17 docs tagged with "Software"

The Camera Traps application is both a simulator and IoT device software for utilizing machine learning on the edge in field research. The first implementation specializes in applying computer vision (detection and classification) to wildlife images for animal ecology studies. Two operational modes are supported: "simulation" mode and "demo" mode. When executed in simulation mode, the software serves as a test bed for studying ML models, protocols and techniques that optimize storage, execution time, power and accuracy. It requires an input dataset of images to act as the images that would be generated an IoT camera device; it uses these images to drive the simulation.

The Cyberinfrastructure Knowledge Network (CKN) is an extensible and portable distributed framework designed to optimize AI at the edge—particularly in dynamic environments where workloads may change suddenly (for example, in response to motion detection). CKN enhances edge–cloud collaboration by using historical data, graph representations, and adaptable deployment of AI models to satisfy changing accuracy‑and‑latency demands on edge devices.

Architectual Overview

CKN facilitates seamless connectivity between edge devices and the cloud through event streaming, enabling real‑time data capture and processing. By leveraging event‑stream processing, it captures, aggregates, and stores historical system‑performance data in a knowledge graph that models application behaviour and guides model selection and deployment at the edge.

- This dataset captures both tabular metadata and graph representations from deep learning training workflows, extracted via TensorFlow's XLA compiler.

Features

TapisUI provides a research oriented frontend to interact with Tapis and tenant components. In this case, the ICICLE extension extends TapisUI with custom branding

A dataset designed to support AI-driven resource estimation like runtime prediction to support HPC scheduling optimization by leveraging compiler-level High-Level Optimizer (HLO) graph features and deep learning workload metadata.

Getting Started

See the full documentation for detailed instructions on creating custom plug‑ins and streaming events to the knowledge graph.

Prerequisites

Quick Start

How to Predict Training Time Using Metadata

View the main TapisUI wiki to learn how to deploy and test TapisUI extensions locally.

This tapis ui extension enables additional icicle specific branding and tabs on tapisui.

1. Create a CKN Topic

Getting Started with the HLO Feature Dataset