def connect_server(self):
from paddle import fluid
from paddle_fl.paddle_fl.core.trainer.fl_trainer import FLTrainerFactory
from paddle_fl.paddle_fl.core.master.fl_job import FLRunTimeJob
import numpy as np
import sys
import logging
self.processLabel.setText('connecting')
trainer_id = self.id
job_path = self.config['path']['job_path']
job = FLRunTimeJob()
job.load_trainer_job(job_path, trainer_id)
job._scheduler_ep = '{}:{}'.format(self.config['scheduler']['ip'],
self.config['scheduler']['port'])
# print(job._trainer_send_program)
self.trainer = MFLTrainerFactory().create_fl_trainer(job)
use_cuda = False
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
self.trainer._current_ep = '{}:{}'.format(
self.config['client']['ip'],
int(self.config['client']['port']) + self.id)
print('prepared ok')
self.trainer.start(place=place)
self.trainer._logger.setLevel(logging.DEBUG)
print('connected ok')
self.processLabel.setText('connected')
self.trainThread = threading.Thread(target=self.train)
self.processLabel.setText('training')
self.trainThread.start()
self.trainThread.join()
self.processLabel.setText('finished')
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)
trainer_id = int(sys.argv[1]) # trainer id for each guest
place = fluid.CPUPlace()
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" # Inform the scheduler IP to trainer
trainer = FLTrainerFactory().create_fl_trainer(job)
trainer._current_ep = "127.0.0.1:{}".format(9000 + trainer_id)
place = fluid.CPUPlace()
trainer.start(place)
r = Gru4rec_Reader()
train_reader = r.reader(train_file_dir, place, batch_size=125)
output_folder = "model_node4"
epoch_i = 0
while not trainer.stop():
epoch_i += 1
train_step = 0
for data in train_reader():
#print(np.array(data['src_wordseq']))
import paddle.fluid as fluid
import logging
import math
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)
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 scheduler IP address to trainer
trainer = FLTrainerFactory().create_fl_trainer(job)
trainer._current_ep = "127.0.0.1:{}".format(9000 + trainer_id)
place = fluid.CPUPlace()
trainer.start(place)
test_program = trainer._main_program.clone(for_test=True)
train_reader = paddle.batch(paddle.reader.shuffle(paddle.dataset.mnist.train(),
buf_size=500),
batch_size=64)
test_reader = paddle.batch(paddle.dataset.mnist.test(), batch_size=64)
img = fluid.layers.data(name='img', shape=[1, 28, 28], dtype='float32')
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
feeder = fluid.DataFeeder(feed_list=[img, label], place=fluid.CPUPlace())
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. 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" job = FLRunTimeJob() job.load_server_job(job_path, server_id) 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.start()
os.system("ls")
os.system("gzip -d {}.tar.gz".format(message))
print("gzip finish")
os.system("tar -xf {}.tar".format(message))
os.system("ls")
zmq_socket.close()
print("close socket")
#program start
if 'server' in message:
server = FLServer()
server_id = 0
job_path = "job_config"
job = FLRunTimeJob()
job.load_server_job(job_path, server_id)
job._scheduler_ep = scheduler_conf["ENDPOINT"]
server.set_server_job(job)
server._current_ep = endpoint
server.start()
else:
def reader():
for i in range(1000):
data_dict = {}
for i in range(3):
data_dict[str(i)] = np.random.rand(1, 5).astype('float32')
data_dict["label"] = np.random.randint(2, size=(1, 1)).astype('int64')
yield data_dict
trainer_id = message.split("trainer")[1]
job_path = "job_config"
#########
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)
# Load configs
####################
trainer_id = int(args.id) # trainer id
job_path = params["federated"]["job_path"]
job = FLRunTimeJob()
job.load_trainer_job(job_path, trainer_id)
job._scheduler_ep = "127.0.0.1:"+ str(params["federated"]["scheduler_port"]) # Inform scheduler IP address to trainer
trainer = FLTrainerFactory().create_fl_trainer(job)
trainer._current_ep = "127.0.0.1:{}".format(params["federated"]["seed_of_clients_port"] + trainer_id)
place = paddle.fluid.CPUPlace()
trainer.start(place)
test_program = trainer._main_program.clone(for_test = True)
# Load data
###############
# dataset = Time_series_loader(distributed = params["federated"]["distributed"], ts_path = params["federated"]["clients_path"], number_of_clients = params["federated"]["number_of_clients"], lookback = params["federated"]["lookback"], lookforward = params["federated"]["lookforward"])
dataset = select_data(params)
train_reader = paddle.batch(reader = dataset.train_data(client = trainer_id),
batch_size = params["federated"]["batch_size"])
def reader():
for i in range(1000):
data_dict = {}
for i in range(3):
data_dict[str(i)] = np.random.rand(1, 5).astype('float32')
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
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 = os.environ['FL_SCHEDULER_SERVICE_HOST'] + ":" + os.environ[
'FL_SCHEDULER_SERVICE_PORT_FL_SCHEDULER']
trainer = FLTrainerFactory().create_fl_trainer(job)
#trainer._current_ep = "127.0.0.1:{}".format(9000+trainer_id)
trainer._current_ep = os.environ['TRAINER0_SERVICE_HOST'] + ":" + os.environ[
'TRAINER0_SERVICE_PORT_TRAINER0']
place = fluid.CPUPlace()
trainer.start(place)
print(trainer._scheduler_ep, trainer._current_ep)
output_folder = "fl_model"
epoch_id = 0
while not trainer.stop():
print("batch %d start train" % (epoch_id))
train_step = 0
for data in reader():
trainer.run(feed=data, fetch=[])
train_step += 1
from paddle_fl.paddle_fl.core.server.fl_server import FLServer
from paddle_fl.paddle_fl.core.master.fl_job import FLRunTimeJob
import json
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--config_path', help="path to the config file")
args = parser.parse_args()
with open(args.config_path, 'r') as fp:
params = json.load(fp)
server = FLServer()
server_id = 0
job_path = params["federated"]["job_path"]
print("job_path: ", job_path)
job = FLRunTimeJob()
job.load_server_job(job_path, server_id)
job._scheduler_ep = "127.0.0.1:" + str(params["federated"]["scheduler_port"])
print("IP address for scheduler: ", job._scheduler_ep)
server.set_server_job(job)
server._current_ep = "127.0.0.1:" + str(params["federated"]["server_port"])
print("IP address for server: ", server._current_ep)
server.start()