How to Fix "AssertionError: CUDA unavailable, invalid device 0 requested"

user_16521752 0
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I'm trying to use my GPU to run the YOLOR model, and I keep getting the error:

Traceback (most recent call last):
  File "D:\yolor\detect.py", line 198, in <module>
    detect()
  File "D:\yolor\detect.py", line 41, in detect
    device = select_device(opt.device)
  File "D:\yolor\utils\torch_utils.py", line 47, in select_device
    assert torch.cuda.is_available(), 'CUDA unavailable, invalid device %s requested' % device  # check availablity
AssertionError: CUDA unavailable, invalid device 0 requested

When I try to check if CUDA is available with the following:

python3
>>import torch
>>print(torch.cuda.is_available())

I get False, which explains the problem. I tried running the command

py -m pip install torch1.9.0+cu111 torchvision0.10.0+cu111 torchaudio==0.9.0 -f https://download.pytorch.org/whl/torch_stable.html

I get the error: ERROR: Invalid requirement: 'torch1.9.0+cu111'

Running nvcc --version, I get:

nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2021 NVIDIA Corporation
Built on Mon_May__3_19:41:42_Pacific_Daylight_Time_2021
Cuda compilation tools, release 11.3, V11.3.109
Build cuda_11.3.r11.3/compiler.29920130_0

Thus, I'm not really sure what the issue is, or how to fix it.

EDIT: As @Ivan pointed out, I added the == sign, but still get False when checking if CUDA is available.

pytorch deep-learning python
3 Answers

You forgot to put the == signs between the packages and the version number. According to the PyTorch installation page:

py -m pip install torch==1.9.0+cu111 torchvision==0.10.0+cu111 torchaudio===0.9.0 -f https://download.pytorch.org/whl/torch_stable.html
user_63313690

Ok after 1 week of pain I have founded this solution

1- After download NVIDIA Driver:

  • Go to your window and search for "NVIDIA Control Panel"
  • Then at the bottom left there should be "System Information"
  • Then look for "CUDA Cores"
  • Mine is 384 (year my laptop is antique) (NVIDIA GeForce GT 750M)
  • For CUDA Cores: 384 (corresponds to CUDA Toolkit 9.0)
  • For CUDA Cores: 387 (corresponds to CUDA Toolkit 9.1)
  • For other CUDA Cores you will need to do some more research yourself because I'm honestly don't know where to find this (if you are curious about where I found the one above, its on the second comments "https://github.com/pytorch/pytorch/issues/4546"

2- (Optional) Download Anaconda

  • This is the system I use, the choice is your
  • If you are using Anaconda and have been installing and uninstall to fix this problem. I recommend you to clean uninstall the environment since in my case my file got crash because of repeated install and un-install
  • Here is the link to show you how to do it "https://www.youtube.com/watch?v=dcvdOuvWI-Q&t=107s"

3- After install the right CUDA toolkit for your system

  • Go to "https://pytorch.org"
  • Put in your system details and install the right PyTorch for your system
  • (Optional) if you use Tensorflow as well, go here and install the right version for your CUDA

4- After all of that, in your Anaconda environment (or any environment you are using), type:

  • import torch
  • print(torch.cuda.is_available())

if return True then good job

if not: good luck on your coming week

hope it help and good luck on your journey with yolor (I'm learning it too)

user_163039920

If you are working with VSCode dev containers maybe you forgot to add the GPU to the container.

This can be fixed adding to .devcontainer/devcontainer.json

"runArgs": ["--gpus", "all"]
user_91109120
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