Mridul Seth

We all know and love our carefully designed CI pipelines, which tests our code and makes sure by adding some code or fixing a bug we aren’t introducing a regression in the codebase. But we often don’t give the same treatment to benchmarking as we give to correctness. The benchmarking tests are usually one off scripts written to test a specific change. In this talk, we will discuss various strategies to test our code for performance regressions using ASV (airspeed velocity) for python projects.

In this talk, we will discuss how we can use ASV (airspeed velocity) and on demand cloud providers to mimic the usual setup of dedicated servers to run benchmarking tests. Currently, a lot of projects use dedicated servers to run their benchmarking suite, which usually is not economical as the majority of the time the hardware is unused. We can get rid of the setup of dedicated hardware + ongoing costs associated with it by only using resources when required. Outline 5 mins: - Why benchmarking? - The usual ways of running the benchmarking suite on dedicated hardware. - Using GitHub Actions reliably for "relative benchmark" testing (A quick shout out to the blog post by Quansight)  10 mins: - How we can use container technology to setup our tests and run them on demand on "dedicated" hardware to cut down on costs and give a lower barrier to entry to set up a benchmarking suite for projects.

This workshop is for data scientists and other programmers who want to add another tool in their data science toolkit. Modelling, analysing and visualising data as networks! Network Science deals with analysing network data, and the data can come from different fields like politics, finance, computer science, law and even Game of Thrones!

In this workshop we will cover the basics of network theory and network thinking, then we will go over some algorithms used to analyse network data. We will take a quick detour to understand the story between linear algebra and networks. We will then jump on to some real world datasets and how to use our newly acquired skills to tackle the data problems. This will be a hands-on and interactive tutorial, so get ready to code your way till the end! By the end of the workshop you should be comfortable with working with network data using the PyData ecosystem (NetworkX, pandas, numpy!). We will roughly follow the following timeline during the workshop (30 mins each) Part A: Introduction to Graphs and the NetworkX API Part B: Graph Algorithms - Hubs: Which nodes are the important nodes in our data? - Paths: Where should I jump next to find my destination? - Structures: Who should I be friends with? Part C: Linear Algebra and Network Science - What do matrices have to do with nodes and edges?