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[doc] add requirement and highlight application (#3516) 

* [doc] add requirement and highlight application

* [doc] link example and application
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@ -38,6 +38,14 @@
<ul> <ul>
<li><a href="#Why-Colossal-AI">Why Colossal-AI</a> </li> <li><a href="#Why-Colossal-AI">Why Colossal-AI</a> </li>
<li><a href="#Features">Features</a> </li> <li><a href="#Features">Features</a> </li>
<li>
<a href="#Colossal-AI-in-the-Real-World">Colossal-AI for Real World Applications</a>
<ul>
<li><a href="#ColossalChat">ColossalChat: An Open-Source Solution for Cloning ChatGPT With a Complete RLHF Pipeline</a></li>
<li><a href="#AIGC">AIGC: Acceleration of Stable Diffusion</a></li>
<li><a href="#Biomedicine">Biomedicine: Acceleration of AlphaFold Protein Structure</a></li>
</ul>
</li>
<li> <li>
<a href="#Parallel-Training-Demo">Parallel Training Demo</a> <a href="#Parallel-Training-Demo">Parallel Training Demo</a>
<ul> <ul>
@ -64,14 +72,6 @@
<li><a href="#OPT-Serving">OPT-175B Online Serving for Text Generation</a></li> <li><a href="#OPT-Serving">OPT-175B Online Serving for Text Generation</a></li>
<li><a href="#BLOOM-Inference">176B BLOOM</a></li> <li><a href="#BLOOM-Inference">176B BLOOM</a></li>
</ul> </ul>
</li>
<li>
<a href="#Colossal-AI-in-the-Real-World">Colossal-AI for Real World Applications</a>
<ul>
<li><a href="#ColossalChat">ColossalChat: An Open-Source Solution for Cloning ChatGPT With a Complete RLHF Pipeline</a></li>
<li><a href="#AIGC">AIGC: Acceleration of Stable Diffusion</a></li>
<li><a href="#Biomedicine">Biomedicine: Acceleration of AlphaFold Protein Structure</a></li>
</ul>
</li> </li>
<li> <li>
<a href="#Installation">Installation</a> <a href="#Installation">Installation</a>
@ -120,6 +120,88 @@ distributed training and inference in a few lines.
- Inference - Inference
- [Energon-AI](https://github.com/hpcaitech/EnergonAI) - [Energon-AI](https://github.com/hpcaitech/EnergonAI)
<p align="right">(<a href="#top">back to top</a>)</p>
## Colossal-AI in the Real World
### ColossalChat
<div align="center">
<a href="https://chat.colossalai.org/">
<img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/Chat-demo.png" width="700" />
</a>
</div>
[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://chat.colossalai.org)
<p id="ColossalChat_scaling" align="center">
<img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/applications/chatgpt/ChatGPT%20scaling.png" width=800/>
</p>
- Up to 7.73 times faster for single server training and 1.42 times faster for single-GPU inference
<p id="ColossalChat-1GPU" align="center">
<img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/applications/chatgpt/ChatGPT-1GPU.jpg" width=450/>
</p>
- 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)
<p id="ColossalChat-LoRA" align="center">
<img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/applications/chatgpt/LoRA%20data.jpg" width=600/>
</p>
- 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
<p align="right">(<a href="#top">back to top</a>)</p>
### 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).
<p id="diffusion_train" align="center">
<img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/Stable%20Diffusion%20v2.png" width=800/>
</p>
- [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).
<p id="diffusion_demo" align="center">
<img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/DreamBooth.png" width=800/>
</p>
- [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.
<p id="inference" align="center">
<img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/Stable%20Diffusion%20Inference.jpg" width=800/>
</p>
- [Inference](https://github.com/hpcaitech/ColossalAI/tree/main/examples/images/diffusion): Reduce inference GPU memory consumption by 2.5x.
<p align="right">(<a href="#top">back to top</a>)</p>
### Biomedicine
Acceleration of [AlphaFold Protein Structure](https://alphafold.ebi.ac.uk/)
<p id="FastFold" align="center">
<img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/FastFold.jpg" width=800/>
</p>
- [FastFold](https://github.com/hpcaitech/FastFold): Accelerating training and inference on GPU Clusters, faster data processing, inference sequence containing more than 10000 residues.
<p id="FastFold-Intel" align="center">
<img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/data%20preprocessing%20with%20Intel.jpg" width=600/>
</p>
- [FastFold with Intel](https://github.com/hpcaitech/FastFold): 3x inference acceleration and 39% cost reduce.
<p id="xTrimoMultimer" align="center">
<img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/xTrimoMultimer_Table.jpg" width=800/>
</p>
- [xTrimoMultimer](https://github.com/biomap-research/xTrimoMultimer): accelerating structure prediction of protein monomers and multimer by 11x.
<p align="right">(<a href="#top">back to top</a>)</p> <p align="right">(<a href="#top">back to top</a>)</p>
## Parallel Training Demo ## Parallel Training Demo
@ -213,88 +295,6 @@ Please visit our [documentation](https://www.colossalai.org/) and [examples](htt
- [BLOOM](https://github.com/hpcaitech/EnergonAI/tree/main/examples/bloom): Reduce hardware deployment costs of 176-billion-parameter BLOOM by more than 10 times. - [BLOOM](https://github.com/hpcaitech/EnergonAI/tree/main/examples/bloom): Reduce hardware deployment costs of 176-billion-parameter BLOOM by more than 10 times.
<p align="right">(<a href="#top">back to top</a>)</p>
## Colossal-AI in the Real World
### ColossalChat
<div align="center">
<a href="https://chat.colossalai.org/">
<img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/Chat-demo.png" width="700" />
</a>
</div>
[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://chat.colossalai.org)
<p id="ColossalChat_scaling" align="center">
<img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/applications/chatgpt/ChatGPT%20scaling.png" width=800/>
</p>
- Up to 7.73 times faster for single server training and 1.42 times faster for single-GPU inference
<p id="ColossalChat-1GPU" align="center">
<img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/applications/chatgpt/ChatGPT-1GPU.jpg" width=450/>
</p>
- 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)
<p id="ColossalChat-LoRA" align="center">
<img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/applications/chatgpt/LoRA%20data.jpg" width=600/>
</p>
- 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
<p align="right">(<a href="#top">back to top</a>)</p>
### 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).
<p id="diffusion_train" align="center">
<img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/Stable%20Diffusion%20v2.png" width=800/>
</p>
- [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).
<p id="diffusion_demo" align="center">
<img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/DreamBooth.png" width=800/>
</p>
- [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.
<p id="inference" align="center">
<img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/Stable%20Diffusion%20Inference.jpg" width=800/>
</p>
- [Inference](https://github.com/hpcaitech/ColossalAI/tree/main/examples/images/diffusion): Reduce inference GPU memory consumption by 2.5x.
<p align="right">(<a href="#top">back to top</a>)</p>
### Biomedicine
Acceleration of [AlphaFold Protein Structure](https://alphafold.ebi.ac.uk/)
<p id="FastFold" align="center">
<img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/FastFold.jpg" width=800/>
</p>
- [FastFold](https://github.com/hpcaitech/FastFold): Accelerating training and inference on GPU Clusters, faster data processing, inference sequence containing more than 10000 residues.
<p id="FastFold-Intel" align="center">
<img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/data%20preprocessing%20with%20Intel.jpg" width=600/>
</p>
- [FastFold with Intel](https://github.com/hpcaitech/FastFold): 3x inference acceleration and 39% cost reduce.
<p id="xTrimoMultimer" align="center">
<img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/xTrimoMultimer_Table.jpg" width=800/>
</p>
- [xTrimoMultimer](https://github.com/biomap-research/xTrimoMultimer): accelerating structure prediction of protein monomers and multimer by 11x.
<p align="right">(<a href="#top">back to top</a>)</p> <p align="right">(<a href="#top">back to top</a>)</p>
## Installation ## Installation
@ -303,6 +303,8 @@ Requirements:
- PyTorch >= 1.11 (PyTorch 2.x in progress) - PyTorch >= 1.11 (PyTorch 2.x in progress)
- Python >= 3.7 - Python >= 3.7
- CUDA >= 11.0 - 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. 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.

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applications/README.md
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@ -7,6 +7,8 @@ The list of applications include:
- [X] [Chatbot](./Chat/README.md) - [X] [Chatbot](./Chat/README.md)
- [ ] Stable Diffusion - [ ] Stable Diffusion
- [ ] Dreambooth - [ ] Dreambooth
- [X] [FastFold](https://github.com/hpcaitech/FastFold): Optimizing AlphaFold (Biomedicine) Training and Inference on GPU Clusters
> Please note that the `Chatbot` application is migrated from the original `ChatGPT` folder. > Please note that the `Chatbot` application is migrated from the original `ChatGPT` folder.
You can find more example code for the base model and functions in the [Examples](https://github.com/hpcaitech/ColossalAI/tree/main/examples) directory.

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@ -38,6 +38,14 @@
<ul> <ul>
<li><a href="#为何选择-Colossal-AI">为何选择 Colossal-AI</a> </li> <li><a href="#为何选择-Colossal-AI">为何选择 Colossal-AI</a> </li>
<li><a href="#特点">特点</a> </li> <li><a href="#特点">特点</a> </li>
<li>
<a href="#Colossal-AI-in-the-Real-World">Colossal-AI 成功案例</a>
<ul>
<li><a href="#ColossalChat">ColossalChat：完整RLHF流程0门槛克隆ChatGPT</a></li>
<li><a href="#AIGC">AIGC: 加速 Stable Diffusion</a></li>
<li><a href="#生物医药">生物医药: 加速AlphaFold蛋白质结构预测</a></li>
</ul>
</li>
<li> <li>
<a href="#并行训练样例展示">并行训练样例展示</a> <a href="#并行训练样例展示">并行训练样例展示</a>
<ul> <ul>
@ -65,14 +73,6 @@
<li><a href="#BLOOM-Inference">1760亿参数 BLOOM</a></li> <li><a href="#BLOOM-Inference">1760亿参数 BLOOM</a></li>
</ul> </ul>
</li> </li>
<li>
<a href="#Colossal-AI-in-the-Real-World">Colossal-AI 成功案例</a>
<ul>
<li><a href="#ColossalChat">ColossalChat：完整RLHF流程0门槛克隆ChatGPT</a></li>
<li><a href="#AIGC">AIGC: 加速 Stable Diffusion</a></li>
<li><a href="#生物医药">生物医药: 加速AlphaFold蛋白质结构预测</a></li>
</ul>
</li>
<li> <li>
<a href="#安装">安装</a> <a href="#安装">安装</a>
<ul> <ul>
@ -117,8 +117,88 @@ Colossal-AI 为您提供了一系列并行组件。我们的目标是让您的
<p align="right">(<a href="#top">返回顶端</a>)</p> <p align="right">(<a href="#top">返回顶端</a>)</p>
## 并行训练样例展示 ## Colossal-AI 成功案例
### ColossalChat
<div align="center">
<a href="https://chat.colossalai.org/">
<img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/Chat-demo.png" width="700" />
</a>
</div>
[ColossalChat](https://github.com/hpcaitech/ColossalAI/tree/main/applications/Chat): 完整RLHF流程0门槛克隆 [ChatGPT](https://openai.com/blog/chatgpt/) [[代码]](https://github.com/hpcaitech/ColossalAI/tree/main/applications/Chat) [[博客]](https://medium.com/@yangyou_berkeley/colossalchat-an-open-source-solution-for-cloning-chatgpt-with-a-complete-rlhf-pipeline-5edf08fb538b) [[在线样例]](https://chat.colossalai.org)
<p id="ColossalChat_scaling" align="center">
<img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/applications/chatgpt/ChatGPT%20scaling.png" width=800/>
</p>
- 最高可提升单机训练速度7.73倍，单卡推理速度1.42倍
<p id="ColossalChat-1GPU" align="center">
<img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/applications/chatgpt/ChatGPT-1GPU.jpg" width=450/>
</p>
- 单卡模型容量最多提升10.3倍
- 最小demo训练流程最低仅需1.62GB显存 (任意消费级GPU)
<p id="ColossalChat-LoRA" align="center">
<img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/applications/chatgpt/LoRA%20data.jpg" width=600/>
</p>
- 提升单卡的微调模型容量3.7倍
- 同时保持高速运行
<p align="right">(<a href="#top">back to top</a>)</p>
### AIGC
加速AIGC(AI内容生成)模型，如[Stable Diffusion v1](https://github.com/CompVis/stable-diffusion) 和 [Stable Diffusion v2](https://github.com/Stability-AI/stablediffusion)
<p id="diffusion_train" align="center">
<img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/Stable%20Diffusion%20v2.png" width=800/>
</p>
- [训练](https://github.com/hpcaitech/ColossalAI/tree/main/examples/images/diffusion): 减少5.6倍显存消耗，硬件成本最高降低46倍(从A100到RTX3060)
<p id="diffusion_demo" align="center">
<img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/DreamBooth.png" width=800/>
</p>
- [DreamBooth微调](https://github.com/hpcaitech/ColossalAI/tree/main/examples/images/dreambooth): 仅需3-5张目标主题图像个性化微调
<p id="inference" align="center">
<img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/Stable%20Diffusion%20Inference.jpg" width=800/>
</p>
- [推理](https://github.com/hpcaitech/ColossalAI/tree/main/examples/images/diffusion): GPU推理显存消耗降低2.5倍
<p align="right">(<a href="#top">返回顶端</a>)</p>
### 生物医药
加速 [AlphaFold](https://alphafold.ebi.ac.uk/) 蛋白质结构预测
<p id="FastFold" align="center">
<img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/FastFold.jpg" width=800/>
</p>
- [FastFold](https://github.com/hpcaitech/FastFold): 加速AlphaFold训练与推理、数据前处理、推理序列长度超过10000残基
<p id="FastFold-Intel" align="center">
<img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/data%20preprocessing%20with%20Intel.jpg" width=600/>
</p>
- [FastFold with Intel](https://github.com/hpcaitech/FastFold): 3倍推理加速和39%成本节省
<p id="xTrimoMultimer" align="center">
<img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/xTrimoMultimer_Table.jpg" width=800/>
</p>
- [xTrimoMultimer](https://github.com/biomap-research/xTrimoMultimer): 11倍加速蛋白质单体与复合物结构预测
<p align="right">(<a href="#top">返回顶端</a>)</p>
## 并行训练样例展示
### GPT-3 ### GPT-3
<p align="center"> <p align="center">
@ -213,87 +293,6 @@ Colossal-AI 为您提供了一系列并行组件。我们的目标是让您的
<p align="right">(<a href="#top">返回顶端</a>)</p> <p align="right">(<a href="#top">返回顶端</a>)</p>
## Colossal-AI 成功案例
### ColossalChat
<div align="center">
<a href="https://chat.colossalai.org/">
<img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/Chat-demo.png" width="700" />
</a>
</div>
[ColossalChat](https://github.com/hpcaitech/ColossalAI/tree/main/applications/Chat): 完整RLHF流程0门槛克隆 [ChatGPT](https://openai.com/blog/chatgpt/) [[代码]](https://github.com/hpcaitech/ColossalAI/tree/main/applications/Chat) [[博客]](https://medium.com/@yangyou_berkeley/colossalchat-an-open-source-solution-for-cloning-chatgpt-with-a-complete-rlhf-pipeline-5edf08fb538b) [[在线样例]](https://chat.colossalai.org)
<p id="ColossalChat_scaling" align="center">
<img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/applications/chatgpt/ChatGPT%20scaling.png" width=800/>
</p>
- 最高可提升单机训练速度7.73倍，单卡推理速度1.42倍
<p id="ColossalChat-1GPU" align="center">
<img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/applications/chatgpt/ChatGPT-1GPU.jpg" width=450/>
</p>
- 单卡模型容量最多提升10.3倍
- 最小demo训练流程最低仅需1.62GB显存 (任意消费级GPU)
<p id="ColossalChat-LoRA" align="center">
<img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/applications/chatgpt/LoRA%20data.jpg" width=600/>
</p>
- 提升单卡的微调模型容量3.7倍
- 同时保持高速运行
<p align="right">(<a href="#top">back to top</a>)</p>
### AIGC
加速AIGC(AI内容生成)模型，如[Stable Diffusion v1](https://github.com/CompVis/stable-diffusion) 和 [Stable Diffusion v2](https://github.com/Stability-AI/stablediffusion)
<p id="diffusion_train" align="center">
<img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/Stable%20Diffusion%20v2.png" width=800/>
</p>
- [训练](https://github.com/hpcaitech/ColossalAI/tree/main/examples/images/diffusion): 减少5.6倍显存消耗，硬件成本最高降低46倍(从A100到RTX3060)
<p id="diffusion_demo" align="center">
<img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/DreamBooth.png" width=800/>
</p>
- [DreamBooth微调](https://github.com/hpcaitech/ColossalAI/tree/main/examples/images/dreambooth): 仅需3-5张目标主题图像个性化微调
<p id="inference" align="center">
<img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/Stable%20Diffusion%20Inference.jpg" width=800/>
</p>
- [推理](https://github.com/hpcaitech/ColossalAI/tree/main/examples/images/diffusion): GPU推理显存消耗降低2.5倍
<p align="right">(<a href="#top">返回顶端</a>)</p>
### 生物医药
加速 [AlphaFold](https://alphafold.ebi.ac.uk/) 蛋白质结构预测
<p id="FastFold" align="center">
<img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/FastFold.jpg" width=800/>
</p>
- [FastFold](https://github.com/hpcaitech/FastFold): 加速AlphaFold训练与推理、数据前处理、推理序列长度超过10000残基
<p id="FastFold-Intel" align="center">
<img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/data%20preprocessing%20with%20Intel.jpg" width=600/>
</p>
- [FastFold with Intel](https://github.com/hpcaitech/FastFold): 3倍推理加速和39%成本节省
<p id="xTrimoMultimer" align="center">
<img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/xTrimoMultimer_Table.jpg" width=800/>
</p>
- [xTrimoMultimer](https://github.com/biomap-research/xTrimoMultimer): 11倍加速蛋白质单体与复合物结构预测
<p align="right">(<a href="#top">返回顶端</a>)</p>
## 安装 ## 安装
环境要求: 环境要求:
@ -301,6 +300,8 @@ Colossal-AI 为您提供了一系列并行组件。我们的目标是让您的
- PyTorch >= 1.11 (PyTorch 2.x 正在适配中) - PyTorch >= 1.11 (PyTorch 2.x 正在适配中)
- Python >= 3.7 - Python >= 3.7
- CUDA >= 11.0 - CUDA >= 11.0
- [NVIDIA GPU Compute Capability](https://developer.nvidia.com/cuda-gpus) >= 7.0 (V100/RTX20 and higher)
- Linux OS
如果你遇到安装问题，可以向本项目 [反馈](https://github.com/hpcaitech/ColossalAI/issues/new/choose)。 如果你遇到安装问题，可以向本项目 [反馈](https://github.com/hpcaitech/ColossalAI/issues/new/choose)。

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@ -4,6 +4,8 @@ Requirements:
- PyTorch >= 1.11 (PyTorch 2.x in progress) - PyTorch >= 1.11 (PyTorch 2.x in progress)
- Python >= 3.7 - Python >= 3.7
- CUDA >= 11.0 - 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 about installation, you may want to raise an [issue](https://github.com/hpcaitech/ColossalAI/issues/new/choose) in this repository. If you encounter any problem about installation, you may want to raise an [issue](https://github.com/hpcaitech/ColossalAI/issues/new/choose) in this repository.

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@ -5,6 +5,8 @@
- PyTorch >= 1.11 (PyTorch 2.x 正在适配中) - PyTorch >= 1.11 (PyTorch 2.x 正在适配中)
- Python >= 3.7 - Python >= 3.7
- CUDA >= 11.0 - CUDA >= 11.0
- [NVIDIA GPU Compute Capability](https://developer.nvidia.com/cuda-gpus) >= 7.0 (V100/RTX20 and higher)
- Linux OS
如果你遇到安装问题，可以向本项目 [反馈](https://github.com/hpcaitech/ColossalAI/issues/new/choose)。 如果你遇到安装问题，可以向本项目 [反馈](https://github.com/hpcaitech/ColossalAI/issues/new/choose)。

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@ -12,6 +12,8 @@
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. 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.
You can find applications such as Chatbot, Stable Diffusion and Biomedicine in the [Applications](https://github.com/hpcaitech/ColossalAI/tree/main/applications) directory.
## Folder Structure ## Folder Structure
```text ```text