No cudnn feature flags remain after 6ba52425 — the devel headers
and libs were dead weight (~1.5 GB).
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Align all 4 Docker images with local dev environment:
- ci-builder: CUDA 12.4.1/Ubuntu 22.04 → 12.9.1/Ubuntu 24.04
- ci-builder-cpu: Debian bookworm → Ubuntu 24.04 (+ explicit rustup)
- foxhunt-runtime: Debian bookworm → Ubuntu 24.04
- foxhunt-training-runtime: CUDA 12.6.3 → 12.9.1, nvrtc 12-6 → 12-9
All images now have glibc 2.39, matching the local build machine.
Binaries compiled locally or in CI will run in any of these containers
without GLIBC_2.3x version mismatch errors.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Both Dockerfiles already install mold v2.35.1 but the registry images
are stale builds without it. This change triggers rebuild-ci-builder
and rebuild-ci-builder-cpu pipeline steps via detect-changes.
.cargo/config.toml uses -fuse-ld=mold — without mold in the image,
linking falls back to the system default (slower).
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
The backtesting crate links libcuda.so.1 at runtime (via candle CUDA).
GPU-less CI nodes need the stub library so binaries can load, but
model_loader tests that actually use CUDA must be skipped. These will
run on gpu-training nodes separately.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
The backtesting crate dynamically links to libcuda.so.1 via candle.
NVIDIA CUDA dev images include stubs at /usr/local/cuda/lib64/stubs/
but only as libcuda.so (not .so.1). Create the symlink both in the
Dockerfile (for future builds) and inline in the test script (for
the current image).
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
