lzhengning
/
JittorMirror
mirror of https://github.com/Jittor/Jittor
1
0
Fork 0
Code Issues Packages Projects Releases Wiki Activity

polish readme

This commit is contained in:
Dun Liang 2021-01-20 14:09:55 +08:00
parent d73b038946
commit c1731cb215
3 changed files with 38 additions and 9 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

14
README.cn.md
View File

@ -4,10 +4,18 @@
[快速开始](#快速开始) | [安装](#安装) | [教程](#教程)
Jittor 是一个基于即时编译和元算子的高性能深度学习框架，整个框架在即时编译的同时，还集成了强大的Op编译器和调优器，为您的模型生成定制化的高性能代码。
Jittor 是一个基于即时编译和元算子的高性能深度学习框架，整个框架在即时编译的同时，还集成了强大的Op编译器和调优器，为您的模型生成定制化的高性能代码。Jittor还包含了丰富的高性能模型库，涵盖范围包括：图像识别，检测，分割，生成，可微渲染，几何学习，强化学习等等。
Jittor前端语言为Python。前端使用了模块化的设计，这是目前最主流的深度学习框架接口设计。后端则使用高性能语言编写，如CUDA，C++。
Jittor前端语言为Python。前端使用了模块化和动态图执行的设计，这是目前最主流的深度学习框架接口设计。后端则使用高性能语言编写，如CUDA，C++。
相关链接：
* [Jittor官网](https://cg.cs.tsinghua.edu.cn/jittor/)
* [Jittor教程](https://cg.cs.tsinghua.edu.cn/jittor/tutorial/)
* [Jittor模型库](https://cg.cs.tsinghua.edu.cn/jittor/resources/)
* [Jittor文档](https://cg.cs.tsinghua.edu.cn/jittor/assets/docs/index.html)
下面的代码演示了如何一步一步使用Python代码，从头对一个双层神经网络建模。
@ -118,7 +126,7 @@ docker run -it --network host jittor/jittor-cuda
## Pip 安装
如果您没有准备好环境，或者使用的不是Ubuntu操作系统， 推荐使用docker安装， 如果您已经装好编译器和对应版本的Python,我们强烈推荐您使用这种方法
如果您没有准备好环境，或者使用的不是Ubuntu操作系统， 推荐使用**docker安装**， 如果您已经装好编译器和对应版本的Python,我们强烈推荐您使用这种方法
(如果无法访问github, 可以通过jittor主页下载):
```bash

12
README.md
View File

@ -3,10 +3,18 @@
[Quickstart](#quickstart) | [Install](#install) | [Tutorial](#tutorial) | [Chinese](./README.cn.md)
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 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. .
The front-end language is Python. Module Design is used in the front-end, which is the most popular design for deeplearning framework interface. The back-end is implemented by high performance language, such as CUDA,C++.
The front-end language is Python. Module Design and Dynamic Graph Execution is used in the front-end, which is the most popular design for deeplearning framework interface. The back-end is implemented by high performance language, such as CUDA,C++.
Related Links:
* [Jittor Website](https://cg.cs.tsinghua.edu.cn/jittor/)
* [Jittor Tutorials](https://cg.cs.tsinghua.edu.cn/jittor/tutorial/)
* [Jittor Models](https://cg.cs.tsinghua.edu.cn/jittor/resources/)
* [Jittor Documents](https://cg.cs.tsinghua.edu.cn/jittor/assets/docs/index.html)
The following example shows how to model a two-layer neural network step by step and train from scratch In a few lines of Python code.

21
README.src.md
View File

@ -5,13 +5,26 @@
[快速开始](#快速开始) | [安装](#安装) | [教程](#教程)
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 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. .
Jittor 是一个基于即时编译和元算子的高性能深度学习框架，整个框架在即时编译的同时，还集成了强大的Op编译器和调优器，为您的模型生成定制化的高性能代码。
Jittor 是一个基于即时编译和元算子的高性能深度学习框架，整个框架在即时编译的同时，还集成了强大的Op编译器和调优器，为您的模型生成定制化的高性能代码。Jittor还包含了丰富的高性能模型库，涵盖范围包括：图像识别，检测，分割，生成，可微渲染，几何学习，强化学习等等。
The front-end language is Python. Module Design is used in the front-end, which is the most popular design for deeplearning framework interface. The back-end is implemented by high performance language, such as CUDA,C++.
The front-end language is Python. Module Design and Dynamic Graph Execution is used in the front-end, which is the most popular design for deeplearning framework interface. The back-end is implemented by high performance language, such as CUDA,C++.
Jittor前端语言为Python。前端使用了模块化和动态图执行的设计，这是目前最主流的深度学习框架接口设计。后端则使用高性能语言编写，如CUDA，C++。
Related Links:
* [Jittor Website](https://cg.cs.tsinghua.edu.cn/jittor/)
* [Jittor Tutorials](https://cg.cs.tsinghua.edu.cn/jittor/tutorial/)
* [Jittor Models](https://cg.cs.tsinghua.edu.cn/jittor/resources/)
* [Jittor Documents](https://cg.cs.tsinghua.edu.cn/jittor/assets/docs/index.html)
相关链接：
* [Jittor官网](https://cg.cs.tsinghua.edu.cn/jittor/)
* [Jittor教程](https://cg.cs.tsinghua.edu.cn/jittor/tutorial/)
* [Jittor模型库](https://cg.cs.tsinghua.edu.cn/jittor/resources/)
* [Jittor文档](https://cg.cs.tsinghua.edu.cn/jittor/assets/docs/index.html)
Jittor前端语言为Python。前端使用了模块化的设计，这是目前最主流的深度学习框架接口设计。后端则使用高性能语言编写，如CUDA，C++。
The following example shows how to model a two-layer neural network step by step and train from scratch In a few lines of Python code.