def create_train_runner(hparams, num_workers):
params = {}
steps_per_epoch = int(hparams.num_examples_per_epoch / hparams.batch_size)
return low_level_runner.TrainLowLevelRunner(iterations=steps_per_epoch,
hparams=hparams,
per_host_v1=True)
input_fn = DistributedPipeline(hparams, num_workers)
runner.initialize(input_fn, params)
mlperf_log.gnmt_print(key=mlperf_log.RUN_START)
runner.build_model(model_fn, params)
return runner
def create_train_runner(hparams):
hparams.tgt_sos_id, hparams.tgt_eos_id = 1, 2
steps_per_epoch = int(hparams.num_examples_per_epoch / hparams.batch_size)
return low_level_runner.TrainLowLevelRunner(iterations=steps_per_epoch,
hparams=hparams)
def create_train_runner(hparams, num_workers):
params = {}
steps_per_epoch = int(hparams.num_examples_per_epoch / hparams.batch_size)
return low_level_runner.TrainLowLevelRunner(iterations=steps_per_epoch,
hparams=hparams,
per_host_v1=True)