Metadata-Version: 2.1
Name: heat
Version: 1.1.0
Summary: A framework for high-performance data analytics and machine learning.
Home-page: https://github.com/helmholtz-analytics/heat
Author: Helmholtz Association
Author-email: martin.siggel@dlr.de
License: UNKNOWN
Description: <div align="center">
          <img src="https://raw.githubusercontent.com/helmholtz-analytics/heat/master/doc/images/logo.png">
        </div>
        
        ---
        
        Heat is a distributed tensor framework for high performance data analytics.
        
        Project Status
        --------------
        
        [![Jenkins](https://img.shields.io/jenkins/build?jobUrl=https%3A%2F%2Fheat-ci.fz-juelich.de%2Fjob%2Fheat%2Fjob%2Fheat%2Fjob%2Fmaster%2F&label=CPU)](https://heat-ci.fz-juelich.de/blue/organizations/jenkins/heat%2Fheat/activity?branch=master)
        [![Jenkins](https://img.shields.io/jenkins/build?jobUrl=https%3A%2F%2Fheat-ci.fz-juelich.de%2Fjob%2FGPU%2520Cluster%2Fjob%2Fmaster%2F&label=GPU)](https://heat-ci.fz-juelich.de/blue/organizations/jenkins/GPU%20Cluster%2Fmaster/activity)
        [![Documentation Status](https://readthedocs.org/projects/heat/badge/?version=latest)](https://heat.readthedocs.io/en/latest/?badge=latest)
        [![codecov](https://codecov.io/gh/helmholtz-analytics/heat/branch/master/graph/badge.svg)](https://codecov.io/gh/helmholtz-analytics/heat)
        [![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
        [![license: MIT](https://img.shields.io/badge/License-MIT-blue.svg)](https://opensource.org/licenses/MIT)
        [![Downloads](https://pepy.tech/badge/heat)](https://pepy.tech/project/heat)
        [![Mattermost Chat](https://img.shields.io/badge/chat-on%20mattermost-blue.svg)](https://mattermost-haf.fz-juelich.de/signup_user_complete/?id=iqrr6pmxb38fzqffa51qqhcu8w)
        
        Goals
        -----
        
        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 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 to tackle your actual Big Data challenges that go beyond the
        computational and memory needs of your laptop and desktop.
        
        Features
        --------
        
        * 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](https://github.com/helmholtz-analytics/heat/blob/master/scripts/tutorial.ipynb)
        right here on Github or in the /scripts directory.
        
        The complete documentation of the latest version is always deployed on
        [Read the Docs](https://heat.readthedocs.io/).
        
        Support Channels
        ----------------
        
        We use [StackOverflow](https://stackoverflow.com/tags/pyheat/) as a forum for questions about Heat.
        If you do not find an answer to your question, then please ask a new question there and be sure to
        tag it with "pyheat".
        
        Requirements
        ------------
        
        Heat requires Python 3.7 or newer.
        Heat is based on [PyTorch](https://pytorch.org/). Specifially, we are exploiting
        PyTorch's support for GPUs *and* MPI parallelism. For MPI support we utilize
        [mpi4py](https://mpi4py.readthedocs.io). Both packages can be installed via pip
        or automatically using the setup.py.
        
        
        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[hdf5,netcdf]
        
        where the part in brackets is a list of optional dependencies. You can omit
        it, if you do not need HDF5 or NetCDF support.
        
        Hacking
        -------
        
        If you want to work with the development version, you can check out the sources using
        
        > $ git clone https://github.com/helmholtz-analytics/heat.git
        
        The installation can then be done from the checked-out sources with
        
        > $ pip install .[hdf5,netcdf,dev]
        
        We welcome contributions from the community, please check out our [Contribution Guidelines](contributing.md) before getting started!
        
        License
        -------
        
        Heat is distributed under the MIT license, see our
        [LICENSE](LICENSE) file.
        
        Citing Heat
        -----------
        
        If you find Heat helpful for your research, please mention it in your academic publications. You can cite:
        
        - Götz, M., Debus, C., Coquelin, D., Krajsek, K., Comito, C., Knechtges, P., Hagemeier, B., Tarnawa, M., Hanselmann, S., Siggel, S., Basermann, A. & Streit, A. (2020). HeAT - a Distributed and GPU-accelerated Tensor Framework for Data Analytics. In 2020 IEEE International Conference on Big Data (Big Data) (pp. 276-287). IEEE, DOI: 10.1109/BigData50022.2020.9378050.
        
        ```
        @inproceedings{heat2020,
            title={{HeAT -- a Distributed and GPU-accelerated Tensor Framework for Data Analytics}},
            author={
              Markus Götz and
              Charlotte Debus and
              Daniel Coquelin and
              Kai Krajsek and
              Claudia Comito and
              Philipp Knechtges and
              Björn Hagemeier and
              Michael Tarnawa and
              Simon Hanselmann and
              Martin Siggel and
              Achim Basermann and
              Achim Streit
            },
            booktitle={2020 IEEE International Conference on Big Data (Big Data)},
            year={2020},
            pages={276-287},
            month={December},
            publisher={IEEE},
            doi={10.1109/BigData50022.2020.9378050}
        }
        ```
        
        Acknowledgements
        ----------------
        
        *This work is supported by the [Helmholtz Association Initiative and
        Networking Fund](https://www.helmholtz.de/en/about_us/the_association/initiating_and_networking/)
        under project number ZT-I-0003 and the Helmholtz AI platform grant.*
        
        ---
        
        <div align="center">
          <a href="https://www.dlr.de/EN/Home/home_node.html"><img src="https://raw.githubusercontent.com/helmholtz-analytics/heat/master/doc/images/dlr_logo.svg" height="50px" hspace="3%" vspace="20px"></a><a href="https://www.fz-juelich.de/portal/EN/Home/home_node.html"><img src="https://raw.githubusercontent.com/helmholtz-analytics/heat/master/doc/images/fzj_logo.svg" height="50px" hspace="3%" vspace="20px"></a><a href="http://www.kit.edu/english/index.php"><img src="https://raw.githubusercontent.com/helmholtz-analytics/heat/master/doc/images/kit_logo.svg" height="50px" hspace="3%" vspace="20px"></a><a href="https://www.helmholtz.de/en/"><img src="https://raw.githubusercontent.com/helmholtz-analytics/heat/master/doc/images/helmholtz_logo.svg" height="50px" hspace="3%" vspace="20px"></a>
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Keywords: data,analytics,tensors,distributed,gpu
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: License :: OSI Approved :: MIT License
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering
Requires-Python: >=3.7
Description-Content-Type: text/markdown
Provides-Extra: docutils
Provides-Extra: hdf5
Provides-Extra: netcdf
Provides-Extra: dev
