make examples more easy to follow

paddle_fl/core/scheduler/agent_master.py

@@ -140,4 +140,3 @@ class FLScheduler(object):
                 if len(finish_training_dict) == len(worker_dict):
                     all_finish_training = True
             time.sleep(5)
-            loop += 1

paddle_fl/examples/ctr_demo/fl_scheduler.py

@@ -2,7 +2,8 @@ from paddle_fl.core.scheduler.agent_master import FLScheduler
 
 worker_num = 2
 server_num = 1
-scheduler = FLScheduler(worker_num,server_num)
+# 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.")

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"
+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"
+server._current_ep = "127.0.0.1:8181" # IP address for server
 server.start()
 print("connect")

paddle_fl/examples/ctr_demo/fl_trainer.py

@@ -19,22 +19,22 @@ 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"
+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.start()
 print(trainer._scheduler_ep, trainer._current_ep)
 output_folder = "fl_model"
-step_i = 0
+epoch_id = 0
 while not trainer.stop():
-    print("batch %d start train" % (step_i))
+    print("batch %d start train" % (epoch_id))
     train_step = 0
     for data in reader():
         trainer.run(feed=data, fetch=[])
         train_step += 1
         if train_step == trainer._step:
             break
-    step_i += 1
-    if step_i % 100 == 0:
+    epoch_id += 1
+    if epoch_id % 5 == 0:
         trainer.save_inference_program(output_folder)
     

paddle_fl/examples/dpsgd_demo/fl_scheduler.py

@@ -2,7 +2,8 @@ from paddle_fl.core.scheduler.agent_master import FLScheduler
 
 worker_num = 4
 server_num = 1
-scheduler = FLScheduler(worker_num,server_num)
+#Define number of worker/server and the port for scheduler
+scheduler = FLScheduler(worker_num,server_num,port=9091) 
 scheduler.set_sample_worker_num(4)
 scheduler.init_env()
 print("init env done.")

paddle_fl/examples/dpsgd_demo/fl_server.py

@@ -21,7 +21,7 @@ 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"
+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"
+server._current_ep = "127.0.0.1:8181" # IP address for server
 server.start()

paddle_fl/examples/dpsgd_demo/fl_trainer.py

@@ -13,7 +13,7 @@ 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"
+job._scheduler_ep = "127.0.0.1:9091" # Inform scheduler IP address to trainer
 trainer = FLTrainerFactory().create_fl_trainer(job)
 trainer._current_ep = "127.0.0.1:{}".format(9000+trainer_id)
 trainer.start()

paddle_fl/examples/dpsgd_demo/run.sh

+unset http_proxy
+unset https_proxy
 python fl_master.py
 sleep 2
 python -u fl_scheduler.py >scheduler.log &

paddle_fl/examples/femnist_demo/fl_scheduler.py

@@ -2,7 +2,8 @@ from paddle_fl.core.scheduler.agent_master import FLScheduler
 
 worker_num = 4
 server_num = 1
-scheduler = FLScheduler(worker_num,server_num)
+# Define the number of worker/server and the port for scheduler
+scheduler = FLScheduler(worker_num,server_num,port=9091)
 scheduler.set_sample_worker_num(4)
 scheduler.init_env()
 print("init env done.")

paddle_fl/examples/femnist_demo/fl_server.py

@@ -7,7 +7,7 @@ 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"
+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"
+server._current_ep = "127.0.0.1:8181" # IP address for server
 server.start()

paddle_fl/examples/femnist_demo/fl_trainer.py

@@ -14,7 +14,7 @@ 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"
+job._scheduler_ep = "127.0.0.1:9091" # Inform the scheduler IP to trainer
 print(job._target_names)
 trainer = FLTrainerFactory().create_fl_trainer(job)
 trainer._current_ep = "127.0.0.1:{}".format(9000+trainer_id)
@@ -40,7 +40,7 @@ def train_test(train_test_program, train_test_feed, train_test_reader):
 epoch_id = 0
 step = 0
 epoch = 3000
-count_by_step = True
+count_by_step = False
 if count_by_step:
 	output_folder = "model_node%d" % trainer_id
 else: 

paddle_fl/examples/femnist_demo/run.sh

+unset http_proxy
+unset https_proxy
 #killall python
 python fl_master.py
 sleep 2

paddle_fl/examples/gru4rec_demo/fl_scheduler.py

@@ -2,7 +2,8 @@ from paddle_fl.core.scheduler.agent_master import FLScheduler
 
 worker_num = 4
 server_num = 1
-scheduler = FLScheduler(worker_num,server_num)
+# Define the number of worker/server and the port for scheduler
+scheduler = FLScheduler(worker_num,server_num,port=9091)
 scheduler.set_sample_worker_num(4)
 scheduler.init_env()
 print("init env done.")

paddle_fl/examples/gru4rec_demo/fl_server.py

@@ -21,7 +21,7 @@ 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"
+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"
+server._current_ep = "127.0.0.1:8181" # IP address for server
 server.start()

paddle_fl/examples/gru4rec_demo/fl_trainer.py

@@ -14,7 +14,7 @@ train_file_dir = "mid_data/node4/%d/" % trainer_id
 job_path = "fl_job_config"
 job = FLRunTimeJob()
 job.load_trainer_job(job_path, trainer_id)
-job._scheduler_ep = "127.0.0.1:9091"
+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.start()

paddle_fl/examples/secagg_demo/fl_scheduler.py

@@ -2,7 +2,8 @@ from paddle_fl.core.scheduler.agent_master import FLScheduler
 
 worker_num = 2
 server_num = 1
-scheduler = FLScheduler(worker_num,server_num)
+# 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.")

paddle_fl/examples/secagg_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"
+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"
+server._current_ep = "127.0.0.1:8181" # IP address for server
 server.start()
 print("connect")

paddle_fl/examples/secagg_demo/fl_trainer.py

@@ -28,7 +28,7 @@ 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"
+job._scheduler_ep = "127.0.0.1:9091" # Inform the scheduler IP to trainer
 trainer = FLTrainerFactory().create_fl_trainer(job)
 trainer.trainer_id = trainer_id
 trainer._current_ep = "127.0.0.1:{}".format(9000+trainer_id)
@@ -75,6 +75,7 @@ while not trainer.stop():
         if step_i % 100 == 0:
             print("Epoch: {0}, step: {1}, accuracy: {2}".format(epoch_id, step_i, accuracy[0]))
 
+    print(step_i)
     avg_loss_val, acc_val = train_test(train_test_program=test_program,
                                        train_test_reader=test_reader,
                                        train_test_feed=feeder)
@@ -82,5 +83,5 @@ while not trainer.stop():
 
     if epoch_id > 40:
         break
-    if step_i % 100 == 0:
+    if epoch_id % 5 == 0:
         trainer.save_inference_program(output_folder)

paddle_fl/examples/submitter_demo/train_program.py

@@ -89,15 +89,15 @@ else:
     trainer.start()
     print(trainer._scheduler_ep, trainer._current_ep)
     output_folder = "fl_model"
-    step_i = 0
+    epoch_id = 0
     while not trainer.stop():
         print("batch %d start train" % (step_i))
-        train_step = 0
+        step_i = 0
         for data in reader():
             trainer.run(feed=data, fetch=[])
-            train_step += 1
+            step_i += 1
             if train_step == trainer._step:
                 break
-        step_i += 1
-        if step_i % 100 == 0:
+        epoch_id += 1
+        if epoch_id % 5 == 0:
             trainer.save_inference_program(output_folder)