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trainer.py
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trainer.py
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import pytorch_lightning as pl
from crar.crar import CRARLightning
import numpy as np
import torch
import argparse
from pytorch_lightning.loggers import TensorBoardLogger
import os
import yaml
from box import Box
from pytorch_lightning.loggers import WandbLogger
def main(hparams):
model = CRARLightning(hparams)
# logger = TensorBoardLogger(save_dir=os.getcwd(), name=hparams.logger_dir)
# logger = WandbLogger(name=hparams.logger_dir.split("/")[1])
# logger.watch(model, log="all", log_freq=10)
grad_clip_norm = 0
if "grad_clip_norm" in hparams:
grad_clip_norm = hparams.optim.grad_clip_norm
trainer = pl.Trainer(
gpus=1,
# logger=logger,
distributed_backend="dp",
max_epochs=hparams.max_epochs,
early_stop_callback=False,
gradient_clip_val=grad_clip_norm,
benchmark=True,
# auto_lr_find=True
# val_check_interval=100,
# log_gpu_memory="all",
)
trainer.fit(model)
if __name__ == "__main__":
# For reproducibility
torch.manual_seed(0)
np.random.seed(0)
parser = argparse.ArgumentParser()
parser.add_argument(
"--env", type=str, default="SimpleMaze-v1", help="gym environment tag"
)
args, _ = parser.parse_known_args()
with open("config.yaml") as f:
config = Box(yaml.load(f, Loader=yaml.FullLoader)[args.env])
main(config)