update document content

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.