Python

Where to store your models if you want to access them rarely? Let’s say you want to be able to access them in few months, but you don’t want to pay for the running server all the time. This may not be the regular issue, but we faced this challenge when working on some PoC that was supposed to be shown to clients every few months. We didn’t want to pay for the server all the time, didn’t have to maintain it, but we wanted to have the model ready to be served in a few minutes. The way I solved this was by wrapping the ML model in a FastAPI app with BentoML and deploying it on Google Cloud Run as a docker container. ...

What if you just don’t like rmd/qmd format anymore? You love jupyter notebooks and that’s your way of working (that’s me). Still, you must generate report from time to time. Fortunately quarto solves this problem brilliantly! The reports look beautifully. Not only htmls what we might expect, but pdfs as well! I wrote a post for Appsilon that has been published on medium regarding this topic. Be sure to check it out, it was a real game-changer for me! ...

Working with quarto files in RStudio is probably the suggested way. However, you may prefer to use VS Code. Especially if you work mainly in python. It’s not a problem by any means! I wrote a post on medium on working with quarto in VS Code on python files. ...

Do you remember good old rmarkdown? --- title: "R markdown" output: html_document --- Hello ~world~ problems! We have a great successor in the form of quarto that is ready for 2022 and beyond! ...

One thing is to train the model, but another is to serve it. In between those two phases, model serialization and deserialization occurs. In simpler words it’s just model saving and loading. It’s important because different methods result in different: Inference speed Model size Python environment size If you are curious what are the ways to serialize model in PyTorch and how they compare, checkout my new post on Appsilon blog. ...

I’d like to inform that the course has finished and the total of 56 people received a graduation certificate (which required passing homeworks for over 70%), congratulations! 🎉 It was a great experience and I’m happy that I was able to teach so many young students! ...

When working with deep learning problems I usually use PyTorch. I like this framework as it gives me a lot of freedom and allows to write code in the pythonic way. Using bare pytorch unfortunately often means writing a lot of boilerplate code, which no-one like. Another problem that arises during longer and larger projects is experiments running and configuration maintenance. I tackle those two problems by using pytorch-lightning and Hydra. Recently I described my approach in a post on the Appsilon blog. ...

I’m happy to announce that I’ll create and teach the course Python LevelUP: Data Science by Appsilon in collaboration with DaftAcademy, part of DaftCode. This will be a five-week course on the basics of Data Science in python. The course materials will be available on GitHub for free. Apart from teaching the classic stuff, I’ll try to show some of the newest and coolest libraries out there. Even if you already know most of this stuff, I really recommend to take a closer look on the unknown packages. ...

This is the second post from micro-series preceding the SFI conference. In the first post I showed how easy it is to do the style transfer at home. Now, I would like to present you how to play with StyleGANv2. StyleGANv2 is the second version of StyleGAN, they are very similar in core principals. We will be working with StyleGANv2 but I will refer to it as SG. There is a lot that can be said/explained about SG. Here I would like to focus on showing how to make your own experiments with SG instead of getting into SG’s details. ...

I wrote another post for Appsilon, this time about deploying streamlit applications on RStudio Connect infrastructure. This post might be totally useless for majority of audience, but a pure gold for a few 💰. What is streamlit you can understand from my previous post. Having a running app in your browser is already cool, but how cool is it to deploy your app on the internet? If you need an enterprise solution for hosting dashboards in RShiny/streamlit, RSConnect is the way! In this blog post I focused on presenting solutions for common problems you might encounter. ...