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BentoML
Package and deploy any ML model as a scalable REST API — batteries included.
0Open Source
MLOps
Overview
BentoML is an open-source framework for building, packaging, and deploying ML models as production-ready REST APIs, with built-in support for batching, adaptive scaling, and containerization.
Key Features
- Model-agnostic: supports PyTorch, TensorFlow, sklearn, LLMs
- Adaptive batching for throughput optimization
- One-command Docker image generation
- BentoCloud for serverless, auto-scaled deployments
- Built-in monitoring, logging, and tracing
- Multi-model pipelines as a single service
Real-World Workflows
Deploy a fine-tuned LLM as a REST API
- 1Define a BentoML Service with your model loading logic
- 2Add an @bentoml.api endpoint with input/output types
- 3Run locally: bentoml serve service:svc
- 4Build and containerize: bentoml build && bentoml containerize
Getting Started
pip install bentoml
import bentoml
from bentoml.io import Text
svc = bentoml.Service('summarizer')
@svc.api(input=Text(), output=Text())
def summarize(text: str) -> str:
# your model inference
return summary
# bentoml serve service:svcCompare Alternatives
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