Francesco Murdaca

Microbenchmarks for AI applications using Red Hat OpenShift on PSI in project Thoth

By Francesco Murdaca October 28, 2019July 1, 2020

Project Thoth is an artificial intelligence (AI) R&D Red Hat research project as part of the Office of the CTO and the AI Center of Excellence (CoE). This project aims to build a knowledge graph and a recommendation system for application stacks based on the collected knowledge, such as machine learning (ML) applications that rely on popular open source ML frameworks and libraries (TensorFlow, PyTorch, MXNet, etc.). In this article, we examine the potential of project Thoth’s infrastructure running in Red Hat Openshift and explore how it can collect performance observations.

Several types of observations are gathered from various domains (like build time, run time and performance, and application binary interfaces (ABI)). These observations are collected through the Thoth system and enrich the knowledge graph automatically. The knowledge graph is then used to learn from the observations. Project Thoth architecture requires multi-namespace deployment in an OpenShift environment, which is run on PnT DevOps Shared Infrastructure (PSI), a shared multi-tenant OpenShift cluster.