microbe-model / scripts /cerebrium_dispatch.py
Miyu Horiuchi
Deploy app from main@a3254bf (no paper/ binaries)
0ed74db
"""Dispatch KOfam scans and/or ESM-2 embeddings to Cerebrium.
Each Cerebrium replica is a stateless HTTP endpoint that handles one genome at
a time (`replica_concurrency = 1`). We fan out across `max_replicas` parallel
in-flight requests; results stream to JSONL as they arrive.
Usage:
# Smoke test: 5 KOfam scans
uv run python scripts/cerebrium_dispatch.py kofam --limit 5
# Smoke test: 5 embeddings
uv run python scripts/cerebrium_dispatch.py embed --limit 5
# Full corpus (defaults: concurrency = max_replicas of the deployed app)
uv run python scripts/cerebrium_dispatch.py kofam
uv run python scripts/cerebrium_dispatch.py embed
"""
from __future__ import annotations
import argparse
import asyncio
import json
import os
import sys
import time
from pathlib import Path
from typing import Any
import httpx
import pandas as pd
import yaml
PROJECT_ID = "p-58781999"
REGION_HOST = "https://api.aws.us-east-1.cerebrium.ai"
APP_CONFIG = {
"kofam": {
"function": "scan_genome",
"concurrency": 10,
"out_path": Path("data/kofam_hits.jsonl"),
"id_field": "genome_accession",
"request_timeout": 180,
"ok_keys": ("ko_hits",),
},
"embed": {
"function": "embed_genome",
"concurrency": 3,
"out_path": Path("data/per_marker_embeddings.jsonl"),
"id_field": "bacdive_id",
"request_timeout": 600,
"ok_keys": ("row",),
},
}
def _read_access_token() -> str:
env_token = os.environ.get("CEREBRIUM_API_KEY") or os.environ.get("CEREBRIUM_INFERENCE_KEY")
if env_token:
return env_token
sys.exit(
"Set CEREBRIUM_API_KEY to a JWT from the dashboard's API Keys section. "
"The CLI's accesstoken doesn't work for inference endpoints."
)
def _load_pending_kofam(limit: int) -> list[dict[str, Any]]:
feats = pd.read_parquet("data/features.parquet")
accs = feats["genome_accession"].dropna().astype(str).unique().tolist()
done: set[str] = set()
out_path = APP_CONFIG["kofam"]["out_path"]
if out_path.exists():
with open(out_path) as fh:
for line in fh:
try:
row = json.loads(line)
except Exception:
continue
acc = row.get("genome_accession") or row.get("accession")
if acc:
done.add(str(acc))
pending = [a for a in accs if a not in done]
if limit:
pending = pending[:limit]
return [{"accession": a} for a in pending]
def _load_pending_embed(limit: int) -> list[dict[str, Any]]:
import microbe_model.config as cfg
pheno = pd.read_parquet("data/bacdive_phenotypes.parquet")
has_genome = pheno["genome_accession"].notna()
label_cols = list(cfg.PHENOTYPE_TARGETS.keys())
has_label = pheno[label_cols].notna().any(axis=1)
ready = pheno[has_genome & has_label].copy()
ready["bacdive_id"] = ready["bacdive_id"].astype(int)
done: set[int] = set()
out_path = APP_CONFIG["embed"]["out_path"]
if out_path.exists():
with open(out_path) as fh:
for line in fh:
try:
done.add(int(json.loads(line)["bacdive_id"]))
except Exception:
continue
pending = ready[~ready["bacdive_id"].isin(done)]
if limit:
pending = pending.head(limit)
return [
{"bacdive_id": int(row["bacdive_id"]), "accession": str(row["genome_accession"])}
for _, row in pending.iterrows()
]
async def _call_once(
client: httpx.AsyncClient, app: str, payload: dict[str, Any], token: str,
timeout: float,
) -> dict[str, Any]:
url = f"{REGION_HOST}/v4/{PROJECT_ID}/{app}/{APP_CONFIG[app]['function']}"
headers = {"Authorization": f"Bearer {token}", "Content-Type": "application/json"}
resp = await client.post(url, headers=headers, json=payload, timeout=timeout)
resp.raise_for_status()
data = resp.json()
if isinstance(data, dict) and "result" in data:
return data["result"]
return data
async def _worker(
app: str, queue: asyncio.Queue, log_fh, results: dict[str, int],
token: str, timeout: float, sem: asyncio.Semaphore,
):
async with httpx.AsyncClient() as client:
while True:
payload = await queue.get()
if payload is None:
queue.task_done()
return
async with sem:
start = time.time()
for attempt in range(3):
try:
out = await _call_once(client, app, payload, token, timeout)
elapsed = time.time() - start
if isinstance(out, dict) and out.get("ok"):
log_fh.write(json.dumps(out.get("row") if app == "embed" else out) + "\n")
log_fh.flush()
results["ok"] += 1
results["elapsed_sum"] += elapsed
else:
results["fail"] += 1
reason = out.get("reason", "?") if isinstance(out, dict) else "non-dict"
print(f" fail {payload}: {reason}", flush=True)
break
except httpx.HTTPStatusError as exc:
if exc.response.status_code in (429, 502, 503, 504):
await asyncio.sleep(2 ** attempt)
continue
results["fail"] += 1
print(f" http {exc.response.status_code} {payload}: {exc.response.text[:200]}",
flush=True)
break
except (httpx.TimeoutException, httpx.TransportError) as exc:
if attempt < 2:
await asyncio.sleep(2 ** attempt)
continue
results["fail"] += 1
print(f" timeout {payload}: {exc}", flush=True)
queue.task_done()
async def _run(app: str, jobs: list[dict[str, Any]], concurrency: int):
cfg = APP_CONFIG[app]
token = _read_access_token()
out_path: Path = cfg["out_path"]
out_path.parent.mkdir(parents=True, exist_ok=True)
queue: asyncio.Queue = asyncio.Queue()
for j in jobs:
await queue.put(j)
for _ in range(concurrency):
await queue.put(None)
results = {"ok": 0, "fail": 0, "elapsed_sum": 0.0}
sem = asyncio.Semaphore(concurrency)
t0 = time.time()
with open(out_path, "a") as log_fh:
workers = [
asyncio.create_task(_worker(
app, queue, log_fh, results, token, cfg["request_timeout"], sem,
))
for _ in range(concurrency)
]
last_report = t0
while any(not w.done() for w in workers):
await asyncio.sleep(15)
now = time.time()
done = results["ok"] + results["fail"]
if done == 0:
continue
rate = done / (now - t0)
remaining = len(jobs) - done
eta = remaining / rate if rate > 0 else float("inf")
if now - last_report >= 30:
print(
f" [{int(now - t0)}s] ok={results['ok']:,} fail={results['fail']:,} "
f"rate={rate:.2f}/s eta={int(eta/60)}min", flush=True,
)
last_report = now
await asyncio.gather(*workers)
elapsed = time.time() - t0
avg_per_ok = results["elapsed_sum"] / max(results["ok"], 1)
print(f"\nDone in {elapsed/60:.1f} min. ok={results['ok']:,} fail={results['fail']:,} "
f"avg/ok={avg_per_ok:.1f}s")
def main() -> None:
parser = argparse.ArgumentParser()
parser.add_argument("app", choices=list(APP_CONFIG.keys()))
parser.add_argument("--limit", type=int, default=0)
parser.add_argument("--concurrency", type=int, default=0,
help="Override default (default: app's max_replicas)")
args = parser.parse_args()
if args.app == "kofam":
jobs = _load_pending_kofam(args.limit)
else:
jobs = _load_pending_embed(args.limit)
if not jobs:
print("Nothing to do.")
return
concurrency = args.concurrency or APP_CONFIG[args.app]["concurrency"]
print(f"Dispatching {len(jobs):,} jobs to Cerebrium app '{args.app}' "
f"at concurrency={concurrency}.")
print(f" Output: {APP_CONFIG[args.app]['out_path']}")
asyncio.run(_run(args.app, jobs, concurrency))
if __name__ == "__main__":
main()