moved containers dir and created docker_files dir
This commit is contained in:
@@ -1,25 +0,0 @@
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# Unsloth fine-tuning container for Qwen 3.5 4B on RTX 3090.
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#
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# Build:
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# docker build -f python/prompt_bench/Dockerfile.finetune -t bill-finetune .
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#
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# Run:
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# docker run --rm --device=nvidia.com/gpu=all --ipc=host \
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# -v $(pwd)/output:/workspace/output \
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# -v $(pwd)/output/finetune_dataset.jsonl:/workspace/dataset.jsonl:ro \
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# -v /zfs/models/hf:/models \
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# bill-finetune \
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# --dataset /workspace/dataset.jsonl \
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# --output-dir /workspace/output/qwen-bill-summarizer
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FROM ghcr.io/unslothai/unsloth:latest
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RUN pip install --no-cache-dir typer
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WORKDIR /workspace
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COPY python/prompt_bench/finetune.py python/prompt_bench/finetune.py
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COPY config/prompts/summarization_prompts.toml config/prompts/summarization_prompts.toml
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COPY python/prompt_bench/__init__.py python/prompt_bench/__init__.py
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COPY python/__init__.py python/__init__.py
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ENTRYPOINT ["python", "-m", "pipelines.prompt_bench.finetune"]
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@@ -1 +0,0 @@
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"""Prompt benchmarking system for evaluating LLMs via vLLM."""
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@@ -1,179 +0,0 @@
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"""Docker container lifecycle management for Unsloth fine-tuning."""
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from __future__ import annotations
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import logging
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import subprocess
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from pathlib import Path
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from typing import Annotated
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import typer
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from pipelines.tools.containers.lib import check_gpu_free
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logger = logging.getLogger(__name__)
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CONTAINER_NAME = "bill-finetune"
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FINETUNE_IMAGE = "bill-finetune:latest"
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REPO_DIR = Path(__file__).resolve().parents[4]
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DEFAULT_HF_CACHE = Path("/zfs/models/hf")
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def build_image() -> None:
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"""Build the fine-tuning Docker image."""
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logger.info("Building fine-tuning image: %s", FINETUNE_IMAGE)
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result = subprocess.run(
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[
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"docker",
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"build",
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"-f",
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str(REPO_DIR / "python/prompt_bench/Dockerfile.finetune"),
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"-t",
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FINETUNE_IMAGE,
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".",
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],
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text=True,
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check=False,
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)
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if result.returncode != 0:
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message = "Failed to build fine-tuning image"
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raise RuntimeError(message)
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logger.info("Image built: %s", FINETUNE_IMAGE)
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def start_finetune(
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*,
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dataset_path: Path,
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output_dir: Path,
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hf_cache: Path = DEFAULT_HF_CACHE,
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) -> None:
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"""Run the fine-tuning container.
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Args:
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dataset_path: Host path to the fine-tuning JSONL dataset.
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output_dir: Host path where the trained model will be saved.
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hf_cache: Host path to HuggingFace model cache (bind-mounted to avoid re-downloading).
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validation_split: Fraction of data held out for validation.
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"""
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dataset_path = dataset_path.resolve()
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output_dir = output_dir.resolve()
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if not dataset_path.is_file():
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message = f"Dataset not found: {dataset_path}"
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raise FileNotFoundError(message)
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output_dir.mkdir(parents=True, exist_ok=True)
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stop_finetune()
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hf_cache = hf_cache.resolve()
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hf_cache.mkdir(parents=True, exist_ok=True)
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command = [
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"docker",
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"run",
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"--name",
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CONTAINER_NAME,
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"--device=nvidia.com/gpu=all",
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"--ipc=host",
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"-v",
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f"{hf_cache}:/root/.cache/huggingface",
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"-v",
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f"{output_dir}:/workspace/output/qwen-bill-summarizer",
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"-v",
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f"{dataset_path}:/workspace/dataset.jsonl:ro",
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FINETUNE_IMAGE,
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"--dataset",
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"/workspace/dataset.jsonl",
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"--output-dir",
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"/workspace/output/qwen-bill-summarizer",
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]
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logger.info("Starting fine-tuning container")
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logger.info(" Dataset: %s", dataset_path)
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logger.info(" Output: %s", output_dir)
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result = subprocess.run(command, text=True, check=False)
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if result.returncode != 0:
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message = f"Fine-tuning container exited with code {result.returncode}"
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raise RuntimeError(message)
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logger.info("Fine-tuning complete. Model saved to %s", output_dir)
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def stop_finetune() -> None:
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"""Stop and remove the fine-tuning container."""
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logger.info("Stopping fine-tuning container")
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subprocess.run(["docker", "stop", CONTAINER_NAME], capture_output=True, check=False)
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subprocess.run(
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["docker", "rm", "-f", CONTAINER_NAME], capture_output=True, check=False
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)
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def logs_finetune() -> str | None:
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"""Return recent logs from the fine-tuning container, or None if not running."""
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result = subprocess.run(
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["docker", "logs", "--tail", "50", CONTAINER_NAME],
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capture_output=True,
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text=True,
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check=False,
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)
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if result.returncode != 0:
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return None
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return result.stdout + result.stderr
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app = typer.Typer(help="Fine-tuning container management.")
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@app.command()
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def build() -> None:
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"""Build the fine-tuning Docker image."""
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build_image()
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@app.command()
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def run(
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dataset: Annotated[Path, typer.Option(help="Fine-tuning JSONL")] = REPO_DIR
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/ "data/finetune_dataset.jsonl",
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output_dir: Annotated[
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Path, typer.Option(help="Where to save the trained model")
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] = REPO_DIR / "data/output/qwen-bill-summarizer",
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hf_cache: Annotated[
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Path, typer.Option(help="Host path to HuggingFace model cache")
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] = DEFAULT_HF_CACHE,
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log_level: Annotated[str, typer.Option(help="Log level")] = "INFO",
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) -> None:
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"""Run fine-tuning inside a Docker container."""
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logging.basicConfig(
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level=log_level, format="%(asctime)s %(levelname)s %(name)s: %(message)s"
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)
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check_gpu_free()
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start_finetune(
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dataset_path=dataset,
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output_dir=output_dir,
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hf_cache=hf_cache,
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)
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@app.command()
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def stop() -> None:
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"""Stop and remove the fine-tuning container."""
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stop_finetune()
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@app.command()
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def logs() -> None:
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"""Show recent logs from the fine-tuning container."""
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output = logs_finetune()
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if output is None:
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typer.echo("No running fine-tuning container found.")
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raise typer.Exit(code=1)
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typer.echo(output)
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def cli() -> None:
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"""Typer entry point."""
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app()
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if __name__ == "__main__":
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cli()
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@@ -1,23 +0,0 @@
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from __future__ import annotations
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import logging
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import subprocess
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logger = logging.getLogger(__name__)
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def check_gpu_free() -> None:
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"""Warn if GPU-heavy processes (e.g. Ollama) are running."""
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result = subprocess.run(
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["nvidia-smi", "--query-compute-apps=pid,process_name", "--format=csv,noheader"],
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capture_output=True,
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text=True,
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check=False,
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)
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if result.returncode != 0:
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logger.warning("Could not query GPU processes: %s", result.stderr.strip())
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return
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processes = result.stdout.strip()
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if processes:
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logger.warning("GPU processes detected:\n%s", processes)
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logger.warning("Consider stopping Ollama (sudo systemctl stop ollama) before benchmarking")
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@@ -1,70 +0,0 @@
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"""Docker container lifecycle management for vLLM."""
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from __future__ import annotations
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import logging
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import subprocess
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logger = logging.getLogger(__name__)
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CONTAINER_NAME = "vllm-bench"
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VLLM_IMAGE = "vllm/vllm-openai:v0.19.0"
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def start_vllm(
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*,
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model: str,
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port: int,
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model_dir: str,
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gpu_memory_utilization: float,
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) -> None:
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"""Start a vLLM container serving the given model.
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Args:
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model: HuggingFace model directory name (relative to model_dir).
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port: Host port to bind.
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model_dir: Host path containing HuggingFace model directories.
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gpu_memory_utilization: Fraction of GPU memory to use (0-1).
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"""
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command = [
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"docker",
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"run",
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"-d",
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"--name",
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CONTAINER_NAME,
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"--device=nvidia.com/gpu=all",
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"--ipc=host",
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"-v",
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f"{model_dir}:/models",
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"-p",
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f"{port}:8000",
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VLLM_IMAGE,
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"--model",
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f"/models/{model}",
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"--served-model-name",
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model,
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"--gpu-memory-utilization",
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str(gpu_memory_utilization),
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"--max-model-len",
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"4096",
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]
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logger.info("Starting vLLM container with model: %s", model)
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stop_vllm()
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result = subprocess.run(command, capture_output=True, text=True, check=False)
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if result.returncode != 0:
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msg = f"Failed to start vLLM container: {result.stderr.strip()}"
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raise RuntimeError(msg)
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logger.info("vLLM container started: %s", result.stdout.strip()[:12])
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def stop_vllm() -> None:
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"""Stop and remove the vLLM benchmark container."""
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logger.info("Stopping vLLM container")
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subprocess.run(["docker", "stop", CONTAINER_NAME], capture_output=True, check=False)
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subprocess.run(["docker", "rm", "-f", CONTAINER_NAME], capture_output=True, check=False)
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subprocess.run(
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["docker", "network", "disconnect", "-f", "bridge", CONTAINER_NAME],
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capture_output=True,
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check=False,
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)
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logger.info("vLLM container stopped and removed")
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