heat 1.7.0
A framework for high-performance data analytics and machine learning.
Requires Python: >=3.10
Keywords: data, analytics, tensors, distributed, gpu

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---

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

# Project Status

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# Table of Contents
  - [What is Heat for?](#what-is-heat-for)
  - [Features](#features)
  - [Getting Started](#getting-started)
  - [Installation](#installation)
    - [Requirements](#requirements)
    - [pip](#pip)
    - [conda](#conda)
  - [Support Channels](#support-channels)
  - [Contribution guidelines](#contribution-guidelines)
    - [Resources](#resources)
  - [License](#license)
  - [Citing Heat](#citing-heat)
  - [FAQ](#faq)
  - [Acknowledgements](#acknowledgements)


# What is Heat for?

Heat builds on [PyTorch](https://pytorch.org/) and [mpi4py](https://mpi4py.readthedocs.io) to provide high-performance computing infrastructure for memory-intensive applications within the NumPy/SciPy ecosystem.


With Heat you can:
- port existing NumPy/SciPy code from single-CPU to multi-node clusters with minimal coding effort;
- exploit the entire, cumulative RAM of your many nodes for memory-intensive operations and algorithms;
- run your NumPy/SciPy code on GPUs (CUDA, ROCm, coming up: Apple MPS).

For a example that highlights the benefits of multi-node parallelism, hardware acceleration, and how easy this can be done with the help of Heat, see, e.g., our [blog post on trucated SVD of a 200GB data set](https://helmholtz-analytics.github.io/heat/2023/06/16/new-feature-hsvd.html).

Check out our [coverage tables](coverage_tables.md) to see which NumPy, SciPy, scikit-learn functions are already supported.

 If you need a functionality that is not yet supported:
  - [search existing issues](https://github.com/helmholtz-analytics/heat/issues) and make sure to leave a comment if someone else already requested it;
  - [open a new issue](https://github.com/helmholtz-analytics/heat/issues/new/choose).


Check out our [features](#features) and the [Heat API Reference](https://heat.readthedocs.io/en/latest/autoapi/index.html) for a complete list of functionalities.

# Features

* High-performance n-dimensional arrays
* CPU, GPU, and distributed computation using MPI
* Powerful data analytics and machine learning methods
* Seamless integration with the NumPy/SciPy ecosystem
* Python array API (work in progress)


# Getting Started

Go to [Quick Start](quick_start.md) for a quick overview. For more details, see [Installation](#installation).

**You can test your setup** by running the [`heat_test.py`](https://github.com/helmholtz-analytics/heat/blob/main/scripts/heat_test.py) script:

```shell
mpirun -n 2 python heat_test.py
```

It should print something like this:

```shell
x is distributed:  True
Global DNDarray x:  DNDarray([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=ht.int32, device=cpu:0, split=0)
Global DNDarray x:
Local torch tensor on rank  0 :  tensor([0, 1, 2, 3, 4], dtype=torch.int32)
Local torch tensor on rank  1 :  tensor([5, 6, 7, 8, 9], dtype=torch.int32)
```

Check out our Jupyter Notebook [**Tutorials**](https://github.com/helmholtz-analytics/heat/blob/main/tutorials/), choose `local` to try things out on your machine, or `hpc` if you have access to an HPC system.

The complete documentation of the latest version is always deployed on
[Read the Docs](https://heat.readthedocs.io/).


<!-- # 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 single-node data analytics and machine learning libraries, and  high-performance computing (HPC). Heat's interface integrates seamlessly with the existing data science ecosystem and makes  writing scalable
scientific and data science applications as effortless as using NumPy.

Heat allows you to tackle your actual Big Data challenges that go beyond the
computational and memory needs of your laptop and desktop.
 -->
# Installation

## Requirements

### Basics
- python >= 3.10
- MPI (OpenMPI, MPICH, Intel MPI, etc.)
- mpi4py >= 3.0.0
- pytorch >= 2.0.0

### Parallel I/O
- h5py
- netCDF4
- zarr

### GPU support
In order to do computations on your GPU(s):
- your CUDA or ROCm installation must match your hardware and its drivers;
- your [PyTorch installation](https://pytorch.org/get-started/locally/) must be compiled with CUDA/ROCm support.

### HPC systems
On most HPC-systems you will not be able to install/compile MPI or CUDA/ROCm yourself. Instead, you will most likely need to load a pre-installed MPI and/or CUDA/ROCm module from the module system. Maybe, you will even find PyTorch, h5py, or mpi4py as (part of) such a module. Note that for optimal performance on GPU, you need to usa an MPI library that has been compiled with CUDA/ROCm support (e.g., so-called "CUDA-aware MPI").


## pip
Install the latest version with

```bash
pip install heat[hdf5,netcdf,zarr]
```
where the part in brackets is a list of optional dependencies. You can omit
it, if you do not need HDF5, NetCDF, or Zarr support.

## **conda**

The conda build includes all dependencies **including OpenMPI**.
```bash
 conda install -c conda-forge heat
 ```

# Support Channels

Go ahead and ask questions on [GitHub Discussions](https://github.com/helmholtz-analytics/heat/discussions). If you found a bug or are missing a feature, then please file a new [issue](https://github.com/helmholtz-analytics/heat/issues/new/choose). You can also get in touch with us on [Mattermost](https://mattermost.hzdr.de/signup_user_complete/?id=3sixwk9okpbzpjyfrhen5jpqfo) (sign up with your GitHub credentials). Once you log in, you can introduce yourself on the `Town Square` channel.


# Contribution guidelines

**We welcome contributions from the community, if you want to contribute to Heat, be sure to review the [Contribution Guidelines](contributing.md) and [Resources](#resources)  before getting started!**

We use [GitHub issues](https://github.com/helmholtz-analytics/heat/issues) for tracking requests and bugs, please see [Discussions](https://github.com/helmholtz-analytics/heat/discussions) for general questions and discussion. You can also get in touch with us on [Mattermost](https://mattermost.hzdr.de/signup_user_complete/?id=3sixwk9okpbzpjyfrhen5jpqfo) (sign up with your GitHub credentials). Once you log in, you can introduce yourself on the `Town Square` channel.

If you’re unsure where to start or how your skills fit in, reach out! You can ask us here on GitHub, by leaving a comment on a relevant issue that is already open.

**If you are new to contributing to open source, [this guide](https://opensource.guide/how-to-contribute/) helps explain why, what, and how to get involved.**


## Resources

* [Heat Tutorials](https://github.com/helmholtz-analytics/heat/tree/main/tutorials)
* [Heat API Reference](https://heat.readthedocs.io/en/latest/autoapi/index.html)

### Parallel Computing and MPI:

* David Henty's [course](https://www.archer2.ac.uk/training/courses/200514-mpi/)
* Wes Kendall's [Tutorials](https://mpitutorial.com/tutorials/)
* Rolf Rabenseifner's [MPI course material](https://www.hlrs.de/training/self-study-materials/mpi-course-material) (including C, Fortran **and** Python via `mpi4py`)

### mpi4py

* [mpi4py docs](https://mpi4py.readthedocs.io/en/stable/tutorial.html)
* [Tutorial](https://www.kth.se/blogs/pdc/2019/08/parallel-programming-in-python-mpi4py-part-1/)
# 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 publications. You can cite: -->

If Heat contributed to a publication, please cite our main paper.

**Preferred Citation:**

Götz, M., Debus, C., Coquelin, D., et al. (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.

```bibtex
@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},
    publisher={IEEE},
    doi={10.1109/BigData50022.2020.9378050}
}
```

### Other Relevant Publications

**For the RSE perspective and latest benchmarks:**

Hoppe, F., et al. (2025). *Engineering a large-scale data analytics and array computing library for research: Heat*. Electronic Communications of the EASST, 83.

```bibtex
@article{heat2025rse,
  title={Engineering a large-scale data analytics and array computing library for research: Heat},
  volume={83},
  url={[https://eceasst.org/index.php/eceasst/article/view/2626](https://eceasst.org/index.php/eceasst/article/view/2626)},
  DOI={10.14279/eceasst.v83.2626},
  journal={Electronic Communications of the EASST},
  author={Hoppe, Fabian and Gutiérrez Hermosillo Muriedas, Juan Pedro and Tarnawa, Michael and Knechtges, Philipp and Hagemeier, Björn and Krajsek, Kai and Rüttgers, Alexander and Götz, Markus and Comito, Claudia},
  year={2025}
}
```

**For the neural networks module (DASO):**

Coquelin, D., et al. (2022). *Accelerating neural network training with distributed asynchronous and selective optimization (DASO)*. J Big Data 9, 14.

```bibtex
@Article{DASO2022,
    author={Coquelin, Daniel and Debus, Charlotte and G{\"o}tz, Markus and von der Lehr, Fabrice and Kahn, James and Siggel, Martin and Streit, Achim},
    title={Accelerating neural network training with distributed asynchronous and selective optimization (DASO)},
    journal={Journal of Big Data},
    year={2022},
    volume={9},
    number={1},
    pages={14},
    doi={10.1186/s40537-021-00556-1}
}
```


**For specific software versions:**
Please use the [Zenodo DOI]([10.5281/zenodo.2531472](https://doi.org/10.5281/zenodo.2531472).) provided with each release.




# FAQ
Work in progress...

  <!-- - Users
  - Developers
  - Students
  - system administrators -->

## 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.*

*This project has received funding from Google Summer of Code (GSoC) in 2022.*

*This work is partially carried out under a [programme](https://activities.esa.int/index.php/4000144045) of, and funded by, the European Space Agency.
Any view expressed in this repository or related publications can in no way be taken to reflect the official opinion of the European Space Agency.*

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