This component implements a time cost estimator for distributed training of large language models (LLMs). It is used to predict the time required to train one batch across multiple GPUs. The predictor module only requires at least a CPU. The computation sampling module needs one or more GPUs, while the communication sampling module requires multiple GPUs, depending on your computing platform.
- This dataset captures both tabular metadata and graph representations from deep learning training workflows, extracted via TensorFlow's XLA compiler.
Convention and Usage
Graph Neural Networks for Trade Flow Prediction
System Architecture and Design Philosophy
This is a visualization tool for results from GNN FoodFlow Model. This portal allows you to explore the data through an interactive map, and to download the filtered results for your own research.
Overview
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.
WAYS to configure HARP to setup applications for profiling:
System Requirements
Follow these steps to set up and run the GNN Food Flow Portal locally.
How to Predict Training Time Using Metadata
How to Implement a Hurdle Model for Trade Prediction
Continuing Multi-Session Missions
iSpLib is an accelerated sparse kernel library with PyTorch interface. This library has an auto-tuner which generates optimized custom sparse kernels based on the user environment. The goal of this library is to provide efficient sparse operations for Graph Neural Network implementations. Currently it has support for CPU-based efficient Sparse Dense Matrix Multiplication (spmm-sum only) with autograd.
A project leveraging Graph Neural Networks (GNNs) to predict food flows between counties and FAF zones for economic planning, infrastructure development, and policy-making. This model predicts food trade flows between U.S. counties and Freight Analysis Framework (FAF) zones using Graph Neural Networks (GNNs). It addresses the challenges of sparsity in trade data by applying a two-stage hurdle model that distinguishes between the presence and magnitude of trade.
SpMM Example
Enviroment
Getting Started with the HLO Feature Dataset
Getting Started with WildWing
An open-source, autonomous and affordable UAS for animal behaviour video monitoring using Parrot Anafi drones to track group-living animals.