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SnapOtter
0309e0f680
- Add Cloudflare Pages deployment for landing page (snapotter.com) and docs (docs.snapotter.com) - Create deploy-landing.yml and update deploy-docs.yml workflows - Update CI to ignore apps/landing/** paths - Fix logo transparency (remove white background) across all apps - Recreate social-preview.png with SnapOtter branding - Update all docs URLs from GitHub Pages to docs.snapotter.com - Update VitePress config: light theme default, fix llms.txt paths - Add .vitepress/cache/ and .env.* to gitignore
91 lines
3.3 KiB
Python
91 lines
3.3 KiB
Python
"""Runtime GPU/CUDA detection utility."""
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import functools
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import json
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import os
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import subprocess
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import sys
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def emit_info(msg):
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"""Emit an informational JSON message to stderr for the bridge to capture."""
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print(json.dumps({"info": msg}), file=sys.stderr, flush=True)
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@functools.lru_cache(maxsize=1)
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def gpu_available():
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"""Return True if a usable CUDA GPU is present at runtime."""
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# Allow explicit disable via env var (set to "false" or "0")
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override = os.environ.get("SNAPOTTER_GPU")
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if override is not None and override.lower() in ("0", "false", "no"):
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return False
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# Use torch.cuda as the source of truth when available. It actually
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# probes the hardware. Fall back to onnxruntime provider detection
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# when torch is not installed (e.g. CPU-only images without PyTorch).
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try:
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import torch
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avail = torch.cuda.is_available()
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if avail:
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name = torch.cuda.get_device_name(0)
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print(f"[gpu] CUDA available via torch: {name}", file=sys.stderr, flush=True)
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else:
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print("[gpu] torch loaded but CUDA not available", file=sys.stderr, flush=True)
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return avail
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except ImportError as e:
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print(f"[gpu] torch not importable: {e}", file=sys.stderr, flush=True)
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# Fallback: check if onnxruntime-gpu is installed and CUDA EP is available,
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# then verify an actual NVIDIA GPU is present via nvidia-smi.
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try:
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import onnxruntime as _ort
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providers = _ort.get_available_providers()
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if "CUDAExecutionProvider" not in providers:
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return False
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# CUDA EP is compiled in — verify hardware is actually present.
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# nvidia-smi is the most reliable cross-platform check.
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result = subprocess.run(
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["nvidia-smi", "--query-gpu=name", "--format=csv,noheader"],
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capture_output=True, text=True, timeout=5,
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)
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if result.returncode == 0 and result.stdout.strip():
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print(f"[gpu] CUDA available via ONNX Runtime + nvidia-smi: {result.stdout.strip()}",
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file=sys.stderr, flush=True)
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return True
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return False
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except (ImportError, FileNotFoundError, subprocess.TimeoutExpired):
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return False
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def onnx_providers():
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"""Return (providers, device) tuple.
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providers: ONNX Runtime execution providers in priority order.
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device: "cuda" or "cpu" — reflects which hardware will actually be used.
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"""
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if gpu_available():
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return (["CUDAExecutionProvider", "CPUExecutionProvider"], "cuda")
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emit_info("No GPU detected, processing on CPU")
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return (["CPUExecutionProvider"], "cpu")
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def safe_onnx_session(model_path, providers=None):
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"""Create an ONNX Runtime InferenceSession with graceful CUDA EP fallback.
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Returns (session, device) where device is "cuda" or "cpu".
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"""
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import onnxruntime as ort
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device = "cpu"
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if providers is None:
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providers, device = onnx_providers()
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try:
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session = ort.InferenceSession(model_path, providers=providers)
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return session, device
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except Exception as e:
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if "CUDAExecutionProvider" in providers:
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emit_info(f"CUDA init failed ({e}), falling back to CPU")
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session = ort.InferenceSession(model_path, providers=["CPUExecutionProvider"])
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return session, "cpu"
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raise
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