https://github.com/tybrucechen/tutorial-conda-cudnn-nccl-installation-for-pytorch
This is a tutorial for installing CUDA (v11.8) and cuDNN (8.6.9) to enable programming torch with GPU. It also mentions about implementation of NCCL for distributed GPU DNN model training.
https://github.com/tybrucechen/tutorial-conda-cudnn-nccl-installation-for-pytorch
cuda-installation nccl pytorch-installation ubuntu ubuntu-server windows-10
Last synced: about 2 months ago
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This is a tutorial for installing CUDA (v11.8) and cuDNN (8.6.9) to enable programming torch with GPU. It also mentions about implementation of NCCL for distributed GPU DNN model training.
- Host: GitHub
- URL: https://github.com/tybrucechen/tutorial-conda-cudnn-nccl-installation-for-pytorch
- Owner: TyBruceChen
- Created: 2024-05-23T13:15:58.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-12-20T06:57:50.000Z (5 months ago)
- Last Synced: 2025-02-14T08:27:32.638Z (3 months ago)
- Topics: cuda-installation, nccl, pytorch-installation, ubuntu, ubuntu-server, windows-10
- Language: Jupyter Notebook
- Homepage:
- Size: 1.79 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Conda-Installation-Tutorial-Windows10 (for Linux (Ubuntu18), click [here](https://github.com/TyBruceChen/Tutorial-Conda-cuDNN-NCCL-installation-for-Pytorch/blob/main/README_virtual_env_Ubuntu18_server.md)) (for Pytorch distributed GPU training with NCCL (as well as by [Accelerate class](https://huggingface.co/docs/accelerate/v0.12.0/en/basic_tutorials/launch)), click [here](https://github.com/TyBruceChen/Tutorial-Conda-cuDNN-NCCL-installation-for-Pytorch/blob/main/README_nccl_distributed_compute.md))
This is a tutorial for installing CUDA (v11.8) and cuDNN (8.6.9) to enable programming Pytorch with GPU. It also mentioned about the solution of unabling for Pytorch to detect the CUDA core.
**Claim:** This tutorial was done when I came back from abroad at NAU. I found my computer like a stranger so I devoted myself to re-install the whole system. Thus the CUDA environment needs to be re-configured, where I have met several obstacles while doing this although it's my third (or fourth time...? I do not remember) to do this. So I decided to do a full-scope tutorial to record the problem I met and its corresponding solution which may help me in the future and others.
**Suggestion: Install the CUDA first then install the corresponding CUDA-compatible Pytorch**
### 1. Check the compatibility:
1. Check whether your GPU is compatible with CUDA (and the supported CUDA version) at https://developer.nvidia.com/cuda-gpus, and update your driver.
2. Confirm the version of CUDA that you want and can install with (you can search for a specific version). In this tutorial, it's v11.8.
3. Find the compatible version of cuDNN (plug-in for optimizing AI training) at https://developer.download.nvidia.com/compute/cudnn/redist/cudnn/windows-x86_64/. In this tutorial, it's 8.6.0.
### 2. Install CUDA:
If the high version fails to install, you can try the older version.
Here I check all the checkboxes.
### 3. Verify the installation of CUDA:
First go the settings -> edit system environment variable (path) -> ```Environment Variables...```
-> Under System variables, ensure the existence of these two paths as below:
-> Also under this column, find ```Path```, double click to open, and add these two paths if they did not exist:
Second, to verify the system has already detected installed CUDA, type the command ```nvcc --version``` in the command prompt, the displayed version should match with the CUDA version you just installed.
### 4. Add Plug-in: cuDNN to CUDA:
Copy all the files (folders) of the downloaded cuDNN zip file that is compatible with your CUDA version, and paste them under the CUDA folder (in my case, it's ```C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.8\```) to finish those paste action at one time.
### 5. Installation of compatible Pytorch:
This step was where I got stuck and spent most of my time working it out.
Run the similar command to install cuda corresponding version of Pytorch: ```pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu118``` (here cu118 indicates the CUDA version is 11.8). The proper size of the main package of this installation should be near 2.4GB.
### 6. Verification:
```
import torch
torch.cuda.is_available()
```
should return ```True``` value
### 7. Some torch functions that may helps debugging if error occurs:
Click [here](https://github.com/TyBruceChen/Conda-Installation-Tutorial-Windows-/blob/main/debug.ipynb)