File size: 2,587 Bytes
68e589c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 |
# 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.add_argument(
"--virtual-display",
action="store_true",
help="Whether to create a virtual display for video rendering",
)
parser.set_defaults(algo="ppo", env="CartPole-v1", seed=1)
args = parser.parse_args()
print(args)
if args.virtual_display:
from pyvirtualdisplay import Display
virtual_display = Display(visible=0, size=(1400, 900))
virtual_display.start()
delattr(args, "virtual_display")
# pool_size isn't a TrainArg so must be removed from args
pool_size = args.pool_size
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)
|