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)