ccbb5cbc9e
Operator hit an outage after the machine slept overnight: the agent showed "curator unreachable" in a loop while curator's API (lease) was actually fine and the browser could still load images — just slowly. Root cause is a feedback loop in the new pipeline: every download streams a full original through curator's single Python file-serving path, and the autoscaler grows DOWNLOADERS whenever the buffer is empty. When downloads are merely SLOW/failing, the buffer is empty for that reason — so the agent piled on more concurrent large-file GETs, saturating curator's web workers + NFS, which slowed curator (and its browser) further and produced more failures → more downloaders. Classic congestion collapse. - Failure-aware autoscaling: if transient download failures rose since the last decision, SHRINK the downloader pool toward the floor instead of growing — the empty buffer is caused by failures, not the GPU starving. It ramps back up only once downloads succeed again. - DL_MAX 24 → 8: 24 concurrent large-file downloads through one Python serving path is too many; 8 keeps a fast GPU fed without stampeding curator. - fetch_image timeout 180 → (10, 60): the read timeout is between-bytes, so a large-but-flowing download still completes, but a stuck/dead connection fails in 60s instead of hanging a downloader for 3 min and piling up stuck requests. Build marker 2026-07-01.4. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa
130 lines
5.2 KiB
Python
130 lines
5.2 KiB
Python
"""HTTP client for the FabledCurator GPU-job API.
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The agent's ONLY contact with FC — lease/submit/heartbeat/fail + fetch image
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bytes, all over HTTP with the bearer token. No DB/Redis.
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"""
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import requests
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from requests.adapters import HTTPAdapter
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from urllib3.util.retry import Retry
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class FcClient:
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def __init__(self, base_url: str, token: str, agent_id: str):
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self.base = base_url.rstrip("/")
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self.agent_id = agent_id
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# Main session: NO in-request retry — lease/fetch are cheap to redo and
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# the worker loop already backs off + re-leases on failure. (Auto-retrying
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# a lease could double-claim a batch if a response is lost.)
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self.s = self._session(token)
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# Submit session: retry in-place, because by submit time the GPU work is
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# already DONE — a momentary blip (dropped connection, gateway 5xx during
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# a curator redeploy) must not throw that work away and force a full
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# re-download + recompute on another agent. A duplicate submit after a
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# lost response is harmless: the job is already closed, so it just returns
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# 409 lease_invalid (a no-op). Idempotent enough to retry POST safely.
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retry = Retry(
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total=3, connect=3, read=3, status=3,
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backoff_factor=0.5, # ~0.5s, 1s, 2s between tries
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status_forcelist=(500, 502, 503, 504), # transient server/gateway
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allowed_methods=frozenset({"POST"}),
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raise_on_status=False, # let raise_for_status decide
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)
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self._submit_s = self._session(token, retry)
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@staticmethod
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def _session(token: str, retry: Retry | None = None) -> requests.Session:
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s = requests.Session()
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s.headers["Authorization"] = f"Bearer {token}"
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# Many worker threads share a Session; the default pool (10) would
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# throttle them + spam "connection pool is full". Size it for the cap.
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adapter = HTTPAdapter(
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pool_connections=64, pool_maxsize=64, max_retries=retry or 0
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)
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s.mount("http://", adapter)
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s.mount("https://", adapter)
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return s
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def lease(self, batch_size: int) -> list[dict]:
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r = self.s.post(
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f"{self.base}/api/gpu/jobs/lease",
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json={"agent_id": self.agent_id, "batch_size": batch_size},
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timeout=30,
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)
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r.raise_for_status()
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return r.json().get("jobs", [])
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def submit(self, job_id: int, regions: list[dict], replace_kinds: list[str]) -> dict:
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r = self._submit_s.post(
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f"{self.base}/api/gpu/jobs/submit",
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json={
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"agent_id": self.agent_id, "job_id": job_id,
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"regions": regions, "replace_kinds": replace_kinds,
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},
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timeout=120,
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)
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r.raise_for_status()
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return r.json()
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def submit_embedding(self, job_id: int, embedding: list, version: str) -> dict:
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"""Post a whole-image SigLIP embedding (the 'embed' task) → image_record."""
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r = self._submit_s.post(
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f"{self.base}/api/gpu/jobs/submit_embedding",
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json={
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"agent_id": self.agent_id, "job_id": job_id,
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"embedding": embedding, "embedding_version": version,
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},
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timeout=120,
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)
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r.raise_for_status()
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return r.json()
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def heartbeat(self, job_ids: list[int]) -> None:
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try:
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self.s.post(
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f"{self.base}/api/gpu/jobs/heartbeat",
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json={"agent_id": self.agent_id, "job_ids": job_ids},
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timeout=30,
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)
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except requests.RequestException:
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pass
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def fail(self, job_id: int, error: str) -> None:
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try:
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self.s.post(
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f"{self.base}/api/gpu/jobs/fail",
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json={"agent_id": self.agent_id, "job_id": job_id, "error": error},
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timeout=30,
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)
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except requests.RequestException:
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pass
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def release(self, job_ids: list[int]) -> None:
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# Graceful hand-back on stop so orphaned work is re-leased at once.
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if not job_ids:
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return
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try:
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self.s.post(
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f"{self.base}/api/gpu/jobs/release",
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json={"agent_id": self.agent_id, "job_ids": job_ids},
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timeout=30,
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)
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except requests.RequestException:
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pass
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def fetch_image(self, image_url: str) -> bytes:
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# image_url is a server-relative path ("/images/...").
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# timeout=(connect, read): the read timeout is BETWEEN-BYTES, not total,
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# so a large-but-flowing download still completes — but a stuck/dead
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# connection (curator overloaded) fails in 60s instead of hanging a
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# downloader for 180s and piling up concurrent stuck requests on curator.
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r = self.s.get(f"{self.base}{image_url}", timeout=(10, 60))
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r.raise_for_status()
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return r.content
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def queue_status(self) -> dict:
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# Short timeout: this backs the UI /status poll, so a busy curator must
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# not hang the page for long (the GPU meters poll /gpu separately).
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r = self.s.get(f"{self.base}/api/gpu/status", timeout=5)
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r.raise_for_status()
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return r.json()
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