Files
geointel/scripts/supervise_container_yolo_training.py
T

168 lines
6.4 KiB
Python

#!/usr/bin/env python3
"""Resume one checkpointed YOLO run after container recreation, fail closed."""
from __future__ import annotations
import argparse
import json
import subprocess
import sys
import time
from datetime import UTC, datetime
from pathlib import Path
SCRIPT_DIR = Path(__file__).resolve().parent
if str(SCRIPT_DIR) not in sys.path:
sys.path.insert(0, str(SCRIPT_DIR))
from training_release_manifest import ( # noqa: E402
TrainingReleaseError,
assert_training_release_eligible,
)
def container_running(container: str) -> bool:
result = subprocess.run(
["docker", "inspect", "--format", "{{.State.Running}}", container],
capture_output=True, text=True, check=False,
)
return result.returncode == 0 and result.stdout.strip() == "true"
def training_active(container: str, run_marker: str) -> bool:
result = subprocess.run(
["docker", "top", container, "-eo", "pid,args"],
capture_output=True, text=True, check=False,
)
return result.returncode == 0 and any(
"train" in line and run_marker in line
for line in result.stdout.splitlines()
)
def write_state(path: Path, payload: dict) -> None:
payload["updated_at"] = datetime.now(UTC).isoformat()
temporary = path.with_suffix(path.suffix + ".tmp")
temporary.write_text(json.dumps(payload, indent=2), encoding="utf-8")
temporary.replace(path)
def load_completion_command(path: Path) -> list[str]:
command = json.loads(path.read_text(encoding="utf-8"))
if not isinstance(command, list) or not command or not all(isinstance(x, str) and x for x in command):
raise ValueError("completion command must be a non-empty JSON list of non-empty strings")
return command
def verify_training_release(
*,
train_yaml: Path,
corpus_manifest: Path,
fixture_mode: bool,
) -> None:
"""Re-check the exact release before each detached resume command."""
assert_training_release_eligible(
train_yaml=train_yaml,
corpus_manifest=corpus_manifest,
fixture_mode=fixture_mode,
)
def main() -> int:
parser = argparse.ArgumentParser()
parser.add_argument("--container", required=True)
parser.add_argument("--host-run-dir", type=Path, required=True)
parser.add_argument("--container-checkpoint", required=True)
parser.add_argument("--train-yaml", type=Path, required=True)
parser.add_argument("--corpus-manifest", type=Path, required=True)
parser.add_argument(
"--fixture-mode",
action="store_true",
help="Only valid for an explicitly fixture-only frozen corpus release.",
)
parser.add_argument("--run-marker", required=True)
parser.add_argument("--yolo", default="/opt/geointel/venv/bin/yolo")
parser.add_argument("--poll-seconds", type=int, default=30)
parser.add_argument("--max-resumes", type=int, default=20)
parser.add_argument("--completion-command-json", type=Path)
parser.add_argument("--once", action="store_true")
args = parser.parse_args()
if args.poll_seconds < 1 or args.max_resumes < 1:
raise SystemExit("poll-seconds and max-resumes must be positive")
state_path = args.host_run_dir / "supervisor-state.json"
state = {"schema_version": 1, "status": "monitoring", "resume_count": 0}
if state_path.is_file():
state.update(json.loads(state_path.read_text(encoding="utf-8")))
while True:
if (args.host_run_dir / "results.png").is_file():
if state.get("completion_handoff_started"):
state["status"] = "training_finished_handoff_already_started"
write_state(state_path, state)
return 0
if args.completion_command_json:
try:
command = load_completion_command(args.completion_command_json)
except (OSError, json.JSONDecodeError, ValueError) as exc:
state["status"] = "invalid_completion_command"
state["completion_handoff_error"] = str(exc)
write_state(state_path, state)
return 4
result = subprocess.run(command, check=False)
if result.returncode != 0:
state["status"] = "completion_handoff_failed"
state["completion_handoff_returncode"] = result.returncode
write_state(state_path, state)
return 5
state["completion_handoff_started"] = True
state["completion_command"] = command
state["status"] = "training_finished_handoff_started"
else:
state["status"] = "training_finished"
write_state(state_path, state)
return 0
checkpoint = args.host_run_dir / "weights" / "last.pt"
if not checkpoint.is_file() or checkpoint.stat().st_size < 1024 * 1024:
state["status"] = "checkpoint_missing_or_incomplete"
write_state(state_path, state)
return 2
if container_running(args.container) and not training_active(args.container, args.run_marker):
if int(state["resume_count"]) >= args.max_resumes:
state["status"] = "resume_budget_exhausted"
write_state(state_path, state)
return 3
try:
verify_training_release(
train_yaml=args.train_yaml,
corpus_manifest=args.corpus_manifest,
fixture_mode=args.fixture_mode,
)
except TrainingReleaseError as exc:
state["status"] = "training_release_verification_failed"
state["training_release_error"] = str(exc)
write_state(state_path, state)
return 6
result = subprocess.run(
["docker", "exec", "-d", args.container, args.yolo, "train",
f"resume={args.container_checkpoint}", "device=0"],
check=False,
)
if result.returncode == 0:
state["resume_count"] = int(state["resume_count"]) + 1
state["status"] = "resumed"
else:
state["status"] = "resume_command_failed"
write_state(state_path, state)
else:
state["status"] = "monitoring"
write_state(state_path, state)
if args.once:
return 0
time.sleep(args.poll_seconds)
if __name__ == "__main__":
raise SystemExit(main())