HeAT - Helmholtz Analytics Toolkit

HeAT is a flexible and seamless open-source software for high performance data analytics and machine learning. It provides highly optimized algorithms and data structures for tensor computations using CPUs, GPUs and distributed cluster systems on top of MPI. The goal of HeAT is to fill the gap between data analytics and machine learning libraries with a strong focus on on single-node performance on the one hand, and traditional high-performance computing (HPC) on the other. HeAT's generic Python-first programming interface integrates seamlessly with the existing data science ecosystem and makes it as effortless as using numpy to write scalable scientific and data science applications.

HeAT allows you tackle your actual Big Data challenges that go beyond the computational and memory needs of your laptop and desktop.

• Python API
• High-performance n-dimensional tensors
• CPU, GPU and distributed computation using MPI
• Powerful data analytics and machine learning methods
• Abstracted communication via split tensors

The development of HeAT is mainly driven by our HAF partners from DLR, FZJ, and KIT.

Releases

Packaged versions of HeAT are released on the Python Package Index (PyPI) and can be easily installed via

$ pip install heat

Date	Version
2021-04-30	1.0.0
2020-11-13	0.5.1
2020-09-25	0.5.0
2020-05-27	0.4.0
2020-02-19	0.3.0
2020-01-30	0.2.2
2019-12-19	0.2.1
2019-05-29	0.1.0

Source Code

HeAT is available as an open source project on GitHub: https://github.com/helmholtz-analytics/heat.