Files
pytorch-lightning/examples/new_project_templates/trainer_cpu_template.py
T
William Falcon bf09060fef Fixes #292 (#303)
* early stopping callback is not default

* added a default logger

* added default checkpoint callback

* added default checkpoint/loggers

* added default checkpoint/loggers

* updated docs

* cleaned demos

* cleaned demos

* cleaned demos

* clean up docs around loggers

* clean up docs around loggers

* clean up docs around loggers

* clean up docs around loggers

* clean up docs around loggers

* clean up docs around loggers

* clean up docs around loggers

* clean up docs around loggers

* clean up docs around loggers

* clean up docs around loggers

* clean up docs around loggers

* clean up docs around loggers

* clean up docs around loggers
2019-10-04 19:48:57 -04:00

54 lines
1.4 KiB
Python

import os
import sys
from test_tube import HyperOptArgumentParser, Experiment
from pytorch_lightning import Trainer
from pytorch_lightning.utilities.arg_parse import add_default_args
from pytorch_lightning.callbacks.pt_callbacks import EarlyStopping, ModelCheckpoint
from examples.new_project_templates.lightning_module_template import LightningTemplateModel
def main(hparams):
"""
Main training routine specific for this project
:param hparams:
:return:
"""
# init experiment
exp = Experiment(
name=hparams.tt_name,
debug=hparams.debug,
save_dir=hparams.tt_save_path,
version=hparams.hpc_exp_number,
autosave=False,
description=hparams.tt_description
)
exp.argparse(hparams)
exp.save()
# build model
model = LightningTemplateModel(hparams)
# configure trainer
trainer = Trainer(experiment=exp)
# train model
trainer.fit(model)
if __name__ == '__main__':
# use default args given by lightning
root_dir = os.path.split(os.path.dirname(sys.modules['__main__'].__file__))[0]
parent_parser = HyperOptArgumentParser(strategy='random_search', add_help=False)
add_default_args(parent_parser, root_dir)
# allow model to overwrite or extend args
parser = LightningTemplateModel.add_model_specific_args(parent_parser)
hyperparams = parser.parse_args()
# train model
main(hyperparams)