lightning/pl_examples/basic_examples/gpu_template.py

80 lines
1.9 KiB
Python

"""
Runs a model on a single node across N-gpus.
"""
import os
from argparse import ArgumentParser
import numpy as np
import torch
from pl_examples.basic_examples.lightning_module_template import LightningTemplateModel
from pytorch_lightning import Trainer
SEED = 2334
torch.manual_seed(SEED)
np.random.seed(SEED)
def main(hparams):
"""
Main training routine specific for this project
:param hparams:
"""
# ------------------------
# 1 INIT LIGHTNING MODEL
# ------------------------
model = LightningTemplateModel(hparams)
# ------------------------
# 2 INIT TRAINER
# ------------------------
trainer = Trainer(
gpus=hparams.gpus,
distributed_backend=hparams.distributed_backend,
use_amp=hparams.use_16bit
)
# ------------------------
# 3 START TRAINING
# ------------------------
trainer.fit(model)
if __name__ == '__main__':
# ------------------------
# TRAINING ARGUMENTS
# ------------------------
# these are project-wide arguments
root_dir = os.path.dirname(os.path.realpath(__file__))
parent_parser = ArgumentParser(add_help=False)
# gpu args
parent_parser.add_argument(
'--gpus',
type=int,
default=2,
help='how many gpus'
)
parent_parser.add_argument(
'--distributed_backend',
type=str,
default='dp',
help='supports three options dp, ddp, ddp2'
)
parent_parser.add_argument(
'--use_16bit',
dest='use_16bit',
action='store_true',
help='if true uses 16 bit precision'
)
# each LightningModule defines arguments relevant to it
parser = LightningTemplateModel.add_model_specific_args(parent_parser, root_dir)
hyperparams = parser.parse_args()
# ---------------------
# RUN TRAINING
# ---------------------
main(hyperparams)