Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

Added extra steps to update the Cudnnn DLL found in the Torch packages #4459

Merged
merged 8 commits into from
Sep 7, 2023
Merged

Added extra steps to update the Cudnnn DLL found in the Torch packages #4459

Changes from all commits
Commits
Show all changes
8 commits
Select commit Hold shift + click to select a range
04c0a83
Added extra steps to update the Cudnnn DLL found in the Torch packages
Keerigan45 Sep 5, 2023
53f2369
Update 030_INSTALL_CUDA_AND_ROCM.md
hipsterusername Sep 5, 2023
3afa73c
Update 030_INSTALL_CUDA_AND_ROCM.md
Millu Sep 6, 2023
6506ce3
Updated "\" to be escaped in markdown
Millu Sep 6, 2023
e16598c
Merge branch 'main' into patch-2
Millu Sep 6, 2023
cfc4caf
Update 030_INSTALL_CUDA_AND_ROCM.md
Keerigan45 Sep 7, 2023
40a0b2c
Update 030_INSTALL_CUDA_AND_ROCM.md
Keerigan45 Sep 7, 2023
252adb9
Fixed typos
Millu Sep 7, 2023
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
24 changes: 24 additions & 0 deletions docs/installation/030_INSTALL_CUDA_AND_ROCM.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -57,6 +57,30 @@ familiar with containerization technologies such as Docker.
For downloads and instructions, visit the [NVIDIA CUDA Container
Runtime Site](https://developer.nvidia.com/nvidia-container-runtime)

### cuDNN Installation for 40/30 Series Optimization* (Optional)

1. Find the InvokeAI folder
2. Click on .venv folder - e.g., YourInvokeFolderHere\\.venv
3. Click on Lib folder - e.g., YourInvokeFolderHere\\.venv\Lib
4. Click on site-packages folder - e.g., YourInvokeFolderHere\\.venv\Lib\site-packages
5. Click on Torch directory - e.g., YourInvokeFolderHere\InvokeAI\\.venv\Lib\site-packages\torch
6. Click on the lib folder - e.g., YourInvokeFolderHere\\.venv\Lib\site-packages\torch\lib
7. Copy everything inside the folder and save it elsewhere as a backup.
8. Go to __https://developer.nvidia.com/cudnn__
9. Login or create an Account.
10. Choose the newer version of cuDNN. **Note:**
There are two versions, 11.x or 12.x for the differents architectures(Turing,Maxwell Etc...) of GPUs.
You can find which version you should download from [this link](https://docs.nvidia.com/deeplearning/cudnn/support-matrix/index.html).
13. Download the latest version and extract it from the download location
14. Find the bin folder E\cudnn-windows-x86_64-__Whatever Version__\bin
15. Copy and paste the .dll files into YourInvokeFolderHere\\.venv\Lib\site-packages\torch\lib **Make sure to copy, and not move the files**
16. If prompted, replace any existing files

**Notes:**
* If no change is seen or any issues are encountered, follow the same steps as above and paste the torch/lib backup folder you made earlier and replace it. If you didn't make a backup, you can also uninstall and reinstall torch through the command line to repair this folder.
* This optimization is intended for the newer version of graphics card (40/30 series) but results have been seen with older graphics card.


### Torch Installation

When installing torch and torchvision manually with `pip`, remember to provide
Expand Down
Loading