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')
