Merge pull request #81 from qjing666/document

update document content

README.md

 
 <img src='https://github.com/PaddlePaddle/PaddleFL/blob/master/docs/source/_static/FL-logo.png' width = "400" height = "160">
 
-[DOC](https://paddlefl.readthedocs.io/en/latest/) | [Quick Start](https://paddlefl.readthedocs.io/en/latest/instruction.html) | [中文](./README_cn.md)
+[DOC](https://paddlefl.readthedocs.io/en/latest/) | [Quick Start](https://paddlefl.readthedocs.io/en/latest/compile_and_intall.html) | [中文](./README_cn.md)
 
 PaddleFL is an open source federated learning framework based on PaddlePaddle. Researchers can easily replicate and compare different federated learning algorithms with PaddleFL. Developers can also benefit from PaddleFL in that it is easy to deploy a federated learning system in large scale distributed clusters. In PaddleFL, serveral federated learning strategies will be provided with application in computer vision, natural language processing, recommendation and so on. Application of traditional machine learning training strategies such as Multi-task learning, Transfer Learning in Federated Learning settings will be provided. Based on PaddlePaddle's large scale distributed training and elastic scheduling of training job on Kubernetes, PaddleFL can be easily deployed based on full-stack open sourced software.
 
@@ -42,7 +42,7 @@ We **highly recommend** to run PaddleFL in Docker
 ```sh
 #Pull and run the docker
 docker pull hub.baidubce.com/paddlefl/paddle_fl:latest
-docker run --name <docker_name> --net=host -it -v $PWD:/root <image id> /bin/bash
+docker run --name <docker_name> --net=host -it -v $PWD:/paddle <image id> /bin/bash
 
 #Install paddle_fl
 pip install paddle_fl

README_cn.md

@@ -39,7 +39,7 @@ PaddleFL 中主要提供两种解决方案：**Data Parallel** 以及 **Federate
 ```sh
 #Pull and run the docker
 docker pull hub.baidubce.com/paddlefl/paddle_fl:latest
-docker run --name <docker_name> --net=host -it -v $PWD:/root <image id> /bin/bash
+docker run --name <docker_name> --net=host -it -v $PWD:/paddle <image id> /bin/bash
 
 #Install paddle_fl
 pip install paddle_fl

docs/source/examples/md/dpsgd-example.md

@@ -213,4 +213,4 @@ while not trainer.stop():
 
 To show the effectiveness of DPSGD-based federated learning with PaddleFL, a simulated experiment is conducted on an open source dataset MNIST. From the figure given below, model evaluation results are similar between DPSGD-based federated learning and traditional parameter server training when the overall privacy budget *epsilon* is 1.3 or 0.13. 
 
-<img src="fl_dpsgd_benchmark.png" height=400 width=600 hspace='10'/> <br />
+<img src="_static/fl_dpsgd_benchmark.png" height=400 width=600 hspace='10'/> <br />

docs/source/examples/md/gru4rec_examples.md

@@ -109,4 +109,4 @@ wget https://paddle-zwh.bj.bcebos.com/gru4rec_paddlefl_benchmark/gru4rec_benchma
 | 1/4 of the whole dataset | private training | - | 0.269 | 
 | 1/4 of the whole dataset | private training | - | 0.282 | 
 
-<img src="fl_benchmark.png" height=300 width=500 hspace='10'/> <br />
+<img src="_static/fl_benchmark.png" height=300 width=500 hspace='10'/> <br />

docs/source/examples/md/uci_demo.md

 ## Instructions for PaddleFL-MPC UCI Housing Demo
 
-([简体中文](./README_CN.md)|English)
-
 This document introduces how to run UCI Housing demo based on Paddle-MPC, which has two ways of running, i.e., single machine and multi machines.
 
 ### 1. Running on Single Machine

docs/source/md/introduction.md

@@ -8,7 +8,8 @@ Data is becoming more and more expensive nowadays, and sharing of raw data is ve
 
 ## Overview of PaddleFL
 
-<img src='../../../images/FL-framework.png' width = "1000" height = "320" align="middle"/>
+<img src='_static/FL-framework.png' width = "1000" height = "320" align="middle"/>
+
 
 In PaddleFL, horizontal and vertical federated learning strategies will be implemented according to the categorization given in [4]. Application demonstrations in natural language processing, computer vision and recommendation will be provided in PaddleFL.
 
@@ -36,7 +37,7 @@ Besides, PFM is implemented based on secure multi-party computation (MPC) to ena
 
 ### Data Parallel
 
-<img src='images/FL-training.png' width = "1000" height = "400" align="middle"/>
+<img src='_static/FL-training.png' width = "1000" height = "400" align="middle"/>
 
 In Data Parallel, components for defining a federated learning task and training a federated learning job are as follows:
 
@@ -60,7 +61,7 @@ In Data Parallel, components for defining a federated learning task and training
 
 ### Federated Learning with MPC
 
-<img src='../../../images/PFM-overview.png' width = "1000" height = "446" align="middle"/>
+<img src='_static/PFM-overview.png' width = "1000" height = "446" align="middle"/>
 
 Paddle FL MPC implements secure training and inference tasks based on the underlying MPC protocol like ABY3[11], which is a high efficient three-party computing model.