Add to ORKGHeAT is a distributed tensor framework for high performance data analytics. The goal of HeAT is to fill the gap between machine learning libraries that have a strong focus on exploiting GPUs for performance, and traditional, distributed high-performance computing (HPC). The basic idea is to provide a generic, distributed tensor library with machine learning methods based on it
Release Notes Heat v1.0 comes with some major updates: new module nn for data-parallel neural netwo...
To cope with the rapid growth in available data, the efficiency of data analysis and machine learnin...
This repository contains data, code, and related artefacts supporting the following publication: "H...
HeAT is a distributed tensor framework for high performance data analytics. The goal of HeAT is to f...
We present the Helmholtz Analytics Toolkit (HeAT), a scientific big data analytics library for HPC s...
This work introduces the Helmholtz Analytics Toolkit (HeAT), a scientific big data analytics library...
We present the Helmholtz Analytics Toolkit (HeAT), a scientific big data analytics library for HPC s...
Born out of a large-scale collaboration in applied sciences, Heat [1, 2] is an open-source Python li...
This talk presents the Helmholtz Analytics Toolkit (HeAT), a HPC data analytics library for scientif...
When it comes to enhancing exploitation of massive data, machine learning and AI methods are very mu...
In order to cope with the exponential growth in available data, the efficiency of data analysis and ...
The HeAT v0.4.0 release is now available. We are striving to be as NumPy-API-compatible as possible ...
To cope with the rapid growth in available data, theefficiency of data analysis and machine learning...
This release includes many important updates (see below). We particularly would like to thank our en...
Highlights We have been selected as a mentoring organization for Google Summer of Code, and we alre...
Release Notes Heat v1.0 comes with some major updates: new module nn for data-parallel neural netwo...
To cope with the rapid growth in available data, the efficiency of data analysis and machine learnin...
This repository contains data, code, and related artefacts supporting the following publication: "H...
HeAT is a distributed tensor framework for high performance data analytics. The goal of HeAT is to f...
We present the Helmholtz Analytics Toolkit (HeAT), a scientific big data analytics library for HPC s...
This work introduces the Helmholtz Analytics Toolkit (HeAT), a scientific big data analytics library...
We present the Helmholtz Analytics Toolkit (HeAT), a scientific big data analytics library for HPC s...
Born out of a large-scale collaboration in applied sciences, Heat [1, 2] is an open-source Python li...
This talk presents the Helmholtz Analytics Toolkit (HeAT), a HPC data analytics library for scientif...
When it comes to enhancing exploitation of massive data, machine learning and AI methods are very mu...
In order to cope with the exponential growth in available data, the efficiency of data analysis and ...
The HeAT v0.4.0 release is now available. We are striving to be as NumPy-API-compatible as possible ...
To cope with the rapid growth in available data, theefficiency of data analysis and machine learning...
This release includes many important updates (see below). We particularly would like to thank our en...
Highlights We have been selected as a mentoring organization for Google Summer of Code, and we alre...
Release Notes Heat v1.0 comes with some major updates: new module nn for data-parallel neural netwo...
To cope with the rapid growth in available data, the efficiency of data analysis and machine learnin...
This repository contains data, code, and related artefacts supporting the following publication: "H...