List of videos

Lightning Talk 3 [EuroPython 2021 - Talk - 2021-07-30 - Optiver] [Online] License: This video is licensed under the CC BY-NC-SA 4.0 license: https://creativecommons.org/licenses/by-nc-sa/4.0/ Please see our speaker release agreement for details: https://ep2021.europython.eu/events/speaker-release-agreement/

Python the Bad Parts [EuroPython 2021 - Keynote - 2021-07-30 - Optiver] [Online] By Joannah Nanjekye License: This video is licensed under the CC BY-NC-SA 4.0 license: https://creativecommons.org/licenses/by-nc-sa/4.0/ Please see our speaker release agreement for details: https://ep2021.europython.eu/events/speaker-release-agreement/

Analyzing COVID Vaccine Popularity in Europe Using FastAPI, Postgres & Azure [EuroPython 2021 - Talk - 2021-07-30 - Ni] [Online] By Rabeea Emad FastAPI is one of the fastest growing Python web frameworks – in part due to its ease of usage and high performance. In this talk, I will teach you how you can rapidly process European covid vaccine popularity data using Fire, place it into Azure Database for Postgres (for free!) and then leverage FastAPI to serve them through a back-end. Finally, I will launch the backend online through Azure App Service and use Jupyter to visualize the data from the API to answer interesting questions! Come join me and let the adventure begin! License: This video is licensed under the CC BY-NC-SA 4.0 license: https://creativecommons.org/licenses/by-nc-sa/4.0/ Please see our speaker release agreement for details: https://ep2021.europython.eu/events/speaker-release-agreement/

Measuring memory: Python memory profilers and when to use them [EuroPython 2021 - Talk - 2021-07-30 - Optiver] [Online] By Itamar Turner-Trauring If your program is using too much memory, it can crash, thrash, or just run more slowly. To reduce memory usage, you need to measure it somehow. There are a number of tools to measure Python memory usage, however. Which one should you pick? This depends heavily on your use case. In this talk you will learn about the different ways memory problems manifest in Python, the memory usage patterns of different application types, and which tools to use in each situation. For web applications, the typical problem is memory leaks: memory that is never freed adds up if your program runs forever. That means you need tools to help you identify the source of memory leaks, whether in Python or in C extensions. For data processing applications like data science or scientific computing, the problem is typically caused by processing large amounts of data. Here you need to find the peak memory usage, and what code what responsible for it. License: This video is licensed under the CC BY-NC-SA 4.0 license: https://creativecommons.org/licenses/by-nc-sa/4.0/ Please see our speaker release agreement for details: https://ep2021.europython.eu/events/speaker-release-agreement/

Python and Flutter application for Colouring and Enhancing Old Photos [EuroPython 2021 - Talk - 2021-07-30 - Parrot [Data Science]] [Online] By Utkarsh Mishra A photo is an image that represents an external form of a person or a thing from the past. It communicates any past incident or activity to the present or future, reminding them of the melodious memories they have. In the 19th century most photographs were monochromatic, so people hand-coloured them, often for purely aesthetic reasons. Photos with colour just looked nicer on the mantle. Contemporary artists still add colour for those reasons. Colourization is a time-consuming, labour-intensive process when done manually. Realistically capturing undertones in the skin is especially difficult. You may have to add subtle layers of pink, orange, yellow, or blue. Experts recommend finding a colour photo featuring similar skin tones to use as a reference. To reduce such a manual time-consuming process to a few seconds or a minute, we built a Deep Learning model to colour old black and white images. It uses an autoencoder with skip connections built using TensorFlow backend with Keras to retain details and features from the original image during the conversion. After the coloured image is obtained, a gamma correction filter is being applied to increase the contrast of the image after which an optional operation of super-resolution is performed in accordance with the condition of image size less than 500 pixels. These operations are built into an API which is called using a Flutter Mobile application that takes image input from user from gallery or camera, crop it to get the required section and passed it to the API, which then executes the above processes and the resultant image is passed as output back to the flutter application. The results of this work will help guide the computer vision developers to incorporate the feature of colouring old images and photos to multiple domains and to further enhance these models by adding variants to them. Outline - Introduction to Deep Learning and the model architecture [10 minutes] - Introduction to API using Flask and Frontend Flutter app [10 minutes] - Application Demo [5 minutes] - Q&A [5 minutes] License: This video is licensed under the CC BY-NC-SA 4.0 license: https://creativecommons.org/licenses/by-nc-sa/4.0/ Please see our speaker release agreement for details: https://ep2021.europython.eu/events/speaker-release-agreement/

Python monorepos: what, why and how [EuroPython 2021 - Talk - 2021-07-30 - Brian] [Online] By Benjy Weinberger As organizations and repos grow, we have to choose how to manage codebases in a scalable way. We have two architectural alternatives: Multirepo: split the codebase into increasing numbers of small repos, along team or project boundaries. Monorepo: Maintain one large repository containing code for many projects and libraries, with multiple teams collaborating across it. In this talk we'll discuss the advantages of monorepos for Python codebases, and the kinds of tooling and processes we can use to make working in a Python monorepo effective. At the end of this talk you will understand the tradeoffs of different codebase architecture choices, and how to evaluate tooling and processes that keep your repo humming along at scale. No prior knowledge is required. License: This video is licensed under the CC BY-NC-SA 4.0 license: https://creativecommons.org/licenses/by-nc-sa/4.0/ Please see our speaker release agreement for details: https://ep2021.europython.eu/events/speaker-release-agreement/

Developing Flask Applications for Google Cloud [EuroPython 2021 - Talk - 2021-07-30 - Brian] [Online] By Abby Carey Learn how to build Flask apps for Cloud Run, one of Google Cloud's serverless platforms. To accelerate the Cloud Run development loop, we'll be using Cloud Code, an IDE plugin that makes working with Cloud Run easy. We'll also cover how to integrate products like Cloud SQL, Pub/Sub, and Secret Manager. License: This video is licensed under the CC BY-NC-SA 4.0 license: https://creativecommons.org/licenses/by-nc-sa/4.0/ Please see our speaker release agreement for details: https://ep2021.europython.eu/events/speaker-release-agreement/

Formalizing a Language [EuroPython 2021 - Talk - 2021-07-30 - Ni] [Online] By Jeremiah Paige What is formal grammar, how does it create a programming language and what does Python's grammar look like. In Python 3.9 the grammar parsing engine was changed; find out some of the reasons why and what this change has allowed in terms of future language evolution. License: This video is licensed under the CC BY-NC-SA 4.0 license: https://creativecommons.org/licenses/by-nc-sa/4.0/ Please see our speaker release agreement for details: https://ep2021.europython.eu/events/speaker-release-agreement/

Graph neural networks for information extraction with PyTorch [EuroPython 2021 - Talk - 2021-07-30 - Parrot [Data Science]] [Online] By Augusto Stoffel In this talk, I will provide a primer on a class of deep learning models known as graph neural networks (GNNs). These models generalize the well-known convolutional neural networks (CNNs) ubiquitous in computer vision, and the approach of the talk will emphasize that analogy. Specifically, after a quick general recap on graphs, we will see how to think of an image as a graph where nodes represent pixels and edges represent the relationship of being a neighbor. After expressing the formulas for a CNN in terms of this graph, it is not hard to imagine ways to pass from the graph of an image to arbitrary graphs. This way, one naturally arrives at a basic GNN architecture. Other recent advances, for instance graph attention networks, can also be surveyed, time permitting. We will then survey the existing Python implementations and supporting libraries, with a focus on the PyTorch framework and on the PyTorch Geometric library in particular. Next, I will turn to applications in the field of NLP, and in particular information extraction. I will focus on the problem of understanding documents in tabular format. Unlike regular paragraphs of text, these documents contain more information than just an ordered sequence of words. We will see how graphs can be used to encode the spatial disposition of words, capturing enough information to allow training deep learning models with good accuracy and generalization capabilities. License: This video is licensed under the CC BY-NC-SA 4.0 license: https://creativecommons.org/licenses/by-nc-sa/4.0/ Please see our speaker release agreement for details: https://ep2021.europython.eu/events/speaker-release-agreement/