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 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)
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():
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"
job = FLRunTimeJob()
job.load_trainer_job(job_path, int(trainer_id))
job._scheduler_ep = scheduler_conf["ENDPOINT"]
trainer = FLTrainerFactory().create_fl_trainer(job)
trainer._current_ep = endpoint
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("epoch %d start train" % (epoch_id))
step_i = 0
for data in reader():
trainer.run(feed=data, fetch=[])
step_i += 1
if step_i == trainer._step: