# Pyrallel - Parallel Data Analytics in Python
Overview: experimental project to investigate distributed computation patterns for machine learning and other semi-interactive data analytics tasks.
Scope:
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.
## Dependencies
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.
## License
Simplified BSD.
## History
This project started at the [PyCon 2012 PyData sprint](http://wiki.ipython.org/PyCon12Sprint) as a set of proof of concept [IPython.parallel scripts](https://github.com/ogrisel/pycon-pydata-sprint).
1 maintainer
Metadata
1 maintainer
Release files for pyrallel 0.2.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | Actions |
|---|---|---|---|---|
| pyrallel-0.2.1.tar.gz | 7.9 kB | Without provenance | Details for pyrallel-0.2.1.tar.gz |
Release files / pyrallel-0.2.1.tar.gz
| Download URL | pyrallel-0.2.1.tar.gz |
|---|---|
| Size | 7.9 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
b57cfdf7dfc14628d7c3b738e0e23d8750a5ffc1ac2aae83d2216cd1549b6b30
|
|
BLAKE2b-256 checksum How to use checksums |
cab842036676c89dcc92c90e74f8cb5ccc60bc2da860bef3115068806ef639ef
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
Security for pyrallel 0.2.1
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
No provenance found
None of the files in this release were uploaded with provenance metadata.
A note on package security
PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.
To learn more about protecting yourself from malicious actors, visit our package security documentation.
