## Step 1: Define Federated Learning Compile-Time

We define very simple multiple layer perceptron for demonstration. When multiple organizations
agree to share data knowledge through PaddleFL, a model can be defined with agreement from these organizations. A FLJob can be generated and saved. Programs needed to be run each node will be generated separately in FLJob.

```python
import paddle.fluid as fluid
import paddle_fl as fl
from paddle_fl.core.master.job_generator import JobGenerator
from paddle_fl.core.strategy.fl_strategy_base import FLStrategyFactory

class Model(object):
    def __init__(self):
        pass

    def mlp(self, inputs, label, hidden_size=128):
        self.concat = fluid.layers.concat(inputs, axis=1)
        self.fc1 = fluid.layers.fc(input=self.concat, size=256, act='relu')
        self.fc2 = fluid.layers.fc(input=self.fc1, size=128, act='relu')
	self.predict = fluid.layers.fc(input=self.fc2, size=2, act='softmax')
        self.sum_cost = fluid.layers.cross_entropy(input=self.predict, label=label)
        self.accuracy = fluid.layers.accuracy(input=self.predict, label=label)
        self.loss = fluid.layers.reduce_mean(self.sum_cost)
        self.startup_program = fluid.default_startup_program()

inputs = [fluid.layers.data( \
            name=str(slot_id), shape=[5],
	    dtype="float32")
	  for slot_id in range(3)]
label = fluid.layers.data( \
            name="label",
            shape=[1],
            dtype='int64')

model = Model()
model.mlp(inputs, label)

job_generator = JobGenerator()
optimizer = fluid.optimizer.SGD(learning_rate=0.1)
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 inputs], [model.predict.name])

build_strategy = FLStrategyFactory()
build_strategy.fed_avg = True
build_strategy.inner_step = 1
strategy = build_strategy.create_fl_strategy()

endpoints = ["127.0.0.1:8181"]
output = "fl_job_config"
job_generator.generate_fl_job(
    strategy, server_endpoints=endpoints, worker_num=2, output=output)
```

## Step 2: Issue FL Job to Organizations

We can define a secure service to send programs to each node in FLJob. There are two types of nodes in distributed federated learning job. One is FL Server, the other is FL Trainer. A FL Trainer is owned by individual organization and an organization can have multiple FL Trainers given different amount of data knowledge the organization is willing to share. A FL Server is owned by a secure distributed training cluster. By means of security of the cluster, all organizations participated in the Federated Training Job should agree to trust the cluster is secure.

## 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
# Define the number of worker/server and the port for scheduler
scheduler = FLScheduler(worker_num,server_num,port=9091)
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
from paddle_fl.core.trainer.fl_trainer import FLTrainerFactory
from paddle_fl.core.master.fl_job import FLRunTimeJob
import numpy as np
import sys

def reader():
    for i in range(1000):
        data_dict = {}
        for i in range(3):
            data_dict[str(i)] = np.random.rand(1, 5).astype('float32')
        data_dict["label"] = np.random.randint(2, size=(1, 1)).astype('int64')
        yield data_dict

trainer_id = int(sys.argv[1]) # trainer id for each guest
job_path = "fl_job_config"
job = FLRunTimeJob()
job.load_trainer_job(job_path, trainer_id)
job._scheduler_ep = "127.0.0.1:9091" # Inform the scheduler IP to trainer
trainer = FLTrainerFactory().create_fl_trainer(job)
trainer.start()

output_folder = "fl_model"
step_i = 0
while not trainer.stop():
    step_i += 1
    print("batch %d start train" % (step_i))
    trainer.run(feed=data, fetch=[])
    if trainer_id == 0:
        print("start saving model")
        trainer.save_inference_program(output_folder)
    if step_i >= 100:
       break
```

On FL Server Node, a training script is defined as follows:

```python
import paddle_fl as fl
import paddle.fluid as fluid
from paddle_fl.core.server.fl_server import FLServer
from paddle_fl.core.master.fl_job import FLRunTimeJob
server = FLServer()
server_id = 0
job_path = "fl_job_config"
job = FLRunTimeJob()
job.load_server_job(job_path, server_id)
job._scheduler_ep = "127.0.0.1:9091" # IP address for scheduler
server.set_server_job(job)
server._current_ep = "127.0.0.1:8181" # IP address for server
server.start()
```
