level=logging.DEBUG)
logger = logging.getLogger("FLTrainer")
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())
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
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