def main():
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument("-v",
"--version",
action="version",
version="OpenNMT-tf %s" % __version__)
parser.add_argument("--config",
required=True,
nargs="+",
help="List of configuration files.")
parser.add_argument(
"--auto_config",
default=False,
action="store_true",
help="Enable automatic configuration values.",
)
parser.add_argument(
"--model_type",
default="",
choices=list(sorted(catalog.list_model_names_from_catalog())),
help="Model type from the catalog.",
)
parser.add_argument("--model",
default="",
help="Custom model configuration file.")
parser.add_argument(
"--run_dir",
default="",
help="If set, model_dir will be created relative to this location.",
)
parser.add_argument(
"--data_dir",
default="",
help="If set, data files are expected to be relative to this location.",
)
parser.add_argument(
"--checkpoint_path",
default=None,
help=("Specific checkpoint or model directory to load "
"(when a directory is set, the latest checkpoint is used)."),
)
parser.add_argument(
"--log_level",
default="INFO",
choices=["CRITICAL", "ERROR", "WARNING", "INFO", "DEBUG", "NOTSET"],
help="Logs verbosity.",
)
parser.add_argument("--seed", type=int, default=None, help="Random seed.")
parser.add_argument(
"--gpu_allow_growth",
default=False,
action="store_true",
help="Allocate GPU memory dynamically.",
)
parser.add_argument(
"--intra_op_parallelism_threads",
type=int,
default=0,
help=("Number of intra op threads (0 means the system picks "
"an appropriate number)."),
)
parser.add_argument(
"--inter_op_parallelism_threads",
type=int,
default=0,
help=("Number of inter op threads (0 means the system picks "
"an appropriate number)."),
)
parser.add_argument(
"--mixed_precision",
default=False,
action="store_true",
help="Enable mixed precision.",
)
parser.add_argument(
"--eager_execution",
default=False,
action="store_true",
help="Enable TensorFlow eager execution.",
)
subparsers = parser.add_subparsers(help="Run type.", dest="run_type")
subparsers.required = True
parser_train = subparsers.add_parser("train", help="Training.")
parser_train.add_argument(
"--with_eval",
default=False,
action="store_true",
help="Enable automatic evaluation.",
)
parser_train.add_argument(
"--num_gpus",
type=int,
default=1,
help="Number of GPUs to use for in-graph replication.",
)
parser_train.add_argument(
"--horovod",
default=False,
action="store_true",
help="Enable Horovod training mode.",
)
parser_eval = subparsers.add_parser("eval", help="Evaluation.")
parser_eval.add_argument("--features_file",
nargs="+",
default=None,
help="Input features files.")
parser_eval.add_argument("--labels_file",
default=None,
help="Output labels files.")
parser_infer = subparsers.add_parser("infer", help="Inference.")
parser_infer.add_argument("--features_file",
nargs="+",
required=True,
help="Run inference on this file.")
parser_infer.add_argument(
"--predictions_file",
default="",
help=
("File used to save predictions. If not set, predictions are printed "
"on the standard output."),
)
parser_infer.add_argument(
"--log_prediction_time",
default=False,
action="store_true",
help="Logs some prediction time metrics.",
)
parser_export = subparsers.add_parser("export", help="Model export.")
parser_export.add_argument(
"--output_dir",
"--export_dir",
required=True,
help="The directory of the exported model.",
)
parser_export.add_argument(
"--format",
"--export_format",
choices=exporters.list_exporters(),
default="saved_model",
help="Format of the exported model.",
)
parser_score = subparsers.add_parser("score", help="Scoring.")
parser_score.add_argument("--features_file",
nargs="+",
required=True,
help="Features file.")
parser_score.add_argument("--predictions_file",
default=None,
help="Predictions to score.")
parser_average_checkpoints = subparsers.add_parser(
"average_checkpoints", help="Checkpoint averaging.")
parser_average_checkpoints.add_argument(
"--output_dir",
required=True,
help="The output directory for the averaged checkpoint.",
)
parser_average_checkpoints.add_argument(
"--max_count",
type=int,
default=8,
help="The maximal number of checkpoints to average.",
)
parser_update_vocab = subparsers.add_parser(
"update_vocab", help="Update model vocabularies in checkpoint.")
parser_update_vocab.add_argument(
"--output_dir",
required=True,
help="The output directory for the updated checkpoint.",
)
parser_update_vocab.add_argument("--src_vocab",
default=None,
help="Path to the new source vocabulary.")
parser_update_vocab.add_argument("--tgt_vocab",
default=None,
help="Path to the new target vocabulary.")
# When using an option that takes multiple values just before the run type,
# the run type is treated as a value of this option. To fix this issue, we
# inject a placeholder option just before the run type to clearly separate it.
parser.add_argument("--placeholder",
action="store_true",
help=argparse.SUPPRESS)
run_types = set(subparsers.choices.keys())
args = sys.argv[1:]
for i, arg in enumerate(args):
if arg in run_types:
args.insert(i, "--placeholder")
break
args = parser.parse_args(args)
if (hasattr(args, "features_file") and args.features_file
and len(args.features_file) == 1):
args.features_file = args.features_file[0]
_initialize_logging(getattr(logging, args.log_level))
tf.config.threading.set_intra_op_parallelism_threads(
args.intra_op_parallelism_threads)
tf.config.threading.set_inter_op_parallelism_threads(
args.inter_op_parallelism_threads)
if args.eager_execution:
tf.config.run_functions_eagerly(True)
gpus = tf.config.list_physical_devices(device_type="GPU")
if hasattr(args, "horovod") and args.horovod:
import horovod.tensorflow as hvd
hvd.init()
is_master = hvd.rank() == 0
if gpus:
local_gpu = gpus[hvd.local_rank()]
tf.config.set_visible_devices(local_gpu, device_type="GPU")
gpus = [local_gpu]
else:
hvd = None
is_master = True
if args.gpu_allow_growth:
for device in gpus:
tf.config.experimental.set_memory_growth(device, enable=True)
# Load and merge run configurations.
config = load_config(args.config)
if args.run_dir:
config["model_dir"] = os.path.join(args.run_dir, config["model_dir"])
if args.data_dir:
config["data"] = _prefix_paths(args.data_dir, config["data"])
if is_master and not tf.io.gfile.exists(config["model_dir"]):
tf.get_logger().info("Creating model directory %s",
config["model_dir"])
tf.io.gfile.makedirs(config["model_dir"])
model = load_model(
config["model_dir"],
model_file=args.model,
model_name=args.model_type,
serialize_model=is_master,
as_builder=True,
)
runner = Runner(
model,
config,
auto_config=args.auto_config,
mixed_precision=args.mixed_precision,
seed=args.seed,
)
if args.run_type == "train":
runner.train(
num_devices=args.num_gpus,
with_eval=args.with_eval,
checkpoint_path=args.checkpoint_path,
hvd=hvd,
)
elif args.run_type == "eval":
metrics = runner.evaluate(
checkpoint_path=args.checkpoint_path,
features_file=args.features_file,
labels_file=args.labels_file,
)
print(metrics)
elif args.run_type == "infer":
runner.infer(
args.features_file,
predictions_file=args.predictions_file,
checkpoint_path=args.checkpoint_path,
log_time=args.log_prediction_time,
)
elif args.run_type == "export":
runner.export(
args.output_dir,
checkpoint_path=args.checkpoint_path,
exporter=exporters.make_exporter(args.format),
)
elif args.run_type == "score":
runner.score(
args.features_file,
args.predictions_file,
checkpoint_path=args.checkpoint_path,
)
elif args.run_type == "average_checkpoints":
runner.average_checkpoints(args.output_dir, max_count=args.max_count)
elif args.run_type == "update_vocab":
runner.update_vocab(args.output_dir,
src_vocab=args.src_vocab,
tgt_vocab=args.tgt_vocab)
def main():
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument("-v",
"--version",
action="version",
version="OpenNMT-tf %s" % __version__)
parser.add_argument("--config",
required=True,
nargs="+",
help="List of configuration files.")
parser.add_argument("--auto_config",
default=False,
action="store_true",
help="Enable automatic configuration values.")
parser.add_argument("--model_type",
default="",
choices=list(
classes_in_module(catalog, public_only=True)),
help="Model type from the catalog.")
parser.add_argument("--model",
default="",
help="Custom model configuration file.")
parser.add_argument(
"--run_dir",
default="",
help="If set, model_dir will be created relative to this location.")
parser.add_argument(
"--data_dir",
default="",
help="If set, data files are expected to be relative to this location."
)
parser.add_argument(
"--checkpoint_path",
default=None,
help=("Specific checkpoint or model directory to load "
"(when a directory is set, the latest checkpoint is used)."))
parser.add_argument(
"--log_level",
default="INFO",
choices=["CRITICAL", "ERROR", "WARNING", "INFO", "DEBUG", "NOTSET"],
help="Logs verbosity.")
parser.add_argument("--seed", type=int, default=None, help="Random seed.")
parser.add_argument("--gpu_allow_growth",
default=False,
action="store_true",
help="Allocate GPU memory dynamically.")
parser.add_argument(
"--intra_op_parallelism_threads",
type=int,
default=0,
help=("Number of intra op threads (0 means the system picks "
"an appropriate number)."))
parser.add_argument(
"--inter_op_parallelism_threads",
type=int,
default=0,
help=("Number of inter op threads (0 means the system picks "
"an appropriate number)."))
parser.add_argument("--mixed_precision",
default=False,
action="store_true",
help="Enable mixed precision.")
subparsers = parser.add_subparsers(help="Run type.", dest="run")
parser_train = subparsers.add_parser("train", help="Training.")
parser_train.add_argument("--with_eval",
default=False,
action="store_true",
help="Enable automatic evaluation.")
parser_train.add_argument(
"--num_gpus",
type=int,
default=1,
help="Number of GPUs to use for in-graph replication.")
parser_eval = subparsers.add_parser("eval", help="Evaluation.")
parser_eval.add_argument("--features_file",
nargs="+",
default=None,
help="Input features files.")
parser_eval.add_argument("--labels_file",
default=None,
help="Output labels files.")
parser_infer = subparsers.add_parser("infer", help="Inference.")
parser_infer.add_argument("--features_file",
nargs="+",
required=True,
help="Run inference on this file.")
parser_infer.add_argument(
"--predictions_file",
default="",
help=
("File used to save predictions. If not set, predictions are printed "
"on the standard output."))
parser_infer.add_argument("--log_prediction_time",
default=False,
action="store_true",
help="Logs some prediction time metrics.")
parser_export = subparsers.add_parser("export", help="Model export.")
parser_export.add_argument("--export_dir",
required=True,
help="The directory of the exported model.")
parser_score = subparsers.add_parser("score", help="Scoring.")
parser_score.add_argument("--features_file",
nargs="+",
required=True,
help="Features file.")
parser_score.add_argument("--predictions_file",
default=None,
help="Predictions to score.")
parser_average_checkpoints = subparsers.add_parser(
"average_checkpoints", help="Checkpoint averaging.")
parser_average_checkpoints.add_argument(
"--output_dir",
required=True,
help="The output directory for the averaged checkpoint.")
parser_average_checkpoints.add_argument(
"--max_count",
type=int,
default=8,
help="The maximal number of checkpoints to average.")
parser_update_vocab = subparsers.add_parser(
"update_vocab", help="Update model vocabularies in checkpoint.")
parser_update_vocab.add_argument(
"--output_dir",
required=True,
help="The output directory for the updated checkpoint.")
parser_update_vocab.add_argument("--src_vocab",
default=None,
help="Path to the new source vocabulary.")
parser_update_vocab.add_argument("--tgt_vocab",
default=None,
help="Path to the new target vocabulary.")
args = parser.parse_args()
if hasattr(args, "features_file") and args.features_file and len(
args.features_file) == 1:
args.features_file = args.features_file[0]
_set_log_level(getattr(logging, args.log_level))
tf.config.threading.set_intra_op_parallelism_threads(
args.intra_op_parallelism_threads)
tf.config.threading.set_inter_op_parallelism_threads(
args.inter_op_parallelism_threads)
if args.gpu_allow_growth:
for device in tf.config.experimental.list_physical_devices(
device_type="GPU"):
tf.config.experimental.set_memory_growth(device, enable=True)
# Load and merge run configurations.
config = load_config(args.config)
if args.run_dir:
config["model_dir"] = os.path.join(args.run_dir, config["model_dir"])
if args.data_dir:
config["data"] = _prefix_paths(args.data_dir, config["data"])
if not tf.io.gfile.exists(config["model_dir"]):
tf.get_logger().info("Creating model directory %s",
config["model_dir"])
tf.io.gfile.makedirs(config["model_dir"])
model = load_model(config["model_dir"],
model_file=args.model,
model_name=args.model_type)
runner = Runner(model,
config,
auto_config=args.auto_config,
mixed_precision=args.mixed_precision,
seed=args.seed)
if args.run == "train":
runner.train(num_devices=args.num_gpus,
with_eval=args.with_eval,
checkpoint_path=args.checkpoint_path)
elif args.run == "eval":
metrics = runner.evaluate(checkpoint_path=args.checkpoint_path,
features_file=args.features_file,
labels_file=args.labels_file)
print(metrics)
elif args.run == "infer":
runner.infer(args.features_file,
predictions_file=args.predictions_file,
checkpoint_path=args.checkpoint_path,
log_time=args.log_prediction_time)
elif args.run == "export":
runner.export(args.export_dir, checkpoint_path=args.checkpoint_path)
elif args.run == "score":
runner.score(args.features_file,
args.predictions_file,
checkpoint_path=args.checkpoint_path)
elif args.run == "average_checkpoints":
runner.average_checkpoints(args.output_dir, max_count=args.max_count)
elif args.run == "update_vocab":
runner.update_vocab(args.output_dir,
src_vocab=args.src_vocab,
tgt_vocab=args.tgt_vocab)