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提交 40d28bc1 编写于 作者: H Hao Wang 提交者： Cheerego
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add en index for en install doc (#541) 

* add en index for install doc,fix some expressions

* Appendix title

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doc/fluid/beginners_guide/basics/learning_materials_en.md 0 → 100644
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# Learning Materials
## The first book to start your journey
Books are the most direct resources to pick up the rationale of a subject. We recommend the following books for you which are categorized into machine learning theory, deep learning theory and programming languages.
### Books for Machine Learning Theory
Machine learning theory is a prerequisite to deep learning. Deep learning, one of the branches of machine learning, has a theoretical basis strongly relevant to machine learning.
There have been various textbooks nowadays, from which we select an easier one for you. Please pay more attention to the chapters involving Neural Networks in the textbook.
book：《Machine Learning》（Zhihua Zhou，Tsinghua University Express, 2016）
### Books for Deep Learning Theory
Having consolidated your basis of machine learning, it is time to dive into deep learning.
It's commonplace that deep learning theory leaves an obscure and abstract impression on learners, and tightly connects with mathematics.
To help you smoothly get started with deep learning, we recommend the following easy-to-go textbook, which features a good explanation of both deep learning theory and its related mathematic basis.
book：《Deep Learning》（Goodfellow, Bengio, Courville)
### Books for Programming Languages
Python：
Python is our recommended programming language. On the one hand, Python is the main supportive language of mainstream deep learning frameworks; On the other hand, Python is easier than other languages for beginners.
Python textbooks abounds in the market, and what lies here is a textbook that ingeniously balanced theoretical knowledge with practical operations. Through resolving the 52 questions in the book, running your answer code, and addressing the problems occurred in this process, you can gradually get the hang of Python.
Book：《Learn Python the Hard Way》（Zed Shaw）
C++：
C++ is adopted widely in low level part of frameworks. After you have gradually mastered basic operations of an open-source framework, programming in C++ is an important skill in the more advanced operations of a framework.
C++ also requires frequent practical exercises like Python mentioned above.
The book lying here is a quick-to-start textbook with introduction to functions and structures, and examples of resolutions.
Book：《Essential C++》（Lippman,S.B.）
## Open Lectures
Besides textbooks, face-to-face instructions from teachers would contribute a robust and quick boost to your learning of new technology. Compared with on-campus lectures, open video lectures can not only make your learning simpler, but also save your time and energy.
Currently, the courses about deep learning are mostly free and public. These courses will facilitate you to comprehend abstract theory embedded in deep learning in a more effortless way, and direct you straightly towards practical applications. Regards to the vitality, operability, continuity, and compactness, we recommend the following courses and their corresponding links are attached afterwards to exempt your time from searching.
### Lectures Aimed at Theory Analysis
[Machine Learning](http://open.163.com/special/opencourse/machinelearning.html) : Delivered by Andrew Ng, Stanford University. This series of lectures encompasses detailed analysis on relevant algorithms.
[Deep Learning](http://speech.ee.ntu.edu.tw/~tlkagk/courses_ML17_2.html) : An online English course delivered by Prof. Hung-yi Lee. It is combined with the abroad research contributions, and at the same time it is suitable for novices to get started and understand deep learning.
The following are several lectures delivered in Chinese:
[AI tech](https://ai.baidu.com/paddlepaddle/player?id=13) : The course named "Master Core AI Technology" organized by Baidu deciphers AI technology to Deep Learning in a comprehensive and fine-grained way. Each lesson lasts for 20 - 30 minutes.
[Programming Languages](https://ai.baidu.com/paddlepaddle/openCourses) Python tutorials，with 20 minutes each lesson, illustrates from the basis to advanced usage.
### PaddlePaddle Hands-on Training
Having equipped with a firm grasp of theory basis and programming ability, you can now commence a practical adventure to PaddlePaddle Fluid, and grow up from a beginner level to a medium or high level.
Our official open courses are presented on the official site. The courses embrace PaddlePaddle practical operations, scenarios applied with PaddlePaddle, and introduction to PaddlePaddle machine learning models. Developers can take full advantage of our official courses to start PaddlePaddle from scratch and gradually move to industrial application.
[Click Here](http://ai.baidu.com/paddlepaddle/openCourses) to embark on your sailing in our official deep learning video lectures.
doc/fluid/beginners_guide/index_en.rst 0 → 100644
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################
Beginner's Guide
################
PaddlePaddle (PArallel Distributed Deep LEarning) is a
simple, efficient and extensible deep learning framework.
Please refer to `PaddlePaddle Github <https://github.com/PaddlePaddle/Paddle>`_ for details, and `release note <../release_note_en.html>`_ for features incorporated in current version.
For beginners of PaddlePaddle, the following documentation will tutor you about installing PaddlePaddle:
- `Installation Manuals <../beginners_guide/install/index_en.html>`_ ：Installation on Ubuntu/CentOS/Windows/MacOS is supported.
The following resources are recommended for novices in deep learning:
- `Resources <../beginners_guide/basics/learning_materials.html>`_ ：Selected books and lectures about machine learning, deep learning and programming languages.
If you have been armed with certain level of deep learning knowledge, and it happens to be the first time to try PaddlePaddle, the following cases of model building will expedite your learning process:
- `Programming with Fluid <../beginners_guide/programming_guide/programming_guide.html>`_ ： Core concepts and basic usage of Fluid
- `Quick Start <../beginners_guide/quick_start/index.html>`_： Two easy-to-go models, linear regression model and digit recognition model, are in place to speed up your study of training neural networks
- `Deep Learning <../beginners_guide/basics/index.html>`_： This section encompasses various fields of fundamental deep learning knowledge, such as image classification, customized recommendation, machine translation, and examples implemented by Fluid are provided.
.. toctree::
:hidden:
install/index_en.rst
basics/learning_materials_en.md
doc/fluid/beginners_guide/install/Tables_en.md
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***
# APPENDIX
<a name="third_party"></a>
# Appendix
## Compile Dependency Table
......@@ -228,6 +229,7 @@ PaddePaddle implements references to various BLAS/CUDA/cuDNN libraries by specif
***
<a name="whls"></a>
</br></br>
## **Installation Package List**
......@@ -322,6 +324,7 @@ PaddePaddle implements references to various BLAS/CUDA/cuDNN libraries by specif
You can find various distributions of PaddlePaddle-gpu in [the Release History](https://pypi.org/project/paddlepaddle-gpu/#history).
***
<a name="dockers"></a>
</br></br>
## Installation Mirrors and Introduction
......@@ -359,7 +362,7 @@ You can find various distributions of PaddlePaddle-gpu in [the Release History](
You can find the docker image for each release of PaddlePaddle in the [DockerHub](https://hub.docker.com/r/paddlepaddle/paddle/tags/).
***
<a name="ciwhls-release"></a>
</br></br>
## **Multi-version whl package list - Release**
......@@ -379,92 +382,90 @@ You can find the docker image for each release of PaddlePaddle in the [DockerHub
<tbody>
<tr>
<td> cpu-noavx-mkl </td>
<td> <a href="http://paddlepaddle.org/download?url=http://paddle-wheel.bj.bcebos.com/1.2.0-cpu-noavx-mkl/paddlepaddle-1.2.0-cp27-cp27mu-linux_x86_64.whl">
paddlepaddle-1.2.0-cp27-cp27mu-linux_x86_64.whl</a></td>
<td> <a href="http://paddlepaddle.org/download?url=http://paddle-wheel.bj.bcebos.com/1.2.0-cpu-noavx-mkl/paddlepaddle-1.2.0-cp27-cp27m-linux_x86_64.whl">
paddlepaddle-1.2.0-cp27-cp27m-linux_x86_64.whl</a></td>
<td> <a href="http://paddlepaddle.org/download?url=http://paddle-wheel.bj.bcebos.com/1.2.0-cpu-noavx-mkl/paddlepaddle-1.2.0-cp35-cp35m-linux_x86_64.whl">
paddlepaddle-1.2.0-cp35-cp35m-linux_x86_64.whl</a></td>
<td> <a href="http://paddlepaddle.org/download?url=http://paddle-wheel.bj.bcebos.com/1.2.0-cpu-noavx-mkl/paddlepaddle-1.2.0-cp36-cp36m-linux_x86_64.whl">
paddlepaddle-1.2.0-cp36-cp36m-linux_x86_64.whl</a></td>
<td> <a href="http://paddlepaddle.org/download?url=http://paddle-wheel.bj.bcebos.com/1.2.0-cpu-noavx-mkl/paddlepaddle-1.2.0-cp37-cp37m-linux_x86_64.whl">
paddlepaddle-1.2.0-cp37-cp37m-linux_x86_64.whl</a></td>
<td> <a href="http://paddlepaddle.org/download?url=http://paddle-wheel.bj.bcebos.com/1.2.1-cpu-noavx-mkl/paddlepaddle-1.2.1-cp27-cp27mu-linux_x86_64.whl">
paddlepaddle-1.2.1-cp27-cp27mu-linux_x86_64.whl</a></td>
<td> <a href="http://paddlepaddle.org/download?url=http://paddle-wheel.bj.bcebos.com/1.2.1-cpu-noavx-mkl/paddlepaddle-1.2.1-cp27-cp27m-linux_x86_64.whl">
paddlepaddle-1.2.1-cp27-cp27m-linux_x86_64.whl</a></td>
<td> <a href="http://paddlepaddle.org/download?url=http://paddle-wheel.bj.bcebos.com/1.2.1-cpu-noavx-mkl/paddlepaddle-1.2.1-cp35-cp35m-linux_x86_64.whl">
paddlepaddle-1.2.1-cp35-cp35m-linux_x86_64.whl</a></td>
<td> <a href="http://paddlepaddle.org/download?url=http://paddle-wheel.bj.bcebos.com/1.2.1-cpu-noavx-mkl/paddlepaddle-1.2.1-cp36-cp36m-linux_x86_64.whl">
paddlepaddle-1.2.1-cp36-cp36m-linux_x86_64.whl</a></td>
<td> <a href="http://paddlepaddle.org/download?url=http://paddle-wheel.bj.bcebos.com/1.2.1-cpu-noavx-mkl/paddlepaddle-1.2.1-cp37-cp37m-linux_x86_64.whl">
paddlepaddle-1.2.1-cp37-cp37m-linux_x86_64.whl</a></td>
</tr>
<tr>
<td> cpu_avx_mkl </td>
<td> <a href="http://paddlepaddle.org/download?url=http://paddle-wheel.bj.bcebos.com/1.2.0-cpu-avx-mkl/paddlepaddle-1.2.0-cp27-cp27mu-linux_x86_64.whl">
paddlepaddle-1.2.0-cp27-cp27mu-linux_x86_64.whl</a></td>
<td> <a href="http://paddlepaddle.org/download?url=http://paddle-wheel.bj.bcebos.com/1.2.0-cpu-avx-mkl/paddlepaddle-1.2.0-cp27-cp27m-linux_x86_64.whl">
paddlepaddle-1.2.0-cp27-cp27m-linux_x86_64.whl</a></td>
<td> <a href="http://paddlepaddle.org/download?url=http://paddle-wheel.bj.bcebos.com/1.2.0-cpu-avx-mkl/paddlepaddle-1.2.0-cp35-cp35m-linux_x86_64.whl">
paddlepaddle-1.2.0-cp35-cp35m-linux_x86_64.whl</a></td>
<td> <a href="http://paddlepaddle.org/download?url=http://paddle-wheel.bj.bcebos.com/1.2.0-cpu-avx-mkl/paddlepaddle-1.2.0-cp36-cp36m-linux_x86_64.whl">
paddlepaddle-1.2.0-cp36-cp36m-linux_x86_64.whl</a></td>
<td> <a href="http://paddlepaddle.org/download?url=http://paddle-wheel.bj.bcebos.com/1.2.0-cpu-avx-mkl/paddlepaddle-1.2.0-cp37-cp37m-linux_x86_64.whl">
paddlepaddle-1.2.0-cp37-cp37m-linux_x86_64.whl</a></td>
<td> <a href="http://paddlepaddle.org/download?url=http://paddle-wheel.bj.bcebos.com/1.2.1-cpu-avx-mkl/paddlepaddle-1.2.1-cp27-cp27mu-linux_x86_64.whl">
paddlepaddle-1.2.1-cp27-cp27mu-linux_x86_64.whl</a></td>
<td> <a href="http://paddlepaddle.org/download?url=http://paddle-wheel.bj.bcebos.com/1.2.1-cpu-avx-mkl/paddlepaddle-1.2.1-cp27-cp27m-linux_x86_64.whl">
paddlepaddle-1.2.1-cp27-cp27m-linux_x86_64.whl</a></td>
<td> <a href="http://paddlepaddle.org/download?url=http://paddle-wheel.bj.bcebos.com/1.2.1-cpu-avx-mkl/paddlepaddle-1.2.1-cp35-cp35m-linux_x86_64.whl">
paddlepaddle-1.2.1-cp35-cp35m-linux_x86_64.whl</a></td>
<td> <a href="http://paddlepaddle.org/download?url=http://paddle-wheel.bj.bcebos.com/1.2.1-cpu-avx-mkl/paddlepaddle-1.2.1-cp36-cp36m-linux_x86_64.whl">
paddlepaddle-1.2.1-cp36-cp36m-linux_x86_64.whl</a></td>
<td> <a href="http://paddlepaddle.org/download?url=http://paddle-wheel.bj.bcebos.com/1.2.1-cpu-avx-mkl/paddlepaddle-1.2.1-cp37-cp37m-linux_x86_64.whl">
paddlepaddle-1.2.1-cp37-cp37m-linux_x86_64.whl</a></td>
</tr>
<tr>
<td> cpu_avx_openblas </td>
<td> <a href="http://paddlepaddle.org/download?url=http://paddle-wheel.bj.bcebos.com/1.2.0-cpu-avx-openblas/paddlepaddle-1.2.0-cp27-cp27mu-linux_x86_64.whl">
paddlepaddle-1.2.0-cp27-cp27mu-linux_x86_64.whl</a></td>
<td> <a href="http://paddlepaddle.org/download?url=http://paddle-wheel.bj.bcebos.com/1.2.0-cpu-avx-openblas/paddlepaddle-1.2.0-cp27-cp27m-linux_x86_64.whl"> paddlepaddle-1.2.0-cp27-cp27m-linux_x86_64.whl</a></td>
<td> <a href="http://paddlepaddle.org/download?url=http://paddle-wheel.bj.bcebos.com/1.2.0-cpu-avx-openblas/paddlepaddle-1.2.0-cp35-cp35m-linux_x86_64.whl">
paddlepaddle-1.2.0-cp35-cp35m-linux_x86_64.whl</a></td>
<td> <a href="http://paddlepaddle.org/download?url=http://paddle-wheel.bj.bcebos.com/1.2.0-cpu-avx-openblas/paddlepaddle-1.2.0-cp36-cp36m-linux_x86_64.whl">
paddlepaddle-1.2.0-cp36-cp36m-linux_x86_64.whl</a></td>
<td> <a href="http://paddlepaddle.org/download?url=http://paddle-wheel.bj.bcebos.com/1.2.0-cpu-avx-openblas/paddlepaddle-1.2.0-cp37-cp37m-linux_x86_64.whl">
paddlepaddle-1.2.0-cp37-cp37m-linux_x86_64.whl</a></td>
<td> <a href="http://paddlepaddle.org/download?url=http://paddle-wheel.bj.bcebos.com/1.2.1-cpu-avx-openblas/paddlepaddle-1.2.1-cp27-cp27mu-linux_x86_64.whl">
paddlepaddle-1.2.1-cp27-cp27mu-linux_x86_64.whl</a></td>
<td> <a href="http://paddlepaddle.org/download?url=http://paddle-wheel.bj.bcebos.com/1.2.1-cpu-avx-openblas/paddlepaddle-1.2.1-cp27-cp27m-linux_x86_64.whl"> paddlepaddle-1.2.1-cp27-cp27m-linux_x86_64.whl</a></td>
<td> <a href="http://paddlepaddle.org/download?url=http://paddle-wheel.bj.bcebos.com/1.2.1-cpu-avx-openblas/paddlepaddle-1.2.1-cp35-cp35m-linux_x86_64.whl">
paddlepaddle-1.2.1-cp35-cp35m-linux_x86_64.whl</a></td>
<td> <a href="http://paddlepaddle.org/download?url=http://paddle-wheel.bj.bcebos.com/1.2.1-cpu-avx-openblas/paddlepaddle-1.2.1-cp36-cp36m-linux_x86_64.whl">
paddlepaddle-1.2.1-cp36-cp36m-linux_x86_64.whl</a></td>
<td> <a href="http://paddlepaddle.org/download?url=http://paddle-wheel.bj.bcebos.com/1.2.1-cpu-avx-openblas/paddlepaddle-1.2.1-cp37-cp37m-linux_x86_64.whl">
paddlepaddle-1.2.1-cp37-cp37m-linux_x86_64.whl</a></td>
</tr>
<tr>
<td> cuda8.0_cudnn5_avx_mkl </td>
<td> <a href="http://paddlepaddle.org/download?url=http://paddle-wheel.bj.bcebos.com/1.2.0-gpu-cuda8-cudnn5-avx-mkl/paddlepaddle_gpu-1.2.0.post85-cp27-cp27mu-linux_x86_64.whl"> paddlepaddle_gpu-1.2.0-cp27-cp27mu-linux_x86_64.whl</a></td>
<td><a href="http://paddlepaddle.org/download?url=http://paddle-wheel.bj.bcebos.com/1.2.0-gpu-cuda8-cudnn5-avx-mkl/paddlepaddle_gpu-1.2.0.post85-cp27-cp27m-linux_x86_64.whl"> paddlepaddle_gpu-1.2.0-cp27-cp27m-linux_x86_64.whl</a></td>
<td><a href="http://paddlepaddle.org/download?url=http://paddle-wheel.bj.bcebos.com/1.2.0-gpu-cuda8-cudnn5-avx-mkl/paddlepaddle_gpu-1.2.0.post85-cp35-cp35m-linux_x86_64.whl"> paddlepaddle_gpu-1.2.0-cp35-cp35m-linux_x86_64.whl</a></td>
<td> <a href="http://paddlepaddle.org/download?url=http://paddle-wheel.bj.bcebos.com/1.2.0-gpu-cuda8-cudnn5-avx-mkl/paddlepaddle_gpu-1.2.0.post85-cp36-cp36m-linux_x86_64.whl">
paddlepaddle_gpu-1.2.0-cp36-cp36m-linux_x86_64.whl</a></td>
<td> <a href="http://paddlepaddle.org/download?url=http://paddle-wheel.bj.bcebos.com/1.2.0-gpu-cuda8-cudnn5-avx-mkl/paddlepaddle_gpu-1.2.0.post85-cp37-cp37m-linux_x86_64.whl">
paddlepaddle_gpu-1.2.0-cp37-cp37m-linux_x86_64.whl</a></td>
<td> <a href="http://paddlepaddle.org/download?url=http://paddle-wheel.bj.bcebos.com/1.2.1-gpu-cuda8-cudnn5-avx-mkl/paddlepaddle_gpu-1.2.1.post85-cp27-cp27mu-linux_x86_64.whl"> paddlepaddle_gpu-1.2.1-cp27-cp27mu-linux_x86_64.whl</a></td>
<td><a href="http://paddlepaddle.org/download?url=http://paddle-wheel.bj.bcebos.com/1.2.1-gpu-cuda8-cudnn5-avx-mkl/paddlepaddle_gpu-1.2.1.post85-cp27-cp27m-linux_x86_64.whl"> paddlepaddle_gpu-1.2.1-cp27-cp27m-linux_x86_64.whl</a></td>
<td><a href="http://paddlepaddle.org/download?url=http://paddle-wheel.bj.bcebos.com/1.2.1-gpu-cuda8-cudnn5-avx-mkl/paddlepaddle_gpu-1.2.1.post85-cp35-cp35m-linux_x86_64.whl"> paddlepaddle_gpu-1.2.1-cp35-cp35m-linux_x86_64.whl</a></td>
<td> <a href="http://paddlepaddle.org/download?url=http://paddle-wheel.bj.bcebos.com/1.2.1-gpu-cuda8-cudnn5-avx-mkl/paddlepaddle_gpu-1.2.1.post85-cp36-cp36m-linux_x86_64.whl">
paddlepaddle_gpu-1.2.1-cp36-cp36m-linux_x86_64.whl</a></td>
<td> <a href="http://paddlepaddle.org/download?url=http://paddle-wheel.bj.bcebos.com/1.2.1-gpu-cuda8-cudnn5-avx-mkl/paddlepaddle_gpu-1.2.1.post85-cp37-cp37m-linux_x86_64.whl">
paddlepaddle_gpu-1.2.1-cp37-cp37m-linux_x86_64.whl</a></td>
</tr>
<tr>
<td> cuda8.0_cudnn7_noavx_mkl </td>
<td> <a href="http://paddlepaddle.org/download?url=http://paddle-wheel.bj.bcebos.com/1.2.0-gpu-cuda8-cudnn7-noavx-mkl/paddlepaddle_gpu-1.2.0-cp27-cp27mu-linux_x86_64.whl"> paddlepaddle_gpu-1.2.0-cp27-cp27mu-linux_x86_64.whl</a></td>
<td><a href="http://paddlepaddle.org/download?url=http://paddle-wheel.bj.bcebos.com/1.2.0-gpu-cuda8-cudnn7-noavx-mkl/paddlepaddle_gpu-1.2.0-cp27-cp27m-linux_x86_64.whl"> paddlepaddle_gpu-1.2.0-cp27-cp27m-linux_x86_64.whl</a></td>
<td><a href="http://paddlepaddle.org/download?url=http://paddle-wheel.bj.bcebos.com/1.2.0-gpu-cuda8-cudnn7-noavx-mkl/paddlepaddle_gpu-1.2.0-cp35-cp35m-linux_x86_64.whl"> paddlepaddle_gpu-1.2.0-cp35-cp35m-linux_x86_64.whl</a></td>
<td> <a href="http://paddlepaddle.org/download?url=http://paddle-wheel.bj.bcebos.com/1.2.0-gpu-cuda8-cudnn7-noavx-mkl/paddlepaddle_gpu-1.2.0-cp36-cp36m-linux_x86_64.whl">
paddlepaddle_gpu-1.2.0-cp36-cp36m-linux_x86_64.whl</a></td>
<td> <a href="http://paddlepaddle.org/download?url=http://paddle-wheel.bj.bcebos.com/1.2.0-gpu-cuda8-cudnn7-noavx-mkl/paddlepaddle_gpu-1.2.0-cp37-cp37m-linux_x86_64.whl">
paddlepaddle_gpu-1.2.0-cp37-cp37m-linux_x86_64.whl</a></td>
<td> <a href="http://paddlepaddle.org/download?url=http://paddle-wheel.bj.bcebos.com/1.2.1-gpu-cuda8-cudnn7-noavx-mkl/paddlepaddle_gpu-1.2.1-cp27-cp27mu-linux_x86_64.whl"> paddlepaddle_gpu-1.2.1-cp27-cp27mu-linux_x86_64.whl</a></td>
<td><a href="http://paddlepaddle.org/download?url=http://paddle-wheel.bj.bcebos.com/1.2.1-gpu-cuda8-cudnn7-noavx-mkl/paddlepaddle_gpu-1.2.1-cp27-cp27m-linux_x86_64.whl"> paddlepaddle_gpu-1.2.1-cp27-cp27m-linux_x86_64.whl</a></td>
<td><a href="http://paddlepaddle.org/download?url=http://paddle-wheel.bj.bcebos.com/1.2.1-gpu-cuda8-cudnn7-noavx-mkl/paddlepaddle_gpu-1.2.1-cp35-cp35m-linux_x86_64.whl"> paddlepaddle_gpu-1.2.1-cp35-cp35m-linux_x86_64.whl</a></td>
<td> <a href="http://paddlepaddle.org/download?url=http://paddle-wheel.bj.bcebos.com/1.2.1-gpu-cuda8-cudnn7-noavx-mkl/paddlepaddle_gpu-1.2.1-cp36-cp36m-linux_x86_64.whl">
paddlepaddle_gpu-1.2.1-cp36-cp36m-linux_x86_64.whl</a></td>
<td> <a href="http://paddlepaddle.org/download?url=http://paddle-wheel.bj.bcebos.com/1.2.1-gpu-cuda8-cudnn7-noavx-mkl/paddlepaddle_gpu-1.2.1-cp37-cp37m-linux_x86_64.whl">
paddlepaddle_gpu-1.2.1-cp37-cp37m-linux_x86_64.whl</a></td>
</tr>
<tr>
<td> cuda8.0_cudnn7_avx_mkl </td>
<td> <a href="http://paddlepaddle.org/download?url=http://paddle-wheel.bj.bcebos.com/1.2.0-gpu-cuda8-cudnn7-avx-mkl/paddlepaddle_gpu-1.2.0.post87-cp27-cp27mu-linux_x86_64.whl"> paddlepaddle_gpu-1.2.0.post87-cp27-cp27mu-linux_x86_64.whl</a></td>
<td><a href="http://paddlepaddle.org/download?url=http://paddle-wheel.bj.bcebos.com/1.2.0-gpu-cuda8-cudnn7-avx-mkl/paddlepaddle_gpu-1.2.0.post87-cp27-cp27m-linux_x86_64.whl"> paddlepaddle_gpu-1.2.0.post87-cp27-cp27m-linux_x86_64.whl</a></td>
<td><a href="http://paddlepaddle.org/download?url=http://paddle-wheel.bj.bcebos.com/1.2.0-gpu-cuda8-cudnn7-avx-mkl/paddlepaddle_gpu-1.2.0.post87-cp35-cp35m-linux_x86_64.whl"> paddlepaddle_gpu-1.2.0.post87-cp35-cp35m-linux_x86_64.whl</a></td>
<td> <a href="http://paddlepaddle.org/download?url=http://paddle-wheel.bj.bcebos.com/1.2.0-gpu-cuda8-cudnn7-avx-mkl/paddlepaddle_gpu-1.2.0.post87-cp36-cp36m-linux_x86_64.whl">
paddlepaddle_gpu-1.2.0.post87-cp36-cp36m-linux_x86_64.whl</a></td>
<td> <a href="http://paddlepaddle.org/download?url=http://paddle-wheel.bj.bcebos.com/1.2.0-gpu-cuda8-cudnn7-avx-mkl/paddlepaddle_gpu-1.2.0.post87-cp37-cp37m-linux_x86_64.whl">
paddlepaddle_gpu-1.2.0.post87-cp37-cp37m-linux_x86_64.whl</a></td>
<td> <a href="http://paddlepaddle.org/download?url=http://paddle-wheel.bj.bcebos.com/1.2.1-gpu-cuda8-cudnn7-avx-mkl/paddlepaddle_gpu-1.2.1.post87-cp27-cp27mu-linux_x86_64.whl"> paddlepaddle_gpu-1.2.1.post87-cp27-cp27mu-linux_x86_64.whl</a></td>
<td><a href="http://paddlepaddle.org/download?url=http://paddle-wheel.bj.bcebos.com/1.2.1-gpu-cuda8-cudnn7-avx-mkl/paddlepaddle_gpu-1.2.1.post87-cp27-cp27m-linux_x86_64.whl"> paddlepaddle_gpu-1.2.1.post87-cp27-cp27m-linux_x86_64.whl</a></td>
<td><a href="http://paddlepaddle.org/download?url=http://paddle-wheel.bj.bcebos.com/1.2.1-gpu-cuda8-cudnn7-avx-mkl/paddlepaddle_gpu-1.2.1.post87-cp35-cp35m-linux_x86_64.whl"> paddlepaddle_gpu-1.2.1.post87-cp35-cp35m-linux_x86_64.whl</a></td>
<td> <a href="http://paddlepaddle.org/download?url=http://paddle-wheel.bj.bcebos.com/1.2.1-gpu-cuda8-cudnn7-avx-mkl/paddlepaddle_gpu-1.2.1.post87-cp36-cp36m-linux_x86_64.whl">
paddlepaddle_gpu-1.2.1.post87-cp36-cp36m-linux_x86_64.whl</a></td>
<td> <a href="http://paddlepaddle.org/download?url=http://paddle-wheel.bj.bcebos.com/1.2.1-gpu-cuda8-cudnn7-avx-mkl/paddlepaddle_gpu-1.2.1.post87-cp37-cp37m-linux_x86_64.whl">
paddlepaddle_gpu-1.2.1.post87-cp37-cp37m-linux_x86_64.whl</a></td>
</tr>
<tr>
<td> cuda9.0_cudnn7_avx_mkl </td>
<td> <a href="http://paddlepaddle.org/download?url=http://paddle-wheel.bj.bcebos.com/1.2.0-gpu-cuda9-cudnn7-avx-mkl/paddlepaddle_gpu-1.2.0.post97-cp27-cp27mu-linux_x86_64.whl"> paddlepaddle_gpu-1.2.0-cp27-cp27mu-linux_x86_64.whl</a></td>
<td><a href="http://paddlepaddle.org/download?url=http://paddle-wheel.bj.bcebos.com/1.2.0-gpu-cuda9-cudnn7-avx-mkl/paddlepaddle_gpu-1.2.0.post97-cp27-cp27m-linux_x86_64.whl"> paddlepaddle_gpu-1.2.0-cp27-cp27m-linux_x86_64.whl</a></td>
<td><a href="http://paddlepaddle.org/download?url=http://paddle-wheel.bj.bcebos.com/1.2.0-gpu-cuda9-cudnn7-avx-mkl/paddlepaddle_gpu-1.2.0.post97-cp35-cp35m-linux_x86_64.whl"> paddlepaddle_gpu-1.2.0-cp35-cp35m-linux_x86_64.whl</a></td>
<td> <a href="http://paddlepaddle.org/download?url=http://paddle-wheel.bj.bcebos.com/1.2.0-gpu-cuda9-cudnn7-avx-mkl/paddlepaddle_gpu-1.2.0.post97-cp36-cp36m-linux_x86_64.whl">
paddlepaddle_gpu-1.2.0-cp36-cp36m-linux_x86_64.whl</a></td>
<td> <a href="http://paddlepaddle.org/download?url=http://paddle-wheel.bj.bcebos.com/1.2.0-gpu-cuda9-cudnn7-avx-mkl/paddlepaddle_gpu-1.2.0.post97-cp37-cp37m-linux_x86_64.whl">
paddlepaddle_gpu-1.2.0-cp37-cp37m-linux_x86_64.whl</a></td>
<td> <a href="http://paddlepaddle.org/download?url=http://paddle-wheel.bj.bcebos.com/1.2.1-gpu-cuda9-cudnn7-avx-mkl/paddlepaddle_gpu-1.2.1.post97-cp27-cp27mu-linux_x86_64.whl"> paddlepaddle_gpu-1.2.1-cp27-cp27mu-linux_x86_64.whl</a></td>
<td><a href="http://paddlepaddle.org/download?url=http://paddle-wheel.bj.bcebos.com/1.2.1-gpu-cuda9-cudnn7-avx-mkl/paddlepaddle_gpu-1.2.1.post97-cp27-cp27m-linux_x86_64.whl"> paddlepaddle_gpu-1.2.1-cp27-cp27m-linux_x86_64.whl</a></td>
<td><a href="http://paddlepaddle.org/download?url=http://paddle-wheel.bj.bcebos.com/1.2.1-gpu-cuda9-cudnn7-avx-mkl/paddlepaddle_gpu-1.2.1.post97-cp35-cp35m-linux_x86_64.whl"> paddlepaddle_gpu-1.2.1-cp35-cp35m-linux_x86_64.whl</a></td>
<td> <a href="http://paddlepaddle.org/download?url=http://paddle-wheel.bj.bcebos.com/1.2.1-gpu-cuda9-cudnn7-avx-mkl/paddlepaddle_gpu-1.2.1.post97-cp36-cp36m-linux_x86_64.whl">
paddlepaddle_gpu-1.2.1-cp36-cp36m-linux_x86_64.whl</a></td>
<td> <a href="http://paddlepaddle.org/download?url=http://paddle-wheel.bj.bcebos.com/1.2.1-gpu-cuda9-cudnn7-avx-mkl/paddlepaddle_gpu-1.2.1.post97-cp37-cp37m-linux_x86_64.whl">
paddlepaddle_gpu-1.2.1-cp37-cp37m-linux_x86_64.whl</a></td>
</tr>
</tbody>
</table>
</p>
<a name="ciwhls"></a>
</br></br>
## **Multi-version whl package list - dev**
......
doc/fluid/beginners_guide/install/compile/compile_CentOS_en.md
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***
# **Compile under CentOS from Source Code**
# **Compile on CentOS from Source Code**
This instruction will show you how to compile PaddlePaddle on a 64-bit desktop or laptop and CentOS. The Centos systems we support must meet the following requirements:
......@@ -166,7 +166,7 @@ Congratulations, you have now completed the process of compiling PaddlePaddle us
8. Switch to a more stable release branch for compilation (support for Python 3.6 and 3.7 is added from the 1.2 branch):
- `git checkout release/1.2.0`
- `git checkout release/1.2`
9. And please create and enter a directory called build:
......
doc/fluid/beginners_guide/install/compile/compile_MacOS_en.md
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***
# **Compile under MacOS from Source Code**
# **Compile on MacOS from Source Code**
This instruction will show you how to compile PaddlePaddle on *64-bit desktops or laptops* and MacOS systems. The MacOS systems we support need to meet the following requirements:
......
doc/fluid/beginners_guide/install/compile/compile_Ubuntu_en.md
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***
# **Compile under Ubuntu from Source Code**
# **Compile on Ubuntu from Source Code**
This instruction describes how to compile PaddlePaddle on *64-bit desktops or laptops* and Ubuntu systems. The Ubuntu systems we support must meet the following requirements:
......@@ -52,7 +52,7 @@ Once you have **properly installed Docker**, you can start **compiling PaddlePad
5. Switch to a more stable release branch to compile: (Note that python 3.6, python 3.7 version are supported from the 1.2 branch)
`git checkout release/1.2.0`
`git checkout release/1.2`
6. Create and enter the /paddle/build path:
......
doc/fluid/beginners_guide/install/compile/compile_Windows_en.md
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***
# **Compile under Windows from Source Code**
# **Compile on Windows from Source Code**
This instruction will show you how to compile PaddlePaddle on a *64-bit desktop or laptop* and Windows 10. The Windows systems we support must meet the following requirements:
......@@ -52,9 +52,9 @@ Please note: The current version does not support NCCL, distributed, AVX, warpct
- `git clone https://github.com/PaddlePaddle/Paddle.git`
- `cd Paddle`
4. Switch to a more stable release branch for compilation (supports 1.2.x and above):
4. Switch to a more stable release branch for compilation :
- `git checkout release/x.x.x`
- `git checkout release/1.2`
5. Create a directory called build and enter it:
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======================
Installation Manuals
======================
The manuals will guide you to install and build PaddlePaddle on your 64-bit desktop or laptop.
The versions of Python currently supported: Python 2.7-3.7
PaddlePaddle currently supports the following environments：
* *Ubuntu 14.04 /16.04 /18.04*
* *CentOS 7 / 6*
* *MacOS 10.11 / 10.12 / 10.13 / 10.14*
* *Windows7 / 8/ 10(Pro/Enterprise)*
Please make sure your environment meets the conditions above.
And the installation assumes your computer possesses 64-bit operating system, and AVX instruction set is supported by the processor, otherwise you should use the version of :code:`no_avx` in `whl package list - Dev <Tables_en.html/#ciwhls>`_ .
- If you are planning to use `pip <https://pypi.org/pypi/>`_ to install PaddlePaddle, please type the following commands directly:
:code:`pip install paddlepaddle` （latest CPU version of PaddlePaddle）
:code:`pip install paddlepaddle-gpu` （latest GPU version of PaddlePaddle）
:code:`pip install paddlepaddle==[pip version]`
where [pip version] can be looked up in `PyPi.org <https://pypi.org/search/?q=PaddlePaddle>`_
- If you are planning to use `docker <https://www.docker.com>`_ to install PaddlePaddle, please type the following commands directly:
:code:`docker run --name [Name of container] -it -v $PWD:/paddle hub.baidubce.com/paddlepaddle/paddle:[docker version] /bin/bash`
where [docker version] can be looked up in `DockerHub <https://hub.docker.com/r/paddlepaddle/paddle/tags/>`_
.. toctree::
:hidden:
install_Ubuntu_en.md
install_CentOS_en.md
install_MacOS_en.md
install_Windows_en.md
compile/fromsource_en.rst
FAQ_en.md
Tables_en.md
doc/fluid/beginners_guide/install/install_CentOS_en.md
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***
# **Install under CentOS**
# **Install on CentOS**
This note will show you how to install PaddlePaddle on a *64-bit desktop or laptop* and CentOS. The CentOS system we support needs to meet the following requirements:
......
doc/fluid/beginners_guide/install/install_MacOS_en.md
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***
# **Install under MacOS**
# **Install on MacOS**
This instruction will show you how to install PaddlePaddle on a *64-bit desktop or laptop* and MacOS system. The MacOS system we support must meet the following requirements.
......
doc/fluid/beginners_guide/install/install_Ubuntu_en.md
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***
# **Install under Ubuntu**
# **Install on Ubuntu**
This instruction describes how to install PaddlePaddle on a *64-bit desktop or laptop and Ubuntu system. The Ubuntu systems we support must meet the following requirements:
This instruction describes how to install PaddlePaddle on a *64-bit desktop or laptop* and Ubuntu system. The Ubuntu systems we support must meet the following requirements:
Please note: Attempts on other systems may cause the installation to fail. Please ensure that your environment meets the conditions. The installation we provide by default requires your computer processor to support the AVX instruction set. Otherwise, please select the version of `no_avx` in the [latest Release installation package list](./Tables.html/#ciwhls-release).
......
doc/fluid/beginners_guide/install/install_Windows_en.md
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***
# **Install under Windows**
# **Install on Windows**
This instruction will show you how to install PaddlePaddle on a 64-bit desktop or laptop and Windows. The Windows systems we support must meet the following requirements.
This instruction will show you how to install PaddlePaddle on Windows. The following conditions must be met before you begin to install:
* *a 64-bit desktop or laptop*
* *Windows 7/8 , Windows 10 Professional/Enterprise Edition*
Please note: Attempts on other systems may cause the installation to fail. Please ensure that your environment meets the conditions. The installation we provide by default requires your computer processor to support the AVX instruction set. Otherwise, please select the version of `no_avx` in [the multi-version whl package installation list](Tables.html/#ciwhls):
**Note** :
Windows can use software such as `cpu-z` to detect whether your processor supports the AVX instruction set.
* The current version does not support NCCL, distributed training, AVX, warpctc and MKL related functions.
The current version does not support NCCL, distributed, AVX, warpctc and MKL related functions.
* Currently, only PaddlePaddle for CPU is supported on Windows.
* *Windows 7/8 and Windows 10 Professional/Enterprise Edition*
## Determine which version to install
* Under Windows, we currently only offer PaddlePaddle that supports CPU.
## Choose an installation method
## Installation Steps
### ***Install using pip***
### ***Install through pip***
We do not provide a quick installation command, please install according to the following steps：
* Check your Python versions
* First, **check that your computer and operating system** meet the following requirements:
Python2.7.15，Python3.5.x，Python3.6.x，Python3.7.x on [Official Python](https://www.python.org/downloads/) are supported.
* Check your pip version
For python2: Python2.7.15 downloaded from official Python
For python3: Use python3.5.x, python3.6.x or python3.7.x downloaded from official Python
Version of pip or pip3 should be equal to or above 9.0.1 .
* Python2.7.x :pip >= 9.0.1
* Python3.5.x, python3.6.x or python3.7.x :pip3 >= 9.0.1
* Install PaddlePaddle
Here's how to install PaddlePaddle:
Execute `pip install paddlepaddle` or `pip3 install paddlepaddle` to download and install PaddlePaddle.
* Use pip install to install PaddlePaddle:
** paddlepaddle's dependency package `recordio` may not be installed with `pip`'s default source, you can use `easy_install recordio` to install. **
** For users who need **the CPU version PaddlePaddle**: `pip install paddlepaddle` or `pip3 install paddlepaddle`. **
Now you have completed the process of installing PaddlePaddle via `pip install`.
## ***Verify installation***
......@@ -47,6 +39,5 @@ After completing the installation, you can use `python` or `python3` to enter th
## ***How to uninstall***
Use the following command to uninstall PaddlePaddle (users who use Docker to install PaddlePaddle, please use the following command in the container containing PaddlePaddle):
Use the following command to uninstall PaddlePaddle : `pip uninstallpaddlepaddle `or `pip3 uninstall paddlepaddle`
* ***CPU version of PaddlePaddle***: `pip uninstallpaddlepaddle `or `pip3 uninstall paddlepaddle`
doc/fluid/index_en.rst
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......@@ -4,8 +4,7 @@
.. toctree::
:maxdepth: 1
getstarted/index_en.rst
build_and_install/index_en.rst
beginners_guide/index_en.rst
design/index_en.rst
howto/index_en.rst
dev/index_en.rst
......
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# Release Notes
## Framework
* new pip installation package is available, which can be run on Windows CPU environment.
* support of python3.6、python3.7
* Reconstruction of memory allocator modular :Allocator. Improvement on memory allocating strategy in CPU environment.
Increase in utility ratio of video memory (disabled by default, use ``FLAGS_allocator_strategy`` to enable it).
* Restriction to the usage of SelectedRows, and fix made to bugs on sparse regulation and sparse optimization.
* Tensor supports DLPack，to facilitate integration of other frameworks or into them.
* OP
* Issues on inference of expand op ``shape`` have been resolved.
* Activation function ``Selu`` is included.
## Inference Engine
* Server Prediction
* GPU supports image fusion, and cooperation with TensorRT to realize image modifying. In common image processing models like Resnet50 and Googlenet, with bs=1, the performance has reached a level 50~100% higher.
* GPU supports DDPG Deep Explore prediction.
* Paddle-TRT supports more models, including Resnet， SE-Resnet， DPN，GoogleNet.
* CPU, GPU, TensorRT and other accelerators are merged into AnalysisPredictor，collectively controlled by AnalysisConfig.
* Add interfaces to call multi-thread mathematic library.
* Support for TensorRT plugins，including `split operator` , `prelu operator` , `avg_pool operator` , `elementwise_mul operator` .
* This version has included JIT CPU Kernel， which is able to perform basic vector operations, partial implementation of common algorithms including ReLU，LSTM and GRU, and automatic runtime switch between AVX and AVX2 instruction set.
* FDSFDF optimized CRF decoding and implementation of LayerNorm on AVX and AVX2 instruction set.
* Issue fixed: AnalysisPredictor on GPU or in the transition from CPU to GPU cannot delete transfer data.
* Issue fixed: Variable has consistent increase of occupied memory of container.
* Issue fixed: `fc_op` cannot process 3-D Tensor
* Issue fixed: on GPU, when running pass, issues happened to Analysis predictor
* Issue fixed: GoogleNet problems on TensorRT
* Promotion of prediction performance
* Max Sequence pool optimization，with single op performance 10% higher.
* `Softmax operator` optimization，with single op performance 14% higher.
* `Layer Norm operator` optimization， inclusive of AVX2 instruction set, with single op performance 5 times higher.
* `Stack operator` optimization，with single op performance 3.6 times higher.
* add depthwise_conv_mkldnn_pass to accelerate MobileNet prediction.
* reduce image analysis time in analysis mode， and the velocity is 70 times quicker.
* DAM open-source model，reached 118.8% of previous version.
* Mobile Endpoint Prediction
* This version has realized winograd algorithm， with the help of which the performance of GoogleNet v1 enjoys a dramatic promotion of 35%.
* improvement on GoogleNet 8bit，14% quicker compared with float.
* support for MobileNet v1 8bit， 20% faster than float.
* support for MobileNet v2 8bit， 19% faster than float.
* FPGA V1 has developed Deconv operator
* Android gpu supports mainstream network models like MobileNet、MobileNetSSD、GoogleNet、SqueezeNet、YOLO、ResNet.
## Model
* CV image classifying tasks publish pre-trained models: MobileNet V1, ResNet101, ResNet152，VGG11
* CV Metric Learning models are extended with loss function arcmargin, and the training method is altered. The new method is to adopt element-wise as pre-trained model, and use pair-wise to make further slight adjustment to improve precision.
* NLP model tasks are newly equipped with LSTM implementation based on cudnn. Compared with the implementation based on PaddingRNN, the cudnn method is 3~5 times quicker under diverse argument settings.
* Distributed word2vec model is included，including the new tree-based softmax operator，negative sampling，in line with classic word2vec algorithms.
* Distributed settings of GRU4Rec、Tag-Space algorithms are added.
* Multi-view Simnet model is optimized， with an additional inference setting.
* Reinforcement learning algorithm DQN is supported.
* Currently compatible python3.x models: Semantic model DAM, reading comprehension BiDAF, machine translation Transformer, language model, reinforcement learning DQN, DoubleDQN model, DuelingDQN model, video classification TSN, Metric Learning, character recognition in natural scenes CRNN-CTC 、OCR Attention，Generative Adversarial Networks ConditionalGAN, DCGAN, CycleGAN, Semantic segmentation ICNET, DeepLab v3+, object detection Faster-RCNN, MobileNet-SSD, PyramidBox, iSE-ResNeXt, ResNet, customized recommendation TagSpace、GRU4Rec、SequenceSemanticRetrieval、DeepCTR、Multiview-Simnet.
## Distributed training
* multi-CPU asynchronous training
* Asynchronous concurrent workers： `AsyncExecutor` is added. With a executive granularity of single training file, it supports lock-less asynchronous worker-end computation in distributed training, and single machine training. Take CTR task as an example, general throughput from single machine training is 14 times larger.
* IO optimization：This version has added compatibility with `AsyncExecutor` to DataFeed; enabled customized universal classification task formats; incorporated CTRReader for CTR tasks to linearly elevate speed of reading data. In PaddleRec/ctr tasks，the general throughput increases by 2 times.
* Better data communication： As for sparse access Dense arguments, like Embedding, the sparse data communication mechanism is adopted. Take tasks of semantic matching for instance, the amount of fetched arguments can be compressed to 1% and below. In searching groundtruth data, the general output reached 15 times more.
* multi-GPU synchronous training
* Issue fixed: In Transformer、Bert models, P2P training mode may be hung.
## Documentation
* API
* Add 13 api guides
* Add 300 entries of Chinese API Reference
* Improve 77 entries of English API Reference, including Examples and argument explanation and other adjustable sections.
* Documentation about installation
* Add installing guide on python3.6、python3.7.
* Add installing guide on windows pip install.
* Book Documentation
* Code examples in Book documentation are substituted with Low level API.
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