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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.

Goals

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.

Among other things, the implementation will allow us to tackle use cases that would otherwise exceed memory limits of a single node.

Features

  • high-performance n-dimensional tensors
  • CPU, GPU and distributed computation using MPI
  • powerful machine learning methods using above mentioned tensors

Requirements

HeAT is based on [PyTorch](https://pytorch.org/). Specifially, we are exploiting PyTorch’s support for GPUs and MPI parallelism. Therefore, PyTorch must be compiled with MPI support when using HeAT. The instructions to install PyTorch in that way are contained in the script [install-torch.sh](install-torch.sh), which we’re also using to install PyTorch in Travis CI.

Installation

Tagged releases are made available on the [Python Package Index (PyPI)](https://pypi.org/project/heat/). 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 https://github.com/helmholtz-analytics/heat.git

License

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

Acknowledgements

This work is supported by the [Helmholtz Association Initiative and Networking](https://www.helmholtz.de/en/about_us/the_association/initiating_and_networking/) Fund under project number ZT-I-0003.

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