Kunpeng BoostKit for SRA

Provides the latest updates in documents of Kunpeng BoostKit for SRA.

Describes the solution architecture, advantages, and key features of Kunpeng BoostKit for SRA.

Provides the list of fixed vulnerabilities in open-source and third-party software involved in the Kunpeng BoostKit software packages.

A high-performance AI operator library optimized for the Kunpeng platform. It includes a deep neural network operator library (KDNN) and an extension operator library (KDNN_EXT).

A recall algorithm library optimized for the Kunpeng platform. It optimizes the instruction set architecture and memory access mechanism of the Kunpeng processor at the bottom layer, improving the computing efficiency and throughput of the recall algorithm. It is especially suitable for high-concurrency recall scenarios.

Guide for porting the oneDNN deep neural network library.

Guide for porting the PyTorch open-source deep learning framework.

Guide for porting the TensorFlow deep learning framework.

Guide for porting TensorFlow Serving, a high-performance system for serving machine learning models.

Guide for porting ScaNN, an open-source vector similarity search library.

Guide for porting the DLRM deep learning recommendation model.

Guide for porting TVM, an open-source deep learning compiler stack.

Guide for porting ONNX Runtime, a high-performance cross-platform engine for accelerating model inference in the ONNX format.

Guide for porting Elasticsearch, a distributed search engine.

Describes how to deploy a benchmarking system to measure the inference performance of search and recommendation models, covering server and client environment setup and performance evaluation during the inference phase.

Provides historical documents of Kunpeng BoostKit for SRA.