V2.2.2+cu121 doesn't use GPU

mahmoodn: <p>Hi,<br>
I don’t know what has happened that my current PyTorch installation (from a year ago) doesn’t use GPU anymore. Apparently, it says the CUDA-11.6 is no longer supported. That should happen if I have updated the installation which as far as I remember, I didn’t do that.</p>
<pre><code class="lang-auto">$ python3 -c "import torch; print(torch.__version__)"
2.2.2+cu121

$ nvidia-smi
Tue Jun 18 15:22:00 2024
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 510.39.01    Driver Version: 510.39.01    CUDA Version: 11.6     |
|-------------------------------+----------------------+----------------------+
...

$ python3 -m torch.utils.collect_env
/usr/lib/python3.8/runpy.py:127: 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
  warn(RuntimeWarning(msg))
Collecting environment information...
/home/mahmood/.local/lib/python3.8/site-packages/torch/cuda/__init__.py:141: UserWarning: CUDA initialization: The NVIDIA driver on your system is too old (found version 11060). Please update your GPU driver by downloading and installing a new version from the URL: http://www.nvidia.com/Download/index.aspx Alternatively, go to: https://pytorch.org to install a PyTorch version that has been compiled with your version of the CUDA driver. (Triggered internally at ../c10/cuda/CUDAFunctions.cpp:108.)
  return torch._C._cuda_getDeviceCount() &gt; 0
PyTorch version: 2.2.2+cu121
Is debug build: False
CUDA used to build PyTorch: 12.1
ROCM used to build PyTorch: N/A

OS: Ubuntu 20.04.3 LTS (x86_64)
GCC version: (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0
Clang version: Could not collect
CMake version: version 3.16.3
Libc version: glibc-2.31

Python version: 3.8.10 (default, Nov 22 2023, 10:22:35)  [GCC 9.4.0] (64-bit runtime)
Python platform: Linux-5.13.0-27-generic-x86_64-with-glibc2.29
Is CUDA available: False
CUDA runtime version: 11.6.55
CUDA_MODULE_LOADING set to: N/A
GPU models and configuration: GPU 0: NVIDIA GeForce RTX 3080
Nvidia driver version: 510.39.01
cuDNN version: Probably one of the following:
/usr/local/cuda-11.2/targets/x86_64-linux/lib/libcudnn.so.8
/usr/local/cuda-11.2/targets/x86_64-linux/lib/libcudnn_adv_infer.so.8
/usr/local/cuda-11.2/targets/x86_64-linux/lib/libcudnn_adv_train.so.8
/usr/local/cuda-11.2/targets/x86_64-linux/lib/libcudnn_cnn_infer.so.8
/usr/local/cuda-11.2/targets/x86_64-linux/lib/libcudnn_cnn_train.so.8
/usr/local/cuda-11.2/targets/x86_64-linux/lib/libcudnn_ops_infer.so.8
/usr/local/cuda-11.2/targets/x86_64-linux/lib/libcudnn_ops_train.so.8
/usr/local/cuda-11.6/targets/x86_64-linux/lib/libcudnn.so.8
/usr/local/cuda-11.6/targets/x86_64-linux/lib/libcudnn_adv_infer.so.8
/usr/local/cuda-11.6/targets/x86_64-linux/lib/libcudnn_adv_train.so.8
/usr/local/cuda-11.6/targets/x86_64-linux/lib/libcudnn_cnn_infer.so.8
/usr/local/cuda-11.6/targets/x86_64-linux/lib/libcudnn_cnn_train.so.8
/usr/local/cuda-11.6/targets/x86_64-linux/lib/libcudnn_ops_infer.so.8
/usr/local/cuda-11.6/targets/x86_64-linux/lib/libcudnn_ops_train.so.8
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
Byte Order:                      Little Endian
Address sizes:                   43 bits physical, 48 bits virtual
CPU(s):                          16
On-line CPU(s) list:             0-15
Thread(s) per core:              2
Core(s) per socket:              8
Socket(s):                       1
NUMA node(s):                    1
Vendor ID:                       AuthenticAMD
CPU family:                      23
Model:                           113
Model name:                      AMD Ryzen 7 3700X 8-Core Processor
Stepping:                        0
Frequency boost:                 enabled
CPU MHz:                         2147.247
CPU max MHz:                     4426.1709
CPU min MHz:                     2200.0000
BogoMIPS:                        7199.68
Virtualization:                  AMD-V
L1d cache:                       256 KiB
L1i cache:                       256 KiB
L2 cache:                        4 MiB
L3 cache:                        32 MiB
NUMA node0 CPU(s):               0-15
Vulnerability Itlb multihit:     Not affected
Vulnerability L1tf:              Not affected
Vulnerability Mds:               Not affected
Vulnerability Meltdown:          Not affected
Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp
Vulnerability Spectre v1:        Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2:        Mitigation; Full AMD retpoline, IBPB conditional, STIBP conditional, RSB filling
Vulnerability Srbds:             Not affected
Vulnerability Tsx async abort:   Not affected
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 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 ibs skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb cat_l3 cdp_l3 hw_pstate ssbd mba ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 cqm rdt_a rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local clzero irperf xsaveerptr rdpru wbnoinvd arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic v_vmsave_vmload vgif v_spec_ctrl umip rdpid overflow_recov succor smca sme sev sev_es

Versions of relevant libraries:
[pip3] flake8==7.0.0
[pip3] numpy==1.23.5
[pip3] numpydoc==0.7.0
[pip3] onnx==1.16.0
[pip3] onnx-graphsurgeon==0.3.27
[pip3] onnxruntime==1.16.3
[pip3] torch==2.2.2
[pip3] torchtext==0.9.0a0+1ac252b
[pip3] torchvision==0.9.0a0+af97ec2
[pip3] triton==2.2.0
[conda] Could not collect
</code></pre>
<p>As you can see <code>Is CUDA available: False</code>, so I want to know is there any way to fix that without CUDA update?</p>

ptrblck: <p>Your driver is too old as it shipped with CUDA 11.6 while the PyTorch binary you have installed uses CUDA 12.1 dependencies. Either install the PyTorch binary with CUDA 11.8 dependencies or update your NVIDIA driver to a compatible version with CUDA 12.</p>