The Distributed Computing Library (dislib) is a library that provides various distributed machine-learning algorithms. It has been implemented on top of PyCOMPSs, with the goal of facilitating the execution of big data analytics algorithms in distributed platforms, such as clusters, clouds, and supercomputers.

Dislib comes with two primary programming interfaces: an API to manage data in a distributed way and an estimator-based interface to work with different machine learning models.

Dislib main data structure is the distributed array (ds-array) that enables to distribute the data sets in multiple nodes of a computing infrastructure. The typical workflow in dislib consists of the following steps:

  • Reading input data into a ds-array.

  • Creating an estimator object.

  • Fitting the estimator with the input data.

  • Getting information from the model’s estimator or applying the model to new data.

Some useful links for more detailed information:

  1. Source code.

  2. Installation.

  3. Tutorial.