Ubuntu – torch.cuda.is_available() returns false, what else should I check?

Content:

I’m having trouble getting PyTorch to recognize CUDA on my system. Here are the details:

System Information:

• OS: Ubuntu 22.04.4 LTS (x86_64) running on WSL2
• Python version: 3.7.16
• PyTorch version: 1.12.0+cu113
• GPU: NVIDIA GeForce GTX 1650 with Max-Q Design
• Nvidia driver version: 537.13

Environment Information:
python -m torch.utils.collect_env

Collecting environment information...
PyTorch version: 1.12.0+cu113
Is debug build: False
CUDA used to build PyTorch: 11.3
ROCM used to build PyTorch: N/A

OS: Ubuntu 22.04.4 LTS (x86_64)
GCC version: (Ubuntu 9.5.0-1ubuntu1~22.04) 9.5.0
Clang version: Could not collect
CMake version: Could not collect
Libc version: glibc-2.17

Python version: 3.7.16 (default, Jan 17 2023, 22:20:44)  [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-5.15.146.1-microsoft-standard-WSL2-x86_64-with-debian-bookworm-sid
Is CUDA available: False
CUDA runtime version: Could not collect
GPU models and configuration: GPU 0: NVIDIA GeForce GTX 1650 with Max-Q Design
Nvidia driver version: 537.13
cuDNN version: Could not collect
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

Versions of relevant libraries:
[pip3] numpy==1.21.6
[pip3] torch==1.12.0+cu113
[pip3] torchaudio==0.12.0+cu113
[pip3] torchvision==0.13.0+cu113
[conda] numpy                     1.21.6                   pypi_0    pypi
[conda] torch                     1.12.0+cu113             pypi_0    pypi
[conda] torchaudio                0.12.0+cu113             pypi_0    pypi
[conda] torchvision               0.13.0+cu113             pypi_0    pypi

Steps I’ve Taken:

1. Verified that CUDA is installed correctly:

   nvcc --version
   

   Output:

   nvcc: NVIDIA (R) Cuda compiler driver
   Copyright (c) 2005-2021 NVIDIA Corporation
   Built on Mon_May__3_19:15:13_PDT_2021
   Cuda compilation tools, release 11.3, V11.3.109
   Build cuda_11.3.r11.3/compiler.29920130_0
   

2. Set environment variables:

   export PATH=/usr/local/cuda-11.3/bin${PATH:+:${PATH}}
   export LD_LIBRARY_PATH=/usr/local/cuda-11.3/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}
   

   OR

   echo 'export PATH=/usr/local/cuda-11.3/bin${PATH:+:${PATH}}' >> ~/.bashrc
   echo 'export LD_LIBRARY_PATH=/usr/local/cuda-11.3/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}' >> ~/.bashrc
   

3. Verified NVIDIA driver status:

   nvidia-smi
   

   Output:

   Sun May 19 03:03:53 2024
   +---------------------------------------------------------------------------------------+
   | NVIDIA-SMI 535.103                Driver Version: 537.13       CUDA Version: 12.2     |
   |-----------------------------------------+----------------------+----------------------+
   | GPU  Name                 Persistence-M | Bus-Id        Disp.A | Volatile Uncorr. ECC |
   | Fan  Temp   Perf          Pwr:Usage/Cap |         Memory-Usage | GPU-Util  Compute M. |
   |                                         |                      |               MIG M. |
   |=========================================+======================+======================|
   |   0  NVIDIA GeForce GTX 1650 ...    On  | 00000000:02:00.0 Off |                  N/A |
   | N/A   50C    P0              13W /  35W |      0MiB /  4096MiB |      0%      Default |
   |                                         |                      |                  N/A |
   +-----------------------------------------+----------------------+----------------------+
   
   +---------------------------------------------------------------------------------------+
   | Processes:                                                                            |
   |  GPU   GI   CI        PID   Type   Process name                            GPU Memory |
   |        ID   ID                                                             Usage      |
   |=======================================================================================|
   |  No running processes found                                                           |
   +---------------------------------------------------------------------------------------+
   

4. Checked if CUDA is available in PyTorch:

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

   Output:

   False
   

Questions:

1. Why is torch.cuda.is_available() returning False?
2. What additional checks or steps should I perform to resolve this issue?

Thank you in advance for your help!