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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.
36 lines
1.1 KiB
Python
36 lines
1.1 KiB
Python
import typer
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from commands import app
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@app.command()
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def train(
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ctx: typer.Context,
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epoch_size: int = typer.Option(10, "--epoch", "-e", help="Number of epochs"),
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batch_size: int = typer.Option(64, "--batch", "-b", help="Batch size"),
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lr: float = typer.Option(1e-4, "--lr", "-l", help="Learning rate"),
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checkpoint_path: str = typer.Option(
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"hash_checkpoint.pt", "--checkpoint", "-c", help="Checkpoint path"
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),
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metrics_path: str = typer.Option(
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"hash_training_metrics.jsonl",
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"--metrics-path",
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"-m",
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help="JSONL metrics path for loss curves; relative to output directory unless absolute",
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),
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log_every: int = typer.Option(
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1,
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"--log-every",
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help="Write one metrics row every N global steps; <=0 disables metrics logging",
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),
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):
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from compressors import train as train_module
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train_module(
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epoch_size=epoch_size,
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batch_size=batch_size,
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lr=lr,
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checkpoint_path=checkpoint_path,
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metrics_path=metrics_path,
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log_every=log_every,
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)
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