CODEWITHSUNDEEP
pythoninstallationgpupytorchconda
user1953366
user1953366

Reputation: 1611

How to use GPU in pytorch?

I tried following steps at: https://pytorch.org/get-started/locally/

First I created a conda environment as:

conda create -n facenet37_2 python=3.7

Then on above site I selected:

PyTorch Build: Stable (1.4)
OS: Linux (I am using Ubuntu 18.04)
Package: conda
Language: python
CUDA: 10.1

and it asked me to run following command:

conda install pytorch torchvision cudatoolkit=10.1 -c pytorch

But after that when I opened python and typed:

import torch
torch.cuda.is_available()

I get False

I have GeForce GT 630M (computeCapability: 2.1). But it is not getting detected. Why? Is it too old and no longer supported? How can I fix the issue?

Edit: Why did I get a negative vote?

Upvotes: 4

Views: 11135

Answers (2)

jodag
jodag

Reputation: 22204

The GeForce GT 630M has compute capability 2.1 and therefore only supports up to CUDA 8.

  • PyTorch binaries dropped support for compute capability <= 5.0 in PyTorch 0.3.1. It's not clear to me if compute capability 2.1 was ever included in the binaries.
  • The PyTorch codebase dropped CUDA 8 support in PyTorch 1.1.0.

Due to the second point there's no way short of changing the PyTorch codebase to make your GPU work with the latest version. Your options are:

  • Install PyTorch without GPU support.
  • Try compiling PyTorch < 1.1.0 from source (instructions). Make sure to checkout the v1.0.1 tag. This will produce a binary with support for your compute capability.
  • If acceptable you could try installing a really old version: PyTorch < 0.3.1 using conda or a wheel and see if that works. It may have compute capability 2.1 support though I can't verify this. See pytorch.org for information. Though it looks like the link to https://download.pytorch.org/whl/cu80/torch_stable.html is broken.

Upvotes: 3

trsvchn
trsvchn

Reputation: 8981

Your GPU architecture is Fermi. Last CUDA version with support for Fermi is 8.0 and you have installed 10.1

The possible solution might be to install older CUDA and PyTorch version.

conda install pytorch==1.0.0 torchvision==0.2.1 cuda80 -c pytorch

Upvotes: 1

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