1bcfbb2335
The point of this service is fast handoff of one GPU between applications. It grew up
around the two on this box, and their names ended up compiled into process matching,
VRAM attribution, busy detection and release calls alike -- about 385 references
across five modules. That made it a script for Ollama and ComfyUI rather than a GPU
arbitrator.
tenants.py describes an application as data: how to recognise its processes, how to
tell whether it is genuinely working, how to ask it for VRAM back, and how much it
matters when two want the card. Ollama, ComfyUI and the desktop compositor ship as
defaults in tenants.json, so behaviour is unchanged, but the arbitration logic no
longer knows any particular name. Endpoints are generic: GET /api/tenants,
GET /api/tenants/{name}, POST /api/tenants/{name}/release -- the last being the
general form of both the Ollama soft-yield and the ComfyUI purge.
Verified by registering a third application on this machine with no code change: the
speech relay that had been showing up only as anonymous "unmanaged VRAM" is now named,
attributed, and probed by the VRAM it holds rather than by an API it does not have.
Because it declares no release strategy, a release request returns 409 explaining that
its memory cannot be reclaimed, instead of reporting a success that did nothing.
Busy probes deliberately cannot use GPU utilisation. It is shared by every tenant, so
it cannot attribute work to one of them -- the mistake that made a stale ComfyUI queue
entry undetectable earlier in this branch. A tenant's own VRAM is the signal.
Writing the tests exposed that the suite had become non-hermetic: classification is now
configuration, so a test asserting "a third-party process is unmanaged" started failing
the moment the speech relay was registered on this machine. An autouse fixture now
isolates every test from the operator's live tenants.json.
Tests: 231 (was 206).
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
1352 lines
60 KiB
Python
1352 lines
60 KiB
Python
"""VRAM Arbitrator and High-Speed Switch Manager for Ollama and ComfyUI."""
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import time
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import httpx
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import psutil
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import logging
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from typing import Dict, List, Any, Optional
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from collections import deque
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import asyncio
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import json
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import websockets
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import overclock_manager
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import ram_optimizer
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import tenants as tenants_mod
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import telemetry_store
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try:
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import pynvml
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pynvml.nvmlInit()
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NVML_AVAILABLE = True
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except Exception as e:
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NVML_AVAILABLE = False
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logger = logging.getLogger("vram_arbitrator")
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OLLAMA_API_BASE = "http://localhost:11434"
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COMFY_API_BASE = "http://127.0.0.1:8188"
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# Circular buffer for transition events (the durable log lives in telemetry_store)
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SWITCH_HISTORY = deque(maxlen=50)
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# Bandwidth thresholds for classifying how a model reached VRAM, calibrated by measuring
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# the same 12.87 GB model loaded cold and warm on this box (2026-08-28):
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#
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# 3.1% resident -> 34.3 s -> 0.38 GB/s
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# 100% resident -> 4.9 s -> 2.63 GB/s
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#
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# The first cut at these numbers assumed a page-cache-fed load would approach the bus
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# rate and set the cache-hit bar at 5 GB/s. It does not: Ollama's load_duration covers
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# host-to-device transfer and model initialisation as well as the file read, so a fully
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# resident model still reports ~2.6 GB/s while the page cache itself reads at 6.4 GB/s.
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# A 5 GB/s bar could therefore never be met, and every warm load was being reported as
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# a partial hit. Thresholds now sit either side of the measured 6.9x separation.
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RAM_HIT_GBPS = 2.0
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PARTIAL_HIT_GBPS = 0.8
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# How long to wait for Ollama's VRAM to actually drain.
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#
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# Ollama will not unload a model mid-generation. With OLLAMA_NUM_PARALLEL=1 our
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# keep_alive:0 request queues behind the running one and takes effect the moment it
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# finishes, so a model that is busy is not failing -- it is finishing, and it will
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# release on its own. Blocking the arbitrator for the length of someone's inference
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# helps nobody: ComfyUI is not gated on our return value, and every second spent
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# blocked is a second the watchdog and profile switching are stalled.
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#
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# So: wait briefly for the common case (an idle model releases in 40-110 ms here),
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# then classify. A caller who genuinely wants to block can ask for a longer wait.
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YIELD_CONFIRM_TIMEOUT_S = 2.0
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YIELD_CONFIRM_TIMEOUT_BLOCKING_S = 30.0
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# A model still holding VRAM while the GPU is pinned is generating, not wedged.
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BUSY_UTIL_PCT = 50
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BUSY_PROBE_S = 0.6
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# Fraction of a model that may sit outside VRAM before we call it starved. A little
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# slack absorbs rounding and KV-cache accounting; beyond it, layers are on the CPU.
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CPU_OFFLOAD_TOLERANCE = 0.02
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# Only intervene when ComfyUI is actually holding enough VRAM to be the cause.
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RECLAIM_MIN_COMFY_BYTES = 512 * 1024 ** 2
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# Ollama's response when a model will not fit. Which of the two failure modes you get
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# depends on configuration: with n_gpu_layers left to Ollama it spills layers to the CPU
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# and reports size_vram < size; with n_gpu_layers pinned (99 on this box) it refuses and
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# returns a hard CUDA OOM instead. Both are handled -- the spill by
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# AutoArbitrator._check_ollama_starved, the hard failure by the retry below.
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OOM_SIGNATURES = ("out of memory", "cudamalloc", "unable to allocate",
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"failed to allocate", "cuda error")
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def describe_unmanaged() -> Dict[str, Any]:
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"""VRAM held by processes this service cannot reclaim, named explicitly."""
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stats = get_gpu_hardware_stats()
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bd = stats.get("breakdown", {}) if stats.get("available") else {}
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entries = bd.get("unmanaged", [])
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return {
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"unmanaged_gb": bd.get("unmanaged_gb", 0.0),
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"processes": entries,
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"note": ("VRAM held by processes outside HyperSwap's control; it cannot be "
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"reclaimed automatically" if entries else
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"no third-party GPU processes are holding VRAM"),
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}
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def looks_like_vram_oom(text: str) -> bool:
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low = (text or "").lower()
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return any(sig in low for sig in OOM_SIGNATURES)
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YIELD_CONFIRM_POLL_S = 0.02
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YIELD_RESIDUAL_BYTES = 256 * 1024 ** 2 # treat <256 MB as "released"
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# Connection-pooled clients. Re-creating an AsyncClient per call meant a fresh TCP
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# handshake on every one of the watchdog's polls.
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_clients: Dict[str, httpx.AsyncClient] = {}
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def _client(base_url: str, timeout: float) -> httpx.AsyncClient:
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key = f"{base_url}|{timeout}"
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c = _clients.get(key)
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if c is None or c.is_closed:
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c = httpx.AsyncClient(
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base_url=base_url,
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timeout=timeout,
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limits=httpx.Limits(max_keepalive_connections=4, max_connections=8),
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)
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_clients[key] = c
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return c
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async def close_clients() -> None:
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for c in list(_clients.values()):
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try:
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await c.aclose()
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except Exception:
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pass
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_clients.clear()
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# Bit flags from nvmlDeviceGetCurrentClocksThrottleReasons, decoded for the governor.
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THROTTLE_REASONS = {
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0x0000000000000001: "gpu_idle",
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0x0000000000000002: "applications_clocks_setting",
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0x0000000000000004: "sw_power_cap",
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0x0000000000000008: "hw_slowdown",
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0x0000000000000010: "sync_boost",
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0x0000000000000020: "sw_thermal_slowdown",
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0x0000000000000040: "hw_thermal_slowdown",
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0x0000000000000080: "hw_power_brake_slowdown",
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0x0000000000000100: "display_clock_setting",
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}
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def decode_throttle_reasons(bits: int) -> List[str]:
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return [name for mask, name in THROTTLE_REASONS.items() if bits & mask]
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def get_process_vram_bytes() -> Dict[str, int]:
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"""Fast NVML-only VRAM attribution, used by the yield barrier's tight poll loop.
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Deliberately avoids psutil lookups: this runs every 20 ms while we wait for VRAM
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to actually drain.
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"""
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out = {"ollama_bytes": 0, "comfyui_bytes": 0, "other_bytes": 0, "free_bytes": 0,
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"desktop_bytes": 0, "unmanaged_bytes": 0, "gpu_util_pct": 0}
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if not NVML_AVAILABLE:
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return out
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try:
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handle = pynvml.nvmlDeviceGetHandleByIndex(0)
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out["free_bytes"] = pynvml.nvmlDeviceGetMemoryInfo(handle).free
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try:
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out["gpu_util_pct"] = pynvml.nvmlDeviceGetUtilizationRates(handle).gpu
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except Exception:
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pass
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procs = list(pynvml.nvmlDeviceGetComputeRunningProcesses(handle))
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try:
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procs += list(pynvml.nvmlDeviceGetGraphicsRunningProcesses(handle))
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except Exception:
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pass
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merged: Dict[int, int] = {}
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for p in procs:
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merged[p.pid] = max(merged.get(p.pid, 0), p.usedGpuMemory or 0)
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for pid, used in merged.items():
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key = _pid_key(pid)
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kind = _PID_KIND_CACHE.get(key) if key else None
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if kind is None:
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kind = _classify_pid(pid)
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if key:
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if len(_PID_KIND_CACHE) >= _PID_KIND_CACHE_MAX:
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_PID_KIND_CACHE.clear()
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_PID_KIND_CACHE[key] = kind
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if kind == "ollama":
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out["ollama_bytes"] += used
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elif kind == "comfy":
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out["comfyui_bytes"] += used
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elif kind == "desktop":
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out["desktop_bytes"] += used
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out["other_bytes"] += used
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else:
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out["unmanaged_bytes"] += used
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out["other_bytes"] += used
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except Exception as e:
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logger.debug(f"get_process_vram_bytes failed: {e}")
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return out
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# Keyed by (pid, process start time) rather than pid alone. Linux recycles PIDs, and a
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# stale entry would attribute a new process's VRAM to Ollama or ComfyUI -- in the same
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# snapshot the yield barrier uses to decide whether VRAM was released.
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_PID_KIND_CACHE: Dict[tuple, str] = {}
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_PID_KIND_CACHE_MAX = 512
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def _pid_key(pid: int) -> Optional[tuple]:
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try:
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return (pid, psutil.Process(pid).create_time())
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except Exception:
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return None
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# Tenant names as used by this module's buckets. The tenant registry is the source of
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# truth for *which* application a process belongs to; these two names are kept because
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# the REST payloads and the dashboard have used them since the beginning.
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_BUCKET_ALIASES = {"comfyui": "comfy"}
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def _pid_key(pid: int) -> Optional[tuple]:
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try:
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return (pid, psutil.Process(pid).create_time())
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except Exception:
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return None
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# Compositors and display servers. Their VRAM is small, permanent and not ours to
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# reclaim, so it should not be confused with a real workload.
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DESKTOP_PROCESS_HINTS = (
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"gnome-shell", "xorg", "gnome-remote-desktop", "mutter", "kwin", "plasmashell",
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"gnome-session", "wayland", "weston", "sddm", "gdm", "picom", "compiz",
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)
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def _classify_pid(pid: int) -> str:
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"""Which tenant owns this GPU process.
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The matching rules used to be substrings compiled into this function, which made the
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two applications on this box part of the arbitrator rather than input to it. They now
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come from the tenant registry, so a third application is a config entry.
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"unmanaged" still means something specific and useful: VRAM held by something with no
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declared way to release it, and therefore headroom this service can never offer.
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"""
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return _BUCKET_ALIASES.get(tenants_mod.classify_pid(pid), tenants_mod.classify_pid(pid))
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def get_gpu_hardware_stats() -> Dict[str, Any]:
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"""Retrieve comprehensive GPU hardware and process metrics via NVML."""
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if not NVML_AVAILABLE:
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return {"available": False, "error": "NVML not initialized"}
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|
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try:
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handle = pynvml.nvmlDeviceGetHandleByIndex(0)
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name = pynvml.nvmlDeviceGetName(handle)
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if isinstance(name, bytes):
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name = name.decode("utf-8")
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|
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mem_info = pynvml.nvmlDeviceGetMemoryInfo(handle)
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util_rates = pynvml.nvmlDeviceGetUtilizationRates(handle)
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temp_c = pynvml.nvmlDeviceGetTemperature(handle, pynvml.NVML_TEMPERATURE_GPU)
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try:
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power_mw = pynvml.nvmlDeviceGetPowerUsage(handle)
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power_w = round(power_mw / 1000.0, 1)
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except Exception:
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power_w = 0.0
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fan_pct = 0
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fans = []
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try:
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num_fans = pynvml.nvmlDeviceGetNumFans(handle)
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for i in range(num_fans):
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try:
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fans.append(pynvml.nvmlDeviceGetFanSpeed_v2(handle, i))
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except Exception:
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pass
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if fans:
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fan_pct = max(fans)
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else:
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fan_pct = pynvml.nvmlDeviceGetFanSpeed(handle)
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fans = [fan_pct]
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except Exception:
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try:
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fan_pct = pynvml.nvmlDeviceGetFanSpeed(handle)
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fans = [fan_pct]
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except Exception:
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fan_pct = 0
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fans = []
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|
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|
try:
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clock_graphics = pynvml.nvmlDeviceGetClockInfo(handle, pynvml.NVML_CLOCK_GRAPHICS)
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clock_mem = pynvml.nvmlDeviceGetClockInfo(handle, pynvml.NVML_CLOCK_MEM)
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except Exception:
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clock_graphics = 0
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clock_mem = 0
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|
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|
# PCIe throughput (KB/s) — TX + RX. Key metric for the RAM-cache
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# PCIe-speed swap thesis (assimilated from pmady/gpu-mcp-server).
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pcie_tx_kbps = 0
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pcie_rx_kbps = 0
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try:
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pcie_tx_kbps = pynvml.nvmlDeviceGetPcieThroughput(handle, pynvml.NVML_PCIE_UTIL_TX_BYTES)
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except Exception:
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|
pass
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|
try:
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pcie_rx_kbps = pynvml.nvmlDeviceGetPcieThroughput(handle, pynvml.NVML_PCIE_UTIL_RX_BYTES)
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except Exception:
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|
pass
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|
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|
# Why the GPU is not running at full clocks — the thermal governor reads this.
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|
throttle_bits = 0
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throttle_reasons: List[str] = []
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|
try:
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throttle_bits = pynvml.nvmlDeviceGetCurrentClocksThrottleReasons(handle)
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|
throttle_reasons = decode_throttle_reasons(throttle_bits)
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except Exception:
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|
pass
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|
|
|
# Power management limit (watts) — the OC ceiling.
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power_limit_w = 0.0
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|
try:
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power_limit_w = round(pynvml.nvmlDeviceGetPowerManagementLimit(handle) / 1000.0, 1)
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except Exception:
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|
pass
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|
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|
# Driver + CUDA version (completeness).
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driver_version = ""
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cuda_version = ""
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|
try:
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dv = pynvml.nvmlSystemGetDriverVersion()
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|
driver_version = dv.decode("utf-8") if isinstance(dv, bytes) else str(dv)
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|
except Exception:
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|
pass
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|
try:
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cv = pynvml.nvmlSystemGetCudaDriverVersion()
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cuda_version = cv # int like 12030 == CUDA 12.3
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|
except Exception:
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|
pass
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|
|
|
# Discover processes on GPU
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|
proc_breakdown = {
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|
"ollama_bytes": 0,
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|
"comfyui_bytes": 0,
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|
"system_bytes": 0,
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|
"desktop_bytes": 0,
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"unmanaged_bytes": 0,
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"unmanaged": [],
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"processes": [],
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# Generic attribution: one entry per tenant, so an application added to the
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# registry is reported without any change here.
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"by_tenant": {},
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}
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|
|
|
try:
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procs = pynvml.nvmlDeviceGetComputeRunningProcesses(handle)
|
|
graphics_procs = pynvml.nvmlDeviceGetGraphicsRunningProcesses(handle)
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|
all_procs = {p.pid: p.usedGpuMemory for p in procs}
|
|
for p in graphics_procs:
|
|
all_procs[p.pid] = max(all_procs.get(p.pid, 0), p.usedGpuMemory or 0)
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|
|
|
for pid, used_mem in all_procs.items():
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|
pname = "Unknown"
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|
cmdline = ""
|
|
try:
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|
proc = psutil.Process(pid)
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|
pname = proc.name()
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|
cmdline = " ".join(proc.cmdline())
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|
except Exception:
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|
pass
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|
|
|
kind = _classify_pid(pid)
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is_ollama = kind == "ollama"
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is_comfy = kind == "comfy"
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|
|
|
if is_ollama:
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proc_breakdown["ollama_bytes"] += used_mem
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|
elif is_comfy:
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proc_breakdown["comfyui_bytes"] += used_mem
|
|
else:
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|
proc_breakdown["system_bytes"] += used_mem
|
|
if kind == "desktop":
|
|
proc_breakdown["desktop_bytes"] += used_mem
|
|
else:
|
|
proc_breakdown["unmanaged_bytes"] += used_mem
|
|
proc_breakdown["unmanaged"].append({
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|
"pid": pid, "name": pname,
|
|
"cmdline": cmdline[:120],
|
|
"vram_mb": round(used_mem / (1024**2), 1),
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|
})
|
|
|
|
proc_breakdown["by_tenant"][kind] = (
|
|
proc_breakdown["by_tenant"].get(kind, 0) + used_mem)
|
|
proc_breakdown["processes"].append({
|
|
"pid": pid,
|
|
"name": pname,
|
|
"cmdline": cmdline[:60],
|
|
"vram_bytes": used_mem,
|
|
"vram_mb": round(used_mem / (1024**2), 1),
|
|
"is_ollama": is_ollama,
|
|
"is_comfy": is_comfy,
|
|
"kind": kind,
|
|
})
|
|
except Exception as e:
|
|
logger.error(f"Error enumerating GPU processes: {e}")
|
|
|
|
total_vram = mem_info.total
|
|
used_vram = mem_info.used
|
|
free_vram = mem_info.free
|
|
|
|
return {
|
|
"available": True,
|
|
"device_name": name,
|
|
"vram_total_bytes": total_vram,
|
|
"vram_total_gb": round(total_vram / (1024**3), 2),
|
|
"vram_used_bytes": used_vram,
|
|
"vram_used_gb": round(used_vram / (1024**3), 2),
|
|
"vram_free_bytes": free_vram,
|
|
"vram_free_gb": round(free_vram / (1024**3), 2),
|
|
"vram_used_pct": round((used_vram / total_vram * 100) if total_vram > 0 else 0, 1),
|
|
"gpu_util_pct": util_rates.gpu,
|
|
"mem_util_pct": util_rates.memory,
|
|
"temperature_c": temp_c,
|
|
"power_w": power_w,
|
|
"power_limit_w": power_limit_w,
|
|
"pcie_tx_kbps": pcie_tx_kbps,
|
|
"pcie_rx_kbps": pcie_rx_kbps,
|
|
"throttle_bits": throttle_bits,
|
|
"throttle_reasons": throttle_reasons,
|
|
"driver_version": driver_version,
|
|
"cuda_version": cuda_version,
|
|
"fan_pct": fan_pct,
|
|
"fans": fans,
|
|
"num_fans": len(fans),
|
|
"clock_graphics_mhz": clock_graphics,
|
|
"clock_mem_mhz": clock_mem,
|
|
"breakdown": {
|
|
"ollama_mb": round(proc_breakdown["ollama_bytes"] / (1024**2), 1),
|
|
"ollama_gb": round(proc_breakdown["ollama_bytes"] / (1024**3), 2),
|
|
"comfyui_mb": round(proc_breakdown["comfyui_bytes"] / (1024**2), 1),
|
|
"comfyui_gb": round(proc_breakdown["comfyui_bytes"] / (1024**3), 2),
|
|
"system_mb": round(proc_breakdown["system_bytes"] / (1024**2), 1),
|
|
"system_gb": round(proc_breakdown["system_bytes"] / (1024**3), 2),
|
|
"desktop_gb": round(proc_breakdown["desktop_bytes"] / (1024**3), 2),
|
|
# VRAM held by workloads this service has no control over. It cannot be
|
|
# reclaimed, so it is permanently unavailable headroom.
|
|
"unmanaged_gb": round(proc_breakdown["unmanaged_bytes"] / (1024**3), 2),
|
|
"unmanaged": proc_breakdown["unmanaged"],
|
|
"by_tenant_gb": {k: round(b / (1024**3), 2)
|
|
for k, b in proc_breakdown["by_tenant"].items()},
|
|
"free_mb": round(free_vram / (1024**2), 1),
|
|
"free_gb": round(free_vram / (1024**3), 2),
|
|
"processes": proc_breakdown["processes"],
|
|
}
|
|
}
|
|
except Exception as e:
|
|
return {"available": False, "error": str(e)}
|
|
|
|
async def get_ollama_live_state() -> Dict[str, Any]:
|
|
"""Get active models, running status, and VRAM expiration from Ollama."""
|
|
state = {
|
|
"online": False,
|
|
"loaded_models": [],
|
|
"active_model_name": None,
|
|
"active_model_vram_gb": 0.0,
|
|
"active_context": 0,
|
|
"expires_at": None,
|
|
"installed_models": [],
|
|
# Ollama silently spills layers to CPU when VRAM is short. size_vram < size is the
|
|
# only externally visible sign, and the cost is roughly an order of magnitude in
|
|
# decode speed, so it is worth surfacing loudly.
|
|
"gpu_fraction": 1.0,
|
|
"cpu_offload_pct": 0.0,
|
|
"partially_offloaded": False,
|
|
}
|
|
try:
|
|
client = _client(OLLAMA_API_BASE, 3.0)
|
|
# Check running models (ps)
|
|
ps_resp = await client.get("/api/ps")
|
|
if ps_resp.status_code == 200:
|
|
state["online"] = True
|
|
models = ps_resp.json().get("models", [])
|
|
state["loaded_models"] = models
|
|
if models:
|
|
first = models[0]
|
|
state["active_model_name"] = first.get("name")
|
|
vram_bytes = first.get("size_vram", first.get("size", 0))
|
|
state["active_model_vram_gb"] = round(vram_bytes / (1024**3), 2)
|
|
state["active_context"] = first.get("context_length", 0)
|
|
state["expires_at"] = first.get("expires_at")
|
|
|
|
# Check all tags
|
|
tags_resp = await client.get("/api/tags")
|
|
if tags_resp.status_code == 200:
|
|
state["installed_models"] = tags_resp.json().get("models", [])
|
|
except Exception as e:
|
|
logger.debug(f"Ollama check error: {e}")
|
|
|
|
return state
|
|
|
|
async def get_comfyui_live_state() -> Dict[str, Any]:
|
|
"""Get prompt queue, device status, and active execution from ComfyUI."""
|
|
state = {
|
|
"online": False,
|
|
"executing": False,
|
|
"queue_remaining": 0,
|
|
"queue_running": 0,
|
|
"current_node": None,
|
|
"current_prompt_id": None,
|
|
"vram_free_mb": 0,
|
|
"vram_total_mb": 0,
|
|
}
|
|
try:
|
|
client = _client(COMFY_API_BASE, 3.0)
|
|
# Check system stats
|
|
stats_resp = await client.get("/system_stats")
|
|
if stats_resp.status_code == 200:
|
|
state["online"] = True
|
|
data = stats_resp.json()
|
|
devices = data.get("devices", [])
|
|
if devices:
|
|
dev = devices[0]
|
|
state["vram_free_mb"] = round(dev.get("vram_free", 0) / (1024**2), 1)
|
|
state["vram_total_mb"] = round(dev.get("vram_total", 0) / (1024**2), 1)
|
|
|
|
# Check queue
|
|
queue_resp = await client.get("/queue")
|
|
if queue_resp.status_code == 200:
|
|
qdata = queue_resp.json()
|
|
running = qdata.get("queue_running", [])
|
|
pending = qdata.get("queue_pending", [])
|
|
state["queue_running"] = len(running)
|
|
state["queue_remaining"] = len(pending)
|
|
state["executing"] = len(running) > 0
|
|
if running:
|
|
state["current_prompt_id"] = running[0][1] if len(running[0]) > 1 else str(running[0])
|
|
except Exception as e:
|
|
logger.debug(f"ComfyUI check error: {e}")
|
|
|
|
return state
|
|
|
|
async def _await_vram_release(baseline_bytes: int,
|
|
timeout_s: float = YIELD_CONFIRM_TIMEOUT_S) -> Dict[str, Any]:
|
|
"""Wait for Ollama's VRAM to drain, distinguishing "busy" from "stuck".
|
|
|
|
Posting keep_alive:0 only *asks* Ollama to unload; the driver frees the allocation
|
|
some milliseconds later, and returning before that happens is how ComfyUI ends up
|
|
allocating into VRAM that is still occupied.
|
|
|
|
But there is a second case the first version of this got wrong. If the model is
|
|
mid-generation it cannot unload at all, and reporting that as a timeout made a
|
|
perfectly healthy cron job look like a 95% failure rate. When the VRAM has not
|
|
moved and the GPU is pinned, the model is working; the queued unload will fire when
|
|
it finishes. That is `busy`, not a failure.
|
|
|
|
Returns an `outcome` of "released", "busy" or "stuck".
|
|
"""
|
|
t0 = time.perf_counter()
|
|
peak_util = 0
|
|
while True:
|
|
snap = get_process_vram_bytes()
|
|
last = snap["ollama_bytes"]
|
|
peak_util = max(peak_util, snap.get("gpu_util_pct", 0))
|
|
elapsed = time.perf_counter() - t0
|
|
|
|
if last <= YIELD_RESIDUAL_BYTES:
|
|
return {
|
|
"outcome": "released",
|
|
"confirmed": True,
|
|
"confirm_ms": round(elapsed * 1000, 2),
|
|
"residual_bytes": last,
|
|
"free_bytes": snap["free_bytes"],
|
|
"gpu_util_pct": snap.get("gpu_util_pct", 0),
|
|
}
|
|
|
|
# Unmoved VRAM plus a pinned GPU means a generation is in flight.
|
|
busy = (elapsed >= BUSY_PROBE_S
|
|
and last >= baseline_bytes - YIELD_RESIDUAL_BYTES
|
|
and peak_util >= BUSY_UTIL_PCT)
|
|
|
|
if busy or elapsed >= timeout_s:
|
|
outcome = "busy" if busy else "stuck"
|
|
return {
|
|
"outcome": outcome,
|
|
"confirmed": False,
|
|
"confirm_ms": round(elapsed * 1000, 2),
|
|
"residual_bytes": last,
|
|
"free_bytes": snap["free_bytes"],
|
|
"gpu_util_pct": snap.get("gpu_util_pct", 0),
|
|
"peak_util_pct": peak_util,
|
|
"error": (
|
|
f"Ollama is mid-generation ({peak_util}% GPU, "
|
|
f"{round(last / (1024**3), 2)} GB held); the queued unload will apply "
|
|
f"when it finishes"
|
|
if outcome == "busy" else
|
|
f"Ollama still holding {round(last / (1024**3), 2)} GB after "
|
|
f"{timeout_s}s with the GPU idle"
|
|
),
|
|
}
|
|
await asyncio.sleep(YIELD_CONFIRM_POLL_S)
|
|
|
|
|
|
# Detached tasks need a strong reference or the loop may garbage-collect them mid-flight.
|
|
_DETACHED: set = set()
|
|
|
|
|
|
def _spawn_detached(coro) -> None:
|
|
task = asyncio.ensure_future(coro)
|
|
_DETACHED.add(task)
|
|
task.add_done_callback(_DETACHED.discard)
|
|
|
|
|
|
async def _confirm_release_later(targets: List[str], baseline_bytes: int,
|
|
max_wait_s: float = 900.0) -> None:
|
|
"""Watch for a queued unload to land after the in-flight generation finishes.
|
|
|
|
Runs detached so the caller is never held for the length of an inference. Logs the
|
|
eventual release so the event log tells the whole story rather than stopping at
|
|
"deferred".
|
|
"""
|
|
t0 = time.perf_counter()
|
|
while (time.perf_counter() - t0) < max_wait_s:
|
|
await asyncio.sleep(0.5)
|
|
snap = get_process_vram_bytes()
|
|
if snap["ollama_bytes"] <= YIELD_RESIDUAL_BYTES:
|
|
waited_ms = round((time.perf_counter() - t0) * 1000, 2)
|
|
_record({
|
|
"event_type": "Ollama VRAM Yield",
|
|
"source": ", ".join(targets)[:200],
|
|
"target": "VRAM 0MB (Kept in RAM)",
|
|
"duration_ms": waited_ms,
|
|
"yield_confirm_ms": waited_ms,
|
|
"cache_status": "RAM-Cached",
|
|
"detail": "released after the in-flight generation completed",
|
|
})
|
|
arbitrator.note_deferred_release(waited_ms)
|
|
logger.info(f"Deferred VRAM yield completed after {round(waited_ms / 1000, 1)}s "
|
|
f"({round(snap['free_bytes'] / (1024**3), 2)} GB free)")
|
|
return
|
|
logger.warning("Deferred VRAM yield never landed within "
|
|
f"{max_wait_s}s for {', '.join(targets)}")
|
|
|
|
|
|
def _record(event: Dict[str, Any]) -> None:
|
|
"""Push an event to both the in-memory ring and the durable store."""
|
|
event.setdefault("ts", time.time())
|
|
event.setdefault("timestamp", time.strftime("%H:%M:%S"))
|
|
SWITCH_HISTORY.appendleft(event)
|
|
telemetry_store.record_event(event, profile=overclock_manager.ACTIVE_PROFILE)
|
|
|
|
|
|
async def instant_free_ollama_vram(model_name: Optional[str] = None,
|
|
confirm: bool = True,
|
|
timeout_s: Optional[float] = None) -> Dict[str, Any]:
|
|
"""Yield Ollama's VRAM and wait for the driver to actually release it.
|
|
|
|
The returned duration_ms is now the real end-to-end release time, not just how long
|
|
the HTTP POST took.
|
|
"""
|
|
t0 = time.perf_counter()
|
|
if model_name:
|
|
targets = [model_name]
|
|
else:
|
|
# Unload *every* resident model, not just loaded_models[0]. Ollama will happily
|
|
# keep several models in VRAM at once; releasing only the first left the rest
|
|
# allocated, which the confirm barrier caught as "still holding 8.2 GB after 3s".
|
|
ollama_state = await get_ollama_live_state()
|
|
targets = [m.get("name") for m in ollama_state.get("loaded_models", []) if m.get("name")]
|
|
if not targets and ollama_state.get("active_model_name"):
|
|
targets = [ollama_state["active_model_name"]]
|
|
|
|
if not targets:
|
|
return {"success": True, "message": "No active Ollama model in VRAM",
|
|
"duration_ms": 0, "confirmed": True}
|
|
|
|
model_name = targets[0] if len(targets) == 1 else f"{len(targets)} models"
|
|
baseline = get_process_vram_bytes()["ollama_bytes"]
|
|
try:
|
|
# Generous client timeout: with OLLAMA_NUM_PARALLEL=1 this request queues behind
|
|
# any running generation, and a short timeout would drop the connection before
|
|
# Ollama ever processed the unload -- losing it entirely.
|
|
client = _client(OLLAMA_API_BASE, 120.0)
|
|
unload_calls = [
|
|
client.post("/api/generate", json={"model": t, "keep_alive": 0})
|
|
for t in targets
|
|
]
|
|
# Do not await the queued unloads; a busy model would block us for the length of
|
|
# its inference. They are fire-and-confirm: the barrier below watches the VRAM.
|
|
_spawn_detached(asyncio.gather(*unload_calls, return_exceptions=True))
|
|
request_ms = round((time.perf_counter() - t0) * 1000, 2)
|
|
|
|
barrier: Dict[str, Any] = {"outcome": "unconfirmed", "confirmed": None,
|
|
"confirm_ms": 0.0, "residual_bytes": baseline}
|
|
if confirm:
|
|
barrier = await _await_vram_release(
|
|
baseline, timeout_s if timeout_s is not None else YIELD_CONFIRM_TIMEOUT_S)
|
|
|
|
if barrier.get("outcome") == "busy":
|
|
# The unload is queued and will fire when the generation ends. Keep watching
|
|
# in the background so the release is still logged and the counters stay true,
|
|
# without holding the caller here for the length of someone's inference.
|
|
_spawn_detached(_confirm_release_later(targets, baseline))
|
|
|
|
duration_ms = round((time.perf_counter() - t0) * 1000, 2)
|
|
freed_gb = round(max(baseline - barrier.get("residual_bytes", 0), 0) / (1024**3), 2)
|
|
|
|
outcome = barrier.get("outcome", "unconfirmed")
|
|
_record({
|
|
"event_type": "Ollama VRAM Yield",
|
|
"source": ", ".join(targets)[:200],
|
|
"target": "VRAM 0MB (Kept in RAM)",
|
|
"duration_ms": duration_ms,
|
|
"yield_confirm_ms": barrier.get("confirm_ms"),
|
|
"cache_status": {
|
|
"released": "RAM-Cached",
|
|
"busy": "Deferred — LLM generating",
|
|
"stuck": "Yield Stalled",
|
|
}.get(outcome, "Yield Unconfirmed"),
|
|
"detail": barrier.get("error"),
|
|
})
|
|
return {
|
|
"success": True,
|
|
"outcome": outcome,
|
|
"model": model_name,
|
|
"models_unloaded": targets,
|
|
"duration_ms": duration_ms,
|
|
"request_ms": request_ms,
|
|
"confirm_ms": barrier.get("confirm_ms"),
|
|
"confirmed": barrier.get("confirmed"),
|
|
"freed_gb": freed_gb,
|
|
"residual_gb": round(barrier.get("residual_bytes", 0) / (1024**3), 2),
|
|
"free_vram_gb": round(barrier.get("free_bytes", 0) / (1024**3), 2),
|
|
"gpu_util_pct": barrier.get("gpu_util_pct"),
|
|
"error": barrier.get("error"),
|
|
}
|
|
except Exception as e:
|
|
return {"success": False, "error": str(e),
|
|
"duration_ms": round((time.perf_counter() - t0) * 1000, 2)}
|
|
|
|
|
|
async def instant_free_comfyui_vram() -> Dict[str, Any]:
|
|
"""Tell ComfyUI to purge loaded diffusion models from VRAM."""
|
|
t0 = time.perf_counter()
|
|
try:
|
|
client = _client(COMFY_API_BASE, 5.0)
|
|
await client.post("/free", json={"unload_models": True, "free_memory": True})
|
|
duration_ms = round((time.perf_counter() - t0) * 1000, 2)
|
|
snap = get_process_vram_bytes()
|
|
_record({
|
|
"event_type": "ComfyUI VRAM Purge",
|
|
"source": "ComfyUI Pipeline",
|
|
"target": "VRAM Free",
|
|
"duration_ms": duration_ms,
|
|
"cache_status": "Cleaned",
|
|
})
|
|
return {"success": True, "duration_ms": duration_ms,
|
|
"free_vram_gb": round(snap["free_bytes"] / (1024**3), 2)}
|
|
except Exception as e:
|
|
return {"success": False, "error": str(e),
|
|
"duration_ms": round((time.perf_counter() - t0) * 1000, 2)}
|
|
|
|
|
|
_MODEL_SIZE_CACHE: Dict[str, int] = {}
|
|
|
|
|
|
def _model_size_bytes(model_name: str) -> int:
|
|
"""On-disk weight size for an Ollama model, used to turn load time into bandwidth."""
|
|
if model_name in _MODEL_SIZE_CACHE:
|
|
return _MODEL_SIZE_CACHE[model_name]
|
|
try:
|
|
for f in ram_optimizer.find_ollama_model_files():
|
|
_MODEL_SIZE_CACHE[f["model"]] = f["size_bytes"]
|
|
except Exception as e:
|
|
logger.debug(f"model size lookup failed: {e}")
|
|
return _MODEL_SIZE_CACHE.get(model_name, 0)
|
|
|
|
|
|
def classify_load(size_bytes: int, load_duration_ms: float) -> Dict[str, Any]:
|
|
"""Classify how a model reached VRAM, from achieved bandwidth rather than a constant.
|
|
|
|
The old rule was `load_duration_ms < 2500`, which called a 27B Q2_K read from NVMe a
|
|
cache hit and a small model read from RAM a cold load. Bandwidth separates them
|
|
cleanly: page cache feeds PCIe at many GB/s, this NVMe does not.
|
|
"""
|
|
if load_duration_ms <= 1.0:
|
|
return {"cache_status": "Already in VRAM", "load_gbps": None, "is_ram_hit": True}
|
|
if not size_bytes:
|
|
# No size on record — fall back to the old heuristic, but say so.
|
|
# Without a size we cannot compute bandwidth at all; this is a guess and is
|
|
# labelled as one. 8s roughly splits the measured warm (4.9s) and cold (34.3s)
|
|
# loads for a mid-size model, but it is meaningless for very small or large ones.
|
|
return {
|
|
"cache_status": "RAM Cache Hit ⚡" if load_duration_ms < 8000 else "Cold Disk Load 💾",
|
|
"load_gbps": None,
|
|
"is_ram_hit": load_duration_ms < 8000,
|
|
"detail": "size unknown, fell back to a duration guess",
|
|
}
|
|
gbps = (size_bytes / (1024**3)) / (load_duration_ms / 1000.0)
|
|
if gbps >= RAM_HIT_GBPS:
|
|
status = "RAM Cache Hit ⚡"
|
|
elif gbps >= PARTIAL_HIT_GBPS:
|
|
status = "Partial Cache 🌤"
|
|
else:
|
|
status = "Cold Disk Load 💾"
|
|
return {"cache_status": status, "load_gbps": round(gbps, 2), "is_ram_hit": gbps >= RAM_HIT_GBPS}
|
|
|
|
|
|
async def switch_ollama_model(target_model: str, keep_alive: str = "30m",
|
|
_retrying: bool = False) -> Dict[str, Any]:
|
|
"""High-speed hot-swap to target Ollama model, tracking swap metrics.
|
|
|
|
If the load fails because the model will not fit, reclaims VRAM from an idle ComfyUI
|
|
and retries once. `_retrying` guards against recursing more than one level.
|
|
"""
|
|
t0 = time.perf_counter()
|
|
cur_state = await get_ollama_live_state()
|
|
prev_model = cur_state.get("active_model_name") or "None"
|
|
|
|
try:
|
|
client = _client(OLLAMA_API_BASE, 180.0)
|
|
resp = await client.post(
|
|
"/api/generate",
|
|
json={"model": target_model, "prompt": "Ready check", "stream": False,
|
|
"keep_alive": keep_alive},
|
|
)
|
|
total_duration_ms = round((time.perf_counter() - t0) * 1000, 2)
|
|
|
|
if resp.status_code == 200:
|
|
data = resp.json()
|
|
load_dur_ms = round(data.get("load_duration", 0) / 1e6, 2)
|
|
eval_dur_ms = round(data.get("eval_duration", 0) / 1e6, 2)
|
|
eval_count = data.get("eval_count", 0)
|
|
tokens_per_sec = round((eval_count / (eval_dur_ms / 1000)) if eval_dur_ms > 0 else 0, 1)
|
|
|
|
size_bytes = _model_size_bytes(target_model)
|
|
cls = classify_load(size_bytes, load_dur_ms)
|
|
|
|
_record({
|
|
"event_type": "LLM Model Switch",
|
|
"source": prev_model,
|
|
"target": target_model,
|
|
"duration_ms": total_duration_ms,
|
|
"load_duration_ms": load_dur_ms,
|
|
"tokens_per_sec": tokens_per_sec,
|
|
"bytes_loaded": size_bytes,
|
|
"load_gbps": cls["load_gbps"],
|
|
"cache_status": cls["cache_status"],
|
|
"detail": cls.get("detail"),
|
|
})
|
|
|
|
return {
|
|
"success": True,
|
|
"prev_model": prev_model,
|
|
"target_model": target_model,
|
|
"total_duration_ms": total_duration_ms,
|
|
"load_duration_ms": load_dur_ms,
|
|
"tokens_per_sec": tokens_per_sec,
|
|
"model_size_gb": round(size_bytes / (1024**3), 2) if size_bytes else None,
|
|
"load_gbps": cls["load_gbps"],
|
|
"cache_status": cls["cache_status"],
|
|
"is_ram_hit": cls["is_ram_hit"],
|
|
"response": data.get("response", ""),
|
|
}
|
|
# A model that will not fit is the exact contention this service exists to
|
|
# resolve. Rather than handing the caller a CUDA OOM, take the VRAM back from an
|
|
# idle ComfyUI and try once more.
|
|
body = resp.text
|
|
if looks_like_vram_oom(body) and not _retrying:
|
|
snap = get_process_vram_bytes()
|
|
if snap["comfyui_bytes"] >= RECLAIM_MIN_COMFY_BYTES:
|
|
logger.warning(
|
|
f"Ollama could not fit '{target_model}' with ComfyUI holding "
|
|
f"{round(snap['comfyui_bytes'] / (1024**3), 2)} GB — reclaiming and retrying")
|
|
purge = await instant_free_comfyui_vram()
|
|
arbitrator.stats["reclaims_for_ollama"] += 1
|
|
arbitrator.last_action = (
|
|
f"Reclaimed {round(snap['comfyui_bytes'] / (1024**3), 2)}GB from ComfyUI so "
|
|
f"'{target_model}' could load")
|
|
_record({
|
|
"event_type": "VRAM Reclaim for Ollama",
|
|
"source": "ComfyUI Pipeline",
|
|
"target": target_model,
|
|
"duration_ms": purge.get("duration_ms"),
|
|
"cache_status": "Reclaimed",
|
|
"detail": f"Ollama OOM: {body[:160]}",
|
|
})
|
|
await asyncio.sleep(0.3)
|
|
retry = await switch_ollama_model(target_model, keep_alive, _retrying=True)
|
|
retry["reclaimed_from_comfyui_gb"] = round(
|
|
snap["comfyui_bytes"] / (1024**3), 2)
|
|
retry["first_attempt_error"] = "CUDA OOM; retried after reclaiming VRAM"
|
|
if not retry.get("success"):
|
|
# Be specific about why the reclaim was not enough. Blaming ComfyUI
|
|
# when a third-party process is holding the memory sends the user
|
|
# looking in the wrong place.
|
|
retry["unmanaged_blockers"] = describe_unmanaged()
|
|
return retry
|
|
return {"success": False, "error": f"HTTP {resp.status_code}: {body}",
|
|
"duration_ms": total_duration_ms,
|
|
"upstream_status": resp.status_code,
|
|
"vram_oom": looks_like_vram_oom(body)}
|
|
except Exception as e:
|
|
return {"success": False, "error": str(e),
|
|
"duration_ms": round((time.perf_counter() - t0) * 1000, 2)}
|
|
|
|
|
|
def get_switch_history() -> List[Dict[str, Any]]:
|
|
return list(SWITCH_HISTORY)
|
|
|
|
|
|
class AutoArbitrator:
|
|
"""Real-time bidirectional background arbitrator for seamless Ollama <-> ComfyUI hot-swapping.
|
|
|
|
Two behavioural changes worth knowing about:
|
|
|
|
* ComfyUI's VRAM is no longer purged 1.5 s after every finished prompt. Iterating on
|
|
a workflow is the common case, and purging between runs forced a full checkpoint
|
|
reload each time. The purge now waits for COMFY_IDLE_PURGE_S of genuinely empty
|
|
queue, and happens immediately only when Ollama actually needs the VRAM.
|
|
* The watchdog no longer polls two ComfyUI endpoints every 300 ms. The WebSocket is
|
|
the primary signal; polling is a fallback that runs at 1 Hz and only hits /queue,
|
|
backing off further while the socket is healthy.
|
|
"""
|
|
|
|
COMFY_IDLE_PURGE_S = 30.0
|
|
WATCHDOG_INTERVAL_S = 1.0
|
|
WATCHDOG_INTERVAL_WS_OK_S = 3.0
|
|
|
|
def __init__(self):
|
|
self.running = False
|
|
self.ws_task: Optional[asyncio.Task] = None
|
|
self.poll_task: Optional[asyncio.Task] = None
|
|
self.idle_task: Optional[asyncio.Task] = None
|
|
self.last_yield_time = 0.0
|
|
self.last_comfy_free_time = 0.0
|
|
self.connected_ws = False
|
|
self.last_action = "Idle"
|
|
self.comfy_was_active = False
|
|
self.comfy_idle_since: Optional[float] = None
|
|
self.oc_profile = None
|
|
self.pending_purge = False
|
|
# While a tuning sweep is running, the arbitrator must not fight it: a ComfyUI
|
|
# benchmark would otherwise trip trigger_comfy_priority, which reapplies the whole
|
|
# 'comfy' profile and silently overwrites the clock the sweep is measuring.
|
|
self.oc_suspended = False
|
|
# Per-model backoff. A model that is mid-generation cannot yield, and asking it
|
|
# again every second just blocks the loop repeatedly for no benefit.
|
|
self._yield_backoff_until: Dict[str, float] = {}
|
|
self._yield_busy_streak: Dict[str, int] = {}
|
|
self.last_reclaim_time = 0.0
|
|
self.last_starvation_check: Optional[Dict[str, Any]] = None
|
|
self.watchdog_branches = {"busy": 0, "completed": 0, "idle_check": 0,
|
|
"bad_status": 0, "error": 0}
|
|
self._running_id: Optional[str] = None
|
|
self._running_since: Optional[float] = None
|
|
self._peak_comfy_bytes = 0
|
|
self.comfy_stale_job: Optional[str] = None
|
|
self.last_watchdog_error: Optional[str] = None
|
|
self.stats = {
|
|
"yields": 0, # release confirmed
|
|
"yield_deferred_busy": 0, # model mid-generation; unload queued behind it
|
|
"yield_stalled": 0, # VRAM held with an idle GPU -- the real failure
|
|
"deferred_releases": 0, # queued unloads that later landed
|
|
"purges": 0,
|
|
"deferred_purges": 0,
|
|
"reclaims_for_ollama": 0, # ComfyUI purged because the LLM was spilling to CPU
|
|
}
|
|
|
|
async def start(self):
|
|
if self.running:
|
|
return
|
|
self.running = True
|
|
self.ws_task = asyncio.create_task(self._ws_listener())
|
|
self.poll_task = asyncio.create_task(self._poll_watchdog())
|
|
self.idle_task = asyncio.create_task(self._idle_purge_loop())
|
|
logger.info("AutoArbitrator background engine started (Bidirectional).")
|
|
try:
|
|
await asyncio.get_running_loop().run_in_executor(
|
|
None, overclock_manager.apply_profile, "balanced")
|
|
self.oc_profile = "balanced"
|
|
except Exception as e:
|
|
logger.warning(f"Startup overclock apply failed: {e}")
|
|
|
|
async def stop(self):
|
|
self.running = False
|
|
for task in (self.ws_task, self.poll_task, self.idle_task):
|
|
if task:
|
|
task.cancel()
|
|
await close_clients()
|
|
logger.info("AutoArbitrator background engine stopped.")
|
|
|
|
# Backoff schedule for a model that keeps reporting busy, in seconds.
|
|
BUSY_BACKOFF_S = (5.0, 15.0, 30.0, 60.0)
|
|
|
|
def note_deferred_release(self, waited_ms: float) -> None:
|
|
"""Called when a queued unload finally lands after a generation finished."""
|
|
self.stats["deferred_releases"] += 1
|
|
self._yield_backoff_until.clear()
|
|
self._yield_busy_streak.clear()
|
|
self.last_action = (f"VRAM released after the LLM finished "
|
|
f"({round(waited_ms / 1000, 1)}s) — ComfyUI can proceed")
|
|
|
|
async def trigger_comfy_priority(self, reason: str = "ComfyUI prompt detected"):
|
|
"""Yield Ollama's VRAM before diffusion allocates, without fighting a busy model."""
|
|
self.comfy_was_active = True
|
|
self.comfy_idle_since = None
|
|
self._apply_oc_profile("comfy")
|
|
now = time.time()
|
|
if now - self.last_yield_time < 1.0:
|
|
return
|
|
|
|
ollama_state = await get_ollama_live_state()
|
|
model = ollama_state.get("active_model_name")
|
|
if not model:
|
|
return
|
|
|
|
# Still finishing an inference we already asked to unload: leave it alone.
|
|
until = self._yield_backoff_until.get(model, 0.0)
|
|
if now < until:
|
|
return
|
|
|
|
logger.info(f"⚡ ComfyUI active ({reason}) -> Auto-yielding Ollama model '{model}'...")
|
|
self.last_yield_time = time.time()
|
|
res = await instant_free_ollama_vram(model, confirm=True)
|
|
outcome = res.get("outcome")
|
|
|
|
if outcome == "released":
|
|
self.stats["yields"] += 1
|
|
self._yield_backoff_until.pop(model, None)
|
|
self._yield_busy_streak.pop(model, None)
|
|
self.last_action = (f"Yielded '{model}' for ComfyUI in "
|
|
f"{res.get('confirm_ms')}ms ({res.get('freed_gb')}GB freed)")
|
|
elif outcome == "busy":
|
|
streak = self._yield_busy_streak.get(model, 0)
|
|
delay = self.BUSY_BACKOFF_S[min(streak, len(self.BUSY_BACKOFF_S) - 1)]
|
|
self._yield_busy_streak[model] = streak + 1
|
|
self._yield_backoff_until[model] = time.time() + delay
|
|
self.stats["yield_deferred_busy"] += 1
|
|
self.last_action = (f"'{model}' is mid-generation ({res.get('gpu_util_pct')}% GPU); "
|
|
f"unload is queued and will apply when it finishes")
|
|
logger.info(f"Yield deferred: {res.get('error')} — backing off {delay}s")
|
|
else:
|
|
self.stats["yield_stalled"] += 1
|
|
self.last_action = (f"⚠ '{model}' holding {res.get('residual_gb')}GB with an idle GPU")
|
|
logger.warning(f"VRAM yield stalled: {res.get('error')}")
|
|
|
|
async def trigger_comfy_completed(self, immediate: bool = False):
|
|
"""Mark the end of a generation. The actual purge is deferred unless forced."""
|
|
self.comfy_was_active = False
|
|
if self.comfy_idle_since is None:
|
|
self.comfy_idle_since = time.time()
|
|
self._apply_oc_profile("ollama")
|
|
if immediate:
|
|
await self._purge_comfy_now("Ollama needs VRAM")
|
|
else:
|
|
self.pending_purge = True
|
|
self.stats["deferred_purges"] += 1
|
|
self.last_action = (f"ComfyUI idle — holding its checkpoints for "
|
|
f"{int(self.COMFY_IDLE_PURGE_S)}s in case you iterate")
|
|
|
|
async def _purge_comfy_now(self, reason: str):
|
|
now = time.time()
|
|
if now - self.last_comfy_free_time < 3.0:
|
|
return
|
|
self.last_comfy_free_time = now
|
|
self.pending_purge = False
|
|
logger.info(f"⚡ Purging ComfyUI VRAM cache ({reason})...")
|
|
res = await instant_free_comfyui_vram()
|
|
self.stats["purges"] += 1
|
|
self.last_action = f"Purged ComfyUI VRAM ({res.get('duration_ms')}ms) — {reason}"
|
|
logger.info(f"ComfyUI purge completed: {res}")
|
|
|
|
async def _idle_purge_loop(self):
|
|
"""Purge ComfyUI's VRAM only after a real idle gap, not between iterations."""
|
|
while self.running:
|
|
try:
|
|
if self.pending_purge and self.comfy_idle_since and not self.comfy_was_active:
|
|
idle_for = time.time() - self.comfy_idle_since
|
|
if idle_for >= self.COMFY_IDLE_PURGE_S:
|
|
await self._purge_comfy_now(
|
|
f"idle {int(idle_for)}s")
|
|
except Exception as e:
|
|
logger.debug(f"idle purge loop error: {e}")
|
|
await asyncio.sleep(2.0)
|
|
|
|
async def request_vram_for_ollama(self, needed_gb: float = 0.0) -> Dict[str, Any]:
|
|
"""Called when Ollama needs VRAM now: purge ComfyUI immediately rather than waiting."""
|
|
snap = get_process_vram_bytes()
|
|
free_gb = snap["free_bytes"] / (1024**3)
|
|
if needed_gb and free_gb >= needed_gb:
|
|
return {"purged": False, "free_gb": round(free_gb, 2), "reason": "enough free VRAM"}
|
|
if snap["comfyui_bytes"] > YIELD_RESIDUAL_BYTES:
|
|
await self._purge_comfy_now(f"Ollama requested {needed_gb or '?'}GB")
|
|
snap = get_process_vram_bytes()
|
|
return {"purged": True, "free_gb": round(snap["free_bytes"] / (1024**3), 2)}
|
|
return {"purged": False, "free_gb": round(free_gb, 2), "reason": "ComfyUI holds no VRAM"}
|
|
|
|
async def _ws_listener(self):
|
|
client_id = "hyperswap-arbitrator"
|
|
ws_url = f"ws://127.0.0.1:8188/ws?clientId={client_id}"
|
|
backoff = 2.0
|
|
|
|
while self.running:
|
|
try:
|
|
async with websockets.connect(ws_url, ping_interval=10, ping_timeout=10) as ws:
|
|
self.connected_ws = True
|
|
backoff = 2.0
|
|
logger.info("AutoArbitrator connected to ComfyUI WebSocket.")
|
|
while self.running:
|
|
msg = await ws.recv()
|
|
if not isinstance(msg, str):
|
|
continue
|
|
try:
|
|
data = json.loads(msg)
|
|
msg_type = data.get("type")
|
|
msg_data = data.get("data", {})
|
|
|
|
if msg_type == "status":
|
|
queue_rem = (msg_data.get("status", {})
|
|
.get("exec_info", {}).get("queue_remaining", 0))
|
|
if queue_rem > 0:
|
|
await self.trigger_comfy_priority(f"Queue remaining: {queue_rem}")
|
|
elif queue_rem == 0 and self.comfy_was_active:
|
|
await self.trigger_comfy_completed()
|
|
elif msg_type in ("execution_start", "execution_cached"):
|
|
await self.trigger_comfy_priority(f"Event: {msg_type}")
|
|
elif msg_type == "executing":
|
|
node = msg_data.get("node")
|
|
if node is not None:
|
|
await self.trigger_comfy_priority(f"Executing node: {node}")
|
|
elif self.comfy_was_active:
|
|
await self.trigger_comfy_completed()
|
|
elif msg_type == "execution_success":
|
|
await self.trigger_comfy_completed()
|
|
elif msg_type == "execution_error":
|
|
logger.warning(f"ComfyUI execution error: {msg_data}")
|
|
await self.trigger_comfy_completed()
|
|
except Exception as e:
|
|
logger.debug(f"WS parse error: {e}")
|
|
except (websockets.exceptions.ConnectionClosed, OSError, asyncio.CancelledError):
|
|
self.connected_ws = False
|
|
except Exception as e:
|
|
self.connected_ws = False
|
|
logger.debug(f"WS connection error: {e}")
|
|
|
|
await asyncio.sleep(backoff)
|
|
backoff = min(backoff * 1.5, 15.0)
|
|
|
|
RECLAIM_COOLDOWN_S = 30.0
|
|
# A queue entry that has claimed to be running this long without the GPU ever going
|
|
# busy is stale, not slow.
|
|
STALE_RUNNING_S = 90.0
|
|
# ComfyUI's own VRAM, not GPU utilisation, is what distinguishes a real job from a
|
|
# stale row. Utilisation is shared: Ollama and any third-party process drive it too,
|
|
# so peak utilisation stayed above any sensible threshold and a stuck entry never
|
|
# looked stale. A real diffusion job loads gigabytes of checkpoint; a dead one holds
|
|
# only the CUDA context.
|
|
STALE_COMFY_BYTES = 1.5 * 1024 ** 3
|
|
|
|
def _comfy_genuinely_busy(self, queue: Dict[str, Any]) -> bool:
|
|
"""Decide whether ComfyUI is really working, not just claiming to be.
|
|
|
|
ComfyUI can leave an entry in queue_running after a job dies -- observed here as
|
|
a WAN 2.1 i2v entry that sat there with the GPU at 0% and ComfyUI holding 0.56 GB.
|
|
Trusting that flag alone made this service believe ComfyUI was permanently busy,
|
|
which meant it evicted the LLM on every poll, never ran the idle purge, and never
|
|
checked whether the LLM had been squeezed onto the CPU. Half the arbitration was
|
|
disabled by one stale row.
|
|
|
|
A running entry is corroborated against GPU utilisation before it is believed.
|
|
"""
|
|
running = queue.get("queue_running") or []
|
|
pending = queue.get("queue_pending") or []
|
|
if pending:
|
|
self._running_since = None
|
|
self._running_id = None
|
|
return True
|
|
if not running:
|
|
self._running_since = None
|
|
self._running_id = None
|
|
self.comfy_stale_job = None
|
|
return False
|
|
|
|
entry = running[0]
|
|
prompt_id = entry[1] if isinstance(entry, (list, tuple)) and len(entry) > 1 else str(entry)
|
|
now = time.time()
|
|
if prompt_id != self._running_id:
|
|
self._running_id = prompt_id
|
|
self._running_since = now
|
|
self._peak_comfy_bytes = 0
|
|
|
|
snap = get_process_vram_bytes()
|
|
self._peak_comfy_bytes = max(self._peak_comfy_bytes, snap.get("comfyui_bytes", 0))
|
|
|
|
elapsed = now - (self._running_since or now)
|
|
if elapsed > self.STALE_RUNNING_S and self._peak_comfy_bytes < self.STALE_COMFY_BYTES:
|
|
if self.comfy_stale_job != prompt_id:
|
|
logger.warning(
|
|
f"ComfyUI reports prompt {prompt_id} running for {int(elapsed)}s while "
|
|
f"holding only {self._peak_comfy_bytes / (1024**3):.2f} GB — no checkpoint "
|
|
f"is loaded, so the queue entry is stale. Ignoring it; otherwise ComfyUI "
|
|
f"looks permanently busy and arbitration stops working.")
|
|
self.comfy_stale_job = prompt_id
|
|
return False
|
|
return True
|
|
|
|
async def _check_ollama_starved(self) -> None:
|
|
"""The other direction: rescue an LLM that ComfyUI has squeezed onto the CPU.
|
|
|
|
Yielding Ollama for ComfyUI was automatic; the reverse never was, despite the
|
|
README calling the arbitration bidirectional. When Ollama cannot fit a model it
|
|
does not fail, it silently places layers on the CPU and runs about an order of
|
|
magnitude slower -- so this is the failure mode a user is least likely to notice
|
|
and most likely to feel.
|
|
|
|
If the LLM is spilling while ComfyUI sits idle holding VRAM, ComfyUI's cached
|
|
checkpoints are the thing to give up.
|
|
"""
|
|
# Every bail-out is recorded. This check silently did nothing while a model sat
|
|
# at 29% on the GPU, and with four separate early returns there was no way to
|
|
# tell which one had fired without guessing.
|
|
now = time.time()
|
|
def bail(reason: str, **extra):
|
|
self.last_starvation_check = {"ts": now, "acted": False,
|
|
"reason": reason, **extra}
|
|
|
|
if self.comfy_was_active:
|
|
return bail("ComfyUI is active; it needs the VRAM itself")
|
|
if (now - self.last_reclaim_time) < self.RECLAIM_COOLDOWN_S:
|
|
return bail("within reclaim cooldown",
|
|
seconds_left=round(self.RECLAIM_COOLDOWN_S
|
|
- (now - self.last_reclaim_time), 1))
|
|
|
|
ollama = await get_ollama_live_state()
|
|
if not ollama.get("partially_offloaded"):
|
|
return bail("LLM is not spilling to CPU",
|
|
gpu_fraction=ollama.get("gpu_fraction"),
|
|
model=ollama.get("active_model_name"))
|
|
|
|
snap = get_process_vram_bytes()
|
|
if snap["comfyui_bytes"] < RECLAIM_MIN_COMFY_BYTES:
|
|
# ComfyUI is not the one holding the memory; nothing we can do here.
|
|
return bail("LLM is spilling but ComfyUI holds too little to help",
|
|
cpu_offload_pct=ollama.get("cpu_offload_pct"),
|
|
comfy_gb=round(snap["comfyui_bytes"] / (1024**3), 2))
|
|
|
|
self.last_starvation_check = {"ts": now, "acted": True,
|
|
"reason": "reclaiming for the LLM",
|
|
"cpu_offload_pct": ollama.get("cpu_offload_pct")}
|
|
|
|
self.last_reclaim_time = now
|
|
model = ollama.get("active_model_name")
|
|
offload = ollama.get("cpu_offload_pct")
|
|
logger.warning(f"⚠ '{model}' is {offload}% on CPU while ComfyUI holds "
|
|
f"{round(snap['comfyui_bytes'] / (1024**3), 2)} GB — reclaiming for the LLM")
|
|
await self._purge_comfy_now(f"LLM spilling {offload}% to CPU")
|
|
self.stats["reclaims_for_ollama"] += 1
|
|
|
|
# Freeing VRAM does not move layers back; only a reload re-places the model. Do
|
|
# that only when the model is idle, never mid-generation.
|
|
after = get_process_vram_bytes()
|
|
if after.get("gpu_util_pct", 0) < BUSY_UTIL_PCT and model:
|
|
logger.info(f"Reloading '{model}' to place it fully on the GPU...")
|
|
await instant_free_ollama_vram(model, confirm=True)
|
|
res = await switch_ollama_model(model, keep_alive="30m")
|
|
recheck = await get_ollama_live_state()
|
|
self.last_action = (
|
|
f"Reclaimed {round(snap['comfyui_bytes'] / (1024**3), 2)}GB from ComfyUI and "
|
|
f"reloaded '{model}' — now {round(recheck.get('gpu_fraction', 0) * 100)}% on GPU"
|
|
if res.get("success") else
|
|
f"Reclaimed VRAM from ComfyUI but reloading '{model}' failed: {res.get('error')}")
|
|
else:
|
|
self.last_action = (f"Reclaimed VRAM from ComfyUI; '{model}' is busy, so it will "
|
|
f"stay partly on CPU until its next load")
|
|
|
|
async def _poll_watchdog(self):
|
|
"""Fallback for when the WebSocket is down. One cheap /queue call, 1 Hz.
|
|
|
|
The previous version hit /system_stats and /queue every 300 ms on fresh TCP
|
|
connections — roughly 6.6 requests/second against ComfyUI, forever.
|
|
"""
|
|
while self.running:
|
|
interval = self.WATCHDOG_INTERVAL_WS_OK_S if self.connected_ws else self.WATCHDOG_INTERVAL_S
|
|
try:
|
|
client = _client(COMFY_API_BASE, 3.0)
|
|
resp = await client.get("/queue")
|
|
if resp.status_code == 200:
|
|
q = resp.json()
|
|
busy = self._comfy_genuinely_busy(q)
|
|
if busy:
|
|
self.watchdog_branches["busy"] += 1
|
|
await self.trigger_comfy_priority("Watchdog saw an active queue")
|
|
elif self.comfy_was_active:
|
|
self.watchdog_branches["completed"] += 1
|
|
await self.trigger_comfy_completed()
|
|
else:
|
|
self.watchdog_branches["idle_check"] += 1
|
|
await self._check_ollama_starved()
|
|
else:
|
|
self.watchdog_branches["bad_status"] += 1
|
|
except Exception as e:
|
|
# This used to swallow everything silently, including anything raised by
|
|
# the starvation check, which is why that check could appear to run and
|
|
# do nothing.
|
|
self.watchdog_branches["error"] += 1
|
|
self.last_watchdog_error = str(e)[:200]
|
|
logger.debug(f"watchdog poll error: {e}")
|
|
await asyncio.sleep(interval)
|
|
|
|
def suspend_oc(self, reason: str = "tuning sweep") -> None:
|
|
self.oc_suspended = True
|
|
logger.info(f"Overclock auto-switching suspended ({reason})")
|
|
|
|
def resume_oc(self, profile: Optional[str] = None) -> None:
|
|
self.oc_suspended = False
|
|
# Forget the cached profile so the next transition actually reapplies.
|
|
self.oc_profile = profile
|
|
logger.info("Overclock auto-switching resumed")
|
|
|
|
def _apply_oc_profile(self, profile: str):
|
|
"""Apply an overclock profile in a background thread; only fire on transition."""
|
|
if self.oc_suspended or self.oc_profile == profile:
|
|
return
|
|
self.oc_profile = profile
|
|
try:
|
|
loop = asyncio.get_running_loop()
|
|
loop.run_in_executor(None, overclock_manager.apply_profile, profile)
|
|
logger.info(f"🎛️ Overclock profile switched -> '{profile}'")
|
|
except Exception as e:
|
|
logger.warning(f"Overclock profile switch failed ({profile}): {e}")
|
|
|
|
def get_status(self) -> Dict[str, Any]:
|
|
idle_for = (time.time() - self.comfy_idle_since) if self.comfy_idle_since else None
|
|
return {
|
|
"running": self.running,
|
|
"connected_ws": self.connected_ws,
|
|
"last_action": self.last_action,
|
|
"mode": "Bidirectional Hot-Swap (ComfyUI <-> Ollama)",
|
|
"comfy_active": self.comfy_was_active,
|
|
"pending_purge": self.pending_purge,
|
|
"comfy_idle_s": round(idle_for, 1) if idle_for is not None else None,
|
|
"idle_purge_after_s": self.COMFY_IDLE_PURGE_S,
|
|
"oc_profile": self.oc_profile,
|
|
"counters": dict(self.stats),
|
|
"last_starvation_check": self.last_starvation_check,
|
|
"comfy_stale_job": self.comfy_stale_job,
|
|
"watchdog_branches": dict(self.watchdog_branches),
|
|
"last_watchdog_error": self.last_watchdog_error,
|
|
"yield_backoff": {m: round(max(t - time.time(), 0), 1)
|
|
for m, t in self._yield_backoff_until.items()
|
|
if t > time.time()},
|
|
}
|
|
|
|
|
|
arbitrator = AutoArbitrator()
|
|
|
|
|