About CUDA, Cudnn

pomad: <p>Hi,<br>
I want to setup a DeepLearing environment for my project.</p>
<p>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!</p>
<p>Now i can see<br>
$ nvidia-smi<br>
Tue Jun 27 11:39:23 2023<br>
±--------------------------------------------------------------------------------------+<br>
| NVIDIA-SMI 530.41.03              Driver Version: 530.41.03    CUDA Version: 12.1     |<br>
|-----------------------------------------±---------------------±---------------------+<br>
| GPU  Name                  Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |<br>
| Fan  Temp  Perf            Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |<br>
|                                         |                      |               MIG M. |<br>
|=========================================+======================+======================|<br>
|   0  NVIDIA GeForce GTX 1080         Off| 00000000:01:00.0 Off |                  N/A |<br>
| N/A   34C    P8                4W /  N/A|    131MiB /  8192MiB |      0%      Default |<br>
|                                         |                      |                  N/A |<br>
±----------------------------------------±---------------------±---------------------+<br>
|   1  NVIDIA GeForce GTX 1080         Off| 00000000:02:00.0 Off |                  N/A |<br>
| N/A   38C    P8                7W /  N/A|      6MiB /  8192MiB |      0%      Default |<br>
|                                         |                      |                  N/A |<br>
±----------------------------------------±---------------------±---------------------+</p>
<p>±--------------------------------------------------------------------------------------+<br>
| Processes:                                                                            |<br>
|  GPU   GI   CI        PID   Type   Process name                            GPU Memory |<br>
|        ID   ID                                                             Usage      |<br>
|=======================================================================================|<br>
|    0   N/A  N/A      1148      G   /usr/lib/xorg/Xorg                          100MiB |<br>
|    0   N/A  N/A      1485      G   /usr/bin/gnome-shell                         28MiB |<br>
|    1   N/A  N/A      1148      G   /usr/lib/xorg/Xorg                            4MiB |<br>
±--------------------------------------------------------------------------------------+</p>
<p>It has Cuda version 12.1 as can be seen from the output!<br>
and then<br>
$ nvcc --version<br>
nvcc: NVIDIA (R) Cuda compiler driver<br>
Copyright (c) 2005-2019 NVIDIA Corporation<br>
Built on Sun_Jul_28_19:07:16_PDT_2019<br>
Cuda compilation tools, release 10.1, V10.1.243</p>
<p>shows cuda V10 !!</p>
<p>but for my deeplearning env, i have used conda to activate an ENV!<br>
So after activating, i installed pytorch with the cuda 11.8 version!</p>
<p>Now inside my env using vscode in python :<br>
Pytorch Version: 2.0.1<br>
Cuda Availablity: True<br>
GPU Count: 2<br>
CUDA version: 11.8<br>
cuDNN version: 8700<br>
GPU Count: 2<br>
GPU Name: NVIDIA GeForce GTX 1080<br>
_CudaDeviceProperties(name=‘NVIDIA GeForce GTX 1080’, major=6, minor=1, total_memory=8105MB, multi_processor_count=20)</p>
<p>I’m not sure if my system has conflicts as the cuda and cudnn version?<br>
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?</p>
<p>For now i want to run a program that needs libcudnn8=8.1.0.77-1+cuda11.2<br>
What should i do?</p>
<p>Are CONDA ENVs isolated from each other and also to the original system?<br>
can i have multiple cuda and cudnn in the same ENV or in the same system with different ENVs?</p>
<p>Thanks<br>
Best regards</p>

ptrblck: <p>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.</p>

pomad: <p><a class="mention" href="/u/ptrblck">@ptrblck</a><br>
Thanks for the prompt reply!<br>
So you mean that each conda ENV are isolated from the others?<br>
I mean can i install cuda 12.1 on the base system and create a new ENV with diffrenet Cuda and Cudnns?</p>
<p>How can i install old version of cudnn and cuda ( libcudnn8=8.1.0.77-1+cuda11.2)  along with the pytorch 2.1?<br>
Does my GPU supports cuda 12.1 or just A100, V100, T4 supports it?</p>
<p>Best regards</p>

ptrblck: <aside class="quote no-group" data-username="pomad" data-post="3" data-topic="182963">
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<img loading="lazy" alt="" width="24" height="24" src="https://discuss.pytorch.org/user_avatar/discuss.pytorch.org/pomad/48/61674_2.png" class="avatar"> pomad:</div>
<blockquote>
<p>I mean can i install cuda 12.1 on the base system and create a new ENV with diffrenet Cuda and Cudnns?</p>
</blockquote>
</aside>
<p>Yes, because as I mentioned before the locally installed CUDA toolkit won’t even be used by the PyTorch binaries.</p>
<aside class="quote no-group" data-username="pomad" data-post="3" data-topic="182963">
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<div class="quote-controls"></div>
<img loading="lazy" alt="" width="24" height="24" src="https://discuss.pytorch.org/user_avatar/discuss.pytorch.org/pomad/48/61674_2.png" class="avatar"> pomad:</div>
<blockquote>
<p>How can i install old version of cudnn and cuda ( libcudnn8=8.1.0.77-1+cuda11.2) along with the pytorch 2.1?</p>
</blockquote>
</aside>
<p>You can select the desired PyTorch and CUDA runtime on our website and copy/paste the install command.</p>
<aside class="quote no-group" data-username="pomad" data-post="3" data-topic="182963">
<div class="title">
<div class="quote-controls"></div>
<img loading="lazy" alt="" width="24" height="24" src="https://discuss.pytorch.org/user_avatar/discuss.pytorch.org/pomad/48/61674_2.png" class="avatar"> pomad:</div>
<blockquote>
<p>Does my GPU supports cuda 12.1 or just A100, V100, T4 supports it?</p>
</blockquote>
</aside>
<p>Your Pascal GPU is supported in CUDA 12.1.</p>