feat(compressors): add JSONL training metrics logging with CLI controls

- Add write_training_metrics() in new compressors/training_metrics.py
  for appending epoch/step/lr/component rows as JSON Lines
- Wire --metrics-path and --log-every CLI options into train.py, passing
  them to the training loop so metrics rows are written every N steps
- Accept absolute metrics paths or paths relative to output directory
- Add quantization component to loss log alongside existing distill/contrastive
- Replace inline torch.device() with get_device() utility
- Add test_hash_training_metrics.py covering multi-row JSONL append

Infrastructure:
- Pin torch 2.7.1 + CUDA 12.8 index for Linux/Windows in pyproject.toml
- Add .justfile rsync upload recipe with .stignore exclusion
- Exclude **/__marimo__ from rsync in .stignore

Dependencies updated: numpy 2.4.5, pandas 3.0.3, black 26.5.0,
click 8.4.0, contourpy, etc.

mini-nav/compressors/training_metrics.py

@@ -0,0 +1,37 @@
+"""Utilities for recording hash compressor training metrics."""
+
+from __future__ import annotations
+
+import json
+from pathlib import Path
+from typing import Mapping
+
+
+def write_training_metrics(
+    path: str | Path,
+    *,
+    epoch: int,
+    step: int,
+    lr: float,
+    components: Mapping[str, float],
+) -> None:
+    """Append one training metrics row as JSON Lines.
+
+    The JSONL format keeps training logging cheap and easy to resume: every
+    training step is an independent row that plotting scripts can stream later.
+    """
+    metrics_path = Path(path)
+    metrics_path.parent.mkdir(parents=True, exist_ok=True)
+
+    row = {
+        "epoch": int(epoch),
+        "step": int(step),
+        "lr": float(lr),
+        "total": float(components["total"]),
+        "contrastive": float(components["contrastive"]),
+        "distill": float(components["distill"]),
+        "quantization": float(components["quantization"]),
+    }
+
+    with metrics_path.open("a", encoding="utf-8") as f:
+        f.write(json.dumps(row, ensure_ascii=False) + "\n")