bentoml 1.4.1

BentoML: The easiest way to serve AI apps and models

Unified Model Serving Framework

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

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 BentoCloud.

Getting started

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

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}")

Deploying your first Bento

To deploy your BentoML Service code, configure the runtime environment in service.py.

+ my_image = bentoml.images.PythonImage(python_version="3.11") \
+        .requirements_file("requirements.txt")


@bentoml.service(
+    image=my_image,
    resources={"cpu": "4"},
    traffic={"timeout": 30},
)

Then, choose one of the following ways for deployment:

🐳 Docker Container

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

☁️ BentoCloud

BentoCloud provides compute infrastructure for rapid and reliable GenAI adoption. It helps speed up your BentoML development process leveraging cloud compute resources, and simplify how you deploy, scale and operate BentoML in production.

Sign up for BentoCloud for personal access; for enterprise use cases, contact our team.

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

# Deploy from current directory:
bentoml deploy

For detailed explanations, read the Hello World example.

Examples

• LLMs: Llama 3.2, Mixtral, Solar, and Mistral.
• 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.

Advanced topics

• 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.

Community

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.

Contributing

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!

Usage tracking and feedback

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

License

Apache License 2.0