gjb117 / bentoml

Build Production-Grade AI Applications

BentoML: The Unified AI Application Framework

BentoML is a framework for building reliable, scalable, and cost-efficient AI applications. It comes with everything you need for model serving, application packaging, and production deployment.

👉 Join our Slack community!

Highlights

🏄 Freedom to build with any AI models

• Import from any model hub or bring your own models built with frameworks like PyTorch, TensorFlow, Keras, Scikit-Learn, and XGBoost.
• Support all major ML frameworks, model formats, and model runtime.
• Run and debug your BentoML apps locally on Mac, Windows, or Linux.

🍭 Simplify modern AI application architecture

• Python-first! Effortlessly scale complex AI workloads.
• Enable GPU inference without the headache.
• Compose multiple models to run concurrently or sequentially, over multiple GPUs or Nodes.
• Natively integrates with MLFlow, LangChain, Kubeflow, Triton, Spark, Ray, and many more to complete your production AI stack.

🍱 Bento is the container for AI apps

• Open standard for building AI apps, pack your code, model files, dependencies, and runtime configurations in one Bento “to-go”.
• Auto-generate API servers, supporting REST API, gRPC, and long-running inference jobs.
• Auto-generate Docker container images with just one command.

🚀 Deploy Anywhere

• One-click deployment to ☁️ BentoCloud, the Serverless platform made for hosting AI apps.
• Scalable BentoML deployment with 🦄️ Yatai on Kubernetes.
• Deploy auto-generated container images anywhere docker runs.

Documentation

• Installation: pip install bentoml
• Full Documentation: docs.bentoml.com
• Tutorial: Intro to BentoML

🛠️ What you can build with BentoML

• OpenLLM - An open platform for operating large language models (LLMs) in production.
• StableDiffusion - Create your own text-to-image service with any diffusion models.
• CLIP-API-service - Embed images and sentences, object recognition, visual reasoning, image classification, and reverse image search.
• Transformer NLP Service - Online inference API for Transformer NLP models.
• Distributed TaskMatrix(Visual ChatGPT) - Scalable deployment for TaskMatrix from Microsoft.
• Fraud Detection - Online model serving with custom XGBoost model.
• OCR as a Service - Turn any OCR models into online inference API endpoints.
• Replace Anything - Combining the power of Segment Anything and Stable Diffusion.
• Check out more examples here.

Getting Started

Save or import models in BentoML local model store:

import bentoml
import transformers

pipe = transformers.pipeline("text-classification")

bentoml.transformers.save_model(
  "text-classification-pipe",
  pipe,
  signatures={
    "__call__": {"batchable": True}  # Enable dynamic batching for model
  }
)

View all models saved locally:

$ bentoml models list
 Tag                                     Module                Size        Creation Time
 text-classification-pipe:kn6mr3aubcuf…  bentoml.transformers  256.35 MiB  2023-05-17 14:36:25

Define how your model runs in a service.py file:

import bentoml

model_runner = bentoml.models.get("text-classification-pipe").to_runner()

svc = bentoml.Service("text-classification-service", runners=[model_runner])

@svc.api(input=bentoml.io.Text(), output=bentoml.io.JSON())
async def classify(text: str) -> str:
    results = await model_runner.async_run(text)
    return results[0]

Now, run the API service locally:

bentoml serve service.py:svc

Sent a prediction request:

$ curl -X POST -H "Content-Type: text/plain" --data "BentoML is awesome" http://localhost:3000/classify

{"label":"POSITIVE","score":0.9129443168640137}%

Define how a Bento can be built for deployment, with bentofile.yaml:

service: 'service.py:svc'
name: text-classification-svc
include:
  - 'service.py'
python:
  packages:
  - torch>=2.0
  - transformers

Build a Bento and generate a docker image:

$ bentoml build
...
Successfully built Bento(tag="text-classification-svc:mc322vaubkuapuqj").

$ bentoml containerize text-classification-svc
Building OCI-compliant image for text-classification-svc:mc322vaubkuapuqj with docker
...
Successfully built Bento container for "text-classification-svc" with tag(s) "text-classification-svc:mc322vaubkuapuqj"

$ docker run -p 3000:3000 text-classification-svc:mc322vaubkuapuqj

For a more detailed user guide, check out the BentoML Tutorial.

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Community

BentoML supports billions of model runs per day and is used by thousands of organizations around the globe.

Join our Community Slack 💬, where thousands of AI application developers contribute to the project and help each other.

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!

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Usage Reporting

BentoML collects usage data that helps our team to improve the product. Only BentoML's internal API calls are being reported. We strip out as much potentially sensitive information as possible, and we will never collect user code, model data, model names, or stack traces. Here's the code 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 environment variable BENTOML_DO_NOT_TRACK=True:

export BENTOML_DO_NOT_TRACK=True

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License

Apache License 2.0