https://github.com/ritchieng/the-incredible-rapids
The Incredible RAPIDS: a curated list of tutorials, papers, projects, communities and more relating to RAPIDS.
https://github.com/ritchieng/the-incredible-rapids
blazingsql cudf cugraph cuml rapids
Last synced: about 2 months ago
JSON representation
The Incredible RAPIDS: a curated list of tutorials, papers, projects, communities and more relating to RAPIDS.
- Host: GitHub
- URL: https://github.com/ritchieng/the-incredible-rapids
- Owner: ritchieng
- Created: 2019-10-02T01:52:13.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2019-10-02T02:03:17.000Z (over 5 years ago)
- Last Synced: 2025-02-05T14:13:26.751Z (3 months ago)
- Topics: blazingsql, cudf, cugraph, cuml, rapids
- Size: 1.95 KB
- Stars: 7
- Watchers: 4
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# The Incredible RAPIDS
This is a curated list of tutorials, projects, libraries, videos, papers, books and anything related to the RAPIDS. Feel free to make a pull request to contribute to this list.
## Official Repositories
- [cuDF, something like Pandas on GPU](https://github.com/rapidsai/cudf)
- [cuML, something like Scikit-learn on GPU](https://github.com/rapidsai/cuml)
- [cuGraph, GPU Graph Analytics](https://github.com/rapidsai/cugraph)
- [BlazingSQL, ETL data directly to GPU](https://github.com/BlazingDB/pyBlazing)
## Official Guides
- [Notebooks](https://github.com/rapidsai/notebooks)
- [Community Notebooks](https://github.com/rapidsai/notebooks-contrib)
## cuDF
- [GPU Fractional Differencing to make time series stationary](https://github.com/ritchieng/fractional_differencing_gpu)
- [Even faster but less intuitive GPU Fractional Differencing](https://github.com/rapidsai/gQuant/blob/develop/notebooks/07_fractional_differencing.ipynb)
- [Using NVIDIA RAPIDS to mine Seattle Parking Data](https://github.com/drabastomek/rapids-notebooks)
## Credits
This is based off my more long-standing established repository [The Incredible PyTorch](https://github.com/ritchieng/the-incredible-pytorch) and also the newly-established [The Incredible TensorFlow 2](https://github.com/ritchieng/the-incredible-tensorflow).
## Contributions
Do feel free to contribute!
You can raise an issue or submit a pull request, whichever is more convenient for you. The guideline is simple: just follow the format of the previous bullet point. **Take note, implementations are strictly based on TensorFlow 2 onwards only.**