File size: 4,223 Bytes
b05c680 |
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 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 |
# Support for PyTorch mps mode (https://pytorch.org/docs/stable/notes/mps.html)
import os
os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"
import dataclasses
import shutil
import wandb
import yaml
from dataclasses import dataclass
from torch.utils.tensorboard.writer import SummaryWriter
from typing import Any, Dict, Optional, Sequence
from shared.callbacks.eval_callback import EvalCallback
from runner.config import Config, EnvHyperparams, RunArgs
from runner.env import make_env, make_eval_env
from runner.running_utils import (
ALGOS,
load_hyperparams,
set_seeds,
get_device,
make_policy,
plot_eval_callback,
hparam_dict,
)
from shared.stats import EpisodesStats
@dataclass
class TrainArgs(RunArgs):
wandb_project_name: Optional[str] = None
wandb_entity: Optional[str] = None
wandb_tags: Sequence[str] = dataclasses.field(default_factory=list)
def train(args: TrainArgs):
print(args)
hyperparams = load_hyperparams(args.algo, args.env, os.getcwd())
print(hyperparams)
config = Config(args, hyperparams, os.getcwd())
wandb_enabled = args.wandb_project_name
if wandb_enabled:
wandb.tensorboard.patch(
root_logdir=config.tensorboard_summary_path, pytorch=True
)
wandb.init(
project=args.wandb_project_name,
entity=args.wandb_entity,
config=hyperparams, # type: ignore
name=config.run_name,
monitor_gym=True,
save_code=True,
tags=args.wandb_tags,
)
wandb.config.update(args)
tb_writer = SummaryWriter(config.tensorboard_summary_path)
set_seeds(args.seed, args.use_deterministic_algorithms)
env = make_env(
config, EnvHyperparams(**config.env_hyperparams), tb_writer=tb_writer
)
device = get_device(config.device, env)
policy = make_policy(args.algo, env, device, **config.policy_hyperparams)
algo = ALGOS[args.algo](policy, env, device, tb_writer, **config.algo_hyperparams)
eval_env = make_eval_env(config, EnvHyperparams(**config.env_hyperparams))
record_best_videos = config.eval_params.get("record_best_videos", True)
callback = EvalCallback(
policy,
eval_env,
tb_writer,
best_model_path=config.model_dir_path(best=True),
**config.eval_params,
video_env=make_eval_env(
config, EnvHyperparams(**config.env_hyperparams), override_n_envs=1
)
if record_best_videos
else None,
best_video_dir=config.best_videos_dir,
)
algo.learn(config.n_timesteps, callback=callback)
policy.save(config.model_dir_path(best=False))
eval_stats = callback.evaluate(n_episodes=10, print_returns=True)
plot_eval_callback(callback, tb_writer, config.run_name)
log_dict: Dict[str, Any] = {
"eval": eval_stats._asdict(),
}
if callback.best:
log_dict["best_eval"] = callback.best._asdict()
log_dict.update(hyperparams)
log_dict.update(vars(args))
with open(config.logs_path, "a") as f:
yaml.dump({config.run_name: log_dict}, f)
best_eval_stats: EpisodesStats = callback.best # type: ignore
tb_writer.add_hparams(
hparam_dict(hyperparams, vars(args)),
{
"hparam/best_mean": best_eval_stats.score.mean,
"hparam/best_result": best_eval_stats.score.mean
- best_eval_stats.score.std,
"hparam/last_mean": eval_stats.score.mean,
"hparam/last_result": eval_stats.score.mean - eval_stats.score.std,
},
None,
config.run_name,
)
tb_writer.close()
if wandb_enabled:
wandb.run.summary["num_parameters"] = policy.num_parameters()
wandb.run.summary[
"num_trainable_parameters"
] = policy.num_trainable_parameters()
shutil.make_archive(
os.path.join(wandb.run.dir, config.model_dir_name()),
"zip",
config.model_dir_path(),
)
shutil.make_archive(
os.path.join(wandb.run.dir, config.model_dir_name(best=True)),
"zip",
config.model_dir_path(best=True),
)
wandb.finish()
|