115 lines
3.4 KiB
Python
115 lines
3.4 KiB
Python
"""
|
|
Runs a model on a single node across N-gpus.
|
|
"""
|
|
import os
|
|
import numpy as np
|
|
import torch
|
|
|
|
from test_tube import HyperOptArgumentParser, Experiment
|
|
from pytorch_lightning.models.trainer import Trainer
|
|
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
|
|
|
|
from examples.new_project_templates.lightning_module_template import LightningTemplateModel
|
|
|
|
SEED = 2334
|
|
torch.manual_seed(SEED)
|
|
np.random.seed(SEED)
|
|
|
|
|
|
def main(hparams):
|
|
"""
|
|
Main training routine specific for this project
|
|
:param hparams:
|
|
:return:
|
|
"""
|
|
# ------------------------
|
|
# 1 INIT LIGHTNING MODEL
|
|
# ------------------------
|
|
print('loading model...')
|
|
model = LightningTemplateModel(hparams)
|
|
print('model built')
|
|
|
|
# ------------------------
|
|
# 2 INIT TEST TUBE EXP
|
|
# ------------------------
|
|
|
|
# init experiment
|
|
exp = Experiment(
|
|
name=hyperparams.experiment_name,
|
|
save_dir=hyperparams.test_tube_save_path,
|
|
autosave=False,
|
|
description='test demo'
|
|
)
|
|
|
|
exp.argparse(hparams)
|
|
exp.save()
|
|
|
|
# ------------------------
|
|
# 3 DEFINE CALLBACKS
|
|
# ------------------------
|
|
model_save_path = '{}/{}/{}'.format(hparams.model_save_path, exp.name, exp.version)
|
|
early_stop = EarlyStopping(
|
|
monitor='val_acc',
|
|
patience=3,
|
|
verbose=True,
|
|
mode='max'
|
|
)
|
|
|
|
checkpoint = ModelCheckpoint(
|
|
filepath=model_save_path,
|
|
save_best_only=True,
|
|
verbose=True,
|
|
monitor='val_loss',
|
|
mode='min'
|
|
)
|
|
|
|
# ------------------------
|
|
# 4 INIT TRAINER
|
|
# ------------------------
|
|
trainer = Trainer(
|
|
experiment=exp,
|
|
checkpoint_callback=checkpoint,
|
|
early_stop_callback=early_stop,
|
|
gpus=hparams.gpus,
|
|
distributed_backend='ddp'
|
|
)
|
|
|
|
# ------------------------
|
|
# 5 START TRAINING
|
|
# ------------------------
|
|
trainer.fit(model)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
|
|
# dirs
|
|
root_dir = os.path.dirname(os.path.realpath(__file__))
|
|
demo_log_dir = os.path.join(root_dir, 'pt_lightning_demo_logs')
|
|
checkpoint_dir = os.path.join(demo_log_dir, 'model_weights')
|
|
test_tube_dir = os.path.join(demo_log_dir, 'test_tube_data')
|
|
|
|
# although we user hyperOptParser, we are using it only as argparse right now
|
|
parent_parser = HyperOptArgumentParser(strategy='grid_search', add_help=False)
|
|
|
|
# gpu args
|
|
parent_parser.add_argument('--gpus', type=str, default='-1',
|
|
help='how many gpus to use in the node.'
|
|
' value -1 uses all the gpus on the node')
|
|
parent_parser.add_argument('--test_tube_save_path', type=str, default=test_tube_dir,
|
|
help='where to save logs')
|
|
parent_parser.add_argument('--model_save_path', type=str, default=checkpoint_dir,
|
|
help='where to save model')
|
|
parent_parser.add_argument('--experiment_name', type=str, default='pt_lightning_exp_a',
|
|
help='test tube exp name')
|
|
|
|
# allow model to overwrite or extend args
|
|
parser = LightningTemplateModel.add_model_specific_args(parent_parser, root_dir)
|
|
hyperparams = parser.parse_args()
|
|
|
|
# ---------------------
|
|
# RUN TRAINING
|
|
# ---------------------
|
|
# run on HPC cluster
|
|
print('RUNNING INTERACTIVE MODE ON GPUS. gpu ids: %i' % hyperparams.gpus)
|
|
main(hyperparams)
|