List of videos

EuroPython 2022 - Dance with shadows: stubs, patch and mock - presented by María Andrea Vignau [Liffey Hall 2 on 2022-07-13] A comprehensive but simple introduction to the use of fake objects. Explain how to inject this object and use in test using patch and the awesome and powerful mock objects . Last, I present some very interesting specialized libraries for mocking on web development. Outline 0:00 I present the key factors to use fake objects, and present some dangers. 3:00 Discuss some wanted characteristics in this kind of components. 6:00 Patching: how to do that and some common mistakes. After that I present patch scopes and some disadvantage in the use of this technique. 10:00 Inverse dependency as an possible alternative to patch 13:00 Mocks properties: return value, side effect and specs. Using mocks as spy functions or wrappers. Asserting on callings. 21:00 Using special libraries for mocking. Presenting pyvcr and moto." This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License http://creativecommons.org/licenses/by-nc-sa/4.0/

EuroPython 2022 - How to craft awesome Machine Learning demos with Python - presented by Omar Sanseviero [Liffey Hall 2 on 2022-07-13] Building interactive Machine Learning demos is now easier than ever. With Open Source libraries such as Gradio and Streamlit, you can use Python to craft demos, and use Spaces to share them with the rest of the ML ecosystem as well as non-ML people. Learning to create graphic interfaces for models is extremely useful for sharing with other people interesting in them. All of this leverages free, open-source tools that anyone can use." This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License http://creativecommons.org/licenses/by-nc-sa/4.0/

EuroPython 2022 - Applying machine learning capabilities to wearable IoT devices for boxing technique management - presented by Anthony I. Joseph [Liffey Hall 2 on 2022-07-13] Internet of Things (IoT) devices are becoming more advanced through additional sensors, reduced size and increased computational power. In particular, this increase in computational power allows one to run previously-trained machine learning algorithms natively on an IoT device. This presents an exciting opportunity: IoT devices often feature a variety of onboard sensors which can be used as inputs into a machine learning algorithm. This talk will use the presenter's boxing training as a practical example of applying sensor data to a machine learning algorithm. In particular, this talk will demonstrate using motion sensor data obtained on an Arduino Nano 33 BLE Sense configured with TensorFlow Lite. This talk will discuss the entire analytical process from problem and data analysis through to algorithm training and deployment. It will also discuss boxing concepts and how these concepts are modelled in an IoT context. The links to code are provided below: - https://github.com/ajosephau/boxing_tracker_nano_ble_sense - https://github.com/ajosephau/boxing_tracker_wio_terminal The slide deck is available here: - https://www.dropbox.com/s/7448hf8q9umnnse/Wearable%20Tech%20boxing%20technique.pdf?dl=0" This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License http://creativecommons.org/licenses/by-nc-sa/4.0/

EuroPython 2022 - The beginner’s data science project checklist - presented by Sara Iris Garcia [Liffey Hall 2 on 2022-07-13] In this talk you will learn tips on: - Defining requirements - Outlining a data science project - Reproducibility and Readability checklist - Best practices for writing Documentation - Useful python tools - Tips on presenting your findings This is a talk is intended for beginner audience." This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License http://creativecommons.org/licenses/by-nc-sa/4.0/

EuroPython 2022 - An Introduction to Apache TVM - presented by Leandro Nunes [Liffey Hall 2 on 2022-07-13] This talk will present an introduction to Apache TVM using its Python API, and will include a demonstration using examples of deep learning models being executed in CPUs and Microcontrollers. Apache TVM is a very flexible compilation stack for deep learning models, supporting many input formats such as TensorFlow, TFLite, Keras, PyTorch, ONNX, etc. as well as many target hardware like CPUs, GPUs and neural networks accelerators. This talk will present a walkthrough of TVM Python API from installation to usage, demonstrating its features using a series of quick practical projects. The high-level agenda is: - TVM in a nutshell (a brief description of what is TVM) - How to install - Introduction to TVM Python API - Practical demos: Compiling and tuning a model - Compiling and running a model on an embedded target - Final Remarks" This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License http://creativecommons.org/licenses/by-nc-sa/4.0/

EuroPython 2022 - Building a Just-in-Time Python FaaS Platform with Unikraft - presented by Felipe Huici, Alexander Jung [Liffey Hall 1 on 2022-07-13] Unikraft [1] is a unikernel (specialized virtual machine) project. Unikraft is able to target a specific application (e.g., a web server such as NGINX) and transparently build an entire software stack, from the operating system all the way up to systems libraries, that includes only the parts that the application needs and nothing more. Such specialization results in extremely short boot times (a few milliseconds compared to hundreds or thousands for Linux VMs), small image sizes and memory consumption (e.g., a few MBs vs. hundreds of MBs) and a minimal attack surface, to name a few benefits. The short boot times also allow us to bring Unikraft VMs up just-in-time, as a request for a service arrives, and to bring the instance back down (or suspend it) when the request is over, allowing for even greater efficiency. In addition, Unikraft images are single address space: in cloud environments strong isolation is provided by the hypervisor, so for single application/single tenant VMs it does not make sense to have a kernel/user-space divide. The end result is higher efficiency in performance, with Unikraft yielding noticeably higher throughout than Linux [2]. Regarding application support, we have put great effort towards making Unikraft as POSIX compatible as possible. Unikraft provides a syscall shim-layer and support for the musl libc, allowing us to run unmodified versions of Python. In terms of orchestration, we have integrated Unikraft with major frameworks such as Kubernetes and Prometheus. This, along with extensive debugging facilities should make Unikraft easy to both use and develop for. [1] https://unikraft.org/ [2] https://dl.acm.org/doi/10.1145/3447786.3456248 (best paper award) This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License http://creativecommons.org/licenses/by-nc-sa/4.0/

EuroPython 2022 - Making AI Happen at Your Company - presented by Alexander CS Hendorf [Liffey Hall 1 on 2022-07-13] Many incumbents are transitioning to new technologies while their businesses operate on systems that are years or decades old. Introducing new technologies is not just about introducing Open Source or introducing community culture or working agile or SCRUM or explaining the complicated technology stuff to executives. The truth is: it requires all of it and likely even more. Mastering innovation requires having many balls in the air at once. In this talk I’ll present a transformation use case of an established player including our best practices and anti-patterns. We will discuss the following aspects: * From idea to strategy * Assessing the status quo * Introducing Python and Open Source and what to use (or not) * Legacy is in the the house, still * Getting all departments on the same page * Introducing a community-driven collaborative culture This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License http://creativecommons.org/licenses/by-nc-sa/4.0/

EuroPython 2022 - Classifying LEGO Bricks with Machine Learning - presented by Piotr Rybak [Liffey Hall 1 on 2022-07-13] During this talk, I will walk you through my journey to create the Lego bricks recognition application in Python. I will start with dataset creation and introduce some Lego-specific concepts and resources. Then, I will explain a few different Machine Learning approaches to solve Lego bricks recognition task – classification, detection, and metric learning. Finally, I will show what has worked, what hasn’t, and how you can play with it yourself. This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License http://creativecommons.org/licenses/by-nc-sa/4.0/

EuroPython 2022 - I have to Confess, I still Love Pandas - presented by Cheuk Ting Ho [Liffey Hall 1 on 2022-07-13] In this talk, Cheuk will reapproach Pandas as someone who is a fluent user of the library. Cheuk will review why Pandas is useful to work with data and what advantage it has over the alternatives methods. As Pandas has many functionalities, Cheuk will dissect the discussion on the potential usage of Pandas in day-to-day data science workflow: data inspection, data cleaning, feature engineering etc. After discussing the advantage of using Pandas, Cheuk will discuss the flip side. What makes Pandas a difficult tool to use at first. Here Cheuk will share how she used it efficiently with new users and some fundamental concepts about Pandas. This talk is for Pandas users (new and old) or potential users. Those who are familiar with Pandas may get a refreshing idea from a new angle about the tool. And for the new users, it will be a good startup guide to make the journey of using it a bit easier. This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License http://creativecommons.org/licenses/by-nc-sa/4.0/