Claude
b2d064573f
Matches the existing vendor/easy-asterisk convention (used by services/asterisk.sh) instead of two one-off top-level directories that cluttered the repo root and didn't look like anything else next to setup.sh, lib/, services/, extras/. Only the two services' own SRC_DIR path resolution and header comments needed updating — nothing else in the repo referenced the old ./ai-stack / ./paintplus paths. Also documents vendor/ in README.md's Layout section.
101 lines
3.5 KiB
Python
101 lines
3.5 KiB
Python
#!/usr/bin/env python3
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"""
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GPU setup script — runs at container startup.
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Uses the same detection logic as the backend (gpu_detect.py) to show
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exactly which models will be used before the server starts.
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Non-fatal: any failure just prints a warning and startup continues.
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"""
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import os
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import sys
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def main():
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print("Detecting GPU capabilities…")
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try:
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import torch
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except ImportError:
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print("⚠ PyTorch not installed — GPU detection skipped")
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return
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if torch.cuda.is_available():
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props = torch.cuda.get_device_properties(0)
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free_b, total_b = torch.cuda.mem_get_info(0)
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vram_total = total_b / (1024 ** 3)
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vram_free = free_b / (1024 ** 3)
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major, minor = props.major, props.minor
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cc = f"{major}.{minor}"
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fp16 = major >= 6
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bf16 = major >= 8
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fp8 = major > 8 or (major == 8 and minor >= 9)
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int8 = major >= 7
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tc = major >= 7
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flags = []
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if fp16: flags.append("fp16")
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if bf16: flags.append("bf16")
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if fp8: flags.append("fp8")
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if int8: flags.append("int8")
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if tc: flags.append("tensor-cores")
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print(f"✓ GPU : {props.name}")
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print(f" VRAM : {vram_total:.1f} GB total | {vram_free:.1f} GB free")
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print(f" Compute : CC {cc} ({', '.join(flags) or 'fp32 only'})")
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if major < 6:
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print(f" ⚠ Pre-Pascal (CC {cc}): using fp32 — effective VRAM budget halved")
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elif hasattr(torch.backends, "mps") and torch.backends.mps.is_available():
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print("✓ Apple Silicon MPS GPU detected (fp32 mode)")
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vram_total = vram_free = 0.0
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else:
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print("⚠ No GPU detected — AI inference will use CPU (very slow)")
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vram_total = vram_free = 0.0
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provider = os.environ.get("AI_PROVIDER", "").lower()
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if provider != "local_gpu":
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print(f" AI_PROVIDER={provider!r} — local GPU not active, skipping model selection")
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return
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# Import and run the full detection to show what was selected
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try:
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sys.path.insert(0, "/app")
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from app.services.gpu_detect import detect_gpu
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info = detect_gpu()
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print(f"\n Effective VRAM : {info.effective_vram_gb:.1f} GB (tier: {info.tier})")
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print("\n Model selection:")
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printed: set = set()
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for op, spec in info.recommended.items():
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if spec is None:
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print(f" {op:<12} → (none — will use existing upscaler)")
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elif spec.model_id not in printed:
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print(f" {op:<12} → [{spec.family}] {spec.model_id}")
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print(f" mem_opt={spec.memory_opt} res={spec.native_res}px ~{spec.vram_fp16_gb}GB fp16")
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printed.add(spec.model_id)
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else:
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print(f" {op:<12} → (same as above: {spec.model_id})")
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for w in info.warnings:
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print(f"\n ⚠ {w}")
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auto_dl = os.environ.get("AUTO_DOWNLOAD_MODELS", "true").lower()
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print()
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if auto_dl == "true":
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print(" AUTO_DOWNLOAD_MODELS=true")
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print(" → Model files will download in background at startup.")
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print(" → First request loads from local disk (20-60s, not internet).")
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print(" → Track progress: GET /api/gpu/prefetch-status")
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else:
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print(" AUTO_DOWNLOAD_MODELS=false — models download on first request.")
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except Exception as exc:
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print(f" (Could not run full detection: {exc})")
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print()
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if __name__ == "__main__":
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main()
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