def main(_argv):
model_configs = maybe_load_yaml(DEFAULT_EVAL_CONFIGS)
# load flags from config file
model_configs = load_from_config_path(FLAGS.config_paths, model_configs)
# replace parameters in configs_file with tf FLAGS
model_configs = update_eval_model_configs(model_configs, FLAGS)
model_configs = deep_merge_dict(model_configs, ModelConfigs.load(FLAGS.model_dir))
model_configs = update_eval_model_configs(model_configs, FLAGS)
runner = EvalExperiment(model_configs=model_configs)
runner.run()
def main(_argv):
model_configs = maybe_load_yaml(DEFAULT_EVAL_CONFIGS)
# load flags from config file
model_configs = load_from_config_path(FLAGS.config_paths, model_configs)
# replace parameters in configs_file with tf FLAGS
model_configs = update_eval_model_configs(model_configs, FLAGS)
model_configs = deep_merge_dict(model_configs,
ModelConfigs.load(FLAGS.model_dir))
model_configs = update_eval_model_configs(model_configs, FLAGS)
runner = EvalExperiment(model_configs=model_configs)
runner.run()
def main(_argv):
# load flags from config file
model_configs = load_from_config_path(FLAGS.config_paths)
# replace parameters in configs_file with tf FLAGS
model_configs = update_configs_from_flags(model_configs, FLAGS,
EVAL_ARGS.keys())
model_configs = deep_merge_dict(model_configs,
ModelConfigs.load(FLAGS.model_dir))
model_configs = update_configs_from_flags(model_configs, FLAGS,
EVAL_ARGS.keys())
runner = EvalExperiment(model_configs=model_configs)
runner.run()
def main(_argv):
model_configs = maybe_load_yaml(DEFAULT_INFER_CONFIGS)
# load flags from config file
model_configs = load_from_config_path(FLAGS.config_paths, model_configs)
# replace parameters in configs_file with tf FLAGS
model_configs = update_infer_model_configs(model_configs, FLAGS)
model_dirs = FLAGS.model_dir.strip().split(",")
if len(model_dirs) == 1:
model_configs = deep_merge_dict(model_configs, ModelConfigs.load(model_dirs[0]))
model_configs = update_infer_model_configs(model_configs, FLAGS)
runner = InferExperiment(model_configs=model_configs)
else:
runner = EnsembleExperiment(model_configs=model_configs, model_dirs=model_dirs,
weight_scheme=FLAGS.weight_scheme)
runner.run()
def main(_argv):
# load flags from config file
model_configs = load_from_config_path(FLAGS.config_paths)
# replace parameters in configs_file with tf FLAGS
model_configs = update_configs_from_flags(model_configs, FLAGS,
INFER_ARGS.keys())
model_dirs = FLAGS.model_dir.strip().split(",")
ip, port = FLAGS.server_address.split(":")
addr = (ip, int(port))
server = TranslateServer(addr, TranslateRequestHandler)
server.init_experiment(model_configs=model_configs,
model_dirs=model_dirs,
weight_scheme=FLAGS.weight_scheme)
server.serve_forever()
def main(_argv):
model_configs = maybe_load_yaml(DEFAULT_INFER_CONFIGS)
# load flags from config file
model_configs = load_from_config_path(FLAGS.config_paths, model_configs)
# replace parameters in configs_file with tf FLAGS
model_configs = update_infer_model_configs(model_configs, FLAGS)
model_dirs = FLAGS.model_dir.strip().split(",")
if len(model_dirs) == 1:
model_configs = deep_merge_dict(model_configs,
ModelConfigs.load(model_dirs[0]))
runner = InferExperiment(model_configs=model_configs)
else:
runner = EnsembleExperiment(model_configs=model_configs,
model_dirs=model_dirs,
weight_scheme=FLAGS.weight_scheme)
runner.run()
def main(_argv):
# load flags from config file
model_configs = load_from_config_path(FLAGS.config_paths)
# replace parameters in configs_file with tf FLAGS
model_configs = update_configs_from_flags(model_configs, FLAGS, TRAIN_ARGS.keys())
model_dir = model_configs["model_dir"]
if not gfile.Exists(model_dir):
gfile.MakeDirs(model_dir)
if "CUDA_VISIBLE_DEVICES" not in os.environ.keys():
raise OSError("need CUDA_VISIBLE_DEVICES environment variable")
tf.logging.info("CUDA_VISIBLE_DEVICES={}".format(os.environ["CUDA_VISIBLE_DEVICES"]))
training_runner = TrainingExperiment(
model_configs=model_configs)
training_runner.run()
def main(_argv):
model_configs = maybe_load_yaml(DEFAULT_TRAIN_CONFIGS)
# load flags from config file
model_configs = load_from_config_path(FLAGS.config_paths, model_configs)
# replace parameters in configs_file with tf FLAGS
model_configs = update_train_model_configs(model_configs, FLAGS)
model_dir = model_configs["model_dir"]
if not gfile.Exists(model_dir):
gfile.MakeDirs(model_dir)
if "CUDA_VISIBLE_DEVICES" not in os.environ.keys():
raise OSError("need CUDA_VISIBLE_DEVICES environment variable")
tf.logging.info("CUDA_VISIBLE_DEVICES={}".format(os.environ["CUDA_VISIBLE_DEVICES"]))
training_runner = TrainingExperiment(
model_configs=model_configs)
training_runner.run()
