About CUDA, Cudnn

I want to setup a DeepLearing environment for my project.

After installing ubuntu 20.04 on my MSI GT83VR 6RF which has two Nvidia GTX 1080 gpus, I then installed Nvidia Driver 530 on it! after that i then installed CUDA and Cudnn on it! from the website!

Now i can see
$ nvidia-smi
Tue Jun 27 11:39:23 2023
| NVIDIA-SMI 530.41.03 Driver Version: 530.41.03 CUDA Version: 12.1 |
| 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 1080 Off| 00000000:01:00.0 Off | N/A |
| N/A 34C P8 4W / N/A| 131MiB / 8192MiB | 0% Default |
| | | N/A |
| 1 NVIDIA GeForce GTX 1080 Off| 00000000:02:00.0 Off | N/A |
| N/A 38C P8 7W / N/A| 6MiB / 8192MiB | 0% Default |
| | | N/A |

| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
| 0 N/A N/A 1148 G /usr/lib/xorg/Xorg 100MiB |
| 0 N/A N/A 1485 G /usr/bin/gnome-shell 28MiB |
| 1 N/A N/A 1148 G /usr/lib/xorg/Xorg 4MiB |

It has Cuda version 12.1 as can be seen from the output!
and then
$ nvcc --version
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2019 NVIDIA Corporation
Built on Sun_Jul_28_19:07:16_PDT_2019
Cuda compilation tools, release 10.1, V10.1.243

shows cuda V10 !!

but for my deeplearning env, i have used conda to activate an ENV!
So after activating, i installed pytorch with the cuda 11.8 version!

Now inside my env using vscode in python :
Pytorch Version: 2.0.1
Cuda Availablity: True
GPU Count: 2
CUDA version: 11.8
cuDNN version: 8700
GPU Count: 2
GPU Name: NVIDIA GeForce GTX 1080
_CudaDeviceProperties(name=‘NVIDIA GeForce GTX 1080’, major=6, minor=1, total_memory=8105MB, multi_processor_count=20)

I’m not sure if my system has conflicts as the cuda and cudnn version?
Ho to fix this conflict? ( do i need to remove all the cuda and cudnn and create a new ENV with desired cuda and cudnn inside each one?

For now i want to run a program that needs libcudnn8=
What should i do?

Are CONDA ENVs isolated from each other and also to the original system?
can i have multiple cuda and cudnn in the same ENV or in the same system with different ENVs?

Best regards

Your locally installed CUDA toolkit(s) won’t be used unless you build PyTorch from source or a custom CUDA extension. The PyTorch binaries (pip wheels and conda binaries) ship with their own CUDA dependencies and just require a properly installed NVIDIA driver.

Thanks for the prompt reply!
So you mean that each conda ENV are isolated from the others?
I mean can i install cuda 12.1 on the base system and create a new ENV with diffrenet Cuda and Cudnns?

How can i install old version of cudnn and cuda ( libcudnn8= along with the pytorch 2.1?
Does my GPU supports cuda 12.1 or just A100, V100, T4 supports it?

Best regards

Yes, because as I mentioned before the locally installed CUDA toolkit won’t even be used by the PyTorch binaries.

You can select the desired PyTorch and CUDA runtime on our website and copy/paste the install command.

Your Pascal GPU is supported in CUDA 12.1.