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) 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) trainer.start() 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) input = fluid.layers.data(name='input', shape=[1, 28, 28], dtype='float32') label = fluid.layers.data(name='label', shape=[1], dtype='int64') feeder = fluid.DataFeeder(feed_list=[input, label], place=fluid.CPUPlace()) def train_test(train_test_program, train_test_feed, train_test_reader): acc_set = [] for test_data in train_test_reader():
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 if train_step == trainer._step: break
batch_transforms=batch_trans, batch_size=1, shuffle=True, drop_empty=True, inputs_def=inputs_def)() return data_loader 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) trainer.start(fluid.CUDAPlace(trainer_id)) test_program = trainer._main_program.clone(for_test=True) image = fluid.layers.data(name='image', shape=[3, None, None], dtype='float32', lod_level=0) im_info = fluid.layers.data(name='im_info', shape=[None, 3], dtype='float32', lod_level=0) im_id = fluid.layers.data(name='im_id', shape=[None, 1], dtype='int64', lod_level=0)