mirror of https://github.com/Jittor/Jittor
update installation in README
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README.cn.md
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README.cn.md
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@ -2,6 +2,7 @@
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[快速开始](#快速开始) | [安装](#安装) | [教程](#教程) | [English](./README.md)
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@ -18,6 +19,7 @@ Jittor前端语言为Python。前端使用了模块化和动态图执行的设
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* [Jittor文档](https://cg.cs.tsinghua.edu.cn/jittor/assets/docs/index.html)
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* [Github](https://github.com/jittor/jittor)， [Gitee](https://gitee.com/jittor/jittor)
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* [Jittor 论坛](https://discuss.jittor.org/)
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* [Jittor 精选仓库](https://github.com/Jittor/jittor/blob/master/AWESOME-JITTOR-LIST.md)
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* 即时通信: QQ Group(761222083)
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@ -88,40 +90,18 @@ for i,(x,y) in enumerate(get_data(n)):
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## 安装
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Jittor框架对环境要求如下:
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Jittor 支持**Linux**(e.g. Ubuntu/CentOS/Arch), **macOS**,**Windows**， 其中**Linux**和**macOS**的依赖如下：
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* Python：版本 >= 3.7
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* C++编译器 （需要下列至少一个）
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- g++ （>=5.4.0 for linux）
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- clang （>=8.0 for mac）
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* GPU 编译器（可选）：nvcc >=10.0
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* GPU 加速库（可选）：cudnn-dev (cudnn开发版, 推荐使用tar安装方法，[参考链接](https://docs.nvidia.com/deeplearning/cudnn/install-guide/index.html#installlinux-tar))
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Jittor 目前还支持主流国产Linux操作系统，如统信、麒麟、普华、龙芯Loongnix，安装方式可参考 Linux pip安装方法，准备好python和g++即可。
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**Windows**对环境的要求为：
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* Python：版本 >= 3.8(建议从Python官网安装：<https://www.python.org/downloads/windows/>)
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* x86_64处理器
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* Windows 10及以上。
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如果您不希望手动配置环境，我们推荐使用 Docker 进行安装。
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除此之外，您还可以使用 pip 安装和手动安装。
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注意1：macOS 用户需要安装额外依赖，请参考 [macOS 安装](#macOS-安装)。
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| OS | CPU | Python | Compiler | (Optional) GPU platform |
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|--------------------------------------------------------|-------------------------------------|--------|--------------|---------------------------------------------|
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| Linux<br>(Ubuntu, CentOS, Arch, <br>UOS, KylinOS, ...) | x86 <br>x86_64 <br>ARM <br>loongson | >= 3.7 | g++ >=5.4 | Nvidia CUDA >= 10.0, [cuDNN](https://docs.nvidia.com/deeplearning/cudnn/install-guide/index.html#installlinux-tar) <br> or [AMD ROCm](https://docs.amd.com/) >= 4.0 <br> or [Hygon DCU DTK](https://tycloud.hpccube.com/doc/1.0.6/11277/general-handbook/software-tutorial/jittor.html) >= 22.04 |
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| macOS <br>(>= 10.14 Mojave) | intel<br>Apple Silicon | >= 3.7 | clang >= 8.0 | - |
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| Windows 10 & 11 | x86_64 | [>= 3.8](https://www.python.org/downloads/windows/) | - | Nvidia CUDA >= 10.2 [cuDNN](https://docs.nvidia.com/deeplearning/cudnn/install-guide/index.html#install-windows) |
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Jittor 提供了三种安装方法：pip、docker和手动安装：
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## Pip 安装
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@ -142,11 +122,11 @@ jittor会自动在路径中寻找合适的编译器, 如果您希望手动指定
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### macOS 安装
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macOS 请使用 [homebrew](https://brew.sh) 安装额外的依赖 (python>=3.7, onednn)。
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macOS 请使用 [homebrew](https://brew.sh) 安装额外的依赖。
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```bash
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brew install python@3.7 onednn libomp
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brew install onednn libomp
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```
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之后您可以通过 pip 安装 jittor，并测试是否可以成功运行。
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@ -157,7 +137,7 @@ python3.7 -m pip install jittor
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python3.7 -m jittor.test.test_example
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```
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目前在macOS中，jittor 只支持 CPU 计算。
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目前在 macOS 中，jittor 只支持 CPU 计算。
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### Windows安装
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@ -439,3 +419,4 @@ Jittor目前由[清华大学计算机图形学组](https://cg.cs.tsinghua.edu.cn
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如LICENSE.txt文件中所示，Jittor使用Apache 2.0版权协议。
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33
README.md
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README.md
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@ -91,34 +91,14 @@ We provide some jupyter notebooks to help you quick start with Jittor.
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Jittor environment requirements:
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* System: **Linux**(e.g. Ubuntu/CentOS/Arch), **macOS**, or **Windows**, enviroment requirements of **Linux** and **Mac** are list below:
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| OS | CPU | Python | Compiler | (Optional) GPU platform |
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|--------------------------------------------------------|-------------------------------------|--------|--------------|---------------------------------------------|
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| Linux<br>(Ubuntu, CentOS, Arch, <br>UOS, KylinOS, ...) | x86 <br>x86_64 <br>ARM <br>loongson | >= 3.7 | g++ >=5.4 | Nvidia CUDA >= 10.0, [cuDNN](https://docs.nvidia.com/deeplearning/cudnn/install-guide/index.html#installlinux-tar) <br> or [AMD ROCm](https://docs.amd.com/) >= 4.0 <br> or [Hygon DCU DTK](https://tycloud.hpccube.com/doc/1.0.6/11277/general-handbook/software-tutorial/jittor.html) >= 22.04 |
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| macOS <br>(>= 10.14 Mojave) | intel<br>Apple Silicon | >= 3.7 | clang >= 8.0 | - |
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| Windows 10 & 11 | x86_64 | [>= 3.8](https://www.python.org/downloads/windows/) | - | Nvidia CUDA >= 10.2 [cuDNN](https://docs.nvidia.com/deeplearning/cudnn/install-guide/index.html#install-windows) |
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* Python version >= 3.7
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* CPU compiler (require at least one of the following)
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* g++ (>=5.4.0)
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* clang (>=8.0)
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* GPU compiler (optional)
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* nvcc (>=10.0 for g++ or >=10.2 for clang)
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* GPU library: cudnn-dev (recommend tar file installation, [reference link](https://docs.nvidia.com/deeplearning/cudnn/install-guide/index.html#installlinux-tar))
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**Windows** requirements atr:
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* Python: version >= 3.8(recommend install from <https://www.python.org/downloads/windows/>)
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* x86_64 CPU processor
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* Windows 10 or above
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Note#1: macOS users have to install additional dependencies, see [macOS install](#macOS-install).
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Jittor offers three ways to install: pip, docker, or manual.
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@ -142,7 +122,7 @@ python3.7 -m jittor.test.test_example
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Please first install additional dependencies with [homebrew](https://brew.sh).
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```bash
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brew install python@3.7 onednn libomp
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brew install onednn libomp
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```
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@ -433,3 +413,4 @@ Jittor is currently maintained by the [Tsinghua CSCG Group](https://cg.cs.tsingh
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Jittor is Apache 2.0 licensed, as found in the LICENSE.txt file.
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@ -5,7 +5,7 @@
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[Quickstart](#quickstart) | [Install](#install) | [Tutorial](#tutorial) | [Chinese](./README.cn.md)
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[快速开始](#快速开始) | [安装](#安装) | [教程](#教程)
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[快速开始](#快速开始) | [安装](#安装) | [教程](#教程) | [English](./README.md)
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Jittor is a high-performance deep learning framework based on JIT compiling and meta-operators. The whole framework and meta-operators are compiled just-in-time. A powerful op compiler and tuner are integrated into Jittor. It allowed us to generate high-performance code with specialized for your model. Jittor also contains a wealth of high-performance model libraries, including: image recognition, detection, segmentation, generation, differentiable rendering, geometric learning, reinforcement learning, etc. .
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@ -22,6 +22,7 @@ Related Links:
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* [Jittor Documents](https://cg.cs.tsinghua.edu.cn/jittor/assets/docs/index.html)
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* [Github](https://github.com/jittor/jittor), [Gitee](https://gitee.com/jittor/jittor)
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* [Jittor Forum](https://discuss.jittor.org/)
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* [Awesome Jittor List](https://github.com/Jittor/jittor/blob/master/AWESOME-JITTOR-LIST.md)
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* IM: QQ Group(761222083)
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相关链接：
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@ -31,6 +32,7 @@ Related Links:
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* [Jittor文档](https://cg.cs.tsinghua.edu.cn/jittor/assets/docs/index.html)
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* [Github](https://github.com/jittor/jittor)， [Gitee](https://gitee.com/jittor/jittor)
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* [Jittor 论坛](https://discuss.jittor.org/)
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* [Jittor 精选仓库](https://github.com/Jittor/jittor/blob/master/AWESOME-JITTOR-LIST.md)
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* 即时通信: QQ Group(761222083)
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@ -115,52 +117,17 @@ We provide some jupyter notebooks to help you quick start with Jittor.
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## 安装
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Jittor框架对环境要求如下:
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Jittor 支持**Linux**(e.g. Ubuntu/CentOS/Arch), **macOS**,**Windows**， 其中**Linux**和**macOS**的依赖如下：
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* Python：版本 >= 3.7
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* C++编译器 （需要下列至少一个）
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- g++ （>=5.4.0 for linux）
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- clang （>=8.0 for mac）
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* GPU 编译器（可选）：nvcc >=10.0
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* GPU 加速库（可选）：cudnn-dev (cudnn开发版, 推荐使用tar安装方法，[参考链接](https://docs.nvidia.com/deeplearning/cudnn/install-guide/index.html#installlinux-tar))
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Jittor 目前还支持主流国产Linux操作系统，如统信、麒麟、普华、龙芯Loongnix，安装方式可参考 Linux pip安装方法，准备好python和g++即可。
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**Windows**对环境的要求为：
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* Python：版本 >= 3.8(建议从Python官网安装：<https://www.python.org/downloads/windows/>)
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* x86_64处理器
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* Windows 10及以上。
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如果您不希望手动配置环境，我们推荐使用 Docker 进行安装。
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除此之外，您还可以使用 pip 安装和手动安装。
|
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注意1：macOS 用户需要安装额外依赖，请参考 [macOS 安装](#macOS-安装)。
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Jittor 提供了三种安装方法：pip、docker和手动安装：
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Jittor environment requirements:
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* System: **Linux**(e.g. Ubuntu/CentOS/Arch), **macOS**, or **Windows**, enviroment requirements of **Linux** and **Mac** are list below:
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| OS | CPU | Python | Compiler | (Optional) GPU platform |
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|--------------------------------------------------------|-------------------------------------|--------|--------------|---------------------------------------------|
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| Linux<br>(Ubuntu, CentOS, Arch, <br>UOS, KylinOS, ...) | x86 <br>x86_64 <br>ARM <br>loongson | >= 3.7 | g++ >=5.4 | Nvidia CUDA >= 10.0, [cuDNN](https://docs.nvidia.com/deeplearning/cudnn/install-guide/index.html#installlinux-tar) <br> or [AMD ROCm](https://docs.amd.com/) >= 4.0 <br> or [Hygon DCU DTK](https://tycloud.hpccube.com/doc/1.0.6/11277/general-handbook/software-tutorial/jittor.html) >= 22.04 |
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| macOS <br>(>= 10.14 Mojave) | intel<br>Apple Silicon | >= 3.7 | clang >= 8.0 | - |
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| Windows 10 & 11 | x86_64 | [>= 3.8](https://www.python.org/downloads/windows/) | - | Nvidia CUDA >= 10.2 [cuDNN](https://docs.nvidia.com/deeplearning/cudnn/install-guide/index.html#install-windows) |
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* Python version >= 3.7
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* CPU compiler (require at least one of the following)
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* g++ (>=5.4.0)
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* clang (>=8.0)
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* GPU compiler (optional)
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* nvcc (>=10.0 for g++ or >=10.2 for clang)
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* GPU library: cudnn-dev (recommend tar file installation, [reference link](https://docs.nvidia.com/deeplearning/cudnn/install-guide/index.html#installlinux-tar))
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**Windows** requirements atr:
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* Python: version >= 3.8(recommend install from <https://www.python.org/downloads/windows/>)
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* x86_64 CPU processor
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* Windows 10 or above
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Note#1: macOS users have to install additional dependencies, see [macOS install](#macOS-install).
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Jittor 提供了三种安装方法：pip、docker和手动安装：
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Jittor offers three ways to install: pip, docker, or manual.
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@ -186,12 +153,12 @@ jittor会自动在路径中寻找合适的编译器, 如果您希望手动指定
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### macOS install
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macOS 请使用 [homebrew](https://brew.sh) 安装额外的依赖 (python>=3.7, onednn)。
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macOS 请使用 [homebrew](https://brew.sh) 安装额外的依赖。
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Please first install additional dependencies with [homebrew](https://brew.sh).
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```bash
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brew install python@3.7 onednn libomp
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brew install onednn libomp
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```
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之后您可以通过 pip 安装 jittor，并测试是否可以成功运行。
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@ -203,7 +170,7 @@ python3.7 -m pip install jittor
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python3.7 -m jittor.test.test_example
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```
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目前在macOS中，jittor 只支持 CPU 计算。
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目前在 macOS 中，jittor 只支持 CPU 计算。
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Currently jittor only supports CPU in macOS.
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@ -19,23 +19,69 @@ def check_is_en(src):
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return en_cnt == len(src)
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def check_is_both(src):
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if src.startswith("!"):
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return True
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return len(src) < 2
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def splite_markdown_blocks(src):
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''' split markdown document into text, code, table blocks
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'''
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blocks = []
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block = ""
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status = "text"
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def commit_block():
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blocks.append((block, status))
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for line in src.split('\n'):
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line = line + "\n"
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if line.startswith("```"):
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assert status in ["text", "code"]
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if status == "text":
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commit_block()
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status = "code"
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block = line
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elif status == "code":
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block += line
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commit_block()
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status = "text"
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block = ""
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elif line.strip().startswith('|') and line.strip().endswith('|'):
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assert status in ["text", "table"]
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if status == "text":
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commit_block()
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status = "table"
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block = line
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else:
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block += line
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else:
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if status == "table":
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commit_block()
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status = "text"
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block = line
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else:
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block += line
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if status != "code":
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commit_block()
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return blocks
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for mdname in all_src_md:
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print(mdname)
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with open(mdname, "r", encoding='utf8') as f:
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src = f.read()
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src = src.split("```")
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en_src = []
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cn_src = []
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for i, s in enumerate(src):
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if i%2==1:
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en_src.append(s)
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cn_src.append(s)
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src_blocks = splite_markdown_blocks(src)
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en_src = ""
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cn_src = ""
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for block, status in src_blocks:
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if status == "code" or status == "table":
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en_src += block
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cn_src += block
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else:
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en_s = []
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cn_s = []
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for line in s.split('\n'):
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for line in block.split('\n'):
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if check_is_both(line):
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en_s.append(line)
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cn_s.append(line)
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@ -43,10 +89,9 @@ for mdname in all_src_md:
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en_s.append(line)
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else:
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cn_s.append(line)
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en_src.append("\n".join(en_s))
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cn_src.append("\n".join(cn_s))
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en_src = "```".join(en_src)
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cn_src = "```".join(cn_src)
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en_src += "\n".join(en_s)
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cn_src += "\n".join(cn_s)
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with open(mdname.replace(".src.md", ".md"), 'w', encoding='utf8') as f:
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f.write(en_src)
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with open(mdname.replace(".src.md", ".cn.md"), 'w', encoding='utf8') as f:
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|
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