test readme

docs/source/examples/md/gru4rec_examples.md

-# Gru4Rec for session-based recommendation
+# Train gru4rec model with FedAvg Strategy
 
+This doc introduce how to use PaddleFL to train model with Fl Strategy.
 
-[Graph Convolutional Network \(GCN\)](https://arxiv.org/abs/1609.02907) is a powerful neural network designed for machine learning on graphs. Based on PGL, we reproduce GCN algorithms and reach the same level of indicators as the paper in citation network benchmarks.
+### Dependencies
+- paddlepaddle>=1.6
+
+### How to install PaddleFL
+please use the python which has installed paddlepaddle.
+```sh
+python setup.py install
+```
+
+### Model
+[Gru4rec](https://arxiv.org/abs/1511.06939) is the classical session-based recommendation model. The details implement by paddlepaddle is [here](https://github.com/PaddlePaddle/models/tree/develop/PaddleRec/gru4rec).
+
+
+### Datasets
+We use [Rsc15](https://2015.recsyschallenge.com) dataset as our data. 
 
-### Simple example to build GCN
+```sh
+#download data
+cd example/gru4rec_demo
+sh download.sh
+```
+
+### How to work in PaddleFL
+PaddleFL has two period , CompileTime and RunTime. In CompileTime, define a federated learning task by fl_master. In RunTime, train a federated learning job by fl_server and fl_trainer .
+
+### How to work in CompileTime
+In this example, we implement it in fl_master.py
+```sh
+# please run fl_master to generate fl_job
+python fl_master.py
+```
+In fl_master.py,  we first define FL-Strategy, User-Defined-Program and Distributed-Config. Then FL-Job-Generator generate FL-Job for federated server and worker.
+```python
+# define model
+model = Model()
+model.gru4rec_network()
+
+# define JobGenerator and set model config
+# feed_name and target_name are config for save model.
+job_generator = JobGenerator()
+optimizer = fluid.optimizer.SGD(learning_rate=2.0)
+job_generator.set_optimizer(optimizer)
+job_generator.set_losses([model.loss])
+job_generator.set_startup_program(model.startup_program)
+job_generator.set_infer_feed_and_target_names(
+    [x.name for x in model.inputs], [model.loss.name, model.recall.name])
+
+# define FL-Strategy , we now support two flstrategy, fed_avg and dpsgd. Inner_step means fl_trainer locally train inner_step mini-batch.
+build_strategy = FLStrategyFactory()
+build_strategy.fed_avg = True
+build_strategy.inner_step = 1
+strategy = build_strategy.create_fl_strategy()
+
+# define Distributed-Config and generate fl_job 
+endpoints = ["127.0.0.1:8181"]
+output = "fl_job_config"
+job_generator.generate_fl_job(
+    strategy, server_endpoints=endpoints, worker_num=2, output=output)
+
+```
 
 To build a gcn layer, one can use our pre-defined ```pgl.layers.gcn``` or just write a gcn layer with message passing interface.
 ```python