University research labs have a different structure than corporate research labs and their compute setups. In this post, I list useful Python resources for starting in a university machine learning lab.
An \(\alpha\omega\epsilon s \sigma m \epsilon\) Pelican plugin to render math in JavaScript libraries like KaTeX. This plugin makes sure that equations are preserved in the Markdown and Restructured Text parsers and get reproduced properly in HTML for a JavaScript renderer to process.
New release of Databench that switches the backend from Flask to Tornado, fully supports Python 2 and 3, transpiles ES6 to legacy JavaScript and runs unit tests and coverage on every commit.
Benchmarks for the latest parallel features in pysparkling. It shows good scaling for processing with multiple CPU cores. The example contains only a simple computation which shows that hyperthreading is not very effective in this case.
A pure Python implementation of Apache Spark's RDD interfaces. pysparkling does not depend on Java and has a small execution overhead. It can be a fast test runner for Spark applications.
A short project to visualize the social Twitter graph of people at Wildcard. The backend is particularly efficient in the number of API calls. The visualization is interactive in d3.js.