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        "author_email": "martin.siggel@dlr.de",
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        "description": "HeAT - Helmholtz Analytics Toolkit\n==================================\n\n![HeAT Logo](doc/images/logo_HeAT.png)\n\nHeAT is a distributed tensor framework for high performance data analytics.\n\nProject Status\n--------------\n\n[![Build Status](https://travis-ci.com/helmholtz-analytics/heat.svg?branch=master)](https://travis-ci.com/helmholtz-analytics/heat)\n[![Documentation Status](https://readthedocs.org/projects/heat/badge/?version=latest)](https://heat.readthedocs.io/en/latest/?badge=latest)\n[![codecov](https://codecov.io/gh/helmholtz-analytics/heat/branch/master/graph/badge.svg)](https://codecov.io/gh/helmholtz-analytics/heat)\n\nGoals\n-----\n\nHeAT 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.\n\nHeAT allows you tackle your actual Big Data challenges that go beyond the computational and memory needs of your laptop and desktop.\n\nFeatures\n--------\n\n* High-performance n-dimensional tensors\n* CPU, GPU and distributed computation using MPI\n* Powerful data analytics and machine learning methods\n* Abstracted communication via split tensors\n* Python API\n\nGetting Started\n---------------\n\nCheck out our Jupyter Notebook [tutorial](https://github.com/helmholtz-analytics/heat/blob/master/scripts/tutorial.ipynb) right here on Github or in the /scripts directory.\n\nRequirements\n------------\n\nHeAT is based on [PyTorch](https://pytorch.org/). Specifially, we are exploiting\nPyTorch's support for GPUs *and* MPI parallelism. For MPI support we utilize \n[mpi4py](https://mpi4py.readthedocs.io). Both packages can be installed via pip or automatically using the setup.py.\n\n\nInstallation\n------------\n\nTagged releases are made available on the\n[Python Package Index (PyPI)](https://pypi.org/project/heat/). You can typically\ninstall the latest version with\n\n> $ pip install heat\n\nIf you want to work with the development version, you can checkout the sources using\n\n> $ git clone https://github.com/helmholtz-analytics/heat.git\n\nLicense\n-------\n\nHeAT is distributed under the MIT license, see our\n[LICENSE](LICENSE) file.\n\nAcknowledgements\n----------------\n\n*This work is supported by the [Helmholtz Association Initiative and\nNetworking](https://www.helmholtz.de/en/about_us/the_association/initiating_and_networking/)\nFund under project number ZT-I-0003.*",
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