Second VPR option for REQUEST-vpr-embeddings.md, alongside the in-process
EigenPlaces. SALAD is near-SOTA for place recognition but its deps
(pytorch_lightning, pytorch_metric_learning) are heavy and version-sensitive, so
it runs in a SEPARATE process with those deps quarantined in a
--system-site-packages venv (shares the main torch, adds only the extras). The
main engine venv never imports them — verified clean.

- codai/api/vpr_salad_server.py: stdin image-path -> stdout JSON embedding server
  (same protocol as dinov2-embed), GPU-preferred with CPU fallback, announces its
  true width (8448) on ready.
- embeddings.py: 'salad'/'dinov2-salad' -> 'vpr-server' backend that spawns the
  server via /cache/salad-venv/bin/python; reuses the dinov2cpp reader/cleanup.
- Self-healing: _EmbeddingModel gains a respawn hook. If the manager kills the
  child under VRAM pressure but keeps the cached model, the embed path now
  respawns the server in place instead of failing forever with a stale handle
  (this was causing persistent "process died" 500s once salad was evicted).
- packaging/setup-salad-venv.sh: idempotent creator for the isolated venv.

Both VPR models pass the decisive ordering test on same-place/different-place
image sets (zero overlap between same-place min and different-place max); SALAD's
margin (0.516 vs 0.150) is wider than EigenPlaces' (0.479 vs 0.349). Verified:
8448-d L2=1.0, deterministic, ~0.2s warm / ~8s cold, GPU, subprocess isolation
intact. Config: models.json 'salad' + alias 'dinov2-salad', engine nvidia.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01LoSpEthysqmseCc6Geizty