"BentoML provides our research teams a streamlined way to quickly iterate on their POCs and when ready, deploy their AI services at scale. In addition, the flexible architecture allows us to showcase and deploy many different types of models and workflows from Computer Vision to …

BentoML is a Unified Inference Platform for deploying and scaling AI models with production-grade reliability, all without the complexity of managing infrastructure. It enables your developers to build AI systems 10x faster with custom models, scale efficiently in your cloud, and maintain complete control over security and compliance .

BentoML CLI commands have usage documentation. You can learn more by running bentoml--help. The --help flag also applies to sub-commands for viewing detailed usage of a command, like bentoml build--help.

BentoML allows you to run and test your code locally, so that you can quickly validate your code with local compute resources. Clone the repository and choose your desired project.

Since v1.3.20, BentoML introduces a new Python SDK for configuring the runtime environment of a Bento. You can set it alongside your BentoML Service code in service.py. Essentially, the runtime env...

BentoCloud provides fast and scalable infrastructure for building and scaling AI applications with BentoML in the cloud. Install the dependencies and log in to BentoCloud through the BentoML CLI. If you don’t have a BentoCloud account, sign up here for free.

In the Summarization class, the BentoML Service retrieves a pre-trained model and initializes a pipeline for text summarization. The summarize method serves as the API endpoint. It accepts a string input with a sample provided, processes it through …

@bentoml.service: Converts this class into a BentoML Service. You can optionally set configurations like timeout and GPU resources to use on BentoCloud. We recommend you use an NVIDIA A100 GPU of 80 GB for optimal performance.

This project consists of two main components: a BentoML Service that serves a LangGraph agent as REST APIs and an LLM that generates text. The LLM can be an external API like Claude 3.5 Sonnet or an open-source model served via BentoML (Ministral-8B-Instruct-2410 in this example).

In BentoML, you use the bentoml.monitor context manager to log data related to model inference. It allows you to specify a monitoring session where you can log various data types. This ensures that logging is structured and organized, making it easier to analyze the data later.