BATCH_SIZE = 64
train_reader = paddle.batch(paddle.reader.shuffle(paddle.dataset.mnist.train(),
buf_size=500),
batch_size=BATCH_SIZE)
test_reader = paddle.batch(paddle.dataset.mnist.test(), batch_size=BATCH_SIZE)
trainer_num = 2
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
trainer = FLTrainerFactory().create_fl_trainer(job)
trainer.trainer_id = trainer_id
trainer._current_ep = "127.0.0.1:{}".format(9000 + trainer_id)
trainer.trainer_num = trainer_num
trainer.key_dir = "./keys/"
trainer.start()
output_folder = "fl_model"
epoch_id = 0
step_i = 0
inputs = fluid.layers.data(name='x', shape=[1, 28, 28], dtype='float32')
label = fluid.layers.data(name='y', shape=[1], dtype='int64')
feeder = fluid.DataFeeder(feed_list=[inputs, label], place=fluid.CPUPlace())
# for test
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
trainer.start()
print(trainer._scheduler_ep, trainer._current_ep)
output_folder = "fl_model"
epoch_id = 0
while not trainer.stop():
print("batch %d start train" % (step_i))
step_i = 0
for data in reader():
trainer.run(feed=data, fetch=[])
step_i += 1
if train_step == trainer._step:
break
epoch_id += 1
if epoch_id % 5 == 0:
import math
import random
import json
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"
trainer = FLTrainerFactory().create_fl_trainer(job)
trainer._current_ep = "127.0.0.1:{}".format(9000 + trainer_id)
trainer.start()
print(trainer._step)
test_program = trainer._main_program.clone(for_test=True)
def data_generater(trainer_id, inner_step, batch_size, count_by_step):
train_file = open(
"./femnist_data/train/all_data_%d_niid_0_keep_0_train_9.json" %
trainer_id, 'r')
test_file = open(
"./femnist_data/test/all_data_%d_niid_0_keep_0_test_9.json" %
trainer_id, 'r')
json_train = json.load(train_file)
json_test = json.load(test_file)
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
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"
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:
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']
trainer.start()
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
import paddle
import paddle.fluid as fluid
import logging
import math
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)
trainer = FLTrainerFactory().create_fl_trainer(job)
trainer.start()
test_program = trainer._main_program.clone(for_test=True)
alldata = pd.read_csv('alldata.csv')
print(len(alldata))
# In[3]:
label = alldata['Label'].to_frame()
alldata = alldata.drop('Company', 1)
alldata = alldata.drop('Label', 1)
# In[4]:
from paddle_fl.core.master.fl_job import FLRunTimeJob
from paddle_fl.reader.gru4rec_reader import Gru4rec_Reader
import paddle.fluid as fluid
import numpy as np
import sys
import os
import logging
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)
trainer = FLTrainerFactory().create_fl_trainer(job)
trainer.start()
r = Gru4rec_Reader()
train_reader = r.reader(train_file_dir, place, batch_size = 125)
output_folder = "model_node4"
step_i = 0
while not trainer.stop():
step_i += 1
print("batch %d start train" % (step_i))
for data in train_reader():
#print(np.array(data['src_wordseq']))
ret_avg_cost = trainer.run(feed=data,
fetch=["mean_0.tmp_0"])
avg_ppl = np.exp(ret_avg_cost[0])
from paddle_fl.reader.gru4rec_reader import Gru4rec_Reader
import paddle.fluid as fluid
import numpy as np
import sys
import os
import logging
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"
trainer = FLTrainerFactory().create_fl_trainer(job)
trainer._current_ep = "127.0.0.1:{}".format(9000+trainer_id)
trainer.start()
r = Gru4rec_Reader()
train_reader = r.reader(train_file_dir, place, batch_size = 125)
output_folder = "model_node4"
step_i = 0
while not trainer.stop():
step_i += 1
print("batch %d start train" % (step_i))
train_step = 0
for data in train_reader():
#print(np.array(data['src_wordseq']))
ret_avg_cost = trainer.run(feed=data,
