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import json
import math
import os
import signal
import subprocess
import sys
import time
from pathlib import Path
from typing import Dict, List, Optional, Tuple
PROJECT_ROOT = Path(__file__).resolve().parents[1]
if str(PROJECT_ROOT) not in sys.path:
sys.path.insert(0, str(PROJECT_ROOT))
from rr_label_study.oven_study import _aggregate_summary, _episode_dirs
def _chunk_specs(
total_episodes: int,
episode_offset: int,
max_episodes: Optional[int],
num_workers: int,
) -> List[Tuple[int, int]]:
remaining = max(0, total_episodes - episode_offset)
if max_episodes is not None:
remaining = min(remaining, max_episodes)
if remaining <= 0:
return []
worker_count = min(num_workers, remaining)
chunk_size = math.ceil(remaining / worker_count)
specs: List[Tuple[int, int]] = []
for worker_index in range(worker_count):
start = episode_offset + worker_index * chunk_size
count = min(chunk_size, episode_offset + remaining - start)
if count > 0:
specs.append((start, count))
return specs
def _launch_xvfb(display_num: int, log_path: Path) -> subprocess.Popen:
log_handle = log_path.open("w", encoding="utf-8")
return subprocess.Popen(
[
"Xvfb",
f":{display_num}",
"-screen",
"0",
"1280x1024x24",
"+extension",
"GLX",
"+render",
"-noreset",
],
stdout=log_handle,
stderr=subprocess.STDOUT,
start_new_session=True,
)
def _launch_worker(
worker_dir: Path,
display_num: int,
dataset_root: str,
episode_offset: int,
max_episodes: int,
checkpoint_stride: int,
template_episode_index: int,
max_frames: Optional[int],
) -> Tuple[subprocess.Popen, subprocess.Popen]:
worker_dir.mkdir(parents=True, exist_ok=True)
xvfb = _launch_xvfb(display_num, worker_dir.joinpath("xvfb.log"))
time.sleep(1.0)
runtime_dir = Path(f"/tmp/rr_label_study_display_{display_num}")
runtime_dir.mkdir(parents=True, exist_ok=True)
command = [
sys.executable,
str(PROJECT_ROOT.joinpath("scripts", "run_oven_label_study.py")),
"--dataset-root",
dataset_root,
"--result-dir",
str(worker_dir),
"--episode-offset",
str(episode_offset),
"--max-episodes",
str(max_episodes),
"--checkpoint-stride",
str(checkpoint_stride),
"--template-episode-index",
str(template_episode_index),
]
if max_frames is not None:
command.extend(["--max-frames", str(max_frames)])
env = os.environ.copy()
env["DISPLAY"] = f":{display_num}"
env["XDG_RUNTIME_DIR"] = str(runtime_dir)
worker_log = worker_dir.joinpath("worker.log").open("w", encoding="utf-8")
process = subprocess.Popen(
command,
stdout=worker_log,
stderr=subprocess.STDOUT,
env=env,
cwd=str(PROJECT_ROOT),
start_new_session=True,
)
return xvfb, process
def _stop_process(process: subprocess.Popen) -> None:
if process.poll() is not None:
return
try:
os.killpg(process.pid, signal.SIGTERM)
except ProcessLookupError:
return
try:
process.wait(timeout=10)
except subprocess.TimeoutExpired:
try:
os.killpg(process.pid, signal.SIGKILL)
except ProcessLookupError:
pass
def _collect_metrics(base_result_dir: Path) -> List[Dict[str, object]]:
metrics: List[Dict[str, object]] = []
for metrics_path in sorted(base_result_dir.glob("worker_*/episode*.metrics.json")):
with metrics_path.open("r", encoding="utf-8") as handle:
metrics.append(json.load(handle))
return metrics
def main(argv: Optional[List[str]] = None) -> int:
parser = argparse.ArgumentParser()
parser.add_argument(
"--dataset-root",
default="/workspace/data/bimanual_take_tray_out_of_oven_train_128",
)
parser.add_argument(
"--result-dir",
default="/workspace/reveal_retrieve_label_study/results/oven_parallel",
)
parser.add_argument("--num-workers", type=int, default=4)
parser.add_argument("--episode-offset", type=int, default=0)
parser.add_argument("--max-episodes", type=int)
parser.add_argument("--checkpoint-stride", type=int, default=16)
parser.add_argument("--template-episode-index", type=int, default=0)
parser.add_argument("--base-display", type=int, default=110)
parser.add_argument("--max-frames", type=int)
args = parser.parse_args(argv)
dataset_root = Path(args.dataset_root)
all_episodes = _episode_dirs(dataset_root)
chunk_specs = _chunk_specs(
total_episodes=len(all_episodes),
episode_offset=args.episode_offset,
max_episodes=args.max_episodes,
num_workers=args.num_workers,
)
if not chunk_specs:
raise RuntimeError("no episodes selected for parallel run")
result_dir = Path(args.result_dir)
result_dir.mkdir(parents=True, exist_ok=True)
workers: List[Tuple[subprocess.Popen, subprocess.Popen]] = []
worker_meta: List[Dict[str, object]] = []
try:
for worker_index, (episode_offset, episode_count) in enumerate(chunk_specs):
display_num = args.base_display + worker_index
worker_dir = result_dir.joinpath(f"worker_{worker_index:02d}")
xvfb, process = _launch_worker(
worker_dir=worker_dir,
display_num=display_num,
dataset_root=args.dataset_root,
episode_offset=episode_offset,
max_episodes=episode_count,
checkpoint_stride=args.checkpoint_stride,
template_episode_index=args.template_episode_index,
max_frames=args.max_frames,
)
workers.append((xvfb, process))
worker_meta.append(
{
"worker_index": worker_index,
"display_num": display_num,
"episode_offset": episode_offset,
"episode_count": episode_count,
}
)
for meta, (_, process) in zip(worker_meta, workers):
return_code = process.wait()
meta["return_code"] = return_code
if return_code != 0:
worker_index = int(meta["worker_index"])
worker_log = result_dir.joinpath(f"worker_{worker_index:02d}", "worker.log")
raise RuntimeError(
f"worker {worker_index} failed with code {return_code}; see {worker_log}"
)
finally:
for xvfb, process in workers:
_stop_process(process)
_stop_process(xvfb)
episode_metrics = _collect_metrics(result_dir)
summary = _aggregate_summary(episode_metrics)
with result_dir.joinpath("parallel_workers.json").open("w", encoding="utf-8") as handle:
json.dump(worker_meta, handle, indent=2)
with result_dir.joinpath("parallel_summary.json").open("w", encoding="utf-8") as handle:
json.dump(summary, handle, indent=2)
print(json.dumps(summary, indent=2))
return 0
if __name__ == "__main__":
raise SystemExit(main())
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