This webinar provides a "guided tour" through the core features of Ray.
These features provide a grammar or "pattern language" for building robust, high-performance distributed systems at scale.
We'll focus on Python coding examples – examined in much more detail than in the docs or API reference. For each pattern, we'll look at primary sources, and compare/contrast with how that form of parallelization works in other popular platforms. Then we'll show examples of parallelizing Python code using this – including ways to distribute workloads for other popular Python libraries used in data science work. All of the code is available in Jupyter notebooks on GitHub, and these examples can be run on your laptop.
