v0.17.5

What's changed

Bug fix: GPU Docker images now install the CUDA build of PyTorch

The GPU Docker images (ludwig-gpu, ludwig-ray-gpu) were incorrectly shipping the CPU build of PyTorch (torch==2.12.0 without a +cu* suffix) despite being GPU images. This meant GPU training silently fell back to CPU.

Root cause (two issues):

1. The --force-reinstall step used --extra-index-url instead of --index-url. With --extra-index-url, pip checks PyPI first and finds the CPU wheel (torch==2.12.0) there, so it never looks at the PyTorch CUDA index.

2. The CUDA index suffix was cu124, but torch==2.12.0 is not published on the cu124 index (which only goes up to 2.6.0+cu124). The fix switches to cu126, where torch==2.12.0+cu126 is available.

Fix:

• Changed --extra-index-url → --index-url on the force-reinstall step in both GPU Dockerfiles so pip goes exclusively to the PyTorch wheel server.
• Changed cu124 → cu126 throughout both GPU Dockerfiles (including the Ray base image tag).

Verified locally: torch==2.12.0+cu126 with CUDA build version: 12.6 confirmed inside the rebuilt image.

Updated Docker images (0.17.5)

• ludwigai/ludwig:0.17.5 — CPU
• ludwigai/ludwig-gpu:0.17.5 — CUDA 12.6 (torch 2.12.0+cu126)
• ludwigai/ludwig-ray:0.17.5 — CPU + Ray
• ludwigai/ludwig-ray-gpu:0.17.5 — CUDA 12.6 + Ray (torch 2.12.0+cu126)