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Experimental parallel data analysis toolkit.

branch: master

Pyrallel - Parallel Data Analytics in Python

Overview: experimental project to investigate distributed computation patterns for machine learning and other semi-interactive data analytics tasks.


  • focus on small to medium dataset that fits in memory on a small (10+ nodes) to medium cluster (100+ nodes).

  • focus on small to medium data (with data locality when possible).

  • focus on CPU bound tasks (e.g. training Random Forests) while trying to limit disk / network access to a minimum.

  • do not focus on HA / Fault Tolerance (yet).

  • do not try to invent new set of high level programming abstractions (yet): use a low level programming model (IPython.parallel) to finely control the cluster elements and messages transfered and help identify what are the practical underlying constraints in distributed machine learning setting.

Disclaimer: the public API of this library will probably not be stable soon as the current goal of this project is to experiment.


The usual suspects: Python 2.7, NumPy, SciPy.

Fetch the development version (master branch) from:

StarCluster develop branch and its IPCluster plugin is also required to easily startup a bunch of nodes with IPython.parallel setup.

Patterns currently under investigation

  • Asynchronous & randomized hyper-parameters search (a.k.a. Randomized Grid Search) for machine learning models

  • Share numerical arrays efficiently over the nodes and make them available to concurrently running Python processes without making copies in memory using memory-mapped files.

  • Distributed Random Forests fitting.

  • Ensembling heterogeneous library models.

  • Parallel implementation of online averaged models using a MPI AllReduce, for instance using MiniBatchKMeans on partitioned data.

See the content of the examples/ folder for more details.




This project started at the PyCon 2012 PyData sprint as a set of proof of concept IPython.parallel scripts.

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