add import codes in README

python/paddle_fl/paddle_fl/examples/ctr_demo/README.md

@@ -33,6 +33,11 @@ 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
+import paddle.fluid as fluid
+import paddle_fl.paddle_fl as fl
+from paddle_fl.paddle_fl.core.master.job_generator import JobGenerator
+from paddle_fl.paddle_fl.core.strategy.fl_strategy_base import FLStrategyFactory
+
 class Model(object):
     def __init__(self):
         pass
@@ -93,6 +98,8 @@ python -u fl_trainer.py 1  >trainer1.log &
 In fl_scheduler.py, we let server and trainers to do registeration.
 
 ```python
+from paddle_fl.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
@@ -105,6 +112,11 @@ scheduler.start_fl_training()
 In fl_server.py, we load and run the FL server job.  
 
 ```python
+import paddle_fl.paddle_fl as fl
+import paddle.fluid as fluid
+from paddle_fl.paddle_fl.core.server.fl_server import FLServer
+from paddle_fl.paddle_fl.core.master.fl_job import FLRunTimeJob
+
 server = FLServer()
 server_id = 0
 job_path = "fl_job_config"
@@ -119,6 +131,14 @@ server.start()
 In fl_trainer.py, we load and run the FL trainer job, then evaluate the accuracy with test data and compute the privacy budget. The DataSet is ramdomly generated. 
 
 ```python
+import sys
+import time
+import logging
+import numpy as np
+import paddle.fluid as fluid
+from paddle_fl.paddle_fl.core.trainer.fl_trainer import FLTrainerFactory
+from paddle_fl.paddle_fl.core.master.fl_job import FLRunTimeJob
+
 def reader():
     for i in range(1000):
         data_dict = {}

python/paddle_fl/paddle_fl/examples/dpsgd_demo/README.md

@@ -50,6 +50,12 @@ 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
+import paddle.fluid as fluid
+import paddle_fl.paddle_fl as fl
+from paddle_fl.paddle_fl.core.master.job_generator import JobGenerator
+from paddle_fl.paddle_fl.core.strategy.fl_strategy_base import FLStrategyFactory
+import math
+
 class Model(object):
     def __init__(self):
         pass
@@ -111,6 +117,8 @@ python -u fl_trainer.py 3 >trainer3.log &
 In fl_scheduler.py, we let server and trainers to do registeration.
 
 ```python
+from paddle_fl.paddle_fl.core.scheduler.agent_master import FLScheduler
+
 worker_num = 4
 server_num = 1
 #Define number of worker/server and the port for scheduler
@@ -123,6 +131,11 @@ scheduler.start_fl_training()
 In fl_server.py, we load and run the FL server job.  
 
 ```python
+import paddle_fl.paddle_fl as fl
+import paddle.fluid as fluid
+from paddle_fl.paddle_fl.core.server.fl_server import FLServer
+from paddle_fl.paddle_fl.core.master.fl_job import FLRunTimeJob
+
 server = FLServer()
 server_id = 0
 job_path = "fl_job_config"
@@ -137,6 +150,15 @@ server.start()
 In fl_trainer.py, we load and run the FL trainer job, then evaluate the accuracy with test data and compute the privacy budget.  
 
 ```python
+from paddle_fl.paddle_fl.core.trainer.fl_trainer import FLTrainerFactory
+from paddle_fl.paddle_fl.core.master.fl_job import FLRunTimeJob
+import numpy
+import sys
+import paddle
+import paddle.fluid as fluid
+import logging
+import math
+
 trainer_id = int(sys.argv[1]) # trainer id for each guest
 job_path = "fl_job_config"
 job = FLRunTimeJob()

python/paddle_fl/paddle_fl/examples/femnist_demo/README.md

@@ -41,6 +41,11 @@ 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
+import paddle.fluid as fluid
+import paddle_fl.paddle_fl as fl
+from paddle_fl.paddle_fl.core.master.job_generator import JobGenerator
+from paddle_fl.paddle_fl.core.strategy.fl_strategy_base import FLStrategyFactory
+
 class Model(object):
     def __init__(self):
         pass
@@ -110,6 +115,8 @@ done
 In fl_scheduler.py, we let server and trainers to do registeration. 
 
 ```python
+from paddle_fl.paddle_fl.core.scheduler.agent_master import FLScheduler
+
 worker_num = 4
 server_num = 1
 # Define the number of worker/server and the port for scheduler
@@ -122,6 +129,11 @@ scheduler.start_fl_training()
 In fl_server.py, we load and run the FL server job.  
 
 ```python
+import paddle_fl.paddle_fl as fl
+import paddle.fluid as fluid
+from paddle_fl.paddle_fl.core.server.fl_server import FLServer
+from paddle_fl.paddle_fl.core.master.fl_job import FLRunTimeJob
+
 server = FLServer()
 server_id = 0
 job_path = "fl_job_config"
@@ -136,6 +148,16 @@ server.start()
 In fl_trainer.py, we load and run the FL trainer job.  
 
 ```python
+from paddle_fl.paddle_fl.core.trainer.fl_trainer import FLTrainerFactory
+from paddle_fl.paddle_fl.core.master.fl_job import FLRunTimeJob
+import paddle_fl.paddle_fl.dataset.femnist as femnist
+import numpy
+import sys
+import paddle
+import paddle.fluid as fluid
+import logging
+import math
+
 trainer_id = int(sys.argv[1])  # trainer id for each guest
 job_path = "fl_job_config"
 job = FLRunTimeJob()

python/paddle_fl/paddle_fl/examples/generate_job_from_program/README.md

@@ -20,6 +20,11 @@ pip install paddle_fl
 In program_saver.py, you can defind a model. And save the program in to 'load_file'
 
 ```python
+import os
+import json
+import paddle.fluid as fluid
+from paddle_fl.paddle_fl.core.master.job_generator import JobGenerator
+
 input = fluid.layers.data(name='input', shape=[1, 28, 28], dtype="float32")
 label = fluid.layers.data(name='label', shape=[1], dtype='int64')
 feeder = fluid.DataFeeder(feed_list=[input, label], place=fluid.CPUPlace())
@@ -44,6 +49,10 @@ job_generator.save_program(program_path, [input, label],
 In fl_master.py, you can load the program in 'load_file' and transfer it into an fl program.
 
 ```python
+import paddle_fl.paddle_fl as fl
+import paddle.fluid as fluid
+from paddle_fl.paddle_fl.core.master.job_generator import JobGenerator
+from paddle_fl.paddle_fl.core.strategy.fl_strategy_base import FLStrategyFactory
 build_strategy = FLStrategyFactory()
 build_strategy.fed_avg = True
 build_strategy.inner_step = 10
@@ -72,6 +81,8 @@ python -u fl_trainer.py 1  >trainer1.log &
 In fl_scheduler.py, we let server and trainers to do registeration.
 
 ```python
+from paddle_fl.paddle_fl.core.scheduler.agent_master import FLScheduler
+
 worker_num = 2
 server_num = 1
 #Define number of worker/server and the port for scheduler
@@ -84,6 +95,11 @@ scheduler.start_fl_training()
 In fl_server.py, we load and run the FL server job.
 
 ```python
+import paddle_fl.paddle_fl as fl
+import paddle.fluid as fluid
+from paddle_fl.paddle_fl.core.server.fl_server import FLServer
+from paddle_fl.paddle_fl.core.master.fl_job import FLRunTimeJob
+
 server = FLServer()
 server_id = 0
 job_path = "fl_job_config"
@@ -98,6 +114,15 @@ server.start()
 In fl_trainer.py, we load and run the FL trainer job, then evaluate the accuracy with test data.
 
 ```python
+from paddle_fl.paddle_fl.core.trainer.fl_trainer import FLTrainerFactory
+from paddle_fl.paddle_fl.core.master.fl_job import FLRunTimeJob
+import numpy
+import sys
+import paddle
+import paddle.fluid as fluid
+import logging
+import math
+
 trainer_id = int(sys.argv[1])  # trainer id for each guest
 job_path = "fl_job_config"
 job = FLRunTimeJob()

python/paddle_fl/paddle_fl/examples/gru4rec_demo/README.md

@@ -39,6 +39,11 @@ 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
+import paddle.fluid as fluid
+import paddle_fl.paddle_fl as fl
+from paddle_fl.paddle_fl.core.master.job_generator import JobGenerator
+from paddle_fl.paddle_fl.core.strategy.fl_strategy_base import FLStrategyFactory
+
 # define model
 model = Model()
 model.gru4rec_network()

python/paddle_fl/paddle_fl/examples/secagg_demo/README.md

@@ -50,6 +50,11 @@ 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
+import paddle.fluid as fluid
+import paddle_fl.paddle_fl as fl
+from paddle_fl.paddle_fl.core.master.job_generator import JobGenerator
+from paddle_fl.paddle_fl.core.strategy.fl_strategy_base import FLStrategyFactory
+
 def linear_regression(self, inputs, label):
         param_attrs = fluid.ParamAttr(
             name="fc_0.b_0",
@@ -110,6 +115,8 @@ python3 -u fl_trainer.py 1 >log/trainer1.log &
 In fl_scheduler.py, we let server and trainers to do registeration.
 
 ```python
+from paddle_fl.paddle_fl.core.scheduler.agent_master import FLScheduler
+
 worker_num = 2
 server_num = 1
 #Define number of worker/server and the port for scheduler
@@ -123,6 +130,11 @@ scheduler.start_fl_training()
 In fl_server.py, we load and run the FL server job.  
 
 ```python
+import paddle_fl.paddle_fl as fl
+import paddle.fluid as fluid
+from paddle_fl.paddle_fl.core.server.fl_server import FLServer
+from paddle_fl.paddle_fl.core.master.fl_job import FLRunTimeJob
+
 server = FLServer()
 server_id = 0
 job_path = "fl_job_config"
@@ -135,6 +147,19 @@ server.start()
 In fl_trainer.py, we prepare the MNIST dataset, load and run the FL trainer job, then evaluate the accuracy.  Before training , we first prepare the party's private key and other party's public key. Then, each party generates a random noise using Diffie-Hellman key aggregate protocol with its private key and each other's public key [1]. If the other party's id is larger than this party's id, the model parameters add this random noise. If the other party's id is less than this party's id, the model parameters subtract this random noise. So, and the model parameters is masked before uploading to the server. Finally, the random noises can be removed when aggregating the masked parameters from all the parties.
 
 ```python
+import numpy
+import sys
+import logging
+import time
+import datetime
+import math
+import hashlib
+import hmac
+import paddle
+import paddle.fluid as fluid
+from paddle_fl.paddle_fl.core.trainer.fl_trainer import FLTrainerFactory
+from paddle_fl.paddle_fl.core.master.fl_job import FLRunTimeJob
+
 logging.basicConfig(filename="log/test.log", filemode="w", format="%(asctime)s %(name)s:%(levelname)s:%(message)s", datefmt="%d-%M-%Y %H:%M:%S", level=logging.DEBUG)
 logger = logging.getLogger("FLTrainer")