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A framework for high performance data analytics and machine learning.

Project description

![HeAT Logo](doc/images/logo_HeAT.png)

HeAT is a distributed tensor framework for high performance data analytics.


HeAT is a flexible and seamless open-source software for high performance data analytics and machine learnings. 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, and traditional high-performance computing (HPC). 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.


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

Getting Started

Check out our Jupyter Notebook [tutorial]( right here on Github or in the /scripts directory.


HeAT is based on [PyTorch]( Specifially, we are exploiting PyTorch’s support for GPUs and MPI parallelism. For MPI support we utilize [mpi4py]( Both packages can be installed via pip or automatically using the


Tagged releases are made available on the [Python Package Index (PyPI)]( You can typically install the latest version with

> $ pip install heat

If you want to work with the development version, you can checkout the sources using

> $ git clone


HeAT is distributed under the MIT license, see our [LICENSE](LICENSE) file.


This work is supported by the [Helmholtz Association Initiative and Networking]( Fund under project number ZT-I-0003.

Project details

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