PyTorch's CUDA 12.8 and 12.9 builds start at Turing since torch 2.8, as
CUDA 13 does, so on a V100 both ComfyUI builds were installed, 10 GB each,
and failed on the card with "no kernel image". The cu129 build now needs
7.5, and a third, on PyTorch's CUDA 12.6 torch, carries Volta and Pascal.
The open kernel module, NVIDIA's default since 560, writes "Open Kernel
Module for x86_64 580.82.07" in /proc/driver/nvidia/version, and the word
after "Module" was taken as the driver: "for". With the driver unknown,
every build was tried whatever driver it needs.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Every template and handler message in internal/web goes through a JSON
catalogue under internal/web/locales/<code>/, with English as the fallback.
The language is the person's choice (a cookie set by /_lang/{code}), else
Accept-Language, else landing.language. Tests refuse a key some language
lacks, a translation with other arguments than English, and template text
left in English.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
A node that cannot run the CUDA 13 build installs the same version's
CUDA 12.9 build from vLLM's GitHub release, with PyTorch's cu129 torch;
the install computes on the GPU before it counts. The build can be
chosen per node — auto, cu13, cu129, none — on the cluster page, with
ctl engine-build, or over the admin API; the control plane keeps the
choice and tells the node at once.
Refs #2.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
