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']))
ret_avg_cost = trainer.run(feed=data, fetch=["mean_0.tmp_0"])
train_step += 1
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
# print(job._trainer_send_program)
trainer = FLTrainerFactory().create_fl_trainer(job)
use_cuda = False
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
trainer._current_ep = "127.0.0.1:8192"
trainer.start(place=place)
trainer._logger.setLevel(logging.DEBUG)
g = reader()
if trainer_id > 0:
for i in range(trainer_id):
next(g)
data = next(g)
print(data)
output_folder = "fl_model"
step_i = 0
while not trainer.stop():
step_i += 1
print("batch %d start train" % step_i)
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"])
val_reader = paddle.batch(reader=dataset.val_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 = 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:
break
epoch_id += 1
if epoch_id % 5 == 0:
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
if train_step == trainer._step:
break
epoch_id += 1
if epoch_id % 5 == 0:
