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