def main(unused_argv):
"""Freeze a model to a GraphDef proto."""
if FLAGS.use_tpu:
dual_net.freeze_graph_tpu(FLAGS.model_path)
else:
dual_net.freeze_graph(FLAGS.model_path, FLAGS.use_trt,
FLAGS.trt_max_batch_size, FLAGS.trt_precision)
def main(unused_argv):
"""Freeze a model to a GraphDef proto."""
if FLAGS.use_tpu:
dual_net.freeze_graph_tpu(FLAGS.model_path)
elif FLAGS.trt_batch > 0:
dual_net.freeze_graph(FLAGS.model_path, True, FLAGS.trt_batch)
else:
dual_net.freeze_graph(FLAGS.model_path)
def main(argv):
"""Train on examples and export the updated model weights."""
tf_records = argv[1:]
logging.info("Training on %s records: %s to %s",
len(tf_records), tf_records[0], tf_records[-1])
with utils.logged_timer("Training"):
train(*tf_records)
if FLAGS.export_path:
dual_net.export_model(FLAGS.export_path)
if FLAGS.freeze:
if FLAGS.use_tpu:
dual_net.freeze_graph_tpu(FLAGS.export_path)
else:
dual_net.freeze_graph(FLAGS.export_path)
def main(unused_argv):
"""Freeze a model to a GraphDef proto."""
# Use last GPU for freeze
os.environ["CUDA_VISIBLE_DEVICES"] = "7"
if FLAGS.use_tpu:
dual_net.freeze_graph_tpu(FLAGS.model_path)
elif FLAGS.trt_batch > 0:
dual_net.freeze_graph(FLAGS.model_path, True, FLAGS.trt_batch)
else:
dual_net.freeze_graph(FLAGS.model_path)
icomm = MPI.Comm.Get_parent()
if icomm != MPI.COMM_NULL:
icomm.barrier()
icomm.Disconnect()
def main(argv):
"""Train on examples and export the updated model weights."""
tf_records = argv[1:]
logging.info("Training on %s records: %s to %s", len(tf_records),
tf_records[0], tf_records[-1])
with utils.logged_timer("Training"):
estimator = train(*tf_records)
if FLAGS.export_path:
dual_net.export_model(FLAGS.export_path)
estimator.export_saved_model(FLAGS.export_path,
serving_input_receiver_fn())
else:
estimator.export_saved_model('saved_model',
serving_input_receiver_fn())
if FLAGS.freeze:
if FLAGS.use_tpu:
dual_net.freeze_graph_tpu(FLAGS.export_path)
else:
dual_net.freeze_graph(FLAGS.export_path)
def main(argv):
"""Train on examples and export the updated model weights."""
if FLAGS.dist_train:
hvd.init()
mll.global_batch_size(FLAGS.train_batch_size)
mll.lr_rates(FLAGS.lr_rates)
mll.lr_boundaries(FLAGS.lr_boundaries)
tf_records = argv[1:]
logging.info("Training on %s records: %s to %s", len(tf_records),
tf_records[0], tf_records[-1])
with utils.logged_timer("Training"):
train(*tf_records)
if (not FLAGS.dist_train) or hvd.rank() == 0:
if FLAGS.export_path:
dual_net.export_model(FLAGS.export_path)
if FLAGS.freeze:
if FLAGS.use_tpu:
dual_net.freeze_graph_tpu(FLAGS.export_path)
else:
dual_net.freeze_graph(FLAGS.export_path)
