warnings.simplefilter("ignore") import tensorflow as tf import horovod.tensorflow as hvd from utils import hvd_utils from runtime import Runner from utils.cmdline_helper import parse_cmdline if __name__ == "__main__": tf.logging.set_verbosity(tf.logging.ERROR) FLAGS = parse_cmdline() RUNNING_CONFIG = tf.contrib.training.HParams( mode=FLAGS.mode, # ======= Directory HParams ======= # log_dir=FLAGS.results_dir, model_dir=FLAGS.results_dir, summaries_dir=FLAGS.results_dir, data_dir=FLAGS.data_dir, data_idx_dir=FLAGS.data_idx_dir, # ========= Model HParams ========= # n_classes=1001, input_format='NHWC', compute_format=FLAGS.data_format,
import tensorflow as tf import horovod.tensorflow as hvd import dllogger from utils import hvd_utils from runtime import Runner from model.resnet import model_architectures from utils.cmdline_helper import parse_cmdline if __name__ == "__main__": tf.logging.set_verbosity(tf.logging.ERROR) FLAGS = parse_cmdline(model_architectures.keys()) hvd.init() if hvd.rank() == 0: log_path = os.path.join(FLAGS.results_dir, FLAGS.log_filename) os.makedirs(FLAGS.results_dir, exist_ok=True) dllogger.init(backends=[ dllogger.JSONStreamBackend(verbosity=dllogger.Verbosity.VERBOSE, filename=log_path), dllogger.StdOutBackend(verbosity=dllogger.Verbosity.VERBOSE) ]) else: dllogger.init(backends=[]) dllogger.log(data=vars(FLAGS), step='PARAMETER')