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

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 BentoCloud.

Install BentoML:

# Requires Python≥3.9
pip install -U bentoml

Define APIs in a service.py file.

from __future__ import annotations

import bentoml

@bentoml.service(
    resources={"cpu": "4"}
)
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 the service code locally (serving at http://localhost:3000 by default):

pip install torch transformers  # additional dependencies for local run

bentoml serve service.py:Summarization

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}")

To deploy your BentoML Service code, first create a bentofile.yaml file to define its dependencies and environments. Find the full list of bentofile options here.

service: 'service:Summarization' # Entry service import path
include:
  - '*.py' # Include all .py files in current directory
python:
  packages: # Python dependencies to include
    - torch
    - transformers
docker:
  python_version: "3.11"

Then, choose one of the following ways for deployment:

bentoml build

bentoml containerize summarization:latest

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

# After signup, run the following command to create an API token:
bentoml cloud login

# Deploy from current directory:
bentoml deploy .

For detailed explanations, read Quickstart.

• LLMs: Llama 3.1, Mixtral, Solar, Mistral, and more
• Image Generation: Stable Diffusion 3 Medium, Stable Video Diffusion, Stable Diffusion XL Turbo, ControlNet, LCM LoRAs, and more.
• Text Embeddings: SentenceTransformers
• Audio: ChatTTS, XTTS, WhisperX, Bark
• Computer Vision: YOLO
• Multimodal: BLIP, CLIP
• RAG: RAG-as-a-Service with custom models

Check out the examples folder for more sample code and usage.

• Model composition
• Workers and model parallelization
• Adaptive batching
• GPU inference
• Distributed serving systems
• Concurrency and autoscaling
• Model loading and Model Store
• Observability
• BentoCloud deployment

See Documentation for more tutorials and guides.

Get involved and join our Community Slack 💬, where thousands of AI/ML engineers help each other, contribute to the project, and talk about building AI products.

To report a bug or suggest a feature request, use GitHub Issues.

There are many ways to contribute to the project:

• Report bugs and "Thumbs up" on issues that are relevant to you.
• Investigate issues and review other developers' pull requests.
• Contribute code or documentation to the project by submitting a GitHub pull request.
• Check out the Contributing Guide and Development Guide to learn more.
• Share your feedback and discuss roadmap plans in the #bentoml-contributors channel here.

Thanks to all of our amazing contributors!

The BentoML framework collects anonymous usage data that helps our community improve the product. Only BentoML's internal API calls are being reported. This excludes any sensitive information, such as user code, model data, model names, or stack traces. Here's the code used for usage tracking. You can opt-out of usage tracking by the --do-not-track CLI option:

bentoml [command] --do-not-track

Or by setting the environment variable:

export BENTOML_DO_NOT_TRACK=True

Apache License 2.0