Welcome to the RAISE-DRI project repository!
RAISE-DRI (Research and AI at Scale using Digital Research Infrastructure Resources) is an initiative supported by the Digital Research Alliance of Canada (the Alliance) through the DRI EDIA Champions Pilot Program. This project is committed to empowering equity-deserving researchers in Canada with the skills and knowledge needed to leverage the Digital Research Infrastructure (DRI) for scalable and impactful research.
RAISE-DRI offers hybrid workshops and self-guided tutorials covering a range of topics in artificial intelligence, data science, and high-performance computing, including:
- Introduction to Artificial Intelligence (AI)
- Introduction to Data Science
- Fundamentals of Deep Learning
- Deep Learning (generative AI, LLMs, etc.)
- Introduction to High-Performance Computing (HPC) and Advanced Computing Resources (ARC)
- Strategies for Managing and Analyzing Ultra-High-Dimensional Data
These resources are designed to equip researchers with the knowledge and confidence to use the Alliance's DRI ecosystem in their work.
Inspired by Cookie Cutter Data Science.
├── LICENSE
├── README.md <- The top-level README for users of this project.
├── CODE_OF_CONDUCT.md <- Guidelines for users and contributors of the project.
├── CONTRIBUTING.md <- Information on how to contribute to the project.
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├── docs <- Code for RAISE-DRI website
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├── workshop_examples <- Practice examples from
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│ └──mnist <- Examples using the "mnist" dataset
| ├──mnist_classification.ipynb <- Jupyter notebook example
| ├──mnist_colab.pdf <- Instructions for using Google Colab
| ├──mnist_cluster.pdf <- Instructions for using ARC resources
| └──mnist <- ARC example
| └──README.md <- Instructions for downloading "mnist" dataset
| ├──download-dataset.py <- Download "mnist" data
| ├──main.py <- Runs classification model
| ├──requirements.txt <- Specifies required packages
| └──submit_job.sh <- SLURM script for running model on ARC server
└──
Maintainers
This repository has been set up and maintained by Kaitlyn Wade as part of the Digital Research Alliance of Canada's DRI EDIA Champions Program.
Please create an issue to share references or ideas related to the development of this project.
- Install all-contributors bot
- Connect repo with Zenodo
- Add cff file for citation
For any queries or concerns, you can directly reach out to Kaitlyn Wade by emailing [email protected].
This work is licensed under the MIT license (code) and Creative Commons Attribution 4.0 International license (for documentation). You are free to share and adapt the material for any purpose, even commercially, as long as you provide attribution (give appropriate credit, provide a link to the license, and indicate if changes were made) in any reasonable manner, but not in any way that suggests the licensor endorses you or your use and with no additional restrictions.
RAISE-DRI has funding and support from the following organizations:
- Digital Research Alliance of Canada's DRI EDIA Champions Pilot Program
- OLS Open Seeds Mentoring and Training Program
This repository uses the template created and maintained by The Turing Way team members and shared under CC-BY 4.0 for reuse: https://github.com/alan-turing-institute/reproducible-project-template.
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This project follows the all-contributors specification. Contributions of any kind welcome!