BentoML
FreeThe easiest way to serve AI apps and models
FreeFree tier
About BentoML
BentoML is a Python library for building online serving systems optimized for AI apps and model inference. It allows users to turn any model inference script into a REST API server with minimal code and standard Python type hints. Features include automatic Docker image generation, dynamic batching, model parallelism, multi-stage pipelines, and multi-model inference-graph orchestration. It supports any ML framework, modality, and inference runtime, and can be deployed to production via Docker containers or BentoCloud.
Key Features
Build model inference APIs with a few lines of Python code
Automatic Docker image generation for reproducible deployment
Dynamic batching, model parallelism, and multi-stage pipelines
Multi-model inference-graph orchestration
Support for any ML framework, modality, and inference runtime
Fully customizable with standard Python type hints
Seamless deployment to production via Docker or BentoCloud
Pros & Cons
Pros
- Simplifies deployment of AI models to production with minimal code
- Supports any ML framework and inference runtime
- Automatic Docker image generation ensures reproducibility
- Dynamic batching and model parallelism maximize hardware utilization
- Fully customizable API definitions using Python type hints
- Open-source and free to use
Best For
Turning ML model inference scripts into scalable REST API serversBuilding high-performance inference APIs with GPU utilizationDeploying multi-model serving systems with complex pipelinesDeveloping and testing AI services locally before production deploymentCreating custom task queues and business logic around model inference
FAQ
What is BentoML?
BentoML is a Python library for building online serving systems optimized for AI apps and model inference, enabling easy creation of APIs and multi-model serving.
How do I install BentoML?
Install BentoML using pip: pip install -U bentoml. Requires Python 3.9 or later.
How do I create a model inference API with BentoML?
Define a service class with the @bentoml.service decorator and add an API method with @bentoml.api. The example in the documentation shows a Summarization service that uses a Hugging Face pipeline.
Can I deploy BentoML services to Docker?
Yes, BentoML automatically generates Docker images for your models, making it simple to deploy to any environment with Docker.