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"""Run training
"""
import os
import torch
try:
torch.multiprocessing.set_start_method("spawn")
import torch.multiprocessing as mp
mp.set_start_method("spawn")
except RuntimeError:
pass
import logging
import sys
# Tired of seeing these warnings
import warnings
import hydra
from omegaconf import DictConfig
from sqlalchemy import exc as sa_exc
warnings.filterwarnings("ignore", category=sa_exc.SAWarning)
logging.basicConfig(stream=sys.stdout, level=logging.ERROR)
os.environ["HYDRA_FULL_ERROR"] = "1"
# this file can be run for example using
# python run.py experiment=example_simple
@hydra.main(config_path="configs/", config_name="config.yaml", version_base="1.2")
def main(config: DictConfig):
"""Runs training"""
# Imports should be nested inside @hydra.main to optimize tab completion
# Read more here: https://github.com/facebookresearch/hydra/issues/934
from pvnet.training import train
from pvnet.utils import extras, print_config
# A couple of optional utilities:
# - disabling python warnings
# - easier access to debug mode
# - forcing debug friendly configuration
# - forcing multi-gpu friendly configuration
# You can safely get rid of this line if you don't want those
extras(config)
# Pretty print config using Rich library
if config.get("print_config"):
print_config(config, resolve=True)
# Train model
return train(config)
if __name__ == "__main__":
main()
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