https://github.com/outofai/tiny-cuda-nn-wheels
This repository facilitates the creation of Python wheel files (.whl) from the tiny-cuda-nn project to streamline the installation process on Google Colab.
https://github.com/outofai/tiny-cuda-nn-wheels
deep-learning google-colab hash-grid hashgrid kaggle kaggle-notebook machine-learning positional-embedding python-wheels tiny-cuda tiny-cuda-nn tinycudann
Last synced: about 2 months ago
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This repository facilitates the creation of Python wheel files (.whl) from the tiny-cuda-nn project to streamline the installation process on Google Colab.
- Host: GitHub
- URL: https://github.com/outofai/tiny-cuda-nn-wheels
- Owner: OutofAi
- License: other
- Created: 2024-04-17T09:40:54.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-02-18T03:02:44.000Z (4 months ago)
- Last Synced: 2025-03-26T18:18:25.881Z (2 months ago)
- Topics: deep-learning, google-colab, hash-grid, hashgrid, kaggle, kaggle-notebook, machine-learning, positional-embedding, python-wheels, tiny-cuda, tiny-cuda-nn, tinycudann
- Homepage:
- Size: 137 KB
- Stars: 9
- Watchers: 1
- Forks: 3
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- Funding: .github/FUNDING.yml
- License: LICENSE.txt
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README
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This repository facilitates the creation of Python wheel files (.whl) from the [tiny-cuda-nn project](https://github.com/NVlabs/tiny-cuda-nn) to streamline the installation process on Google Colab and Kaggle. This is to circumvent the 20 minutes build requirement for tiny-cuda-nn on Google colab and Kaggle, when done from the source, to reduce it to few seconds!
_(All relevant credits and licenses are attributed to Nvidia. The materials and software licenses from the original tiny-cuda-nn repository are not included in this repository. Please refer to the original project for licensing details.)_
The current format for the wheel names includes a release postfix that signifies the compute compatibility of the relevant graphics card (i.e. compute compatibility of 8.6 is .post86), for simplcity you can use the code below for Google Colab for the relevant GPU model, but if you want to run it locally on your machine you can always identify the compute compatibility thorugh this page based of your graphics card https://developer.nvidia.com/cuda-gpus
It also uses a release postfix for cuda and torch .post{arch-type}{cuda-version}{torch-version} that signifies the torch and cuda compatibility.
# Google Colab Usage:
For T4 GPU
```
!curl -L "https://github.com/OutofAi/tiny-cuda-nn-wheels/releases/download/1.7.3/tinycudann-1.7.post75251124-cp311-cp311-linux_x86_64.whl" -o tinycudann-1.7.post75251124-cp311-cp311-linux_x86_64.whl
!pip install tinycudann-1.7.post75251124-cp311-cp311-linux_x86_64.whl --force-reinstall
import tinycudann as tcnn
```
For A100 GPU and L4 GPU
```
!curl -L "https://github.com/OutofAi/tiny-cuda-nn-wheels/releases/download/1.7.3/tinycudann-1.7.post89251124-cp311-cp311-linux_x86_64.whl" -o tinycudann-1.7.post89251124-cp311-cp311-linux_x86_64.whl
!pip install tinycudann-1.7.post89251124-cp311-cp311-linux_x86_64.whl --force-reinstall
import tinycudann as tcnn
```
# Kaggle Notebook Usage:
For T4 GPU
```
!curl -L "https://github.com/OutofAi/tiny-cuda-nn-wheels/releases/download/Kaggle-T4/tinycudann-1.7-cp310-cp310-linux_x86_64.whl" -o tinycudann-1.7-cp310-cp310-linux_x86_64.whl
!python -m pip install tinycudann-1.7-cp310-cp310-linux_x86_64.whl --force-reinstall --no-cache-dir
import tinycudann as tcnn
```
For P100 GPU
```
!curl -L "https://github.com/OutofAi/tiny-cuda-nn-wheels/releases/download/Kaggle-P100/tinycudann-1.7-cp310-cp310-linux_x86_64.whl" -o tinycudann-1.7-cp310-cp310-linux_x86_64.whl
!python -m pip install tinycudann-1.7-cp310-cp310-linux_x86_64.whl --force-reinstall --no-cache-dir
import tinycudann as tcnn
```