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# Support for PyTorch mps mode (https://pytorch.org/docs/stable/notes/mps.html)
import os
os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"
import itertools
from argparse import Namespace
from multiprocessing import Pool
from typing import Any, Dict
from runner.running_utils import base_parser
from runner.train import train, TrainArgs
def args_dict(algo: str, env: str, seed: str, args: Namespace) -> Dict[str, Any]:
d = vars(args).copy()
d.update(
{
"algo": algo,
"env": env,
"seed": seed,
}
)
return d
if __name__ == "__main__":
parser = base_parser()
parser.add_argument(
"--wandb-project-name",
type=str,
default="rl-algo-impls",
help="WandB project namme to upload training data to. If none, won't upload.",
)
parser.add_argument(
"--wandb-entity",
type=str,
default=None,
help="WandB team of project. None uses default entity",
)
parser.add_argument(
"--wandb-tags", type=str, nargs="*", help="WandB tags to add to run"
)
parser.add_argument(
"--pool-size", type=int, default=1, help="Simultaneous training jobs to run"
)
parser.set_defaults(
algo="ppo",
env="MountainCarContinuous-v0",
seed=[1, 2, 3],
pool_size=3,
)
args = parser.parse_args()
print(args)
if args.pool_size == 1:
from pyvirtualdisplay.display import Display
virtual_display = Display(visible=False, size=(1400, 900))
virtual_display.start()
# pool_size isn't a TrainArg so must be removed from args
pool_size = min(args.pool_size, len(args.seed))
delattr(args, "pool_size")
algos = args.algo if isinstance(args.algo, list) else [args.algo]
envs = args.env if isinstance(args.env, list) else [args.env]
seeds = args.seed if isinstance(args.seed, list) else [args.seed]
if all(len(arg) == 1 for arg in [algos, envs, seeds]):
train(TrainArgs(**args_dict(algos[0], envs[0], seeds[0], args)))
else:
# Force a new process for each job to get around wandb not allowing more than one
# wandb.tensorboard.patch call per process.
with Pool(pool_size, maxtasksperchild=1) as p:
train_args = [
TrainArgs(**args_dict(algo, env, seed, args))
for algo, env, seed in itertools.product(algos, envs, seeds)
]
p.map(train, train_args)
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