Menu
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.
751
Hardware requirements. Note: TensorFlow binaries use AVX instructions which may not run on older CPUs. The following GPU-enabled devices are supported: NVIDIA® GPU card with CUDA® architectures 3.5, 5.0, 6.0, 7.0, 7.5, 8.0 and higher.
TensorFlow provides strong support for distributing deep learning across multiple GPUs. TensorFlow is an open source platform that you can use to develop and train machine learning and deep learning models. TensorFlow operations can leverage both CPUs and GPUs.
Sep.2017 Update: No way to do that without problems and pains. I've tried hard all the ways and even apply below trick to force it run but finally I had to give up. If you are serious with Tensorflow just go ahead and buy 3.0 compute capability GPU.
This is a trick to force tensorflow run on 2.0 compute capability GPU (not officially):
Open it with Notepad++ or something similar
Search for the first occurence of 3\.5.*5\.2 using regex
You see the 3.0 before 3.5*5.2, change it to 2.0
I changed as above and can do simple calculation with GPU, but get stuck with strange and unknown issues when try with practical projects(those projects run well with 3.0 compute capability GPU)
37
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.
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.
86
If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
Donate Us With
