I have a Windows 10 laptop with a GEFORCE GTX 1050. On the NVIDIA developers site it is NOT listed as being supported by the CUDA tools, and therefore, I first assumed, I would not be ble to run GPU supported tensorflow on this machine. However that was a mistake: it is possible and the speed improvement is impressive.
- Download the CUDA 8.0 toolkit from https://developer.nvidia.com/cuda-downloads
- Install it, without the drivers, ignore the error message that says it does not see a supported GPU
- Also download and install the patch from the above website.
- Create a (free) developers account on the developers site.
- Download cudNN 6.0 after logging in. Also download the installtion guide.
- Copy the cudNN files to the location C:\Program Files\NVIDIA GPU
Computing Toolkit\CUDA\v8.0 - Check whether the Path for CUDA has been set (see the installation guide if you do not know how to do that).
That’s it. Very simple but it took me hours to figure out.
In R, now you can download the keras package and issues the commands:
libabry(keras)
install_keras(tensorflow="gpu")
and you are ready to rock.
In the first test I ran it was 213 second without gpu and 18 sec with GPU sopprt.
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