Bento

Unified Model Serving Framework

🍱 Build model inference APIs and multi-model serving systems with any open-source or custom AI models. 👉 Join our forum!

What is BentoML?

BentoML is a Python library for building online serving systems optimized for AI apps and model inference.

• 🍱 Easily build APIs for Any AI/ML Model. Turn any model inference script into a REST API server with just a few lines of code and standard Python type hints.
• 🐳 Docker Containers made simple. No more dependency hell! Manage your environments, dependencies and model versions with a simple config file. BentoML automatically generates Docker images, ensures reproducibility, and simplifies how you deploy to different environments.
• 🧭 Maximize CPU/GPU utilization. Build high performance inference APIs leveraging built-in serving optimization features like dynamic batching, model parallelism, multi-stage pipeline and multi-model inference-graph orchestration.
• 👩‍💻 Fully customizable. Easily implement your own APIs or task queues, with custom business logic, model inference and multi-model composition. Supports any ML framework, modality, and inference runtime.
• 🚀 Ready for Production. Develop, run and debug locally. Seamlessly deploy to production with Docker containers or Kubernetes.

Getting started

Install BentoML:

# Requires Python≥3.9
pip install -U bentoml

Define APIs in a service.py file.

import bentoml

@bentoml.service(
    image=bentoml.images.Image(python_version="3.11").python_packages("torch", "transformers"),
)
class Summarization:
    def __init__(self) -> None:
        import torch
        from transformers import pipeline

        device = "cuda" if torch.cuda.is_available() else "cpu"
        self.pipeline = pipeline('summarization', device=device)

    @bentoml.api(batchable=True)
    def summarize(self, texts: list[str]) -> list[str]:
        results = self.pipeline(texts)
        return [item['summary_text'] for item in results]

💻 Run locally

Install PyTorch and Transformers packages to your Python virtual environment.

pip install torch transformers  # additional dependencies for local run

Run the service code locally (serving at http://localhost:3000 by default):

bentoml serve

You should expect to see the following output.

[INFO] [cli] Starting production HTTP BentoServer from "service:Summarization" listening on http://localhost:3000 (Press CTRL+C to quit)
[INFO] [entry_service:Summarization:1] Service Summarization initialized

Now you can run inference from your browser at http://localhost:3000 or with a Python script:

import bentoml

with bentoml.SyncHTTPClient('http://localhost:3000') as client:
    summarized_text: str = client.summarize([bentoml.__doc__])[0]
    print(f"Result: {summarized_text}")

🐳 Deploy using Docker

Run bentoml build to package necessary code, models, dependency configs into a Bento - the standardized deployable artifact in BentoML:

bentoml build

Ensure Docker is running. Generate a Docker container image for deployment:

bentoml containerize summarization:latest

Run the generated image:

docker run --rm -p 3000:3000 summarization:latest

Examples

• LLMs: Llama 3.2, Mistral, DeepSeek Distil, and more.
• Image Generation: Stable Diffusion 3 Medium, Stable Video Diffusion, Stable Diffusion XL Turbo, ControlNet, and LCM LoRAs.
• Embeddings: SentenceTransformers and ColPali
• Audio: ChatTTS, XTTS, WhisperX, Bark
• Computer Vision: YOLO and ResNet
• Advanced examples: Function calling, LangGraph, CrewAI

Check out the full list for more sample code and usage.

以上为 README 的开头部分。其余内容请阅读原文。 查看完整 README ↗