mirror of https://github.com/hpcaitech/ColossalAI
53 lines
3.0 KiB
Markdown
53 lines
3.0 KiB
Markdown
# Colossal-AI Examples
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## Table of Contents
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- [Colossal-AI Examples](#colossal-ai-examples)
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- [Table of Contents](#table-of-contents)
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- [Overview](#overview)
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- [Folder Structure](#folder-structure)
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- [Integrate Your Example With Testing](#integrate-your-example-with-testing)
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## Overview
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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.
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## Folder Structure
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```text
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└─ examples
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└─ images
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└─ vit
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└─ test_ci.sh
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└─ train.py
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└─ README.md
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└─ ...
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└─ ...
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```
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## Invitation to open-source contribution
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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!
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You may contact us or participate in the following ways:
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1. [Leaving a Star ⭐](https://github.com/hpcaitech/ColossalAI/stargazers) to show your like and support. Thanks!
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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).
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3. Join the Colossal-AI community on
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[Slack](https://join.slack.com/t/colossalaiworkspace/shared_invite/zt-z7b26eeb-CBp7jouvu~r0~lcFzX832w),
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and [WeChat(微信)](https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/WeChat.png "qrcode") to share your ideas.
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4. Send your official proposal to email contact@hpcaitech.com
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Thanks so much to all of our amazing contributors!
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## Integrate Your Example With Testing
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Regular checks are important to ensure that all examples run without apparent bugs and stay compatible with the latest API.
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Colossal-AI runs workflows to check for examples on a on-pull-request and weekly basis.
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When a new example is added or changed, the workflow will run the example to test whether it can run.
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Moreover, Colossal-AI will run testing for examples every week.
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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.
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1. Create a script called `test_ci.sh` in your example folder
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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.
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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.
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4. Implement the logic such as dependency setup and example execution
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