The minimum required Cuda capability is 3.5

Question

After installing TensorFlow and its dependencies on a g2.2xlarge EC2 instance I tried to run an MNIST example from the getting started page:

python tensorflow/m         


        

Answer 1

There is a section in the official installation page that guides you to enable Cuda 3, but you need to build Tensorflow from source.

$ TF_UNOFFICIAL_SETTING=1 ./configure

# Same as the official settings above

WARNING: You are configuring unofficial settings in TensorFlow. Because some
external libraries are not backward compatible, these settings are largely
untested and unsupported.

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 lib64
Setting up Cuda bin
Setting up Cuda nvvm
Configuration finished

Answer 2

There is a simple trick. You don't even have to build TF from sources.

In the file tensorflow\python\_pywrap_tensorflow.pyd there are two occurences of regex 3\.5.*5\.2. Just replace both 3.5 with 3.0.

Tested on Windows 10, Anaconda 4.2.13, Python 3.5.2, TensorFlow 0.12, CUDA 8, NVidia GTX 660m (CUDA cap. 3.0).

Answer 3

Currently only GPUs with compute capability >= 3.5 are officially supported. However, GitHub user @infojunkie has offered a patch that makes it possible to use TensorFlow with a GPU with compute capability 3.0.

The official fix is in development. Meanwhile, check out the discussion on the GitHub issue for adding this support.