# Colossal-AI Tutorial Hands-on

## Introduction

Welcome to the [Colossal-AI](https://github.com/hpcaitech/ColossalAI) tutorial, which has been accepted as official tutorials by top conference [SC](https://sc22.supercomputing.org/), [AAAI](https://aaai.org/Conferences/AAAI-23/), [PPoPP](https://ppopp23.sigplan.org/), etc.


[Colossal-AI](https://github.com/hpcaitech/ColossalAI), a unified deep learning system for the big model era, integrates
many advanced technologies such as multi-dimensional tensor parallelism, sequence parallelism, heterogeneous memory management,
large-scale optimization, adaptive task scheduling, etc. By using Colossal-AI, we could help users to efficiently and
quickly deploy large AI model training and inference, reducing large AI model training budgets and scaling down the labor cost of learning and deployment.

### 🚀 Quick Links

[**Colossal-AI**](https://github.com/hpcaitech/ColossalAI) |
[**Paper**](https://arxiv.org/abs/2110.14883) |
[**Documentation**](https://www.colossalai.org/) |
[**Issue**](https://github.com/hpcaitech/ColossalAI/issues/new/choose) |
[**Slack**](https://join.slack.com/t/colossalaiworkspace/shared_invite/zt-z7b26eeb-CBp7jouvu~r0~lcFzX832w)

## Table of Content

 - Multi-dimensional Parallelism [[code]](https://github.com/hpcaitech/ColossalAI/tree/main/examples/tutorial/hybrid_parallel)
 - Sequence Parallelism [[code]](https://github.com/hpcaitech/ColossalAI/tree/main/examples/tutorial/sequence_parallel)
 - Large Batch Training Optimization [[code]](https://github.com/hpcaitech/ColossalAI/tree/main/examples/tutorial/large_batch_optimizer)
 - Automatic Parallelism [[code]](https://github.com/hpcaitech/ColossalAI/tree/main/examples/tutorial/auto_parallel)
 - Fine-tuning and Inference for OPT [[code]](https://github.com/hpcaitech/ColossalAI/tree/main/examples/tutorial/opt)
 - Optimized AlphaFold [[code]](https://github.com/hpcaitech/ColossalAI/tree/main/examples/tutorial/fastfold)
 - Optimized Stable Diffusion [[code]](https://github.com/hpcaitech/ColossalAI/tree/main/examples/images/diffusion)


## Discussion

Discussion about the [Colossal-AI](https://github.com/hpcaitech/ColossalAI) project is always welcomed! We would love to exchange ideas with the community to better help this project grow.
If you think there is a need to discuss anything, you may jump to our [Slack](https://join.slack.com/t/colossalaiworkspace/shared_invite/zt-z7b26eeb-CBp7jouvu~r0~lcFzX832w).

If you encounter any problem while running these tutorials, you may want to raise an [issue](https://github.com/hpcaitech/ColossalAI/issues/new/choose) in this repository.

## 🛠️ Setup environment
You should use `conda` to create a virtual environment, we recommend **python 3.8**, e.g. `conda create -n colossal python=3.8`. This installation commands are for CUDA 11.3, if you have a different version of CUDA, please download PyTorch and Colossal-AI accordingly.

```
# install torch
# visit https://pytorch.org/get-started/locally/ to download other versions
pip install torch==1.12.1+cu113 torchvision==0.13.1+cu113 torchaudio==0.12.1 --extra-index-url https://download.pytorch.org/whl/cu113

# install latest ColossalAI
# visit https://colossalai.org/download to download corresponding version of Colossal-AI
pip install colossalai==0.1.11rc3+torch1.12cu11.3 -f https://release.colossalai.org
```

You can run `colossalai check -i` to verify if you have correctly set up your environment 🕹️.
![](https://raw.githubusercontent.com/hpcaitech/public_assets/main/examples/tutorial/colossalai%20check%20-i.png)

If you encounter messages like `please install with cuda_ext`, do let me know as it could be a problem of the distribution wheel. 😥

Then clone the Colossal-AI repository from GitHub.
```bash
git clone https://github.com/hpcaitech/ColossalAI.git
cd ColossalAI/examples/tutorial
```
