pre-refactor 041426
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rpotter6298
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"""
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Dispatch phase 4 experiment runs to the distributed job server.
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Reads experiment_grid.json, checks which runs already have complete 10x5 results,
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and submits the rest. Skips runs marked needs_implementation.
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Usage:
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python -m v3.scripts.main.phase4.dispatch_phase4 \
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--server http://hades:8765 --token hypertower
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# Dry run (print what would be submitted, don't actually submit):
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python -m v3.scripts.main.phase4.dispatch_phase4 \
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--server http://hades:8765 --token hypertower --dry-run
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# Override number of reps (default 10):
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python -m v3.scripts.main.phase4.dispatch_phase4 \
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--server http://hades:8765 --token hypertower --reps 4
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"""
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from __future__ import annotations
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import argparse
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import json
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import os
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import sys
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from pathlib import Path
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import requests
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# Allow running as `python v3/scripts/main/phase3/dispatch_phase3.py`
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sys.path.insert(0, str(Path(__file__).resolve().parents[4]))
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GRID_PATH = Path(__file__).parent / "experiment_grid.json"
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RESULTS_ROOT = Path(__file__).resolve().parents[4] / "v3" / "results"
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MODULE = "v3.scripts.main.run_cv"
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OUTPUT_DIR = "v3/results"
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REP_SEED_START = 100
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REP_SEED_STEP = 100
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# ── Completion check ──────────────────────────────────────────────────────────
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def _completed_reps(run_name: str, reps: int) -> list[int]:
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"""Return list of rep indices that already have a summary.json (any tower mode)."""
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done = []
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for i in range(reps):
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rep_dir = RESULTS_ROOT / run_name / f"rep{i:02d}" / "binary"
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# Accept any tower mode subdir
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if rep_dir.exists() and any((rep_dir / tm / "summary.json").exists()
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for tm in ("single", "bilateral", "siamese", "ensemble")):
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done.append(i)
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return done
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# ── Server API ────────────────────────────────────────────────────────────────
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class _API:
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def __init__(self, base_url: str, token: str):
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self.base_url = base_url.rstrip("/")
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self._h = {"x-token": token}
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def get(self, path: str, **params) -> object:
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r = requests.get(f"{self.base_url}{path}", headers=self._h, params=params, timeout=10)
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r.raise_for_status()
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return r.json()
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def post(self, path: str, body: dict) -> dict:
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r = requests.post(f"{self.base_url}{path}", headers=self._h, json=body, timeout=10)
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r.raise_for_status()
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return r.json()
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def _queued_reps(jobs: list[dict], run_name: str) -> set[int]:
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"""Return rep indices already pending or running in the server queue."""
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active = set()
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for job in jobs:
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if job["run_name"] != run_name:
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continue
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if job["state"] not in ("pending", "running"):
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continue
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# Extract --rep-index from job args
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try:
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args = job["args"] if isinstance(job["args"], list) else json.loads(job["args"])
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if "--rep-index" in args:
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active.add(int(args[args.index("--rep-index") + 1]))
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except Exception:
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pass
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return active
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def _submit_cv(api: _API, run_name: str, run_args: list[str],
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reps: int, missing: list[int], dry_run: bool):
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"""Submit one job per missing rep."""
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for i in missing:
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seed = REP_SEED_START + i * REP_SEED_STEP
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rep_args = run_args + [
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"--run-name", run_name,
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"--reps", "1",
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"--rep-seed-start", str(seed),
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"--rep-index", str(i),
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]
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body = {
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"run_name": run_name,
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"module": MODULE,
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"args": rep_args,
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"output_dir": OUTPUT_DIR,
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"priority": 0,
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}
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if dry_run:
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print(f" [dry-run] would queue rep{i:02d} seed={seed}")
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else:
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resp = api.post("/jobs", body)
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print(f" queued rep{i:02d} seed={seed} job_id={resp['job_id']}")
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# ── Main ──────────────────────────────────────────────────────────────────────
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def main():
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ap = argparse.ArgumentParser(description=__doc__,
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formatter_class=argparse.RawDescriptionHelpFormatter)
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ap.add_argument("--server", default=os.environ.get("HT_SERVER", ""),
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help="Server URL (or set HT_SERVER)")
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ap.add_argument("--token", default=os.environ.get("HT_TOKEN", ""),
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help="Shared secret (or set HT_TOKEN)")
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ap.add_argument("--reps", type=int, default=10,
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help="Expected number of reps per run (default: 10)")
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ap.add_argument("--grid", type=Path, default=GRID_PATH,
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help="Path to experiment grid JSON (default: experiment_grid.json)")
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ap.add_argument("--dry-run", action="store_true",
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help="Print what would be submitted without actually submitting")
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args = ap.parse_args()
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if not args.dry_run:
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if not args.server:
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ap.error("--server is required (or set HT_SERVER)")
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if not args.token:
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ap.error("--token is required (or set HT_TOKEN)")
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elif not args.server or not args.token:
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print("[dry-run] no --server/--token provided — skipping queue check, showing disk state only")
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grid = json.loads(args.grid.read_text())
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common_args = grid["common_args"]
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api = _API(args.server, args.token) if (args.server and args.token) else None
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# Fetch current server queue once (pending + running)
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server_jobs: list[dict] = []
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if api:
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try:
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all_jobs = api.get("/jobs")
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server_jobs = [j for j in all_jobs if j["state"] in ("pending", "running")]
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print(f"[server] {len(server_jobs)} job(s) currently pending/running in queue")
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except Exception as e:
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print(f"[warn] could not fetch server queue: {e}")
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# Collect all runs: baseline + every group's runs
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all_runs = [grid["baseline"]]
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for group in grid["groups"]:
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if group.get("needs_implementation"):
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print(f"\n[skip] group '{group['name']}' — {group['needs_implementation']}")
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continue
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all_runs.extend(group["runs"])
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submitted_total = 0
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skipped_total = 0
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for run in all_runs:
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run_name = run["run_name"]
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run_args = common_args + run.get("extra_args", [])
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done = set(_completed_reps(run_name, args.reps))
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queued = _queued_reps(server_jobs, run_name)
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accounted = done | queued
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missing = [i for i in range(args.reps) if i not in accounted]
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if not missing:
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if len(done) == args.reps:
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print(f"\n[done] {run_name} ({args.reps}/{args.reps} reps complete)")
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else:
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in_q = sorted(queued - done)
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print(f"\n[skip] {run_name} ({len(done)} done, {len(in_q)} queued: {[f'rep{i:02d}' for i in in_q]})")
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skipped_total += 1
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continue
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parts = []
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if done: parts.append(f"{len(done)} done")
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if queued: parts.append(f"{len(queued - done)} queued")
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status = ", ".join(parts) if parts else "not started"
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print(f"\n[queue] {run_name} ({status}) — submitting {len(missing)} rep(s)")
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_submit_cv(api, run_name, run_args, args.reps, missing, args.dry_run)
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submitted_total += len(missing)
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print(f"\n{'='*50}")
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print(f"Submitted: {submitted_total} jobs | Already accounted for: {skipped_total} runs")
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if grid.get("groups"):
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needs_impl = sum(1 for g in grid["groups"] if g.get("needs_implementation"))
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if needs_impl:
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print(f"Skipped (needs implementation): {needs_impl} group(s)")
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if __name__ == "__main__":
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main()
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@@ -0,0 +1,77 @@
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{
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"_notes": [
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"Phase 4 — Dual CNN architecture comparison (image only).",
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"Goal: isolate the effect of bilateral processing by comparing architectures without clinical data.",
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"Best settings from phase 3 carried forward: iop_ratio_drop_raw, bcd_p05 (p07 did not stack).",
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"common_args are prepended to every run's args list.",
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"All runs use --bridge-mode image_only — no clinical data."
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],
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"common_args": [
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"--eval-mode", "binary",
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"--bridge-mode", "image_only",
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"--epochs", "30",
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"--in-memory-cache",
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"--augment",
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"--tune-binary-threshold",
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"--backbone", "refugelike",
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"--iop-corr-method", "ratio",
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"--iop-drop-raw",
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"--output-root", "v3/results"
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],
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"_common_args_implicit_defaults": {
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"--tower-loss-mode": "bcd",
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"--bcd-prob": "0.5",
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"--warmup-cd-epochs": "0",
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"--bilat-warmup-tower-epochs": "3",
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"--bilat-warmup-fused-epochs": "3"
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},
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"baseline": {
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"run_name": "phase4/single",
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"description": "Single-eye baseline with best phase 3 image settings. Direct comparison point for bilateral modes.",
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"extra_args": ["--tower-mode", "single"]
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},
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"groups": [
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{
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"name": "architecture",
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"description": "Core bilateral architecture comparison.",
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"runs": [
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{
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"run_name": "phase4/ensemble",
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"description": "Ensemble: two independent single-eye forward passes, patient-level average of OD+OS scores.",
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"extra_args": ["--tower-mode", "ensemble"]
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},
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{
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"run_name": "phase4/bilateral",
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"description": "BilateralHT: shared towers, concat OD+OS → learned joint projection MLP → classifier.",
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"extra_args": ["--tower-mode", "bilateral"]
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},
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{
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"run_name": "phase4/siamese",
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"description": "SiameseHT: shared backbone, mean+delta (asymmetry) representation → classifier.",
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"extra_args": ["--tower-mode", "siamese"]
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}
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]
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},
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{
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"name": "loss_bilateral",
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"description": "Test loss function sensitivity in bilateral modes (using winner from architecture group).",
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"runs": [
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{
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"run_name": "phase4/bilateral_loss_all",
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"description": "BilateralHT with all-losses mode — joint training dynamics may differ from single-eye.",
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"extra_args": ["--tower-mode", "bilateral", "--tower-loss-mode", "all"]
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},
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{
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"run_name": "phase4/siamese_loss_all",
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"description": "SiameseHT with all-losses mode.",
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"extra_args": ["--tower-mode", "siamese", "--tower-loss-mode", "all"]
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}
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]
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}
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]
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}
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