_images/dislib-logo-full.png







The Distributed Computing Library (dislib) provides distributed algorithms ready to use as a library. So far, dislib is highly focused on machine learning algorithms, and is greatly inspired by scikit-learn. However, other types of numerical algorithms might be added in the future. The main objective of dislib is to facilitate the execution of big data analytics algorithms in distributed platforms, such as clusters, clouds, and supercomputers.

Dislib has been implemented on top of PyCOMPSs programming model, and it is being developed by the Workflows and Distributed Computing group of the Barcelona Supercomputing Center.

Performance

The following plot shows fit time of some dislib models on the MareNostrum 4 supercomputer (using 8 worker nodes):

_images/performance.png

Labels on the horizontal axis represent algorithm-dataset, where:

  • ALS = AlternatingLeastSquares
  • CSVM = CascadeSVM
  • GMM = GaussianMixture
  • RF = RandomForestClassifier

and:

  • Netflix = The Netflix Prize dataset.
  • ijcnn1 = The ijcnn1 dataset.
  • KDD99 = The KDDCUP 1999 dataset.
  • gaia = The Tycho-Gaia Astrometric Solution dataset [1].
  • 1M and 3M = 1 and 3 million random samples.
  • mnist = The mnist dataset.

Source code

The source code of dislib is available online at Github.

Support

If you have questions or issues about the dislib you can join us in Slack.

Alternatively, you can send us an e-mail to support-compss@bsc.es.

Citing dislib

If you use dislib in a scientific publication, we would appreciate citations to the following paper:

J. Álvarez Cid-Fuentes, S. Solà, P. Álvarez, A. Castro-Ginard, and R. M. Badia, “dislib: Large Scale High Performance Machine Learning in Python,” in Proceedings of the 15th International Conference on eScience, 2019, pp. 96-105

Bibtex:

@inproceedings{dislib,
            title       = {{dislib: Large Scale High Performance Machine Learning in Python}},
            author      = {Javier Álvarez Cid-Fuentes and Salvi Solà and Pol Álvarez and Alfred Castro-Ginard and Rosa M. Badia},
            booktitle   = {Proceedings of the 15th International Conference on eScience},
            pages       = {96-105},
            year        = {2019},
 }

Indices and tables

[1]Michalik, Daniel, Lindegren, Lennart, and Hobbs, David, “The Tycho-Gaia astrometric solution - How to get 2.5 million parallaxes with less than one year of Gaia data,” A&A, vol. 574, p. A115, 2015.