Hello and welcome to my adventures in Scaling Python Machine Learning (ML).

Scaling Python ML


  • A First Look at Dask on ARM on K8s

    After getting the cluster set up in the previous post, it was time to finally play with Dask on the cluster. Thankfully, there are dask-kubernetes and dask-docker projects that provide the framework to do this. Since I’m still new to Dask, I decided to start off by using Dask from a local notebook (in retrospect maybe not the best choice).

  • Setting up K3s (lightweight Kubernetes) with Persistent Volumes and Minio on ARM

    After the last adventure of getting the rack built and acquiring the machines, it was time to set up the software. Originally, I had planned to do this in a day or two, but in practice, it ran like so many other “simple” projects and some things I had assumed would be “super quick” ended up taking much longer than planned.

  • Building the Test Cluster

    To ensure that the results between tests are as comparable as possible, I’m using a consistent hardware setup whenever possible. Rather than use a cloud provider I (with the help of Nova) set up a rack with a few different nodes. Using my own hardware allows me to avoid the noisy neighbor problem with any performance numbers and gives me more control over simulating network partitions. A downside is that the environment is not as easily re-creatable.

  • A First (Brief) Look at Ray on Kubernetes

    After my motorcycle/Vespa crash last year I took some time away from work. While I was out and trying to practice getting my typing speed back up, I decided to play with Ray, which was pretty cool. Ray comes out of the same1 research lab that created the initial work that became the basis of Apache Spark. Like Spark, the primary authors have now started a company (Anyscale) to grow Ray. Unlike Spark, Ray is a Python first library and does not depend on the Java Virtual Machine (JVM) – and as someone who’s spent way more time than she would like getting the JVM and Python to play together, Ray and it’s cohort seem quite promising.

    1. Well… same-ish. It’s technically a bit more complicated because of the way the professors choose to run their labs, but if you look at the advisors you’ll notice a lot of overlap. 

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