created working finetuing pipeline

python/prompt_bench/finetune_container.py

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+"""Docker container lifecycle management for Unsloth fine-tuning."""
+
+from __future__ import annotations
+
+import logging
+import subprocess
+from pathlib import Path
+from typing import Annotated
+
+import typer
+
+from python.prompt_bench.container import check_gpu_free
+
+logger = logging.getLogger(__name__)
+
+CONTAINER_NAME = "bill-finetune"
+FINETUNE_IMAGE = "bill-finetune:latest"
+DOCKERFILE_PATH = "python/prompt_bench/Dockerfile.finetune"
+DEFAULT_HF_CACHE = Path("/zfs/models/hf")
+
+
+def build_image() -> None:
+    """Build the fine-tuning Docker image."""
+    logger.info("Building fine-tuning image: %s", FINETUNE_IMAGE)
+    result = subprocess.run(
+        ["docker", "build", "-f", DOCKERFILE_PATH, "-t", FINETUNE_IMAGE, "."],
+        text=True,
+        check=False,
+    )
+    if result.returncode != 0:
+        message = "Failed to build fine-tuning image"
+        raise RuntimeError(message)
+    logger.info("Image built: %s", FINETUNE_IMAGE)
+
+
+def start_finetune(
+    *,
+    dataset_path: Path,
+    output_dir: Path,
+    hf_cache: Path = DEFAULT_HF_CACHE,
+    validation_split: float = 0.1,
+    epochs: int = 3,
+    batch_size: int = 2,
+    learning_rate: float = 2e-4,
+    lora_rank: int = 32,
+    max_seq_length: int = 4096,
+    save_gguf: bool = False,
+) -> None:
+    """Run the fine-tuning container.
+
+    Args:
+        dataset_path: Host path to the fine-tuning JSONL dataset.
+        output_dir: Host path where the trained model will be saved.
+        hf_cache: Host path to HuggingFace model cache (bind-mounted to avoid re-downloading).
+        validation_split: Fraction of data held out for validation.
+        epochs: Number of training epochs.
+        batch_size: Per-device training batch size.
+        learning_rate: Learning rate for the optimizer.
+        lora_rank: LoRA adapter rank.
+        max_seq_length: Maximum sequence length for training.
+        save_gguf: Whether to also export a GGUF quantized model.
+    """
+    dataset_path = dataset_path.resolve()
+    output_dir = output_dir.resolve()
+
+    if not dataset_path.is_file():
+        message = f"Dataset not found: {dataset_path}"
+        raise FileNotFoundError(message)
+
+    output_dir.mkdir(parents=True, exist_ok=True)
+    stop_finetune()
+
+    hf_cache = hf_cache.resolve()
+    hf_cache.mkdir(parents=True, exist_ok=True)
+
+    command = [
+        "docker",
+        "run",
+        "--name",
+        CONTAINER_NAME,
+        "--device=nvidia.com/gpu=all",
+        "--ipc=host",
+        "-v",
+        f"{hf_cache}:/root/.cache/huggingface",
+        "-v",
+        f"{output_dir}:/workspace/output/qwen-bill-summarizer",
+        "-v",
+        f"{dataset_path}:/workspace/dataset.jsonl:ro",
+        FINETUNE_IMAGE,
+        "--dataset",
+        "/workspace/dataset.jsonl",
+        "--output-dir",
+        "/workspace/output/qwen-bill-summarizer",
+        "--val-split",
+        str(validation_split),
+        "--epochs",
+        str(epochs),
+        "--batch-size",
+        str(batch_size),
+        "--lr",
+        str(learning_rate),
+        "--lora-rank",
+        str(lora_rank),
+        "--max-seq-length",
+        str(max_seq_length),
+    ]
+
+    if save_gguf:
+        command.append("--save-gguf")
+
+    logger.info("Starting fine-tuning container")
+    logger.info("  Dataset:    %s", dataset_path)
+    logger.info("  Val split:  %.0f%%", validation_split * 100)
+    logger.info("  Output:     %s", output_dir)
+    logger.info("  Epochs:     %d", epochs)
+    logger.info("  Batch size: %d", batch_size)
+    logger.info("  LoRA rank:  %d", lora_rank)
+
+    result = subprocess.run(command, text=True, check=False)
+    if result.returncode != 0:
+        message = f"Fine-tuning container exited with code {result.returncode}"
+        raise RuntimeError(message)
+    logger.info("Fine-tuning complete. Model saved to %s", output_dir)
+
+
+def stop_finetune() -> None:
+    """Stop and remove the fine-tuning container."""
+    logger.info("Stopping fine-tuning container")
+    subprocess.run(["docker", "stop", CONTAINER_NAME], capture_output=True, check=False)
+    subprocess.run(["docker", "rm", "-f", CONTAINER_NAME], capture_output=True, check=False)
+
+
+def logs_finetune() -> str | None:
+    """Return recent logs from the fine-tuning container, or None if not running."""
+    result = subprocess.run(
+        ["docker", "logs", "--tail", "50", CONTAINER_NAME],
+        capture_output=True,
+        text=True,
+        check=False,
+    )
+    if result.returncode != 0:
+        return None
+    return result.stdout + result.stderr
+
+
+app = typer.Typer(help="Fine-tuning container management.")
+
+
+@app.command()
+def build() -> None:
+    """Build the fine-tuning Docker image."""
+    build_image()
+
+
+@app.command()
+def run(
+    dataset: Annotated[Path, typer.Option(help="Fine-tuning JSONL")] = Path("output/finetune_dataset.jsonl"),
+    output_dir: Annotated[Path, typer.Option(help="Where to save the trained model")] = Path(
+        "output/qwen-bill-summarizer",
+    ),
+    hf_cache: Annotated[Path, typer.Option(help="Host path to HuggingFace model cache")] = DEFAULT_HF_CACHE,
+    validation_split: Annotated[float, typer.Option("--val-split", help="Fraction held out for validation")] = 0.1,
+    epochs: Annotated[int, typer.Option(help="Training epochs")] = 3,
+    batch_size: Annotated[int, typer.Option(help="Per-device batch size")] = 2,
+    learning_rate: Annotated[float, typer.Option("--lr", help="Learning rate")] = 2e-4,
+    lora_rank: Annotated[int, typer.Option(help="LoRA rank")] = 32,
+    max_seq_length: Annotated[int, typer.Option(help="Max sequence length")] = 4096,
+    save_gguf: Annotated[bool, typer.Option("--save-gguf/--no-save-gguf", help="Also save GGUF")] = False,
+    log_level: Annotated[str, typer.Option(help="Log level")] = "INFO",
+) -> None:
+    """Run fine-tuning inside a Docker container."""
+    logging.basicConfig(level=log_level, format="%(asctime)s %(levelname)s %(name)s: %(message)s")
+    check_gpu_free()
+    start_finetune(
+        dataset_path=dataset,
+        output_dir=output_dir,
+        hf_cache=hf_cache,
+        validation_split=validation_split,
+        epochs=epochs,
+        batch_size=batch_size,
+        learning_rate=learning_rate,
+        lora_rank=lora_rank,
+        max_seq_length=max_seq_length,
+        save_gguf=save_gguf,
+    )
+
+
+@app.command()
+def stop() -> None:
+    """Stop and remove the fine-tuning container."""
+    stop_finetune()
+
+
+@app.command()
+def logs() -> None:
+    """Show recent logs from the fine-tuning container."""
+    output = logs_finetune()
+    if output is None:
+        typer.echo("No running fine-tuning container found.")
+        raise typer.Exit(code=1)
+    typer.echo(output)
+
+
+def cli() -> None:
+    """Typer entry point."""
+    app()
+
+
+if __name__ == "__main__":
+    cli()