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https://github.com/pewdiepie-archdaemon/odysseus.git
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Odysseus v1.0
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pewdiepie-archdaemon
@@ -0,0 +1,204 @@
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from copy import deepcopy
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from fastapi import APIRouter
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def setup_hwfit_routes():
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router = APIRouter(prefix="/api/hwfit", tags=["hwfit"])
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def _apply_manual_hardware(system, manual_mode="", manual_gpu_count="", manual_vram_gb="", manual_ram_gb="", manual_backend=""):
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"""Manual hardware is a "what if I had this setup" simulator —
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REPLACES the detected hardware entirely instead of adding to it.
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The previous additive behavior averaged the manual VRAM across
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all GPUs (base + manual), which meant adding "1× 400 GB" on top
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of "2× 70 GB" only nudged the per-GPU cap from 70 to 180 GB
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(= 540 / 3), so GGUF models bigger than that still didn't surface
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— exactly the "cap stuck at detected level" bug the user hit.
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"""
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manual_mode = (manual_mode or "").lower()
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if manual_mode not in {"gpu", "ram"}:
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return system
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try:
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override_ram_gb = float(manual_ram_gb) if manual_ram_gb else 0
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except ValueError:
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override_ram_gb = 0
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override_ram_gb = max(0.0, override_ram_gb)
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if override_ram_gb:
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# Replace RAM, don't add. The number in the field is the
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# TOTAL system memory the user wants to simulate.
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system["available_ram_gb"] = round(override_ram_gb, 1)
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system["total_ram_gb"] = round(override_ram_gb, 1)
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system["manual_hardware"] = True
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if manual_mode == "ram":
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# RAM-only simulation — wipe GPU entirely so the ranker uses
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# CPU/RAM paths.
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system["has_gpu"] = False
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system["gpu_name"] = None
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system["gpu_vram_gb"] = 0
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system["gpu_count"] = 0
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system["gpus"] = []
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system["gpu_groups"] = []
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system["backend"] = "cpu_x86"
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return system
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try:
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count = int(manual_gpu_count) if manual_gpu_count else 1
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except ValueError:
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count = 1
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try:
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vram_each = float(manual_vram_gb) if manual_vram_gb else 8.0
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except ValueError:
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vram_each = 8.0
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count = max(1, min(count, 16))
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vram_each = max(1.0, vram_each)
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backend = (manual_backend or system.get("backend") or "cuda").lower()
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if backend not in {"cuda", "rocm", "cpu_x86", "cpu_arm"}:
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backend = "cuda"
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total_vram = round(vram_each * count, 1)
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gpu_name = f"Simulated {backend.upper()} GPU" + (f" × {count}" if count > 1 else "")
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system["has_gpu"] = True
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system["gpu_name"] = gpu_name
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system["gpu_vram_gb"] = total_vram
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system["gpu_count"] = count
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system["gpus"] = [
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{"index": i, "name": gpu_name, "vram_gb": vram_each}
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for i in range(count)
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]
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# Single homogeneous pool — vram_each here is the ACTUAL per-GPU
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# VRAM the user entered, not an average. That's the whole point:
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# raising vram_each lifts the per-GPU cap (GGUF, tensor-parallel
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# math) all the way up, not just by a small fraction.
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system["gpu_groups"] = [{
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"name": gpu_name,
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"vram_each": vram_each,
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"count": count,
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"indices": list(range(count)),
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"vram_total": total_vram,
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}]
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system["homogeneous"] = True
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system["backend"] = backend
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return system
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@router.get("/system")
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def get_system(host: str = "", ssh_port: str = "", platform: str = "", fresh: bool = False):
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"""Detect and return current system hardware info. Pass host=user@server for remote.
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fresh=true bypasses the per-host cache (the Rescan button)."""
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from services.hwfit.hardware import detect_system
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return detect_system(host=host, ssh_port=ssh_port, platform=platform, fresh=fresh)
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@router.get("/models")
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def get_models(use_case: str = "", sort: str = "score", limit: int = 50, search: str = "", host: str = "", quant: str = "", gpu_count: str = "", gpu_group: str = "", ssh_port: str = "", platform: str = "", fresh: bool = False, manual_mode: str = "", manual_gpu_count: str = "", manual_vram_gb: str = "", manual_ram_gb: str = "", manual_backend: str = "", ignore_detected_gpu: bool = False, ignore_detected_ram: bool = False):
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"""Rank LLM models against detected hardware and return scored results.
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gpu_count: override GPU count (0 = CPU only, 1-N = simulate N GPUs of the
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active group). gpu_group: index into system.gpu_groups (the homogeneous
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pools) to target — empty/auto = the largest pool. vLLM can only
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tensor-parallel across identical GPUs, so we never mix pools.
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fresh=true bypasses the hardware-detection cache."""
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from services.hwfit.hardware import detect_system
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from services.hwfit.fit import rank_models
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from services.hwfit.models import get_models, model_catalog_path
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system = deepcopy(detect_system(host=host, ssh_port=ssh_port, platform=platform, fresh=fresh))
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if system.get("error"):
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return {"system": system, "models": [], "error": system["error"]}
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if not get_models():
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return {
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"system": system,
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"models": [],
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"error": f"Model catalog missing or empty: {model_catalog_path()}",
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}
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if ignore_detected_gpu:
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system["has_gpu"] = False
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system["gpu_name"] = None
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system["gpu_vram_gb"] = 0
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system["gpu_count"] = 0
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system["gpus"] = []
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system["gpu_groups"] = []
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if ignore_detected_ram:
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system["available_ram_gb"] = 0
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system["total_ram_gb"] = 0
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system = _apply_manual_hardware(system, manual_mode, manual_gpu_count, manual_vram_gb, manual_ram_gb, manual_backend)
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# Keep the raw detection around so the UI can still show the box's full
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# GPU complement even while we rank against one homogeneous pool.
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system["detected_gpu_vram_gb"] = system.get("gpu_vram_gb")
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system["detected_gpu_count"] = system.get("gpu_count")
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groups = system.get("gpu_groups") or []
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# Resolve the target homogeneous pool. Default (auto) = the largest pool,
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# which for a uniform box is simply "all the GPUs" — no behaviour change.
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grp = None
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if groups:
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try:
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gidx = int(gpu_group) if gpu_group != "" else 0
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except ValueError:
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gidx = 0
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if 0 <= gidx < len(groups):
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grp = groups[gidx]
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def _apply_group(g, n):
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n = max(1, min(n, g["count"]))
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system["gpu_count"] = n
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system["gpu_vram_gb"] = round(g["vram_each"] * n, 1)
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system["gpu_name"] = g["name"]
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system["active_group"] = {**g, "use_count": n}
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if gpu_count != "":
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n = int(gpu_count)
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if n == 0:
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# RAM-only mode: rank against system memory, offload allowed.
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system["has_gpu"] = False
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system["gpu_vram_gb"] = 0
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system["gpu_count"] = 0
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system["gpu_only"] = False
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system.pop("active_group", None)
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elif grp:
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_apply_group(grp, n)
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system["gpu_only"] = True
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else:
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# No per-GPU detail (older detection) — assume uniform split.
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single_vram = (system.get("gpu_vram_gb") or 0) / (system.get("gpu_count") or 1)
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system["gpu_count"] = max(1, n)
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system["gpu_vram_gb"] = round(single_vram * max(1, n), 1)
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system["gpu_only"] = True
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elif grp:
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# No explicit count, but we still pin to one pool so heterogeneous
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# boxes rank against a real mixable group, not a fictional VRAM sum.
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# gpu_only stays off here so the default view still surfaces offload.
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_apply_group(grp, grp["count"])
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results = rank_models(system, use_case=use_case or None, limit=limit, search=search or None, sort=sort, quant=quant or None)
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return {"system": system, "models": results}
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@router.get("/image-models")
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def get_image_models(sort: str = "fit", search: str = "", host: str = "", gpu_count: str = "", ssh_port: str = "", platform: str = "", fresh: bool = False, manual_mode: str = "", manual_gpu_count: str = "", manual_vram_gb: str = "", manual_ram_gb: str = "", manual_backend: str = "", ignore_detected_gpu: bool = False, ignore_detected_ram: bool = False):
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"""Rank image generation models against detected hardware."""
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from services.hwfit.hardware import detect_system
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from services.hwfit.image_models import rank_image_models
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system = deepcopy(detect_system(host=host, ssh_port=ssh_port, platform=platform, fresh=fresh))
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if system.get("error"):
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return {"system": system, "models": [], "error": system["error"]}
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if ignore_detected_gpu:
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system["has_gpu"] = False
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system["gpu_name"] = None
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system["gpu_vram_gb"] = 0
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system["gpu_count"] = 0
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system["gpus"] = []
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system["gpu_groups"] = []
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if ignore_detected_ram:
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system["available_ram_gb"] = 0
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system["total_ram_gb"] = 0
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system = _apply_manual_hardware(system, manual_mode, manual_gpu_count, manual_vram_gb, manual_ram_gb, manual_backend)
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# Image models use a single GPU — always use per-GPU VRAM
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gpu_vrams = [float(g.get("vram_gb") or 0) for g in (system.get("gpus") or []) if isinstance(g, dict)]
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single_vram = max(gpu_vrams) if gpu_vrams else ((system.get("gpu_vram_gb") or 0) / max(system.get("gpu_count") or 1, 1))
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system["gpu_vram_gb"] = single_vram
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system["gpu_count"] = 1 if single_vram > 0 else 0
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results = rank_image_models(system, search=search or None, sort=sort)
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return {"system": system, "models": results}
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return router
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