the code being run, and its output).

What differs from usual issues is that cuda seems properly installed and running ./deviceQuery from cuda samples is successful (output).

I have two graphical cards:

• an old GTX 650 used for my monitors (I don't want to use that one with tensorflow)
• a GTX 1060 that I want to dedicate to tensorflow

I use:

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• cuda-8.0 (ls -l /usr/local/cuda/lib64/libcud*)
• cudnn-5.1.10
• python-2.7.12
• nvidia-drivers-375.26 (this was installed by cuda and replaced my distro driver package)

I've tried:

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• adding /usr/local/cuda/bin/ to $PATH
• forcing gpu placement in tensorflow script using with tf.device('/gpu:1'): (and with tf.device('/gpu:0'): when it failed, for good measure)
• whitelisting the gpu I wanted to use with CUDA_VISIBLE_DEVICES, in case the presence of my old unsupported card did cause problems
• running the script with sudo (because why not)

Here are the outputs of nvidia-smi and nvidia-debugdump -l, in case it's useful.

At this point, I feel like I have followed all the breadcrumbs and have no idea what I could try else. I'm not even sure if I'm contemplating a bug or a configuration problem. Any advice about how to debug this would be greatly appreciated. Thanks!

Update: with the help of Yaroslav on github, I gathered more debugging info by raising log level, but it doesn't seem to say much about the device selection : https://gist.github.com/oelmekki/760a37ca50bf58d4f03f46d104b798bb

Update 2: Using theano detects gpu correctly, but interestingly it complains about cuDNN being too recent, then fallback to cpu (code ran, output). Maybe that could be the problem with tensorflow as well?

4 Answers

From the log output, it looks like you are running the CPU version of TensorFlow (PyPI: tensorflow), and not the GPU version (PyPI: tensorflow-gpu). Running the GPU version would either log information about the CUDA libraries, or an error if it failed to load them or open the driver.

If you run the following commands, you should be able to use the GPU in subsequent runs:

In my case:

is not enough. Because when reinstall with:

It's still reinstall tensorflow with cpu not gpu.So, before install tensorfow-pgu, I tried to remove all related tensor folders in site-packages uninstall protobuf, and it works!

For conclusion:

Remove all tensor folders in ~Python35Libsite-packages

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None of the other answers here worked for me. After a bit of tinkering I found that this fixed my issues when dealing with Tensorflow built from binary:

Step 0: Uninstall protobuf

Step 1: Uninstall tensorflow

Step 2: Force reinstall Tensorflow with GPU support

Download Game According To Gpu Of The Device 2

Step 3: If you haven't already, set CUDA_VISIBLE_DEVICES

So for me with 2 GPUs it would be

Might seem dumb but a sudo reboot has fixed the exact same problem for me and a couple others.

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