The native amdsmi from ROCm 7.1 requires libamd_smi.so linked against
glibc 2.38 (Ubuntu 24.04), but the Ray base image is Ubuntu 22.04
(glibc 2.35). This caused vLLM to fail ROCm platform detection with
'No module named amdsmi' / GLIBC_2.38 not found errors.
Solution: Pure-Python amdsmi shim that reads GPU info from sysfs
(/sys/class/drm/*) instead of the native library. Provides the full
API surface used by both vLLM (platform detection, device info) and
PyTorch (device counting, memory/power/temp monitoring).
Tested in-container: vLLM detects RocmPlatform, PyTorch sees GPU
(Radeon 8060S, 128GB, HIP 7.3), DeviceConfig resolves to 'cuda'.
Changes:
- Add amdsmi-shim/ package with sysfs-backed implementation
- Update Dockerfile to install shim after vLLM/torch
- Add amdsmi-shim/ to .dockerignore explicit includes
- Use BuildKit syntax 1.7 with cache mounts for apt/uv
- Switch from pip to uv for 10-100x faster installs (ADR-0014)
- Add OCI Image Spec labels for container metadata
- Add HEALTHCHECK directives for orchestration
- Add .dockerignore to reduce context size
- Update Makefile with buildx and lint target
- Add retry logic to ray-entrypoint.sh
Refs: ADR-0012 (uv), ADR-0014 (Docker best practices)
