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SikongJueluo
4ea567adba
- 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.
38 lines
1.0 KiB
Python
38 lines
1.0 KiB
Python
"""Utilities for recording hash compressor training metrics."""
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from __future__ import annotations
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import json
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from pathlib import Path
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from typing import Mapping
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def write_training_metrics(
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path: str | Path,
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*,
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epoch: int,
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step: int,
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lr: float,
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components: Mapping[str, float],
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) -> None:
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"""Append one training metrics row as JSON Lines.
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The JSONL format keeps training logging cheap and easy to resume: every
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training step is an independent row that plotting scripts can stream later.
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"""
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metrics_path = Path(path)
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metrics_path.parent.mkdir(parents=True, exist_ok=True)
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row = {
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"epoch": int(epoch),
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"step": int(step),
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"lr": float(lr),
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"total": float(components["total"]),
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"contrastive": float(components["contrastive"]),
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"distill": float(components["distill"]),
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"quantization": float(components["quantization"]),
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}
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with metrics_path.open("a", encoding="utf-8") as f:
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f.write(json.dumps(row, ensure_ascii=False) + "\n")
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