Torch.cuda.is_available() return false despite NVIDIA driver properly installed on Linux

edward1: <p>I’m encountering some issue where <code>torch.cuda.is_available()</code> returns <code>False</code> even though I’ve correctly installed the NVIDIA driver on my Linux machine. I’ve tried to reinstall pytorch in a new environment using pip and conda, neither worked either.</p>
<p>Here are the details when I run collect_env:</p>
<pre><code class="lang-auto">&lt;frozen runpy&gt;:128: RuntimeWarning: 'torch.utils.collect_env' found in sys.modules after import of package 'torch.utils', but prior to execution of 'torch.utils.collect_env'; this may result in unpredictable behaviour
Collecting environment information...
/home/edward/Documents/default_venv/lib/python3.11/site-packages/torch/cuda/__init__.py:141: UserWarning: CUDA initialization: CUDA unknown error - this may be due to an incorrectly set up environment, e.g. changing env variable CUDA_VISIBLE_DEVICES after program start. Setting the available devices to be zero. (Triggered internally at ../c10/cuda/CUDAFunctions.cpp:108.)
  return torch._C._cuda_getDeviceCount() &gt; 0
PyTorch version: 2.2.1+cu118
Is debug build: False
CUDA used to build PyTorch: 11.8
ROCM used to build PyTorch: N/A

OS: Debian GNU/Linux 12 (bookworm) (x86_64)
GCC version: (Debian 12.2.0-14) 12.2.0
Clang version: Could not collect
CMake version: Could not collect
Libc version: glibc-2.36

Python version: 3.11.2 (main, Mar 13 2023, 12:18:29) [GCC 12.2.0] (64-bit runtime)
Python platform: Linux-6.1.0-18-amd64-x86_64-with-glibc2.36
Is CUDA available: False
CUDA runtime version: 11.8.89
CUDA_MODULE_LOADING set to: N/A
GPU models and configuration: GPU 0: NVIDIA GeForce RTX 2070
Nvidia driver version: 525.147.05
cuDNN version: Could not collect
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

CPU:
Architecture:                       x86_64
CPU op-mode(s):                     32-bit, 64-bit
Address sizes:                      43 bits physical, 48 bits virtual
Byte Order:                         Little Endian
CPU(s):                             16
On-line CPU(s) list:                0-15
Vendor ID:                          AuthenticAMD
Model name:                         AMD Ryzen 7 2700X Eight-Core Processor
CPU family:                         23
Model:                              8
Thread(s) per core:                 2
Core(s) per socket:                 8
Socket(s):                          1
Stepping:                           2
Frequency boost:                    enabled
CPU(s) scaling MHz:                 72%
CPU max MHz:                        3700.0000
CPU min MHz:                        2200.0000
BogoMIPS:                           7385.93
Flags:                              fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl nonstop_tsc cpuid extd_apicid aperfmperf rapl pni pclmulqdq monitor ssse3 fma cx16 sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb hw_pstate ssbd ibpb vmmcall fsgsbase bmi1 avx2 smep bmi2 rdseed adx smap clflushopt sha_ni xsaveopt xsavec xgetbv1 clzero irperf xsaveerptr arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic v_vmsave_vmload vgif overflow_recov succor smca sev sev_es
Virtualization:                     AMD-V
L1d cache:                          256 KiB (8 instances)
L1i cache:                          512 KiB (8 instances)
L2 cache:                           4 MiB (8 instances)
L3 cache:                           16 MiB (2 instances)
NUMA node(s):                       1
NUMA node0 CPU(s):                  0-15
Vulnerability Gather data sampling: Not affected
Vulnerability Itlb multihit:        Not affected
Vulnerability L1tf:                 Not affected
Vulnerability Mds:                  Not affected
Vulnerability Meltdown:             Not affected
Vulnerability Mmio stale data:      Not affected
Vulnerability Retbleed:             Mitigation; untrained return thunk; SMT vulnerable
Vulnerability Spec rstack overflow: Mitigation; safe RET
Vulnerability Spec store bypass:    Mitigation; Speculative Store Bypass disabled via prctl
Vulnerability Spectre v1:           Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2:           Mitigation; Retpolines, IBPB conditional, STIBP disabled, RSB filling, PBRSB-eIBRS Not affected
Vulnerability Srbds:                Not affected
Vulnerability Tsx async abort:      Not affected

Versions of relevant libraries:
[pip3] mypy-extensions==1.0.0
[pip3] numpy==1.26.2
[pip3] onnx==1.15.0
[pip3] onnxruntime==1.15.1
[pip3] torch==2.2.1+cu118
[pip3] torchaudio==2.2.1+cu118
[pip3] torchvision==0.17.1+cu118
[pip3] triton==2.2.0
[conda] No relevant packages

</code></pre>
<p>Here are the result from nvidia-smi:</p>
<pre><code class="lang-auto">+-----------------------------------------------------------------------------+
| NVIDIA-SMI 525.147.05   Driver Version: 525.147.05   CUDA Version: 12.0     |
|-------------------------------+----------------------+----------------------+
| 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 ...  On   | 00000000:09:00.0  On |                  N/A |
|  0%   39C    P8    23W / 185W |    727MiB /  8192MiB |     25%      Default |
|                               |                      |                  N/A |
+-------------------------------+----------------------+----------------------+
                                                                               
+-----------------------------------------------------------------------------+
| Processes:                                                                  |
|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
|        ID   ID                                                   Usage      |
|=============================================================================|
|    0   N/A  N/A      2673      G   /usr/lib/xorg/Xorg                435MiB |
|    0   N/A  N/A      2874      G   ...ome-remote-desktop-daemon        1MiB |
|    0   N/A  N/A      2906      G   /usr/bin/gnome-shell               58MiB |
|    0   N/A  N/A      3607      G   /usr/lib/insync/insync              2MiB |
|    0   N/A  N/A      4193      G   ...--variations-seed-version       75MiB |
|    0   N/A  N/A      6346      G   ...RendererForSitePerProcess       91MiB |
|    0   N/A  N/A   1417875      G   ...--variations-seed-version       58MiB |
+-----------------------------------------------------------------------------+

</code></pre>
<p>I’ve gone through many post looking for solutions yet have not figured this out yet. Your insights would be greatly appreciated! <img src="https://discuss.pytorch.org/images/emoji/apple/pray.png?v=12" title=":pray:" class="emoji" alt=":pray:" loading="lazy" width="20" height="20"></p>

ptrblck: <p>PyTorch cannot initialize your GPU, which usually points to a driver installation issue and others were running into similar issues e.g. after installing a new NVIDIA driver without rebooting the workstation afterwards.</p>