mirror of https://github.com/hpcaitech/ColossalAI
You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
53 lines
3.0 KiB
53 lines
3.0 KiB
# Colossal-AI Examples
|
|
|
|
## Table of Contents
|
|
|
|
- [Colossal-AI Examples](#colossal-ai-examples)
|
|
- [Table of Contents](#table-of-contents)
|
|
- [Overview](#overview)
|
|
- [Folder Structure](#folder-structure)
|
|
- [Integrate Your Example With Testing](#integrate-your-example-with-testing)
|
|
|
|
## Overview
|
|
|
|
This folder provides several examples accelerated by Colossal-AI. The `tutorial` folder is for everyone to quickly try out the different features in Colossal-AI. Other folders such as `images` and `language` include a wide range of deep learning tasks and applications.
|
|
|
|
## Folder Structure
|
|
|
|
```text
|
|
└─ examples
|
|
└─ images
|
|
└─ vit
|
|
└─ test_ci.sh
|
|
└─ train.py
|
|
└─ README.md
|
|
└─ ...
|
|
└─ ...
|
|
```
|
|
## Invitation to open-source contribution
|
|
Referring to the successful attempts of [BLOOM](https://bigscience.huggingface.co/) and [Stable Diffusion](https://en.wikipedia.org/wiki/Stable_Diffusion), any and all developers and partners with computing powers, datasets, models are welcome to join and build the Colossal-AI community, making efforts towards the era of big AI models!
|
|
|
|
You may contact us or participate in the following ways:
|
|
1. [Leaving a Star ⭐](https://github.com/hpcaitech/ColossalAI/stargazers) to show your like and support. Thanks!
|
|
2. Posting an [issue](https://github.com/hpcaitech/ColossalAI/issues/new/choose), or submitting a PR on GitHub follow the guideline in [Contributing](https://github.com/hpcaitech/ColossalAI/blob/main/CONTRIBUTING.md).
|
|
3. Join the Colossal-AI community on
|
|
[Slack](https://join.slack.com/t/colossalaiworkspace/shared_invite/zt-z7b26eeb-CBp7jouvu~r0~lcFzX832w),
|
|
and [WeChat(微信)](https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/WeChat.png "qrcode") to share your ideas.
|
|
4. Send your official proposal to email contact@hpcaitech.com
|
|
|
|
Thanks so much to all of our amazing contributors!
|
|
|
|
## Integrate Your Example With Testing
|
|
|
|
Regular checks are important to ensure that all examples run without apparent bugs and stay compatible with the latest API.
|
|
Colossal-AI runs workflows to check for examples on a on-pull-request and weekly basis.
|
|
When a new example is added or changed, the workflow will run the example to test whether it can run.
|
|
Moreover, Colossal-AI will run testing for examples every week.
|
|
|
|
Therefore, it is essential for the example contributors to know how to integrate your example with the testing workflow. Simply, you can follow the steps below.
|
|
|
|
1. Create a script called `test_ci.sh` in your example folder
|
|
2. Configure your testing parameters such as number steps, batch size in `test_ci.sh`, e.t.c. Keep these parameters small such that each example only takes several minutes.
|
|
3. Export your dataset path with the prefix `/data` and make sure you have a copy of the dataset in the `/data/scratch/examples-data` directory on the CI machine. Community contributors can contact us via slack to request for downloading the dataset on the CI machine.
|
|
4. Implement the logic such as dependency setup and example execution
|