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https://github.com/hpcaitech/ColossalAI
Making large AI models cheaper, faster and more accessible
https://github.com/hpcaitech/ColossalAI
ai big-model data-parallelism deep-learning distributed-computing foundation-models heterogeneous-training hpc inference large-scale model-parallelism pipeline-parallelism
Last synced: 30 days ago
JSON representation
Making large AI models cheaper, faster and more accessible
- Host: GitHub
- URL: https://github.com/hpcaitech/ColossalAI
- Owner: hpcaitech
- License: apache-2.0
- Created: 2021-10-28T16:19:44.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2024-04-12T08:49:34.000Z (about 2 months ago)
- Last Synced: 2024-04-12T12:18:45.463Z (about 2 months ago)
- Topics: ai, big-model, data-parallelism, deep-learning, distributed-computing, foundation-models, heterogeneous-training, hpc, inference, large-scale, model-parallelism, pipeline-parallelism
- Language: Python
- Homepage: https://www.colossalai.org
- Size: 29.4 MB
- Stars: 37,740
- Watchers: 377
- Forks: 4,233
- Open Issues: 416
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGE_LOG.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Codeowners: .github/CODEOWNERS
Lists
- awesome-for-beginners - Colossal-AI - source deep learning system for large-scale model training and inference with high efficiency and low cost. (Python)
- awesome-stars - hpcaitech/ColossalAI - Making large AI models cheaper, faster and more accessible (Python)
- awesome-production-machine-learning - Colossal-AI - A unified deep learning system for big model era, which helps users to efficiently and quickly deploy large AI model training and inference. (Computation Load Distribution)
- awesome-machine-learning - Colossal-AI - source deep learning system for large-scale model training and inference with high efficiency and low cost. (Python / General-Purpose Machine Learning)
- awesome-deep-learning - Colossal-AI - An Integrated Large-scale Model Training System with Efficient Parallelization Techniques
- awesome-generative-ai - hpcaitech/ColossalAI
- my-awesome-stars - ColossalAI
- awesome - hpcaitech/ColossalAI - Making large AI models cheaper, faster and more accessible (Python)
- awesome-stars - hpcaitech/ColossalAI - Making large AI models cheaper, faster and more accessible (Python)
- awesome-stars - hpcaitech/ColossalAI - Making large AI models cheaper, faster and more accessible (Python)
- awesome-stars - hpcaitech/ColossalAI
- awesome-stars - hpcaitech/ColossalAI - Making large AI models cheaper, faster and more accessible (Python)
- awesome-stars - hpcaitech/ColossalAI - Making large AI models cheaper, faster and more accessible (Python)
- my-awesome - hpcaitech/ColossalAI - Making large AI models cheaper, faster and more accessible (Python)
- awesome-llm-and-aigc - Colossal-AI - AI: A Unified Deep Learning System For Large-Scale Parallel Training". (**[arXiv 2021](https://arxiv.org/abs/2110.14883)**). (Summary)
- awesome-llm-eval - ColossalAI - An integrated large-scale model training system with efficient parallelization techniques. (Frameworks-for-Training / Popular-LLM)
- awesome-machine-learning - Colossal-AI - source deep learning system for large-scale model training and inference with high efficiency and low cost. (Python / General-Purpose Machine Learning)
- awesome-stars - hpcaitech/ColossalAI - Making large AI models cheaper, faster and more accessible (Python)
- Awesome_Multimodel_LLM - Colossal-AI - Making large AI models cheaper, faster, and more accessible. (LLM Training Frameworks)
- Awesome-Deep-Neural-Network-Compression - ColossalAI
- my-awesome-stars - hpcaitech/ColossalAI - Making large AI models cheaper, faster and more accessible (Python)
- my-awesome-stars - hpcaitech/ColossalAI - Making large AI models cheaper, faster and more accessible (Python)
- Awesome-instruction-tuning - ColossalChat
- awesome-stars - hpcaitech/ColossalAI - Making large AI models cheaper, faster and more accessible (Python)
- my-awesome - hpcaitech/ColossalAI - Making large AI models cheaper, faster and more accessible (Python)
- my-awesome-stars - hpcaitech/ColossalAI - Making large AI models cheaper, faster and more accessible (Python)
- awesome-stars - ColossalAI
- awesome-lm-system - ColossalAI
- my-awesome-stars - hpcaitech/ColossalAI - Making large AI models cheaper, faster and more accessible (Python)
- awesome-stars - hpcaitech/ColossalAI - Making large AI models cheaper, faster and more accessible (Python)
- awesome-stars - hpcaitech/ColossalAI - Making large AI models cheaper, faster and more accessible (Python)
- awesome - hpcaitech/ColossalAI - Making large AI models cheaper, faster and more accessible (Python)
- awesome-AI-system - Colossal-AI: A Unified Deep Learning System For Large-Scale Parallel Training ICPP'23
- Next-Generation-AI-awesome - *ColossalAI*
- awesome - hpcaitech/ColossalAI - Making large AI models cheaper, faster and more accessible (Python)
- awesome-llm-json - ColossalAI
- awesome-machine-learning - Colossal-AI - source deep learning system for large-scale model training and inference with high efficiency and low cost. (Python / General-Purpose Machine Learning)
- awesome-stars - hpcaitech/ColossalAI - Making large AI models cheaper, faster and more accessible (Python)
- awesome-stars - hpcaitech/ColossalAI - Making large AI models cheaper, faster and more accessible (Python)
- awesome-machine-learning - Colossal-AI - source deep learning system for large-scale model training and inference with high efficiency and low cost. (Python / General-Purpose Machine Learning)
- awesome-instruction-datasets - ColossalChat
- awesome-stars - hpcaitech/ColossalAI - Making large AI models cheaper, faster and more accessible (Python)
- artsz-awesome - hpcaitech/ColossalAI - Colossal-AI: A Unified Deep Learning System for Big Model Era (Python)
- awesome-starts - hpcaitech/ColossalAI - Making large AI models cheaper, faster and more accessible (Python)
- awesome-stars - hpcaitech/ColossalAI - Making large AI models cheaper, faster and more accessible (Python)
- awesome-stars - hpcaitech/ColossalAI - Making large AI models cheaper, faster and more accessible (ai)
- my-awesome-stars - hpcaitech/ColossalAI - Making large AI models cheaper, faster and more accessible (Python)
- my-awesome - hpcaitech/ColossalAI - model,data-parallelism,deep-learning,distributed-computing,foundation-models,heterogeneous-training,hpc,inference,large-scale,model-parallelism,pipeline-parallelism pushed_at:2024-05 star:38.1k fork:4.3k Making large AI models cheaper, faster and more accessible (Python)
- awesome-open-source-mlops - ColossalAI - An integrated large-scale model training system with efficient parallelization techniques. (Frameworks for Training)
- awesome-llmops - ColossalAI - scale model training system with efficient parallelization techniques. |  | (Training / Frameworks for Training)
- my-awesome-stars - hpcaitech/ColossalAI - Colossal-AI: A Unified Deep Learning System for Large-Scale Parallel Training (Python)
- awesome-stars - hpcaitech/ColossalAI - `★36374` Making large AI models cheaper, faster and more accessible (Python)
- awesome-list - ColossalAI - Provides a collection of parallel components and user-friendly tools to kickstart distributed training and inference in a few lines. (Deep Learning Framework / Deployment & Distribution)
- Awesome-LLMOps - ColossalAI - An integrated large-scale model training system with efficient parallelization techniques. (Frameworks for Training)
- awesome-stars - hpcaitech/ColossalAI - Making large AI models cheaper, faster and more accessible (Python)
- awesome-repositories - hpcaitech/ColossalAI - Making large AI models cheaper, faster and more accessible (Python)
- Awesome-AIGC - [code - an-open-source-solution-for-cloning-chatgpt-with-a-complete-rlhf-pipeline/)] (文本生成 / LLaMA以及扩展)
- AiTreasureBox - hpcaitech/ColossalAI - 06-02_38114_1](https://img.shields.io/github/stars/hpcaitech/ColossalAI.svg)|Making large AI models cheaper, faster and more accessible| (Repos)
- Awesome-AIGC - [code - an-open-source-solution-for-cloning-chatgpt-with-a-complete-rlhf-pipeline/)] (文本生成 / LLaMA以及扩展)
- big-model-awesome - Colossal-AI - AI 提供了一系列并行训练组件，目标是让分布式 AI 模型训练像普通的单 GPU 模型一样简单，并提供的友好工具可以在几行代码内快速开始分布式训练。... [[Docs](https://colossalai.org/zh-Hans/docs/concepts/colossalai_overview/)] (Frameworks)
- Awesome-LLM - Colossal-AI - Making large AI models cheaper, faster, and more accessible. (LLM Training Frameworks)
- Awesome-LLM?tab=readme-ov-file - Colossal-AI - Making large AI models cheaper, faster, and more accessible. (LLM Training Frameworks)
- awesome-stars - hpcaitech/ColossalAI - `★38111` Making large AI models cheaper, faster and more accessible (Python)
README
# Colossal-AI
[](https://www.colossalai.org/)
Colossal-AI: Making large AI models cheaper, faster, and more accessible
Paper |
Documentation |
Examples |
Forum |
Blog
[](https://github.com/hpcaitech/ColossalAI/stargazers)
[](https://github.com/hpcaitech/ColossalAI/actions/workflows/build_on_schedule.yml)
[](https://colossalai.readthedocs.io/en/latest/?badge=latest)
[](https://www.codefactor.io/repository/github/hpcaitech/colossalai)
[](https://huggingface.co/hpcai-tech)
[](https://github.com/hpcaitech/public_assets/tree/main/colossalai/contact/slack)
[](https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/WeChat.png)
| [English](README.md) | [中文](docs/README-zh-Hans.md) |
## Latest News
* [2024/01] [Inference Performance Improved by 46%, Open Source Solution Breaks the Length Limit of LLM for Multi-Round Conversations](https://hpc-ai.com/blog/Colossal-AI-SwiftInfer)
* [2024/01] [Construct Refined 13B Private Model With Just $5000 USD, Upgraded Colossal-AI Llama-2 Open Source](https://hpc-ai.com/blog/colossal-llama-2-13b)
* [2023/11] [Enhanced MoE Parallelism, Open-source MoE Model Training Can Be 9 Times More Efficient](https://www.hpc-ai.tech/blog/enhanced-moe-parallelism-open-source-moe-model-training-can-be-9-times-more-efficient)
* [2023/09] [One Half-Day of Training Using a Few Hundred Dollars Yields Similar Results to Mainstream Large Models, Open-Source and Commercial-Free Domain-Specific LLM Solution](https://www.hpc-ai.tech/blog/one-half-day-of-training-using-a-few-hundred-dollars-yields-similar-results-to-mainstream-large-models-open-source-and-commercial-free-domain-specific-llm-solution)
* [2023/09] [70 Billion Parameter LLaMA2 Model Training Accelerated by 195%](https://www.hpc-ai.tech/blog/70b-llama2-training)
* [2023/07] [HPC-AI Tech Raises 22 Million USD in Series A Funding](https://www.hpc-ai.tech/blog/hpc-ai-tech-raises-22-million-usd-in-series-a-funding-to-fuel-team-expansion-and-business-growth)
* [2023/07] [65B Model Pretraining Accelerated by 38%, Best Practices for Building LLaMA-Like Base Models Open-Source](https://www.hpc-ai.tech/blog/large-model-pretraining)
* [2023/03] [ColossalChat: An Open-Source Solution for Cloning ChatGPT With a Complete RLHF Pipeline](https://medium.com/@yangyou_berkeley/colossalchat-an-open-source-solution-for-cloning-chatgpt-with-a-complete-rlhf-pipeline-5edf08fb538b)
* [2023/03] [Intel and Colossal-AI Partner to Deliver Cost-Efficient Open-Source Solution for Protein Folding Structure Prediction](https://www.hpc-ai.tech/blog/intel-habana)
* [2023/03] [AWS and Google Fund Colossal-AI with Startup Cloud Programs](https://www.hpc-ai.tech/blog/aws-and-google-fund-colossal-ai-with-startup-cloud-programs)
## Table of Contents
-
Why Colossal-AI -
Features -
Colossal-AI for Real World Applications
- Colossal-LLaMA-2: One Half-Day of Training Using a Few Hundred Dollars Yields Similar Results to Mainstream Large Models, Open-Source and Commercial-Free Domain-Specific Llm Solution
- ColossalChat: An Open-Source Solution for Cloning ChatGPT With a Complete RLHF Pipeline
- AIGC: Acceleration of Stable Diffusion
- Biomedicine: Acceleration of AlphaFold Protein Structure
-
Parallel Training Demo
-
Single GPU Training Demo
-
Inference
- SwiftInfer:Breaks the Length Limit of LLM for Multi-Round Conversations with 46% Acceleration
- GPT-3
- OPT-175B Online Serving for Text Generation
- 176B BLOOM
-
Installation
- Use Docker
- Community
- Contributing
- Cite Us
## Why Colossal-AI
Prof. James Demmel (UC Berkeley): Colossal-AI makes training AI models efficient, easy, and scalable.
## Features
Colossal-AI provides a collection of parallel components for you. We aim to support you to write your
distributed deep learning models just like how you write your model on your laptop. We provide user-friendly tools to kickstart
distributed training and inference in a few lines.
- Parallelism strategies
- Data Parallelism
- Pipeline Parallelism
- 1D, [2D](https://arxiv.org/abs/2104.05343), [2.5D](https://arxiv.org/abs/2105.14500), [3D](https://arxiv.org/abs/2105.14450) Tensor Parallelism
- [Sequence Parallelism](https://arxiv.org/abs/2105.13120)
- [Zero Redundancy Optimizer (ZeRO)](https://arxiv.org/abs/1910.02054)
- [Auto-Parallelism](https://arxiv.org/abs/2302.02599)
- Heterogeneous Memory Management
- [PatrickStar](https://arxiv.org/abs/2108.05818)
- Friendly Usage
- Parallelism based on the configuration file
## Colossal-AI in the Real World
### Colossal-LLaMA-2
- 7B: One half-day of training using a few hundred dollars yields similar results to mainstream large models, open-source and commercial-free domain-specific LLM solution.
[[code]](https://github.com/hpcaitech/ColossalAI/tree/main/applications/Colossal-LLaMA-2)
[[blog]](https://www.hpc-ai.tech/blog/one-half-day-of-training-using-a-few-hundred-dollars-yields-similar-results-to-mainstream-large-models-open-source-and-commercial-free-domain-specific-llm-solution)
[[HuggingFace model weights]](https://huggingface.co/hpcai-tech/Colossal-LLaMA-2-7b-base)
[[Modelscope model weights]](https://www.modelscope.cn/models/colossalai/Colossal-LLaMA-2-7b-base/summary)
- 13B: Construct refined 13B private model with just $5000 USD.
[[code]](https://github.com/hpcaitech/ColossalAI/tree/main/applications/Colossal-LLaMA-2)
[[blog]](https://hpc-ai.com/blog/colossal-llama-2-13b)
[[HuggingFace model weights]](https://huggingface.co/hpcai-tech/Colossal-LLaMA-2-13b-base)
[[Modelscope model weights]](https://www.modelscope.cn/models/colossalai/Colossal-LLaMA-2-13b-base/summary)
| Model | Backbone | Tokens Consumed | MMLU (5-shot) | CMMLU (5-shot)| AGIEval (5-shot) | GAOKAO (0-shot) | CEval (5-shot) |
| :-----------------------------: | :--------: | :-------------: | :------------------: | :-----------: | :--------------: | :-------------: | :-------------: |
| Baichuan-7B | - | 1.2T | 42.32 (42.30) | 44.53 (44.02) | 38.72 | 36.74 | 42.80 |
| Baichuan-13B-Base | - | 1.4T | 50.51 (51.60) | 55.73 (55.30) | 47.20 | 51.41 | 53.60 |
| Baichuan2-7B-Base | - | 2.6T | 46.97 (54.16) | 57.67 (57.07) | 45.76 | 52.60 | 54.00 |
| Baichuan2-13B-Base | - | 2.6T | 54.84 (59.17) | 62.62 (61.97) | 52.08 | 58.25 | 58.10 |
| ChatGLM-6B | - | 1.0T | 39.67 (40.63) | 41.17 (-) | 40.10 | 36.53 | 38.90 |
| ChatGLM2-6B | - | 1.4T | 44.74 (45.46) | 49.40 (-) | 46.36 | 45.49 | 51.70 |
| InternLM-7B | - | 1.6T | 46.70 (51.00) | 52.00 (-) | 44.77 | 61.64 | 52.80 |
| Qwen-7B | - | 2.2T | 54.29 (56.70) | 56.03 (58.80) | 52.47 | 56.42 | 59.60 |
| Llama-2-7B | - | 2.0T | 44.47 (45.30) | 32.97 (-) | 32.60 | 25.46 | - |
| Linly-AI/Chinese-LLaMA-2-7B-hf | Llama-2-7B | 1.0T | 37.43 | 29.92 | 32.00 | 27.57 | - |
| wenge-research/yayi-7b-llama2 | Llama-2-7B | - | 38.56 | 31.52 | 30.99 | 25.95 | - |
| ziqingyang/chinese-llama-2-7b | Llama-2-7B | - | 33.86 | 34.69 | 34.52 | 25.18 | 34.2 |
| TigerResearch/tigerbot-7b-base | Llama-2-7B | 0.3T | 43.73 | 42.04 | 37.64 | 30.61 | - |
| LinkSoul/Chinese-Llama-2-7b | Llama-2-7B | - | 48.41 | 38.31 | 38.45 | 27.72 | - |
| FlagAlpha/Atom-7B | Llama-2-7B | 0.1T | 49.96 | 41.10 | 39.83 | 33.00 | - |
| IDEA-CCNL/Ziya-LLaMA-13B-v1.1 | Llama-13B | 0.11T | 50.25 | 40.99 | 40.04 | 30.54 | - |
| **Colossal-LLaMA-2-7b-base** | Llama-2-7B | **0.0085T** | 53.06 | 49.89 | 51.48 | 58.82 | 50.2 |
| **Colossal-LLaMA-2-13b-base** | Llama-2-13B | **0.025T** | 56.42 | 61.80 | 54.69 | 69.53 | 60.3 |
### ColossalChat
[ColossalChat](https://github.com/hpcaitech/ColossalAI/tree/main/applications/Chat): An open-source solution for cloning [ChatGPT](https://openai.com/blog/chatgpt/) with a complete RLHF pipeline.
[[code]](https://github.com/hpcaitech/ColossalAI/tree/main/applications/Chat)
[[blog]](https://medium.com/@yangyou_berkeley/colossalchat-an-open-source-solution-for-cloning-chatgpt-with-a-complete-rlhf-pipeline-5edf08fb538b)
[[demo]](https://www.youtube.com/watch?v=HcTiHzApHm0)
[[tutorial]](https://www.youtube.com/watch?v=-qFBZFmOJfg)
- Up to 10 times faster for RLHF PPO Stage3 Training
- Up to 7.73 times faster for single server training and 1.42 times faster for single-GPU inference
- Up to 10.3x growth in model capacity on one GPU
- A mini demo training process requires only 1.62GB of GPU memory (any consumer-grade GPU)
- Increase the capacity of the fine-tuning model by up to 3.7 times on a single GPU
- Keep at a sufficiently high running speed
### AIGC
Acceleration of AIGC (AI-Generated Content) models such as [Stable Diffusion v1](https://github.com/CompVis/stable-diffusion) and [Stable Diffusion v2](https://github.com/Stability-AI/stablediffusion).
- [Training](https://github.com/hpcaitech/ColossalAI/tree/main/examples/images/diffusion): Reduce Stable Diffusion memory consumption by up to 5.6x and hardware cost by up to 46x (from A100 to RTX3060).
- [DreamBooth Fine-tuning](https://github.com/hpcaitech/ColossalAI/tree/main/examples/images/dreambooth): Personalize your model using just 3-5 images of the desired subject.
- [Inference](https://github.com/hpcaitech/ColossalAI/tree/main/examples/images/diffusion): Reduce inference GPU memory consumption by 2.5x.
### Biomedicine
Acceleration of [AlphaFold Protein Structure](https://alphafold.ebi.ac.uk/)
- [FastFold](https://github.com/hpcaitech/FastFold): Accelerating training and inference on GPU Clusters, faster data processing, inference sequence containing more than 10000 residues.
- [FastFold with Intel](https://github.com/hpcaitech/FastFold): 3x inference acceleration and 39% cost reduce.
- [xTrimoMultimer](https://github.com/biomap-research/xTrimoMultimer): accelerating structure prediction of protein monomers and multimer by 11x.
## Parallel Training Demo
### LLaMA2
- 70 billion parameter LLaMA2 model training accelerated by 195%
[[code]](https://github.com/hpcaitech/ColossalAI/tree/main/examples/language/llama2)
[[blog]](https://www.hpc-ai.tech/blog/70b-llama2-training)
### LLaMA1
- 65-billion-parameter large model pretraining accelerated by 38%
[[code]](https://github.com/hpcaitech/ColossalAI/tree/example/llama/examples/language/llama)
[[blog]](https://www.hpc-ai.tech/blog/large-model-pretraining)
### MoE
- Enhanced MoE parallelism, Open-source MoE model training can be 9 times more efficient
[[code]](https://github.com/hpcaitech/ColossalAI/tree/main/examples/language/openmoe)
[[blog]](https://www.hpc-ai.tech/blog/enhanced-moe-parallelism-open-source-moe-model-training-can-be-9-times-more-efficient)
### GPT-3
- Save 50% GPU resources and 10.7% acceleration
### GPT-2
- 11x lower GPU memory consumption, and superlinear scaling efficiency with Tensor Parallelism
GPT-2.png)
- 24x larger model size on the same hardware
- over 3x acceleration
### BERT
- 2x faster training, or 50% longer sequence length
### PaLM
- [PaLM-colossalai](https://github.com/hpcaitech/PaLM-colossalai): Scalable implementation of Google's Pathways Language Model ([PaLM](https://ai.googleblog.com/2022/04/pathways-language-model-palm-scaling-to.html)).
### OPT
- [Open Pretrained Transformer (OPT)](https://github.com/facebookresearch/metaseq), a 175-Billion parameter AI language model released by Meta, which stimulates AI programmers to perform various downstream tasks and application deployments because of public pre-trained model weights.
- 45% speedup fine-tuning OPT at low cost in lines. [[Example]](https://github.com/hpcaitech/ColossalAI/tree/main/examples/language/opt) [[Online Serving]](https://colossalai.org/docs/advanced_tutorials/opt_service)
Please visit our [documentation](https://www.colossalai.org/) and [examples](https://github.com/hpcaitech/ColossalAI/tree/main/examples) for more details.
### ViT
- 14x larger batch size, and 5x faster training for Tensor Parallelism = 64
### Recommendation System Models
- [Cached Embedding](https://github.com/hpcaitech/CachedEmbedding), utilize software cache to train larger embedding tables with a smaller GPU memory budget.
## Single GPU Training Demo
### GPT-2
- 20x larger model size on the same hardware
- 120x larger model size on the same hardware (RTX 3080)
### PaLM
- 34x larger model size on the same hardware
## Inference
- [SwiftInfer](https://github.com/hpcaitech/SwiftInfer): Inference performance improved by 46%, open source solution breaks the length limit of LLM for multi-round conversations
- [Energon-AI](https://github.com/hpcaitech/EnergonAI): 50% inference acceleration on the same hardware
- [OPT Serving](https://colossalai.org/docs/advanced_tutorials/opt_service): Try 175-billion-parameter OPT online services
- [BLOOM](https://github.com/hpcaitech/EnergonAI/tree/main/examples/bloom): Reduce hardware deployment costs of 176-billion-parameter BLOOM by more than 10 times.
## Installation
Requirements:
- PyTorch >= 1.11 and PyTorch <= 2.1
- Python >= 3.7
- CUDA >= 11.0
- [NVIDIA GPU Compute Capability](https://developer.nvidia.com/cuda-gpus) >= 7.0 (V100/RTX20 and higher)
- Linux OS
If you encounter any problem with installation, you may want to raise an [issue](https://github.com/hpcaitech/ColossalAI/issues/new/choose) in this repository.
### Install from PyPI
You can easily install Colossal-AI with the following command. **By default, we do not build PyTorch extensions during installation.**
```bash
pip install colossalai
```
**Note: only Linux is supported for now.**
However, if you want to build the PyTorch extensions during installation, you can set `CUDA_EXT=1`.
```bash
CUDA_EXT=1 pip install colossalai
```
**Otherwise, CUDA kernels will be built during runtime when you actually need them.**
We also keep releasing the nightly version to PyPI every week. This allows you to access the unreleased features and bug fixes in the main branch.
Installation can be made via
```bash
pip install colossalai-nightly
```
### Download From Source
> The version of Colossal-AI will be in line with the main branch of the repository. Feel free to raise an issue if you encounter any problems. :)
```shell
git clone https://github.com/hpcaitech/ColossalAI.git
cd ColossalAI
# install colossalai
pip install .
```
By default, we do not compile CUDA/C++ kernels. ColossalAI will build them during runtime.
If you want to install and enable CUDA kernel fusion (compulsory installation when using fused optimizer):
```shell
CUDA_EXT=1 pip install .
```
For Users with CUDA 10.2, you can still build ColossalAI from source. However, you need to manually download the cub library and copy it to the corresponding directory.
```bash
# clone the repository
git clone https://github.com/hpcaitech/ColossalAI.git
cd ColossalAI
# download the cub library
wget https://github.com/NVIDIA/cub/archive/refs/tags/1.8.0.zip
unzip 1.8.0.zip
cp -r cub-1.8.0/cub/ colossalai/kernel/cuda_native/csrc/kernels/include/
# install
CUDA_EXT=1 pip install .
```
## Use Docker
### Pull from DockerHub
You can directly pull the docker image from our [DockerHub page](https://hub.docker.com/r/hpcaitech/colossalai). The image is automatically uploaded upon release.
### Build On Your Own
Run the following command to build a docker image from Dockerfile provided.
> Building Colossal-AI from scratch requires GPU support, you need to use Nvidia Docker Runtime as the default when doing `docker build`. More details can be found [here](https://stackoverflow.com/questions/59691207/docker-build-with-nvidia-runtime).
> We recommend you install Colossal-AI from our [project page](https://www.colossalai.org) directly.
```bash
cd ColossalAI
docker build -t colossalai ./docker
```
Run the following command to start the docker container in interactive mode.
```bash
docker run -ti --gpus all --rm --ipc=host colossalai bash
```
## Community
Join the Colossal-AI community on [Forum](https://github.com/hpcaitech/ColossalAI/discussions),
[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 suggestions, feedback, and questions with our engineering team.
## Contributing
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. Send your official proposal to email [email protected]
Thanks so much to all of our amazing contributors!
## CI/CD
We leverage the power of [GitHub Actions](https://github.com/features/actions) to automate our development, release and deployment workflows. Please check out this [documentation](.github/workflows/README.md) on how the automated workflows are operated.
## Cite Us
This project is inspired by some related projects (some by our team and some by other organizations). We would like to credit these amazing projects as listed in the [Reference List](./docs/REFERENCE.md).
To cite this project, you can use the following BibTeX citation.
```
@inproceedings{10.1145/3605573.3605613,
author = {Li, Shenggui and Liu, Hongxin and Bian, Zhengda and Fang, Jiarui and Huang, Haichen and Liu, Yuliang and Wang, Boxiang and You, Yang},
title = {Colossal-AI: A Unified Deep Learning System For Large-Scale Parallel Training},
year = {2023},
isbn = {9798400708435},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3605573.3605613},
doi = {10.1145/3605573.3605613},
abstract = {The success of Transformer models has pushed the deep learning model scale to billions of parameters, but the memory limitation of a single GPU has led to an urgent need for training on multi-GPU clusters. However, the best practice for choosing the optimal parallel strategy is still lacking, as it requires domain expertise in both deep learning and parallel computing. The Colossal-AI system addressed the above challenge by introducing a unified interface to scale your sequential code of model training to distributed environments. It supports parallel training methods such as data, pipeline, tensor, and sequence parallelism and is integrated with heterogeneous training and zero redundancy optimizer. Compared to the baseline system, Colossal-AI can achieve up to 2.76 times training speedup on large-scale models.},
booktitle = {Proceedings of the 52nd International Conference on Parallel Processing},
pages = {766–775},
numpages = {10},
keywords = {datasets, gaze detection, text tagging, neural networks},
location = {Salt Lake City, UT, USA},
series = {ICPP '23}
}
```
Colossal-AI has been accepted as official tutorial by top conferences [NeurIPS](https://nips.cc/), [SC](https://sc22.supercomputing.org/), [AAAI](https://aaai.org/Conferences/AAAI-23/),
[PPoPP](https://ppopp23.sigplan.org/), [CVPR](https://cvpr2023.thecvf.com/), [ISC](https://www.isc-hpc.com/), [NVIDIA GTC](https://www.nvidia.com/en-us/on-demand/session/gtcspring23-S51482/) ,etc.