Pandas on Ray – Make Pandas faster
As a person who LOVES pandas, numpy, scikit, and all things SciPy, I really wish these kinds of posts would take a few seconds to include a link, or maybe just a quick paragraph, to answer one question:<p>What the %$#@ is Ray?<p>I make a habit of doing this myself whenever I do a post like this.  Sure, I was able to look up Ray from the Riselab and figure this out myself, but I wish I didn't have to.<p>From the Ray homepage:<p>Ray is a high-performance distributed execution framework targeted at large-scale machine learning and reinforcement learning applications. It achieves scalability and fault tolerance by abstracting the control state of the system in a global control store and keeping all other components stateless. It uses a shared-memory distributed object store to efficiently handle large data through shared memory, and it uses a bottom-up hierarchical scheduling architecture to achieve low-latency and high-throughput scheduling. It uses a lightweight API based on dynamic task graphs and actors to express a wide range of applications in a flexible manner.<p>Check out the following links!<p>Codebase: <a href="https://github.com/ray-project/ray" rel="nofollow">https://github.com/ray-project/ray</a>
Documentation: <a href="http://ray.readthedocs.io/en/latest/index.html" rel="nofollow">http://ray.readthedocs.io/en/latest/index.html</a>
Tutorial: <a href="https://github.com/ray-project/tutorial" rel="nofollow">https://github.com/ray-project/tutorial</a>
Blog: <a href="https://ray-project.github.io" rel="nofollow">https://ray-project.github.io</a>
Mailing list: ray-dev@googlegroups.com