update

paddle_fl/core/trainer/fl_trainer.py

@@ -13,13 +13,10 @@
 # limitations under the License.
 import paddle.fluid as fluid
 import logging
-<<<<<<< HEAD
 from paddle_fl.core.scheduler.agent_master import FLWorkerAgent
-=======
 import numpy
 import hmac
 from .diffiehellman.diffiehellman import DiffieHellman
->>>>>>> 3145ed186544ac195bb957c22a638461d8e480bd
 
 class FLTrainerFactory(object):
     def __init__(self):
@@ -150,11 +147,6 @@ class FedAvgTrainer(FLTrainer):
             self.exe.run(self._send_program)
         self.cur_step += 1
         return loss
-<<<<<<< HEAD
-=======
-
-    def stop(self):
-        return False
        
  
 class SecAggTrainer(FLTrainer):
@@ -205,8 +197,6 @@ class SecAggTrainer(FLTrainer):
         self._recv_program = job._trainer_recv_program
         self_step = job._strategy._inner_step
         self._param_name_list = job._strategy._param_name_list
->>>>>>> 3145ed186544ac195bb957c22a638461d8e480bd
-        
 
     def reset(self):
         self.cur_step = 0

paddle_fl/examples/ctr_demo/fl_master.py

@@ -47,5 +47,5 @@ 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=5, output=output)
+    strategy, server_endpoints=endpoints, worker_num=2, output=output)
 # fl_job_config will  be dispatched to workers

paddle_fl/examples/ctr_demo/fl_scheduler.py

 from paddle_fl.core.scheduler.agent_master import FLScheduler
 
-worker_num = 5
+worker_num = 2
 server_num = 1
 scheduler = FLScheduler(worker_num,server_num)
-scheduler.set_sample_worker_num(5)
+scheduler.set_sample_worker_num(worker_num)
 scheduler.init_env()
 print("init env done.")
 scheduler.start_fl_training()

paddle_fl/examples/ctr_demo/fl_trainer.py

@@ -23,7 +23,6 @@ job._scheduler_ep = "127.0.0.1:9091"
 trainer = FLTrainerFactory().create_fl_trainer(job)
 trainer._current_ep = "127.0.0.1:{}".format(9000+trainer_id)
 trainer.start()
-
 print(trainer._scheduler_ep, trainer._current_ep)
 output_folder = "fl_model"
 step_i = 0
@@ -38,3 +37,4 @@ while not trainer.stop():
     step_i += 1
     if step_i % 100 == 0:
         trainer.save_inference_program(output_folder)
+    

paddle_fl/examples/ctr_demo/run.sh

@@ -6,7 +6,7 @@ python -u fl_scheduler.py > scheduler.log &
 sleep 5
 python -u fl_server.py >server0.log &
 sleep 2
-for ((i=0;i<5;i++))
+for ((i=0;i<2;i++))
 do 
     python -u fl_trainer.py $i >trainer$i.log &
     sleep 2

paddle_fl/examples/femnist_demo/fl_master.py

@@ -13,6 +13,7 @@ class Model(object):
         self.label = fluid.layers.data(name='label', shape=[1],dtype='int64')
         self.conv_pool_1 = fluid.nets.simple_img_conv_pool(input=self.inputs,num_filters=20,filter_size=5,pool_size=2,pool_stride=2,act='relu')
         self.conv_pool_2 = fluid.nets.simple_img_conv_pool(input=self.conv_pool_1,num_filters=50,filter_size=5,pool_size=2,pool_stride=2,act='relu')
+
         self.predict = self.predict = fluid.layers.fc(input=self.conv_pool_2, size=62, act='softmax')
         self.cost = fluid.layers.cross_entropy(input=self.predict, label=self.label)
         self.accuracy = fluid.layers.accuracy(input=self.predict, label=self.label)

paddle_fl/examples/femnist_demo/fl_trainer.py

@@ -15,6 +15,7 @@ job_path = "fl_job_config"
 job = FLRunTimeJob()
 job.load_trainer_job(job_path, trainer_id)
 job._scheduler_ep = "127.0.0.1:9091"
+print(job._target_names)
 trainer = FLTrainerFactory().create_fl_trainer(job)
 trainer._current_ep = "127.0.0.1:{}".format(9000+trainer_id)
 trainer.start()