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train.py
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train.py
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from utils import TrainOptions
from train import Trainer
from loguru import logger
import argparse
from config import run_grid_search_experiments
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--cfg', type=str, help='cfg file path')
parser.add_argument('--opts', default=[], nargs='*', help='additional options to update config')
parser.add_argument('--cfg_id', type=int, default=0, help='cfg id to run when multiple experiments are spawned')
parser.add_argument('--cluster', default=False, action='store_true', help='creates submission files for cluster')
parser.add_argument('--bid', type=int, default=30, help='amount of bid for cluster')
parser.add_argument('--memory', type=int, default=20000, help='memory amount for cluster')
parser.add_argument('--gpu_min_mem', type=int, default=11000, help='minimum amount of GPU memory')
parser.add_argument('--gpu_arch', default=['tesla', 'quadro', 'rtx'],
nargs='*', help='additional options to update config')
parser.add_argument('--num_cpus', type=int, default=8, help='num cpus for cluster')
args = parser.parse_args()
logger.info(f'Input arguments: \n {args}')
hparams = run_grid_search_experiments(
cfg_id=args.cfg_id,
cfg_file=args.cfg,
bid=args.bid,
use_cluster=args.cluster,
memory=args.memory,
script='train.py',
cmd_opts=args.opts,
gpu_min_mem=args.gpu_min_mem,
gpu_arch=args.gpu_arch,
)
trainer = Trainer(hparams)
trainer.train()