This repo contains recipes for building and running containerized AI and LLM Applications with Podman.
These containerized AI recipes can be used to help developers quickly prototype new AI and LLM based applications locally, without the need for relying on any other externally hosted services. Since they are already containerized, it also helps developers move quickly from prototype to production.
A model server is a program that serves machine-learning models, such as LLMs, and makes their functions available via an API. This makes it easy for developers to incorporate AI into their applications. This repository provides descriptions and code for building several of these model servers.
Many of the sample applications rely on the llamacpp_python model server by
default. This server can be used for various generative AI applications with various models.
However, each sample application can be paired with a variety of model servers.
Learn how to build and run the llamacpp_python model server by following the llamacpp_python model server README.
Recipes consist of at least two components: A model server and an AI application. The model server manages the model, and the AI application provides the specific logic needed to perform some specific task such as chat, summarization, object detection, etc.
There are several sample applications in this repository that can be found in the recipes directory.
They fall under the categories:
Learn how to build and run each application by visiting their README's. For example, learn how to run the chatbot recipe here.
Images for many sample applications and models are available in quay.io. All
currently built images are tracked in
ailab-images.md
Linux Operating System Bootable containers enabled for AI Training
To install our standard git hooks, run the following command:
./install-hooks.shEnsures that training/ilab-wrapper/ilab is duplicated into training/nvidia-bootc/duplicated/ilab-wrapper/ilab
