mirror of https://github.com/hpcaitech/ColossalAI
[doc] updated readme regarding pypi installation (#2406)
Frank Lee
GitHub
parent
d3f5ce9efb
commit
85e045b063
|
|
@ -5,10 +5,10 @@
|
|||
|
||||
Colossal-AI: 一个面向大模型时代的通用深度学习系统
|
||||
|
||||
<h3> <a href="https://arxiv.org/abs/2110.14883"> 论文 </a> |
|
||||
<a href="https://www.colossalai.org/"> 文档 </a> |
|
||||
<a href="https://github.com/hpcaitech/ColossalAI-Examples"> 例程 </a> |
|
||||
<a href="https://github.com/hpcaitech/ColossalAI/discussions"> 论坛 </a> |
|
||||
<h3> <a href="https://arxiv.org/abs/2110.14883"> 论文 </a> |
|
||||
<a href="https://www.colossalai.org/"> 文档 </a> |
|
||||
<a href="https://github.com/hpcaitech/ColossalAI-Examples"> 例程 </a> |
|
||||
<a href="https://github.com/hpcaitech/ColossalAI/discussions"> 论坛 </a> |
|
||||
<a href="https://medium.com/@hpcaitech"> 博客 </a></h3>
|
||||
|
||||
[](https://github.com/hpcaitech/ColossalAI/actions/workflows/build.yml)
|
||||
|
|
@ -35,7 +35,7 @@
|
|||
<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="#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>
|
||||
<li><a href="#GPT-3">GPT-3</a></li>
|
||||
<li><a href="#GPT-2">GPT-2</a></li>
|
||||
|
|
@ -47,14 +47,14 @@
|
|||
</ul>
|
||||
</li>
|
||||
<li>
|
||||
<a href="#单GPU训练样例展示">单GPU训练样例展示</a>
|
||||
<a href="#单GPU训练样例展示">单GPU训练样例展示</a>
|
||||
<ul>
|
||||
<li><a href="#GPT-2-Single">GPT-2</a></li>
|
||||
<li><a href="#PaLM-Single">PaLM</a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li>
|
||||
<a href="#推理-Energon-AI-样例展示">推理 (Energon-AI) 样例展示</a>
|
||||
<a href="#推理-Energon-AI-样例展示">推理 (Energon-AI) 样例展示</a>
|
||||
<ul>
|
||||
<li><a href="#GPT-3-Inference">GPT-3</a></li>
|
||||
<li><a href="#OPT-Serving">1750亿参数OPT在线推理服务</a></li>
|
||||
|
|
@ -62,7 +62,7 @@
|
|||
</ul>
|
||||
</li>
|
||||
<li>
|
||||
<a href="#Colossal-AI-in-the-Real-World">Colossal-AI 成功案例</a>
|
||||
<a href="#Colossal-AI-in-the-Real-World">Colossal-AI 成功案例</a>
|
||||
<ul>
|
||||
<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>
|
||||
|
|
@ -131,7 +131,7 @@ Colossal-AI 为您提供了一系列并行组件。我们的目标是让您的
|
|||
<img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/(updated)GPT-2.png" width=800>
|
||||
|
||||
- 用相同的硬件训练24倍大的模型
|
||||
- 超3倍的吞吐量
|
||||
- 超3倍的吞吐量
|
||||
|
||||
### BERT
|
||||
<img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/BERT.png" width=800/>
|
||||
|
|
@ -145,7 +145,7 @@ Colossal-AI 为您提供了一系列并行组件。我们的目标是让您的
|
|||
<img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/OPT_update.png" width=800/>
|
||||
|
||||
- [Open Pretrained Transformer (OPT)](https://github.com/facebookresearch/metaseq), 由Meta发布的1750亿语言模型，由于完全公开了预训练参数权重，因此促进了下游任务和应用部署的发展。
|
||||
- 加速45%，仅用几行代码以低成本微调OPT。[[样例]](https://github.com/hpcaitech/ColossalAI-Examples/tree/main/language/opt) [[在线推理]](https://service.colossalai.org/opt)
|
||||
- 加速45%，仅用几行代码以低成本微调OPT。[[样例]](https://github.com/hpcaitech/ColossalAI-Examples/tree/main/language/opt) [[在线推理]](https://service.colossalai.org/opt)
|
||||
|
||||
请访问我们的 [文档](https://www.colossalai.org/) 和 [例程](https://github.com/hpcaitech/ColossalAI-Examples) 以了解详情。
|
||||
|
||||
|
|
@ -255,6 +255,28 @@ Colossal-AI 为您提供了一系列并行组件。我们的目标是让您的
|
|||
|
||||
## 安装
|
||||
|
||||
### 从PyPI安装
|
||||
|
||||
您可以用下面的命令直接从PyPI上下载并安装Colossal-AI。我们默认不会安装PyTorch扩展包
|
||||
|
||||
```bash
|
||||
pip install colossalai
|
||||
```
|
||||
|
||||
但是，如果你想在安装时就直接构建PyTorch扩展，您可以设置环境变量`CUDA_EXT=1`.
|
||||
|
||||
```bash
|
||||
CUDA_EXT=1 pip install colossalai
|
||||
```
|
||||
|
||||
**否则，PyTorch扩展只会在你实际需要使用他们时在运行时里被构建。**
|
||||
|
||||
与此同时，我们也每周定时发布Nightly版本，这能让你提前体验到新的feature和bug fix。你可以通过以下命令安装Nightly版本。
|
||||
|
||||
```bash
|
||||
pip install colossalai-nightly
|
||||
```
|
||||
|
||||
### 从官方安装
|
||||
|
||||
您可以访问我们[下载](https://www.colossalai.org/download)页面来安装Colossal-AI，在这个页面上发布的版本都预编译了CUDA扩展。
|
||||
|
|
@ -274,10 +296,10 @@ pip install -r requirements/requirements.txt
|
|||
pip install .
|
||||
```
|
||||
|
||||
如果您不想安装和启用 CUDA 内核融合（使用融合优化器时强制安装）：
|
||||
我们默认在`pip install`时不安装PyTorch扩展，而是在运行时临时编译，如果你想要提前安装这些扩展的话（在使用融合优化器时会用到），可以使用一下命令。
|
||||
|
||||
```shell
|
||||
NO_CUDA_EXT=1 pip install .
|
||||
CUDA_EXT=1 pip install .
|
||||
```
|
||||
|
||||
<p align="right">(<a href="#top">返回顶端</a>)</p>
|
||||
|
|
|
|||
28
README.md
28
README.md
|
|
@ -257,9 +257,32 @@ Acceleration of [AlphaFold Protein Structure](https://alphafold.ebi.ac.uk/)
|
|||
|
||||
## Installation
|
||||
|
||||
### Install from PyPI
|
||||
|
||||
You can easily install Colossal-AI with the following command. **By defualt, we do not build PyTorch extensions during installation.**
|
||||
|
||||
```bash
|
||||
pip install colossalai
|
||||
```
|
||||
|
||||
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 it.**
|
||||
|
||||
We also keep release the nightly version to PyPI on a weekly basis. 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 Official Releases
|
||||
|
||||
You can visit the [Download](https://www.colossalai.org/download) page to download Colossal-AI with pre-built CUDA extensions.
|
||||
You can visit the [Download](https://www.colossalai.org/download) page to download Colossal-AI with pre-built PyTorch extensions.
|
||||
|
||||
|
||||
### Download From Source
|
||||
|
|
@ -270,9 +293,6 @@ You can visit the [Download](https://www.colossalai.org/download) page to downlo
|
|||
git clone https://github.com/hpcaitech/ColossalAI.git
|
||||
cd ColossalAI
|
||||
|
||||
# install dependency
|
||||
pip install -r requirements/requirements.txt
|
||||
|
||||
# install colossalai
|
||||
pip install .
|
||||
```
|
||||
|
|
|
|||
Loading…
Reference in New Issue