fix code style

AUTHORS.md

 | Github account | name |
 |---|---|
 | guru4elephant | Daxiang Dong |
-| frankwhzhang | Wenhui Zhang |
\ No newline at end of file
+| frankwhzhang | Wenhui Zhang |

docs/requirements.txt

@@ -3,4 +3,3 @@ mistune
 sphinx_rtd_theme
 paddlepaddle>=1.6
 zmq
-

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

@@ -181,4 +181,3 @@ 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 />
-

docs/source/examples/md/gru4rec_examples.md

@@ -103,6 +103,3 @@ wget https://paddle-zwh.bj.bcebos.com/gru4rec_paddlefl_benchmark/gru4rec_benchma
 | 1/4 of the whole dataset | private training | - | 0.282 | 
 
 <img src="fl_benchmark.png" height=300 width=500 hspace='10'/> <br />
-
-
-

docs/source/md/introduction.md

@@ -55,4 +55,3 @@ In PaddleFL, components for defining a federated learning task and training a fe
 - Federated Learning Systems deployment methods in Kubernetes.
 
 - Vertical Federated Learning Strategies and more horizontal federated learning strategies will be open sourced.
-

docs/source/md/reference.md

@@ -14,4 +14,4 @@
 
 [7]. Virginia Smith, Chao-Kai Chiang, Maziar Sanjabi, Ameet Talwalkar. **Federated Multi-Task Learning** 2016
 
-[8]. Yang Liu, Tianjian Chen, Qiang Yang. **Secure Federated Transfer Learning.** 2018
\ No newline at end of file
+[8]. Yang Liu, Tianjian Chen, Qiang Yang. **Secure Federated Transfer Learning.** 2018

paddle_fl/core/__init__.py

@@ -22,4 +22,3 @@ from .scheduler.agent_master import FLWorkerAgent
 from .scheduler.agent_master import FLScheduler
 from .submitter.client_base import HPCClient
 from .submitter.client_base import CloudClient
-

paddle_fl/core/master/fl_job.py

@@ -14,11 +14,13 @@
 import os
 import paddle.fluid as fluid
 
+
 class FLJobBase(object):
     """
     FLJobBase is fl job base class, responsible for save and load
     a federated learning job
     """
+
     def __init__(self):
         pass
 
@@ -64,6 +66,7 @@ class FLJobBase(object):
             return fluid.Program.parse_from_string(program_desc_str)
         return None
 
+
 class FLCompileTimeJob(FLJobBase):
     """
     FLCompileTimeJob is a container for compile time job in federated learning.
@@ -71,6 +74,7 @@ class FLCompileTimeJob(FLJobBase):
     are in FLCompileTimeJob. Also, server main programs and server startup programs
     are in this class. FLCompileTimeJob has server endpoints for debugging as well
     """
+
     def __init__(self):
         self._trainer_startup_programs = []
         self._trainer_recv_programs = []
@@ -101,69 +105,59 @@ class FLCompileTimeJob(FLJobBase):
             os.system("mkdir -p %s" % server_folder)
             server_startup = self._server_startup_programs[i]
             server_main = self._server_main_programs[i]
-            self._save_program(
-                server_startup,
-                "%s/server.startup.program" % server_folder)
-            self._save_program(
-                server_main,
-                "%s/server.main.program" % server_folder)
+            self._save_program(server_startup,
+                               "%s/server.startup.program" % server_folder)
+            self._save_program(server_main,
+                               "%s/server.main.program" % server_folder)
+            self._save_readable_program(server_startup,
+                                        "%s/server.startup.program.txt" %
+                                        server_folder)
             self._save_readable_program(
-                server_startup,
-                "%s/server.startup.program.txt" % server_folder)
-            self._save_readable_program(
-                server_main,
-                "%s/server.main.program.txt" % server_folder)
+                server_main, "%s/server.main.program.txt" % server_folder)
             self._save_str_list(self._feed_names,
-                            "%s/feed_names" % server_folder)
+                                "%s/feed_names" % server_folder)
             self._save_str_list(self._target_names,
-                            "%s/target_names" % server_folder)
+                                "%s/target_names" % server_folder)
             self._save_endpoints(self._server_endpoints,
-                            "%s/endpoints" % server_folder)
+                                 "%s/endpoints" % server_folder)
             self._save_strategy(self._strategy,
-                           "%s/strategy.pkl" % server_folder)
+                                "%s/strategy.pkl" % server_folder)
 
         for i in range(trainer_num):
             trainer_folder = "%s/trainer%d" % (folder, i)
             os.system("mkdir -p %s" % trainer_folder)
             trainer_startup = self._trainer_startup_programs[i]
             trainer_main = self._trainer_main_programs[i]
-            self._save_program(
-                trainer_startup,
-                "%s/trainer.startup.program" % trainer_folder)
-            self._save_program(
-                trainer_main,
-                "%s/trainer.main.program" % trainer_folder)
-            self._save_readable_program(
-                trainer_startup,
-                "%s/trainer.startup.program.txt" % trainer_folder)
+            self._save_program(trainer_startup,
+                               "%s/trainer.startup.program" % trainer_folder)
+            self._save_program(trainer_main,
+                               "%s/trainer.main.program" % trainer_folder)
+            self._save_readable_program(trainer_startup,
+                                        "%s/trainer.startup.program.txt" %
+                                        trainer_folder)
             self._save_readable_program(
-                trainer_main,
-                "%s/trainer.main.program.txt" % trainer_folder)
+                trainer_main, "%s/trainer.main.program.txt" % trainer_folder)
             self._save_str_list(self._feed_names,
-                            "%s/feed_names" % trainer_folder)
+                                "%s/feed_names" % trainer_folder)
             self._save_str_list(self._target_names,
-                            "%s/target_names" % trainer_folder)
+                                "%s/target_names" % trainer_folder)
             self._save_endpoints(self._server_endpoints,
-                            "%s/endpoints" % trainer_folder)
+                                 "%s/endpoints" % trainer_folder)
             self._save_strategy(self._strategy,
-                           "%s/strategy.pkl" % trainer_folder)
+                                "%s/strategy.pkl" % trainer_folder)
 
         for i in range(send_prog_num):
             trainer_folder = "%s/trainer%d" % (folder, i)
             trainer_send = self._trainer_send_programs[i]
             trainer_recv = self._trainer_recv_programs[i]
-            self._save_program(
-                trainer_send,
-                "%s/trainer.send.program" % trainer_folder)
-            self._save_program(
-                trainer_recv,
-                "%s/trainer.recv.program" % trainer_folder)
+            self._save_program(trainer_send,
+                               "%s/trainer.send.program" % trainer_folder)
+            self._save_program(trainer_recv,
+                               "%s/trainer.recv.program" % trainer_folder)
             self._save_readable_program(
-                trainer_send,
-                "%s/trainer.send.program.txt" % trainer_folder)
+                trainer_send, "%s/trainer.send.program.txt" % trainer_folder)
             self._save_readable_program(
-                trainer_recv,
-                "%s/trainer.recv.program.txt" % trainer_folder)
+                trainer_recv, "%s/trainer.recv.program.txt" % trainer_folder)
 
 
 class FLRunTimeJob(FLJobBase):
@@ -172,6 +166,7 @@ class FLRunTimeJob(FLJobBase):
     A trainer or a server can load FLRunTimeJob. Only necessary programs
      can be loaded in FLRunTimeJob
     """
+
     def __init__(self):
         self._trainer_startup_program = None
         self._trainer_recv_program = None

paddle_fl/core/master/job_generator.py

@@ -14,6 +14,7 @@
 import paddle.fluid as fluid
 from .fl_job import FLCompileTimeJob
 
+
 class JobGenerator(object):
     """
     A JobGenerator is responsible for generating distributed federated
@@ -21,6 +22,7 @@ class JobGenerator(object):
     need to define a deep learning model together to do horizontal federated
     learning.
     """
+
     def __init__(self):
         # worker num for federated learning
         self._worker_num = 0
@@ -32,7 +34,6 @@ class JobGenerator(object):
         self._feed_names = []
         self._target_names = []
 
-
     def set_optimizer(self, optimizer):
         """
         Set optimizer of current job
@@ -56,8 +57,10 @@ class JobGenerator(object):
         self._startup_prog = startup
 
     def set_infer_feed_and_target_names(self, feed_names, target_names):
-        if not isinstance(feed_names, list) or not isinstance(target_names, list):
-            raise ValueError("input should be list in set_infer_feed_and_target_names")
+        if not isinstance(feed_names, list) or not isinstance(target_names,
+                                                              list):
+            raise ValueError(
+                "input should be list in set_infer_feed_and_target_names")
         '''
         print(feed_names)
         print(target_names)
@@ -76,7 +79,6 @@ class JobGenerator(object):
                         server_endpoints=[],
                         worker_num=1,
                         output=None):
-
         """
         Generate Federated Learning Job, based on user defined configs
 
@@ -130,30 +132,35 @@ class JobGenerator(object):
             startup_program = self._startup_prog.clone()
             main_program = self._losses[0].block.program.clone()
             fl_strategy._build_trainer_program_for_job(
-                trainer_id, program=main_program,
-                ps_endpoints=server_endpoints, trainers=worker_num,
-                sync_mode=True, startup_program=startup_program,
+                trainer_id,
+                program=main_program,
+                ps_endpoints=server_endpoints,
+                trainers=worker_num,
+                sync_mode=True,
+                startup_program=startup_program,
                 job=local_job)
 
         startup_program = self._startup_prog.clone()
         main_program = self._losses[0].block.program.clone()
         fl_strategy._build_server_programs_for_job(
-            program=main_program, ps_endpoints=server_endpoints,
-            trainers=worker_num, sync_mode=True,
-            startup_program=startup_program, job=local_job)
+            program=main_program,
+            ps_endpoints=server_endpoints,
+            trainers=worker_num,
+            sync_mode=True,
+            startup_program=startup_program,
+            job=local_job)
 
         local_job.set_feed_names(self._feed_names)
         local_job.set_target_names(self._target_names)
         local_job.set_strategy(fl_strategy)
         local_job.save(output)
 
-
     def generate_fl_job_for_k8s(self,
-                        fl_strategy,
-                        server_pod_endpoints=[],
-                        server_service_endpoints=[],
-                        worker_num=1,
-                        output=None):
+                                fl_strategy,
+                                server_pod_endpoints=[],
+                                server_service_endpoints=[],
+                                worker_num=1,
+                                output=None):
 
         local_job = FLCompileTimeJob()
         assert len(self._losses) > 0
@@ -168,17 +175,23 @@ class JobGenerator(object):
             startup_program = self._startup_prog.clone()
             main_program = self._losses[0].block.program.clone()
             fl_strategy._build_trainer_program_for_job(
-                trainer_id, program=main_program,
-                ps_endpoints=server_service_endpoints, trainers=worker_num,
-                sync_mode=True, startup_program=startup_program,
+                trainer_id,
+                program=main_program,
+                ps_endpoints=server_service_endpoints,
+                trainers=worker_num,
+                sync_mode=True,
+                startup_program=startup_program,
                 job=local_job)
 
         startup_program = self._startup_prog.clone()
         main_program = self._losses[0].block.program.clone()
         fl_strategy._build_server_programs_for_job(
-            program=main_program, ps_endpoints=server_pod_endpoints,
-            trainers=worker_num, sync_mode=True,
-            startup_program=startup_program, job=local_job)
+            program=main_program,
+            ps_endpoints=server_pod_endpoints,
+            trainers=worker_num,
+            sync_mode=True,
+            startup_program=startup_program,
+            job=local_job)
 
         local_job.set_feed_names(self._feed_names)
         local_job.set_target_names(self._target_names)

paddle_fl/core/scheduler/agent_master.py

@@ -2,6 +2,7 @@ import zmq
 import time
 import random
 
+
 def recv_and_parse_kv(socket):
     message = socket.recv()
     group = message.decode().split("\t")
@@ -10,9 +11,11 @@ def recv_and_parse_kv(socket):
     else:
         return group[0], group[1]
 
+
 WORKER_EP = "WORKER_EP"
 SERVER_EP = "SERVER_EP"
 
+
 class FLServerAgent(object):
     def __init__(self, scheduler_ep, current_ep):
         self.scheduler_ep = scheduler_ep
@@ -29,6 +32,7 @@ class FLServerAgent(object):
             if group[0] == 'INIT':
                 break
 
+
 class FLWorkerAgent(object):
     def __init__(self, scheduler_ep, current_ep):
         self.scheduler_ep = scheduler_ep
@@ -64,7 +68,6 @@ class FLWorkerAgent(object):
         return False
 
 
-
 class FLScheduler(object):
     def __init__(self, worker_num, server_num, port=9091, socket=None):
         self.context = zmq.Context()

paddle_fl/core/server/fl_server.py

@@ -14,8 +14,8 @@
 import paddle.fluid as fluid
 from paddle_fl.core.scheduler.agent_master import FLServerAgent
 
-class FLServer(object):
 
+class FLServer(object):
     def __init__(self):
         self._startup_program = None
         self._main_program = None

paddle_fl/core/strategy/details/checkport.py

@@ -48,8 +48,8 @@ def wait_server_ready(endpoints):
                     not_ready_endpoints.append(ep)
         if not all_ok:
             sys.stderr.write("server not ready, wait 3 sec to retry...\n")
-            sys.stderr.write("not ready endpoints:" + str(not_ready_endpoints) +
-                             "\n")
+            sys.stderr.write("not ready endpoints:" + str(not_ready_endpoints)
+                             + "\n")
             sys.stderr.flush()
             time.sleep(3)
         else:

paddle_fl/core/strategy/details/program_utils.py

@@ -163,7 +163,8 @@ def block_to_code(block, block_idx, fout=None, skip_op_callstack=False):
     indent = 0
 
     print(
-        "{0}{1} // block {2}".format(get_indent_space(indent), '{', block_idx),
+        "{0}{1} // block {2}".format(
+            get_indent_space(indent), '{', block_idx),
         file=fout)
 
     indent += 1

paddle_fl/core/strategy/fl_distribute_transpiler.py

@@ -50,6 +50,7 @@ def log(*args):
     if PRINT_LOG:
         print(args)
 
+
 def same_or_split_var(p_name, var_name):
     return p_name == var_name or p_name.startswith(var_name + ".block")
 
@@ -113,7 +114,9 @@ class FLDistributeTranspiler(object):
 
     def _get_all_remote_sparse_update_op(self, main_program):
         sparse_update_ops = []
-        sparse_update_op_types = ["lookup_table", "nce", "hierarchical_sigmoid"]
+        sparse_update_op_types = [
+            "lookup_table", "nce", "hierarchical_sigmoid"
+        ]
         for op in main_program.global_block().ops:
             if op.type in sparse_update_op_types and op.attr(
                     'remote_prefetch') is True:
@@ -406,12 +409,13 @@ class FLDistributeTranspiler(object):
             # NOTE: single_trainer_var must be created for multi-trainer
             # case to merge grads from multiple trainers
             single_trainer_var = pserver_program.global_block().var(
-                    orig_var_name)
+                orig_var_name)
 
             if self.sync_mode and self.trainer_num > 1:
                 for trainer_id in range(self.trainer_num):
                     var = pserver_program.global_block().create_var(
-                        name="%s.opti.trainer_%d" % (orig_var_name, trainer_id),
+                        name="%s.opti.trainer_%d" %
+                        (orig_var_name, trainer_id),
                         persistable=False,
                         type=v.type,
                         dtype=v.dtype,
@@ -816,7 +820,6 @@ class FLDistributeTranspiler(object):
         iomap = collections.OrderedDict()
         return iomap
 
-
     def _get_lr_ops(self):
         lr_ops = []
         block = self.origin_program.global_block()

paddle_fl/core/strategy/fl_strategy_base.py

@@ -16,11 +16,13 @@ from .fl_distribute_transpiler import FLDistributeTranspiler
 from paddle.fluid.optimizer import SGD
 import paddle.fluid as fluid
 
+
 class FLStrategyFactory(object):
     """
     FLStrategyFactory is a FLStrategy builder
     Users can define strategy config to create different FLStrategy
     """
+
     def __init__(self):
         self._fed_avg = False
         self._dpsgd = False
@@ -86,6 +88,7 @@ class FLStrategyBase(object):
     """
     FLStrategyBase is federated learning algorithm container
     """
+
     def __init__(self):
         self._fed_avg = False
         self._dpsgd = False
@@ -105,17 +108,23 @@ class FLStrategyBase(object):
         for loss in losses:
             optimizer.minimize(loss)
 
-    def _build_trainer_program_for_job(
-            self, trainer_id=0, program=None,
-            ps_endpoints=[], trainers=0,
-            sync_mode=True, startup_program=None,
-            job=None):
+    def _build_trainer_program_for_job(self,
+                                       trainer_id=0,
+                                       program=None,
+                                       ps_endpoints=[],
+                                       trainers=0,
+                                       sync_mode=True,
+                                       startup_program=None,
+                                       job=None):
         pass
 
-    def _build_server_programs_for_job(
-            self, program=None, ps_endpoints=[],
-            trainers=0, sync_mode=True,
-            startup_program=None, job=None):
+    def _build_server_programs_for_job(self,
+                                       program=None,
+                                       ps_endpoints=[],
+                                       trainers=0,
+                                       sync_mode=True,
+                                       startup_program=None,
+                                       job=None):
         pass
 
 
@@ -123,6 +132,7 @@ class DPSGDStrategy(FLStrategyBase):
     """
     DPSGDStrategy: Deep Learning with Differential Privacy. 2016
     """
+
     def __init__(self):
         super(DPSGDStrategy, self).__init__()
 
@@ -162,29 +172,40 @@ class DPSGDStrategy(FLStrategyBase):
         """
         Define Dpsgd optimizer
         """
-        optimizer = fluid.optimizer.Dpsgd(self._learning_rate, clip=self._clip, batch_size=self._batch_size, sigma=self._sigma)
+        optimizer = fluid.optimizer.Dpsgd(
+            self._learning_rate,
+            clip=self._clip,
+            batch_size=self._batch_size,
+            sigma=self._sigma)
         optimizer.minimize(losses[0])
 
-    def _build_trainer_program_for_job(
-            self, trainer_id=0, program=None,
-            ps_endpoints=[], trainers=0,
-            sync_mode=True, startup_program=None,
-            job=None):
+    def _build_trainer_program_for_job(self,
+                                       trainer_id=0,
+                                       program=None,
+                                       ps_endpoints=[],
+                                       trainers=0,
+                                       sync_mode=True,
+                                       startup_program=None,
+                                       job=None):
         transpiler = fluid.DistributeTranspiler()
-        transpiler.transpile(trainer_id,
-                             program=program,
-                             pservers=",".join(ps_endpoints),
-                             trainers=trainers,
-                             sync_mode=sync_mode,
-                             startup_program=startup_program)
+        transpiler.transpile(
+            trainer_id,
+            program=program,
+            pservers=",".join(ps_endpoints),
+            trainers=trainers,
+            sync_mode=sync_mode,
+            startup_program=startup_program)
         main = transpiler.get_trainer_program(wait_port=False)
         job._trainer_startup_programs.append(startup_program)
         job._trainer_main_programs.append(main)
 
-    def _build_server_programs_for_job(
-            self, program=None, ps_endpoints=[],
-            trainers=0, sync_mode=True,
-            startup_program=None, job=None):
+    def _build_server_programs_for_job(self,
+                                       program=None,
+                                       ps_endpoints=[],
+                                       trainers=0,
+                                       sync_mode=True,
+                                       startup_program=None,
+                                       job=None):
         transpiler = fluid.DistributeTranspiler()
         trainer_id = 0
         transpiler.transpile(
@@ -207,6 +228,7 @@ class FedAvgStrategy(FLStrategyBase):
     FedAvgStrategy: this is model averaging optimization proposed in
     H. Brendan McMahan, Eider Moore, Daniel Ramage, Blaise Aguera y Arcas. Federated Learning of Deep Networks using Model Averaging. 2017
     """
+
     def __init__(self):
         super(FedAvgStrategy, self).__init__()
 
@@ -216,28 +238,35 @@ class FedAvgStrategy(FLStrategyBase):
         """
         optimizer.minimize(losses[0])
 
-    def _build_trainer_program_for_job(
-            self, trainer_id=0, program=None,
-            ps_endpoints=[], trainers=0,
-            sync_mode=True, startup_program=None,
-            job=None):
+    def _build_trainer_program_for_job(self,
+                                       trainer_id=0,
+                                       program=None,
+                                       ps_endpoints=[],
+                                       trainers=0,
+                                       sync_mode=True,
+                                       startup_program=None,
+                                       job=None):
         transpiler = FLDistributeTranspiler()
-        transpiler.transpile(trainer_id,
-                             program=program,
-                             pservers=",".join(ps_endpoints),
-                             trainers=trainers,
-                             sync_mode=sync_mode,
-                             startup_program=startup_program)
+        transpiler.transpile(
+            trainer_id,
+            program=program,
+            pservers=",".join(ps_endpoints),
+            trainers=trainers,
+            sync_mode=sync_mode,
+            startup_program=startup_program)
         recv, main, send = transpiler.get_trainer_program()
         job._trainer_startup_programs.append(startup_program)
         job._trainer_main_programs.append(main)
         job._trainer_send_programs.append(send)
         job._trainer_recv_programs.append(recv)
 
-    def _build_server_programs_for_job(
-            self, program=None, ps_endpoints=[],
-            trainers=0, sync_mode=True,
-            startup_program=None, job=None):
+    def _build_server_programs_for_job(self,
+                                       program=None,
+                                       ps_endpoints=[],
+                                       trainers=0,
+                                       sync_mode=True,
+                                       startup_program=None,
+                                       job=None):
         transpiler = FLDistributeTranspiler()
         trainer_id = 0
         transpiler.transpile(
@@ -262,6 +291,7 @@ class SecAggStrategy(FedAvgStrategy):
     Practical Secure Aggregation  for Privacy-Preserving Machine Learning,
     The 24th ACM Conference on Computer and Communications Security ( CCS2017 ).
     """
+
     def __init__(self):
         super(SecAggStrategy, self).__init__()
         self._param_name_list = []

paddle_fl/core/submitter/client_base.py

 import sys
 import os
 
+
 class CloudClient(object):
     def __init__(self):
         pass
-    
+
     def generate_submit_sh(self, job_dir):
         with open() as fout:
             pass
@@ -16,6 +17,7 @@ class CloudClient(object):
     def submit(self, **kwargs):
         pass
 
+
 class HPCClient(object):
     def __init__(self):
         self.conf_dict = {}
@@ -70,27 +72,20 @@ class HPCClient(object):
             fout.write("#!/bin/bash\n")
             fout.write("unset http_proxy\n")
             fout.write("unset https_proxy\n")
-            fout.write("export HADOOP_HOME={}\n".format(
-                self.hadoop_home))
+            fout.write("export HADOOP_HOME={}\n".format(self.hadoop_home))
             fout.write("$HADOOP_HOME/bin/hadoop fs -Dhadoop.job.ugi={}"
                        " -Dfs.default.name={} -rmr {}\n".format(
-                           self.ugi,
-                           self.hdfs_path,
-                           self.hdfs_output))
+                           self.ugi, self.hdfs_path, self.hdfs_output))
             fout.write("MPI_NODE_MEM={}\n".format(self.mpi_node_mem))
             fout.write("{}/bin/qsub_f -N {} --conf qsub.conf "
                        "--hdfs {} --ugi {} --hout {} --files ./package "
                        "-l nodes={},walltime=1000:00:00,pmem-hard={},"
                        "pcpu-soft={},pnetin-soft=1000,"
                        "pnetout-soft=1000 job.sh\n".format(
-                           self.hpc_home,
-                           self.task_name,
-                           self.hdfs_path,
-                           self.ugi,
-                           self.hdfs_output,
+                           self.hpc_home, self.task_name, self.hdfs_path,
+                           self.ugi, self.hdfs_output,
                            int(self.worker_nodes) + int(self.server_nodes),
-                           self.mpi_node_mem,
-                           self.pcpu))
+                           self.mpi_node_mem, self.pcpu))
 
     def generate_job_sh(self, job_dir):
         with open("{}/job.sh".format(job_dir), "w") as fout:
@@ -98,17 +93,23 @@ class HPCClient(object):
             fout.write("WORKDIR=`pwd`\n")
             fout.write("mpirun -npernode 1 mv package/* ./\n")
             fout.write("echo 'current dir: '$WORKDIR\n")
-            fout.write("mpirun -npernode 1 tar -zxvf python.tar.gz > /dev/null\n")
-            fout.write("export LIBRARY_PATH=$WORKDIR/python/lib:$LIBRARY_PATH\n")
+            fout.write(
+                "mpirun -npernode 1 tar -zxvf python.tar.gz > /dev/null\n")
+            fout.write(
+                "export LIBRARY_PATH=$WORKDIR/python/lib:$LIBRARY_PATH\n")
             fout.write("mpirun -npernode 1 python/bin/python -m pip install "
                        "{} --index-url=http://pip.baidu.com/pypi/simple "
                        "--trusted-host pip.baidu.com > /dev/null\n".format(
                            self.wheel))
             fout.write("export PATH=python/bin:$PATH\n")
             if self.monitor_cmd != "":
-                fout.write("mpirun -npernode 1 -timestamp-output -tag-output -machinefile "
-                           "${{PBS_NODEFILE}} python/bin/{} > monitor.log 2> monitor.elog &\n".format(self.monitor_cmd))
-            fout.write("mpirun -npernode 1 -timestamp-output -tag-output -machinefile ${PBS_NODEFILE} python/bin/python train_program.py\n")
+                fout.write(
+                    "mpirun -npernode 1 -timestamp-output -tag-output -machinefile "
+                    "${{PBS_NODEFILE}} python/bin/{} > monitor.log 2> monitor.elog &\n".
+                    format(self.monitor_cmd))
+            fout.write(
+                "mpirun -npernode 1 -timestamp-output -tag-output -machinefile ${PBS_NODEFILE} python/bin/python train_program.py\n"
+            )
             fout.write("if [[ $? -ne 0 ]]; then\n")
             fout.write("    echo 'Failed to run mpi!' 1>&2\n")
             fout.write("    exit 1\n")
@@ -150,4 +151,5 @@ class HPCClient(object):
         # generate job.sh
         self.generate_qsub_conf(jobdir)
         # run submit
-        os.system("cd {};sh submit.sh > submit.log 2> submit.elog &".format(jobdir))
+        os.system("cd {};sh submit.sh > submit.log 2> submit.elog &".format(
+            jobdir))

paddle_fl/core/trainer/diffiehellman/__init__.py

@@ -2,7 +2,6 @@
 
 #
 # (c) Chris von Csefalvay, 2015.
-
 """
 __init__.py is responsible for [brief description here].
 """

paddle_fl/core/trainer/diffiehellman/decorators.py

 # coding=utf-8
 
-
 #
 # The MIT License (MIT)
 #
@@ -21,8 +20,6 @@
 # DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
 # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
 #
-
-
 """
 decorators declares some decorators that ensure the object has the
 correct keys declared when need be.

paddle_fl/core/trainer/diffiehellman/diffiehellman.py

@@ -20,10 +20,6 @@
 # DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
 # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
 #
-
-
-
-
 """
 diffiehellmann declares the main key exchange class.
 """
@@ -41,18 +37,17 @@ import os
 try:
     from ssl import RAND_bytes
     rng = RAND_bytes
-except(AttributeError, ImportError):
+except (AttributeError, ImportError):
     rng = os.urandom
 
+
 class DiffieHellman:
     """
     Implements the Diffie-Hellman key exchange protocol.
 
     """
 
-    def __init__(self,
-                 group=18,
-                 key_length=640):
+    def __init__(self, group=18, key_length=640):
 
         self.key_length = max(200, key_length)
         self.generator = PRIMES[group]["generator"]
@@ -81,7 +76,8 @@ class DiffieHellman:
         self.private_key = key
 
     def verify_public_key(self, other_public_key):
-        return self.prime - 1 > other_public_key > 2 and pow(other_public_key, (self.prime - 1) // 2, self.prime) == 1
+        return self.prime - 1 > other_public_key > 2 and pow(
+            other_public_key, (self.prime - 1) // 2, self.prime) == 1
 
     @requires_private_key
     def generate_public_key(self):
@@ -91,9 +87,7 @@ class DiffieHellman:
         :return: void
         :rtype: void
         """
-        self.public_key = pow(self.generator,
-                              self.private_key,
-                              self.prime)
+        self.public_key = pow(self.generator, self.private_key, self.prime)
 
     @requires_private_key
     def generate_shared_secret(self, other_public_key, echo_return_key=False):
@@ -110,16 +104,17 @@ class DiffieHellman:
         if self.verify_public_key(other_public_key) is False:
             raise MalformedPublicKey
 
-        self.shared_secret = pow(other_public_key,
-                                 self.private_key,
+        self.shared_secret = pow(other_public_key, self.private_key,
                                  self.prime)
         try:
             #python3
-            shared_secret_as_bytes = self.shared_secret.to_bytes(self.shared_secret.bit_length() // 8 + 1, byteorder='big')
+            shared_secret_as_bytes = self.shared_secret.to_bytes(
+                self.shared_secret.bit_length() // 8 + 1, byteorder='big')
         except:
             #python2
             length = self.shared_secret.bit_length() // 8 + 1
-            shared_secret_as_bytes = ('%%0%dx' % (length << 1) % self.shared_secret).decode('hex')[-length:]
+            shared_secret_as_bytes = ('%%0%dx' % (
+                length << 1) % self.shared_secret).decode('hex')[-length:]
         _h = sha256()
         _h.update(bytes(shared_secret_as_bytes))
 

paddle_fl/core/trainer/diffiehellman/exceptions.py

@@ -2,7 +2,6 @@
 
 #
 # (c) Chris von Csefalvay, 2015.
-
 """
 exceptions is responsible for exception handling etc.
 """

paddle_fl/core/trainer/diffiehellman/primes.py

 # coding=utf-8
 
-
 #
 # The MIT License (MIT)
 #
@@ -25,34 +24,39 @@
 # Extracted from: Kivinen, T. and Kojo, M. (2003), _More Modular Exponential (MODP) Diffie-Hellman
 #                                                   groups for Internet Key Exchange (IKE)_.
 #
-
 """
 primes holds the RFC 3526 MODP primes and their generators.
 """
 
 PRIMES = {
     5: {
-        "prime": 0xFFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD129024E088A67CC74020BBEA63B139B22514A08798E3404DDEF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7EDEE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3DC2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F83655D23DCA3AD961C62F356208552BB9ED529077096966D670C354E4ABC9804F1746C08CA237327FFFFFFFFFFFFFFFF,
+        "prime":
+        0xFFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD129024E088A67CC74020BBEA63B139B22514A08798E3404DDEF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7EDEE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3DC2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F83655D23DCA3AD961C62F356208552BB9ED529077096966D670C354E4ABC9804F1746C08CA237327FFFFFFFFFFFFFFFF,
         "generator": 2
     },
     14: {
-        "prime": 0xFFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD129024E088A67CC74020BBEA63B139B22514A08798E3404DDEF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7EDEE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3DC2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F83655D23DCA3AD961C62F356208552BB9ED529077096966D670C354E4ABC9804F1746C08CA18217C32905E462E36CE3BE39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9DE2BCBF6955817183995497CEA956AE515D2261898FA051015728E5A8AACAA68FFFFFFFFFFFFFFFF,
+        "prime":
+        0x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
         "generator": 2
     },
     15: {
-        "prime": 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
+        "prime":
+        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
         "generator": 2
     },
     16: {
-        "prime": 0xFFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD129024E088A67CC74020BBEA63B139B22514A08798E3404DDEF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7EDEE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3DC2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F83655D23DCA3AD961C62F356208552BB9ED529077096966D670C354E4ABC9804F1746C08CA18217C32905E462E36CE3BE39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9DE2BCBF6955817183995497CEA956AE515D2261898FA051015728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6BF12FFA06D98A0864D87602733EC86A64521F2B18177B200CBBE117577A615D6C770988C0BAD946E208E24FA074E5AB3143DB5BFCE0FD108E4B82D120A92108011A723C12A787E6D788719A10BDBA5B2699C327186AF4E23C1A946834B6150BDA2583E9CA2AD44CE8DBBBC2DB04DE8EF92E8EFC141FBECAA6287C59474E6BC05D99B2964FA090C3A2233BA186515BE7ED1F612970CEE2D7AFB81BDD762170481CD0069127D5B05AA993B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934063199FFFFFFFFFFFFFFFF,
+        "prime":
+        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
         "generator": 2
     },
     17: {
-        "prime": 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
+        "prime":
+        0xFFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD129024E088A67CC74020BBEA63B139B22514A08798E3404DDEF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7EDEE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3DC2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F83655D23DCA3AD961C62F356208552BB9ED529077096966D670C354E4ABC9804F1746C08CA18217C32905E462E36CE3BE39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9DE2BCBF6955817183995497CEA956AE515D2261898FA051015728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6BF12FFA06D98A0864D87602733EC86A64521F2B18177B200CBBE117577A615D6C770988C0BAD946E208E24FA074E5AB3143DB5BFCE0FD108E4B82D120A92108011A723C12A787E6D788719A10BDBA5B2699C327186AF4E23C1A946834B6150BDA2583E9CA2AD44CE8DBBBC2DB04DE8EF92E8EFC141FBECAA6287C59474E6BC05D99B2964FA090C3A2233BA186515BE7ED1F612970CEE2D7AFB81BDD762170481CD0069127D5B05AA993B4EA988D8FDDC186FFB7DC90A6C08F4DF435C93402849236C3FAB4D27C7026C1D4DCB2602646DEC9751E763DBA37BDF8FF9406AD9E530EE5DB382F413001AEB06A53ED9027D831179727B0865A8918DA3EDBEBCF9B14ED44CE6CBACED4BB1BDB7F1447E6CC254B332051512BD7AF426FB8F401378CD2BF5983CA01C64B92ECF032EA15D1721D03F482D7CE6E74FEF6D55E702F46980C82B5A84031900B1C9E59E7C97FBEC7E8F323A97A7E36CC88BE0F1D45B7FF585AC54BD407B22B4154AACC8F6D7EBF48E1D814CC5ED20F8037E0A79715EEF29BE32806A1D58BB7C5DA76F550AA3D8A1FBFF0EB19CCB1A313D55CDA56C9EC2EF29632387FE8D76E3C0468043E8F663F4860EE12BF2D5B0B7474D6E694F91E6DCC4024FFFFFFFFFFFFFFFF,
         "generator": 2
     },
     18: {
-        "prime": 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
+        "prime":
+        0xFFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD129024E088A67CC74020BBEA63B139B22514A08798E3404DDEF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7EDEE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3DC2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F83655D23DCA3AD961C62F356208552BB9ED529077096966D670C354E4ABC9804F1746C08CA18217C32905E462E36CE3BE39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9DE2BCBF6955817183995497CEA956AE515D2261898FA051015728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6BF12FFA06D98A0864D87602733EC86A64521F2B18177B200CBBE117577A615D6C770988C0BAD946E208E24FA074E5AB3143DB5BFCE0FD108E4B82D120A92108011A723C12A787E6D788719A10BDBA5B2699C327186AF4E23C1A946834B6150BDA2583E9CA2AD44CE8DBBBC2DB04DE8EF92E8EFC141FBECAA6287C59474E6BC05D99B2964FA090C3A2233BA186515BE7ED1F612970CEE2D7AFB81BDD762170481CD0069127D5B05AA993B4EA988D8FDDC186FFB7DC90A6C08F4DF435C93402849236C3FAB4D27C7026C1D4DCB2602646DEC9751E763DBA37BDF8FF9406AD9E530EE5DB382F413001AEB06A53ED9027D831179727B0865A8918DA3EDBEBCF9B14ED44CE6CBACED4BB1BDB7F1447E6CC254B332051512BD7AF426FB8F401378CD2BF5983CA01C64B92ECF032EA15D1721D03F482D7CE6E74FEF6D55E702F46980C82B5A84031900B1C9E59E7C97FBEC7E8F323A97A7E36CC88BE0F1D45B7FF585AC54BD407B22B4154AACC8F6D7EBF48E1D814CC5ED20F8037E0A79715EEF29BE32806A1D58BB7C5DA76F550AA3D8A1FBFF0EB19CCB1A313D55CDA56C9EC2EF29632387FE8D76E3C0468043E8F663F4860EE12BF2D5B0B7474D6E694F91E6DBE115974A3926F12FEE5E438777CB6A932DF8CD8BEC4D073B931BA3BC832B68D9DD300741FA7BF8AFC47ED2576F6936BA424663AAB639C5AE4F5683423B4742BF1C978238F16CBE39D652DE3FDB8BEFC848AD922222E04A4037C0713EB57A81A23F0C73473FC646CEA306B4BCBC8862F8385DDFA9D4B7FA2C087E879683303ED5BDD3A062B3CF5B3A278A66D2A13F83F44F82DDF310EE074AB6A364597E899A0255DC164F31CC50846851DF9AB48195DED7EA1B1D510BD7EE74D73FAF36BC31ECFA268359046F4EB879F924009438B481C6CD7889A002ED5EE382BC9190DA6FC026E479558E4475677E9AA9E3050E2765694DFC81F56E880B96E7160C980DD98EDD3DFFFFFFFFFFFFFFFFF,
         "generator": 2
     },
 }

paddle_fl/core/trainer/fl_trainer.py

@@ -19,6 +19,7 @@ import hmac
 import hashlib
 from .diffiehellman.diffiehellman import DiffieHellman
 
+
 class FLTrainerFactory(object):
     def __init__(self):
         pass
@@ -65,9 +66,7 @@ class FLTrainer(object):
 
     def run(self, feed, fetch):
         self._logger.debug("begin to run")
-        self.exe.run(self._main_program,
-                      feed=feed,
-                      fetch_list=fetch)
+        self.exe.run(self._main_program, feed=feed, fetch_list=fetch)
         self._logger.debug("end to run current batch")
         self.cur_step += 1
 
@@ -119,37 +118,34 @@ class FedAvgTrainer(FLTrainer):
     def reset(self):
         self.cur_step = 0
 
-    def run_with_epoch(self,reader,feeder,fetch,num_epoch):
+    def run_with_epoch(self, reader, feeder, fetch, num_epoch):
         self._logger.debug("begin to run recv program")
         self.exe.run(self._recv_program)
         epoch = 0
         for i in range(num_epoch):
-                for data in reader():
-                    self.exe.run(self._main_program,
-                           feed=feeder.feed(data),
-                           fetch_list=fetch)
-                self.cur_step += 1
-                epoch += 1
+            for data in reader():
+                self.exe.run(self._main_program,
+                             feed=feeder.feed(data),
+                             fetch_list=fetch)
+            self.cur_step += 1
+            epoch += 1
         self._logger.debug("begin to run send program")
         self.exe.run(self._send_program)
+
     def run(self, feed, fetch):
-        self._logger.debug("begin to run FedAvgTrainer, cur_step=%d, inner_step=%d" %
-                           (self.cur_step, self._step))
+        self._logger.debug(
+            "begin to run FedAvgTrainer, cur_step=%d, inner_step=%d" %
+            (self.cur_step, self._step))
         if self.cur_step % self._step == 0:
             self._logger.debug("begin to run recv program")
             self.exe.run(self._recv_program)
         self._logger.debug("begin to run current step")
-        loss = self.exe.run(self._main_program,
-                     feed=feed,
-                     fetch_list=fetch)
+        loss = self.exe.run(self._main_program, feed=feed, fetch_list=fetch)
         if self.cur_step % self._step == 0:
             self._logger.debug("begin to run send program")
             self.exe.run(self._send_program)
         self.cur_step += 1
         return loss
-       
- 
-
 
 
 class SecAggTrainer(FLTrainer):
@@ -207,24 +203,24 @@ class SecAggTrainer(FLTrainer):
         self.cur_step = 0
 
     def run(self, feed, fetch):
-        self._logger.debug("begin to run SecAggTrainer, cur_step=%d, inner_step=%d" %
-                           (self.cur_step, self._step))
+        self._logger.debug(
+            "begin to run SecAggTrainer, cur_step=%d, inner_step=%d" %
+            (self.cur_step, self._step))
         if self.cur_step % self._step == 0:
             self._logger.debug("begin to run recv program")
             self.exe.run(self._recv_program)
         scope = fluid.global_scope()
         self._logger.debug("begin to run current step")
-        loss = self.exe.run(self._main_program,
-                     feed=feed,
-                     fetch_list=fetch)
+        loss = self.exe.run(self._main_program, feed=feed, fetch_list=fetch)
         if self.cur_step % self._step == 0:
             self._logger.debug("begin to run send program")
             noise = 0.0
             scale = pow(10.0, 5)
-            digestmod=hashlib.sha256
+            digestmod = hashlib.sha256
             # 1. load priv key and other's pub key
             dh = DiffieHellman(group=15, key_length=256)
-            dh.load_private_key(self._key_dir + str(self._trainer_id) + "_priv_key.txt")
+            dh.load_private_key(self._key_dir + str(self._trainer_id) +
+                                "_priv_key.txt")
             key = str(self._step_id).encode("utf-8")
             for i in range(self._trainer_num):
                 if i != self._trainer_id:
@@ -232,7 +228,8 @@ class SecAggTrainer(FLTrainer):
                     public_key = int(f.read())
                     dh.generate_shared_secret(public_key, echo_return_key=True)
                     msg = dh.shared_key.encode("utf-8")
-                    hex_res1 = hmac.new(key=key, msg=msg, digestmod=digestmod).hexdigest()
+                    hex_res1 = hmac.new(key=key, msg=msg,
+                                        digestmod=digestmod).hexdigest()
                     current_noise = int(hex_res1[0:8], 16) / scale
                     if i > self._trainer_id:
                         noise = noise + current_noise
@@ -241,9 +238,11 @@ class SecAggTrainer(FLTrainer):
 
             scope = fluid.global_scope()
             for param_name in self._param_name_list:
-                fluid.global_scope().var(param_name + str(self._trainer_id)).get_tensor().set(
-                    numpy.array(scope.find_var(param_name + str(self._trainer_id)).get_tensor()) + noise, fluid.CPUPlace())
+                fluid.global_scope().var(param_name + str(
+                    self._trainer_id)).get_tensor().set(
+                        numpy.array(
+                            scope.find_var(param_name + str(self._trainer_id))
+                            .get_tensor()) + noise, fluid.CPUPlace())
             self.exe.run(self._send_program)
         self.cur_step += 1
         return loss
-

paddle_fl/dataset/femnist.py

@@ -5,73 +5,84 @@ import tarfile
 import random
 
 
-def download(url,tar_path):
-	r = requests.get(url)
-	with open(tar_path,'wb') as f:
-		f.write(r.content)
+def download(url, tar_path):
+    r = requests.get(url)
+    with open(tar_path, 'wb') as f:
+        f.write(r.content)
 
-def extract(tar_path,target_path):
-	tar = tarfile.open(tar_path, "r:gz")
-	file_names = tar.getnames()
-	for file_name in file_names:
-		tar.extract(file_name,target_path)
 
-	tar.close()
+def extract(tar_path, target_path):
+    tar = tarfile.open(tar_path, "r:gz")
+    file_names = tar.getnames()
+    for file_name in file_names:
+        tar.extract(file_name, target_path)
 
-def train(trainer_id,inner_step,batch_size,count_by_step):
-        target_path = "trainer%d_data" % trainer_id
-        data_path = target_path + "/femnist_data"
-        tar_path = data_path + ".tar.gz"
-        if not os.path.exists(target_path):
-            os.system("mkdir trainer%d_data" % trainer_id)
-        if not os.path.exists(data_path):
-            print("Preparing data...")
-            if not os.path.exists(tar_path):
-               download("https://paddlefl.bj.bcebos.com/leaf/femnist_data.tar.gz",tar_path)
-               extract(tar_path,target_path)
-        def train_data():
-                train_file = open("./trainer%d_data/femnist_data/train/all_data_%d_niid_0_keep_0_train_9.json" % (trainer_id,trainer_id),'r')
-                json_train = json.load(train_file)
-                users = json_train["users"]
-                rand = random.randrange(0,len(users)) # random choose a user from each trainer
-                cur_user = users[rand]
-                print('training using '+cur_user)
-                train_images = json_train["user_data"][cur_user]['x']
-                train_labels = json_train["user_data"][cur_user]['y']
-                if count_by_step:
-                        for i in range(inner_step*batch_size):
-                                yield train_images[i%(len(train_images))], train_labels[i%(len(train_images))]
-                else:
-                        for i in range(len(train_images)):
-                                yield train_images[i], train_labels[i]
+    tar.close()
 
-                train_file.close()
 
-        return train_data
+def train(trainer_id, inner_step, batch_size, count_by_step):
+    target_path = "trainer%d_data" % trainer_id
+    data_path = target_path + "/femnist_data"
+    tar_path = data_path + ".tar.gz"
+    if not os.path.exists(target_path):
+        os.system("mkdir trainer%d_data" % trainer_id)
+    if not os.path.exists(data_path):
+        print("Preparing data...")
+        if not os.path.exists(tar_path):
+            download("https://paddlefl.bj.bcebos.com/leaf/femnist_data.tar.gz",
+                     tar_path)
+            extract(tar_path, target_path)
 
-def test(trainer_id,inner_step,batch_size,count_by_step):
-        target_path = "trainer%d_data" % trainer_id
-        data_path = target_path + "/femnist_data"
-        tar_path = data_path + ".tar.gz"
-        if not os.path.exists(target_path):
-            os.system("mkdir trainer%d_data" % trainer_id)
-        if not os.path.exists(data_path):
-                print("Preparing data...")
-                if not os.path.exists(tar_path):
-                        download("https://paddlefl.bj.bcebos.com/leaf/femnist_data.tar.gz",tar_path)
-                extract(tar_path,target_path)
-        def test_data():
-                test_file = open("./trainer%d_data/femnist_data/test/all_data_%d_niid_0_keep_0_test_9.json" % (trainer_id,trainer_id), 'r')
-                json_test = json.load(test_file)
-                users = json_test["users"]
-                for user in users:
-                        test_images = json_test['user_data'][user]['x']
-                        test_labels = json_test['user_data'][user]['y']
-                        for i in range(len(test_images)):
-                                yield test_images[i], test_labels[i]
+    def train_data():
+        train_file = open(
+            "./trainer%d_data/femnist_data/train/all_data_%d_niid_0_keep_0_train_9.json"
+            % (trainer_id, trainer_id), 'r')
+        json_train = json.load(train_file)
+        users = json_train["users"]
+        rand = random.randrange(
+            0, len(users))  # random choose a user from each trainer
+        cur_user = users[rand]
+        print('training using ' + cur_user)
+        train_images = json_train["user_data"][cur_user]['x']
+        train_labels = json_train["user_data"][cur_user]['y']
+        if count_by_step:
+            for i in range(inner_step * batch_size):
+                yield train_images[i % (len(train_images))], train_labels[i % (
+                    len(train_images))]
+        else:
+            for i in range(len(train_images)):
+                yield train_images[i], train_labels[i]
 
-                test_file.close()
+        train_file.close()
 
-        return test_data
+    return train_data
 
-	
+
+def test(trainer_id, inner_step, batch_size, count_by_step):
+    target_path = "trainer%d_data" % trainer_id
+    data_path = target_path + "/femnist_data"
+    tar_path = data_path + ".tar.gz"
+    if not os.path.exists(target_path):
+        os.system("mkdir trainer%d_data" % trainer_id)
+    if not os.path.exists(data_path):
+        print("Preparing data...")
+        if not os.path.exists(tar_path):
+            download("https://paddlefl.bj.bcebos.com/leaf/femnist_data.tar.gz",
+                     tar_path)
+        extract(tar_path, target_path)
+
+    def test_data():
+        test_file = open(
+            "./trainer%d_data/femnist_data/test/all_data_%d_niid_0_keep_0_test_9.json"
+            % (trainer_id, trainer_id), 'r')
+        json_test = json.load(test_file)
+        users = json_test["users"]
+        for user in users:
+            test_images = json_test['user_data'][user]['x']
+            test_labels = json_test['user_data'][user]['y']
+            for i in range(len(test_images)):
+                yield test_images[i], test_labels[i]
+
+        test_file.close()
+
+    return test_data

paddle_fl/examples/ctr_demo/fl_master.py

@@ -3,6 +3,7 @@ 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
@@ -12,7 +13,8 @@ class Model(object):
         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.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()
@@ -34,8 +36,8 @@ 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])
+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

paddle_fl/examples/ctr_demo/fl_scheduler.py

@@ -3,7 +3,7 @@ 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 = FLScheduler(worker_num, server_num, port=9091)
 scheduler.set_sample_worker_num(worker_num)
 scheduler.init_env()
 print("init env done.")

paddle_fl/examples/ctr_demo/fl_server.py

@@ -21,8 +21,8 @@ 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
+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._current_ep = "127.0.0.1:8181"  # IP address for server
 server.start()
 print("connect")

paddle_fl/examples/ctr_demo/fl_trainer.py

@@ -4,7 +4,12 @@ import numpy as np
 import sys
 import logging
 import time
-logging.basicConfig(filename="test.log", filemode="w", format="%(asctime)s %(name)s:%(levelname)s:%(message)s", datefmt="%d-%M-%Y %H:%M:%S", level=logging.DEBUG)
+logging.basicConfig(
+    filename="test.log",
+    filemode="w",
+    format="%(asctime)s %(name)s:%(levelname)s:%(message)s",
+    datefmt="%d-%M-%Y %H:%M:%S",
+    level=logging.DEBUG)
 
 
 def reader():
@@ -15,13 +20,14 @@ def reader():
         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
+
+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
+job._scheduler_ep = "127.0.0.1:9091"  # Inform the scheduler IP to trainer
 trainer = FLTrainerFactory().create_fl_trainer(job)
-trainer._current_ep = "127.0.0.1:{}".format(9000+trainer_id)
+trainer._current_ep = "127.0.0.1:{}".format(9000 + trainer_id)
 trainer.start()
 print(trainer._scheduler_ep, trainer._current_ep)
 output_folder = "fl_model"
@@ -37,4 +43,3 @@ while not trainer.stop():
     epoch_id += 1
     if epoch_id % 5 == 0:
         trainer.save_inference_program(output_folder)
-    

paddle_fl/version.py

@@ -14,4 +14,3 @@
 """ PaddleFL version string """
 fl_version = "0.1.11"
 module_proto_version = "0.1.11"
-