cuda - How can I make tensorflow run on a GPU with capability 2.x?

Question

I've successfully installed tensorflow (GPU) on Linux Ubuntu 16.04 and made some small changes in order to make it work with the new Ubuntu LTS release.

However, I thought (who knows why) that my GPU met the minimum requirement of a compute capability greater than 3.5. That was not the case since my GeForce 820M has just 2.1. Is there a way of making tensorflow GPU version working with my GPU?

I am asking this question since apparently there was no way of making tensorflow GPU version working on Ubuntu 16.04 but by searching the internet I found out that was not the case and indeed I made it almost work were it not for this unsatisfied requirement. Now I am wondering if this issue with GPU compute capability could be fixed as well.

Answer 1

Recent GPU versions of tensorflow require compute capability 3.5 or higher (and use cuDNN to access the GPU.

cuDNN also requires a GPU of cc3.0 or higher:

cuDNN is supported on Windows, Linux and MacOS systems with Pascal, Kepler, Maxwell, Tegra K1 or Tegra X1 GPUs.

• Kepler = cc3.x
• Maxwell = cc5.x
• Pascal = cc6.x
• TK1 = cc3.2
• TX1 = cc5.3

Fermi GPUs (cc2.0, cc2.1) are not supported by cuDNN.

Older GPUs (e.g. compute capability 1.x) are also not supported by cuDNN.

Note that there has never been either a version of cuDNN or any version of TF that officially supported NVIDIA GPUs less than cc3.0. The initial version of cuDNN started out by requiring cc3.0 GPUs, and the initial version of TF started out by requiring cc3.0 GPUs.