update document

python/paddle_fl/paddle_fl/examples/submitter_demo/README.md

@@ -5,13 +5,14 @@ This document introduces how to submit an FL job to mpi cluster
 ### Dependency
 
 - paddlepaddle>=1.8
+- paddle_fl==0.2.0
 
 ### How to install PaddleFL
 
 Please use pip which has paddlepaddle installed
 
 ```sh
-pip install paddle_fl
+pip install paddle_fl==0.2.0
 ```
 
 ### How it works 
@@ -27,7 +28,7 @@ The information of the cluster is defined in config.txt and will be transmitted
 
 The train_program.py is the executed program in cluster.
 ```sh
-#use the python prepared above to submit job
+#use the python prepared above to generate fl job and submit the job to mpi cluster
 python/bin/python client.py config.txt
 ```
 

python/paddle_fl/paddle_fl/examples/submitter_demo/client.py

@@ -18,16 +18,16 @@ import random
 import zmq
 import time
 import sys
-from paddle_fl.paddle_fl.core.submitter.client_base import HPCClient
-from paddle_fl.paddle_fl.core.scheduler.agent_master import FLScheduler
+from paddle_fl.core.submitter.client_base import HPCClient
+from paddle_fl.core.scheduler.agent_master import FLScheduler
 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
+from paddle_fl.core.master.job_generator import JobGenerator
+from paddle_fl.core.strategy.fl_strategy_base import FLStrategyFactory
 from model import Model
 import tarfile
 
 #random_port = random.randint(60001, 64001)
-random_port = 60001
+random_port = 64001
 print(random_port)
 current_ip = socket.gethostbyname(socket.gethostname())
 endpoints = "{}:{}".format(current_ip, random_port)
@@ -51,8 +51,8 @@ default_dict = {
     "ugi": "",
     "worker_nodes": 5,
     "server_nodes": 1,
-    "hadoop_home": "",
-    "hpc_home": "",
+    "hadoop_home": "/path/to/hadoop",
+    "hpc_home": "/path/to/hpc",
     "package_path": "./package",
     "priority": "high",
     "queue": "",

python/paddle_fl/paddle_fl/examples/submitter_demo/train_program.py

@@ -17,17 +17,17 @@ import random
 import zmq
 import os
 import tarfile
-import paddle_fl.paddle_fl as fl
+import 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
-from paddle_fl.paddle_fl.core.trainer.fl_trainer import FLTrainerFactory
+from paddle_fl.core.server.fl_server import FLServer
+from paddle_fl.core.master.fl_job import FLRunTimeJob
+from paddle_fl.core.trainer.fl_trainer import FLTrainerFactory
 import numpy as np
 import sys
 import logging
 import time
 
-random_port = 60001
+random_port = 64001
 scheduler_conf = {}
 
 #connect to scheduler and get the role and id of the endpoint
@@ -99,8 +99,7 @@ else:
     job._scheduler_ep = scheduler_conf["ENDPOINT"]
     trainer = FLTrainerFactory().create_fl_trainer(job)
     trainer._current_ep = endpoint
-    place = fluid.CPUPlace()
-    trainer.start(place)
+    trainer.start()
     print(trainer._scheduler_ep, trainer._current_ep)
     output_folder = "fl_model"
     epoch_id = 0