import argparse
import datetime
from pathlib import Path
from allennlp.commands import main, Subcommand
from allennlp.commands.train import train_model
from allennlp.common import Params
from allennlp.common.util import import_submodules
from allennlp.models import Model
class MyTrain(Subcommand):
def add_subparser(self, name: str, parser: argparse._SubParsersAction) -> argparse.ArgumentParser:
# pylint: disable=protected-access
description = '''Train the specified model on the specified dataset.'''
subparser = parser.add_parser(name, description=description, help='Train a model')
subparser.add_argument('param_path',
type=str,
help='path to parameter file describing the model to be trained')
subparser.add_argument('-s', '--serialization-dir',
required=False,
default="",
type=str,
help='directory in which to save the model and its logs')
subparser.add_argument('-r', '--recover',
action='store_true',
default=False,
help='recover training from the state in serialization_dir')
subparser.add_argument('-f', '--force',
action='store_true',
required=False,
help='overwrite the output directory if it exists')
subparser.add_argument('-o', '--overrides',
type=str,
default="",
help='a JSON structure used to override the experiment configuration')
subparser.add_argument('-e', '--ext-vars',
type=str,
default=None,
help='Used to provide ext variable to jsonnet')
subparser.add_argument('--fp16',
action='store_true',
required=False,
help='use fp 16 training')
subparser.add_argument('--file-friendly-logging',
action='store_true',
default=False,
help='outputs tqdm status on separate lines and slows tqdm refresh rate')
subparser.set_defaults(func=train_model_from_args)
return subparser
def train_model_from_args(args: argparse.Namespace):
"""
Just converts from an ``argparse.Namespace`` object to string paths.
"""
start_time = datetime.datetime.now().strftime('%b-%d_%H-%M')
if args.serialization_dir:
serialization_dir = args.serialization_dir
else:
path = Path(args.param_path.replace("configs/", "results/")).resolve()
serialization_dir = path.with_name(path.stem) / start_time
train_model_from_file(args.param_path,
serialization_dir,
args.overrides,
args.file_friendly_logging,
args.recover,
args.force,
args.ext_vars)
def train_model_from_file(parameter_filename: str,
serialization_dir: str,
overrides: str = "",
file_friendly_logging: bool = False,
recover: bool = False,
force: bool = False,
ext_vars=None) -> Model:
"""
A wrapper around :func:`train_model` which loads the params from a file.
Parameters
----------
param_path : ``str``
A json parameter file specifying an AllenNLP experiment.
serialization_dir : ``str``
The directory in which to save results and logs. We just pass this along to
:func:`train_model`.
overrides : ``str``
A JSON string that we will use to override values in the input parameter file.
file_friendly_logging : ``bool``, optional (default=False)
If ``True``, we make our output more friendly to saved model files. We just pass this
along to :func:`train_model`.
recover : ``bool`, optional (default=False)
If ``True``, we will try to recover a training run from an existing serialization
directory. This is only intended for use when something actually crashed during the middle
of a run. For continuing training a model on new data, see the ``fine-tune`` command.
"""
# Load the experiment config from a file and pass it to ``train_model``.
params = Params.from_file(parameter_filename, overrides, ext_vars=ext_vars)
return train_model(params, serialization_dir, file_friendly_logging, recover, force)
if __name__ == "__main__":
import_submodules("qa")
import_submodules("modules")
main(prog="ReadingZoo",subcommand_overrides={"train": MyTrain()})
