feat: detect when the resolved torch/CUDA ships no kernels for the local GPU

[golithe]

• Cog resolves CUDA from the framework pin and never consults the GPU it runs on
• On a Blackwell card (sm_120), torch==2.4.1 builds green, and every kernel launch fails with CUDA error: no kernel image is available for execution on the device
• Hard to notice: torch.cuda.is_available() returns True, and PyTorch's own diagnostic is a UserWarning
• There's no fallback either, since the wheel ships neither sm_12x cubins nor PTX to JIT from
• cog run does not work with A100 #389 is the same failure class on an A100 in 2022 (closed when the reporter worked around it); Blackwell makes it current again

This PR adds:

• GPUCompatibilityCheck to cog doctor, modelled on DockerCheck
• Reads compute capability from nvidia-smi --query-gpu=compute_cap (lowest across GPUs, since the image must run on the weakest) and compares the resolved torch/CUDA against the oldest release known to ship kernels for it.

cog doctor output when it fires:

 ⚙  Environment
 ✔ Docker
 ✔ Python version
 ⚠ GPU compatibility
 ⚙    torch==2.4.1 (CUDA 12.4) ships no kernels for sm_120, the compute capability of
      this machine's GPU. The image will build, but every CUDA operation in it will
      fail at runtime with "no kernel image is available for execution on the device".

 ⚙  Found 1 warning.

Notes for review:

• The floors are measured, not read off release notes. I installed each wheel and read torch._C._cuda_getArchFlags(), which works without a GPU
• Each floor is bracketed: the named version ships the kernels, the release below it doesn't. sm_120/sm_100 need torch>=2.7.0 + CUDA>=12.8; sm_90 needs torch>=2.0.1 + CUDA>=11.8
• Both bounds are load-bearing: 2.7.0+cu118 is a genuine 2.7 build with no Blackwell kernels
• Deliberately no rows below sm_90. Every probed wheel already covers Turing/Ampere/Ada, so those floors can't be bracketed; unknown-old devices produce no finding rather than a guess
• SeverityWarning, matching PythonVersionCheck: the image is valid and runs fine on other hardware; it only fails when executed on this machine's GPU
• Silent when there's no GPU, no torch pin, or gpu: false; COG_SKIP_GPU_CHECK=1 skips it when building for different hardware than the local card. Doctor-only, no build-path changes

Testing:

• the comparison is a pure function (evaluateGPUCompat), table-tested without a GPU, including +cu128 local-tag pins and the new-torch/old-CUDA case
• Verified by hand on an RTX 5070 Ti (sm_120): fires on torch==2.4.1, silent on 2.7.1 and on gpu: false

Out of scope (deliberately):

• the floors are hand-entered from measurement (precedent: MinimumTorchVersion and friends in pkg/dockerfile/base.go)
• A probe script in tools/ to regenerate them on demand could be a follow-up; full compatgen integration seems like a poor fit, since new capability majors ship every couple of years and the probe needs multi-GB wheel installs. Happy to add the script here or in a follow-up PR if useful