Tarantella is an open-source, distributed Deep Learning framework designed to speed up the training of deep neural networks, while providing seamless integration with existing sequential training codes.
Built on top of the TensorFlow library, Tarantella relies on its intuitive Keras interface to describe deep learning models and training workflows.
Tarantella is easy to use and does not require any parallel computing expertise.
Tarantella allows you to speed up your AI workflows by providing
scalable Deep Neural Network training on multi-GPU and multi-node systems.
Tarantella comes with a simple, minimalistic API that abstracts away any parallel computing details. It provides a rich technical documentation and tutorials to quickly get started.
Tarantella supports the full Keras API of TensorFlow and lets you easily integrate distributed training in your existing workflows.
Tarantella takes automatic care of the distribution of data and computation in such a way that serial results are reproduced.
Tarantella is a community-driven, open-source framework that builds
on top of TensorFlow.
Tarantella supports CPU and GPU clusters, independently of the hardware type and vendor.
To get you started quickly, download Tarantella from github, check out the installation guidelines, and
the tutorials and documentation.
If you want to contribute to Tarantella, have a look at feature requests, bug reports and the
Tarantella is developed at the Competence Center for High Performance Computing, which is part of the
Fraunhofer Institute for Industrial Mathematics (ITWM).
In close cooperation with industrial and academic partners,
the Competence Center for High Performance Computing develops solutions for the efficient use of increasingly
more complex processors and parallel computers. Our focus lies particularly in the field of HPC tools, such as
parallel filesystems and scalable parallel programming, seismic and visualization, Deep Learning tools and
applications, as well as hardware-software co-design and Green by IT.