update documents

contrib/data_safety_training/image_classification/README.md

@@ -14,16 +14,16 @@
 
 ## Start the service on Server side
 
-```
-#python 
+```sh
+ 
 python server/receiver.py 
 
 ```
 
 ## Start the request on User side 
 
-```
-#python
+```sh
+
 python submitter.py
 
 ```

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

@@ -101,6 +101,7 @@ job_generator.generate_fl_job(
 How to work in RunTime
 
 ```
+python -u fl_scheduler.py >scheduler.log &
 python -u fl_server.py >server0.log &
 python -u fl_trainer.py 0 data/ >trainer0.log &
 python -u fl_trainer.py 1 data/ >trainer1.log &

docs/source/examples/md/gru4rec_examples.md

@@ -70,6 +70,7 @@ job_generator.generate_fl_job(
 ### How to work in RunTime
 
 ```sh 
+python -u fl_scheduler.py >scheduler.log &
 python -u fl_server.py >server0.log &
 python -u fl_trainer.py 0 data/ >trainer0.log &
 python -u fl_trainer.py 1 data/ >trainer1.log &

docs/source/md/introduction.md

@@ -26,7 +26,7 @@ In PaddleFL, horizontal and vertical federated learning strategies will be imple
 
 ## Framework design of PaddleFL
 
-<img src='_static/FL-training.png' width = "1300" height = "310" align="middle"/>
+<img src='_static/FL-training.png' width = "1300" height = "400" align="middle"/>
 
 In PaddleFL, components for defining a federated learning task and training a federated learning job are as follows:
 
@@ -46,6 +46,8 @@ In PaddleFL, components for defining a federated learning task and training a fe
 
 - **FL-Worker**: Each organization participates in federated learning will have one or more federated workers that will communicate with the federated parameter server.
 
+- **FL-scheduler**: Decide which set of trainers can join the training before each updating cycle.
+
 ## On Going and Future Work
 
 - Experimental benchmark with public datasets in federated learning settings.

docs/source/md/quick_start.md

@@ -60,6 +60,20 @@ We can define a secure service to send programs to each node in FLJob. There are
 
 ## Step 3: Start Federated Learning Run-Time
 
+On FL Scheduler Node, number of servers and workers are defined. Besides, the number of workers that participate in each upating cycle is also determined. Finally, the FL Scheduler waits servers and workers to initialize. 
+
+```python 
+from paddle_fl.core.scheduler.agent_master import FLScheduler
+
+worker_num = 2
+server_num = 1
+scheduler = FLScheduler(worker_num,server_num)
+scheduler.set_sample_worker_num(worker_num)
+scheduler.init_env()
+print("init env done.")
+scheduler.start_fl_training()
+
+
 On FL Trainer Node, a training script is defined as follows:
 
 ``` python