| |
| """Print a compact progress snapshot for an omni fine-tuning run.""" |
|
|
| from __future__ import annotations |
|
|
| import argparse |
| import json |
| import subprocess |
| from pathlib import Path |
|
|
|
|
| def parse_args() -> argparse.Namespace: |
| workspace_default = Path(__file__).resolve().parents[2] |
| parser = argparse.ArgumentParser(description="Monitor an omni fine-tuning run.") |
| parser.add_argument("--workspace", type=Path, default=workspace_default) |
| parser.add_argument("--run-id", default="xperience10m_qwen3_omni_32ep") |
| parser.add_argument("--last", type=int, default=5) |
| return parser.parse_args() |
|
|
|
|
| def read_jsonl(path: Path, limit: int) -> list[dict]: |
| if not path.exists(): |
| return [] |
| rows = [] |
| with path.open("r", encoding="utf-8") as fp: |
| for line in fp: |
| line = line.strip() |
| if line: |
| try: |
| rows.append(json.loads(line)) |
| except json.JSONDecodeError: |
| pass |
| return rows[-limit:] |
|
|
|
|
| def nvidia_smi() -> str: |
| cmd = [ |
| "nvidia-smi", |
| "--query-gpu=index,memory.used,memory.total,utilization.gpu", |
| "--format=csv,noheader,nounits", |
| ] |
| try: |
| return subprocess.check_output(cmd, text=True, stderr=subprocess.STDOUT).strip() |
| except (FileNotFoundError, subprocess.CalledProcessError) as exc: |
| return f"nvidia-smi unavailable: {exc}" |
|
|
|
|
| def main() -> int: |
| args = parse_args() |
| root = args.workspace / "results" / "omni_finetune" |
| pipeline_status = root / args.run_id / "pipeline_status.jsonl" |
| train_progress = root / f"{args.run_id}_lora" / "progress.jsonl" |
| metrics = root / f"{args.run_id}_eval" / "metrics.json" |
| log_path = root / args.run_id / "logs" / "pipeline.log" |
|
|
| print(f"Run: {args.run_id}") |
| print(f"Pipeline log: {log_path}") |
| print("\nGPU status: index, used MiB, total MiB, util %") |
| print(nvidia_smi()) |
|
|
| print("\nRecent pipeline phases:") |
| for row in read_jsonl(pipeline_status, args.last): |
| print(json.dumps(row, ensure_ascii=False)) |
|
|
| print("\nRecent training progress:") |
| for row in read_jsonl(train_progress, args.last): |
| print(json.dumps(row, ensure_ascii=False)) |
|
|
| if metrics.exists(): |
| print("\nEval metrics:") |
| payload = json.loads(metrics.read_text(encoding="utf-8")) |
| keys = ["accuracy", "action_macro_f1", "json_validity_rate", "subtask_accuracy", "object_micro_f1"] |
| print(json.dumps({key: payload.get(key) for key in keys}, indent=2)) |
| return 0 |
|
|
|
|
| if __name__ == "__main__": |
| raise SystemExit(main()) |
|
|
