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feat(health): warn when GPU arch isn't supported by PyTorch build

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Applies the compatibility-checker portion of #367. Adds a
check_cuda_compatibility() helper that compares the current device's
compute capability against torch.cuda._get_arch_list() and returns a
human-readable warning if the PyTorch build doesn't support it.

Wired into three places:
  • HealthResponse gains a gpu_compatibility_warning field so clients
    can surface the issue in the UI
  • Startup logs the warning as WARN level
  • _get_gpu_status() appends "[UNSUPPORTED - see logs]" to the GPU
    label shown in settings

Skipped #367's other half — the switch from stable to nightly cu128
wheels across release.yml, build_binary.py, and justfile. That's
redundant with #401's TORCH_CUDA_ARCH_LIST=...12.0+PTX approach and
would introduce non-deterministic builds from shifting nightly
releases.

Co-Authored-By: nyzxor <[email protected]>
Co-Authored-By: Claude Opus 4.6 (1M context) <[email protected]>
This commit is contained in:
James Pine
2026-04-16 02:11:23 -07:00
co-authored by nyzxor Claude Opus 4.6
parent 0317626677
commit 73170d0e92
4 changed files with 62 additions and 2 deletions
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backend/app.py
+16 -2
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@@ -146,11 +146,18 @@ def _get_gpu_status() -> str:
"""Return a human-readable string describing GPU availability."""
backend_type = get_backend_type()
if torch.cuda.is_available():
from .backends.base import check_cuda_compatibility
device_name = torch.cuda.get_device_name(0)
compatible, _warning = check_cuda_compatibility()
is_rocm = hasattr(torch.version, "hip") and torch.version.hip is not None
if is_rocm:
return f"ROCm ({device_name})"
return f"CUDA ({device_name})"
label = f"ROCm ({device_name})"
else:
label = f"CUDA ({device_name})"
if not compatible:
label += " [UNSUPPORTED - see logs]"
return label
elif hasattr(torch.backends, "mps") and torch.backends.mps.is_available():
return "MPS (Apple Silicon)"
elif backend_type == "mlx":
@@ -230,6 +237,13 @@ def _register_lifecycle(application: FastAPI) -> None:
logger.info("Backend: %s", backend_type.upper())
logger.info("GPU: %s", _get_gpu_status())
# Warn if GPU architecture is not supported by this PyTorch build
from .backends.base import check_cuda_compatibility
_compatible, _cuda_warning = check_cuda_compatibility()
if not _compatible:
logger.warning("GPU COMPATIBILITY: %s", _cuda_warning)
from .services.cuda import check_and_update_cuda_binary
create_background_task(check_and_update_cuda_binary())