Edge AI Libraries v1.0.0 (Initial Release)
Release Overview
The Edge AI Libraries v1.0.0 hosts a collection of libraries, microservices, and tools for Edge application development. This project also includes sample applications to showcase the generic AI use cases.
Key Components
| Component | Category | Get Started | Developers Docs |
|---|---|---|---|
| Deep Learning Streamer | Library | Link | API Reference |
| Deep Learning Streamer Pipeline Server | Microservice | Link | API Reference |
| Document Ingestion | Microservice | Link | API Reference |
| Model Registry | Microservice | Link | API Reference |
| Object Store | Microservice | Link | Usage |
| Visual Pipeline and Performance Evaluation Tool | Tool | Link | Build instructions |
| Chat Question and Answer | Sample Application | Link | Build instructions |
| Chat Question and Answer Core | Sample Application | Link | Build instructions |
Highlighted Features
Libraries includes:
- Intel® Deep Learning Streamer (Intel® DL Streamer) Pipeline Framework is an open-source streaming media analytics framework, based on GStreamer* multimedia framework, for creating complex media analytics pipelines for the Cloud or at the Edge.
Microservices includes:
- Deep Learning Streamer Pipeline Server: Built on top of GStreamer, a containerized microservice for development and deployment of video analytics pipeline.
- Model Registry: Providing capabilities to manage lifecycle of an AI model.
- Object Store Microservice: MinIO based object store microservice to build generative AI pipelines.
- Data Ingestion microservice: Data ingestion service loads, parses, and creates embeddings for popular document types like pdf, docx, and txt files.
Sample applications includes:
- Chat Question-and-Answer Core: Chat Question-and-Answer sample application is a foundational Retrieval-Augmented Generation (RAG) pipeline that allows users to ask questions and receive answers, including those based on their own private data corpus.
- Chat Question-and-Answer: Compared to the Chat Question-and-Answer Core implementation, this implementation of Chat Question-and-Answer is a modular microservices based approach with each constituent element of the RAG pipeline bundled as an independent microservice.
Tools includes:
- Visual Pipeline and Platform Evaluation Tool: The Visual Pipeline and Platform Evaluation Tool simplifies hardware selection for AI workloads by allowing you to configure workload parameters, benchmark performance, and analyze key metrics such as throughput, CPU, and GPU usage. With its intuitive interface, the tool provides actionable insights to help you optimize hardware selection and performance.
Known Issues
None
Breaking Changes
None — this is the initial release.