List of videos

"Django Clone From Scratch With Flask EuroPython 2020 - Talk - 2020-07-23 - Ni Online By Abdur-Rahmaan Janhangeer With Python, when thinking of web frameworks, two that come into mind are Django and Flask. Instead of having to choose one of the two, this session shows how you can engineer Flask to achieve a simili-Django app. It's a dive into the internal components of Django and the idea behind. The session covers different Flask development patterns to finally finish with an app that's functionally similar to Django with some extra features in for free. It also lists some caveats identified with such a pattern. This session requires attendees to know Flask and having used Django for building at least one project. At the very least it assumes the knowledge of models, views, controller, templates and migrations. It discusses along the way the educational and pedagogical aspect of learning web frameworks and proposes a syllabus. It discusses the benefits of learning Flask and the necessity of learning the two as a Pythonista. Django is the go-to framework for web development and it's no surprise that most freelancing jobs require Django. Flask is appreciated for the flexibility it gives. The session also touches about how we can better promote Flask through education. The session finishes with the hurdles identified when getting started with web development in Python, pulled from personal teaching experience. License: This video is licensed under the CC BY-NC-SA 3.0 license: https://creativecommons.org/licenses/by-nc-sa/3.0/ Please see our speaker release agreement for details: https://ep2020.europython.eu/events/speaker-release-agreement/ "

"Attractive GUIs with PySimpleGUI EuroPython 2020 - Talk - 2020-07-23 - Ni Online By Ruud van der Ham In the Python landscape, there are several high-end GUI packages. Of course, there’s tkinter which is part of the standard library. And there is wxPython and several versions of Qt. What they share is that they are very flexible but not only have a steep learning curve but also take considerable development effort to realize even a simple user interface. At the other of the spectrum, there are several design tools that either generate code of a layout structure. In many cases, the functionality is rather limited, though. Relatively recent, an independent open-source developer (not me) has made a product that tries to bridge the gap between these two worlds: PySimpleGUI. This package runs on top of tkinter, QT for Windows, wxPython and Remi. In this talk, I want to show the design methodology behind this fascinating product that might change the way people look at GUIs. I will start with live coding a simple yet not evident program that shows the basic concepts. With this, I will demonstrate the design philosophy. PySimpleGUI can also be used to implement a user-friendly interface as opposed to command-line interfaces. From there, I will give an overview of some more elements present in this package. Finally, I will show a full-featured GUI program with several bells and whistles. License: This video is licensed under the CC BY-NC-SA 3.0 license: https://creativecommons.org/licenses/by-nc-sa/3.0/ Please see our speaker release agreement for details: https://ep2020.europython.eu/events/speaker-release-agreement/ "

"Painless Machine Learning in Production EuroPython 2020 - Talk - 2020-07-23 - Parrot Data Science Online By Chase Stevens Developing machine learning models is easy; training, deploying, monitoring, scaling, and maintaining them in an automated fashion - all while maintaining your sanity - is hard. In this session, I'll discuss the infrastructure and tooling my small team of data science practitioners and engineers is using to manage and orchestrate the machine learning model lifecycle, including pitfalls we've encountered along the way. Particular attention will be paid to where we've opted to use off-the-shelf solutions versus developing our own, the importance of developer ergonomics, and how to maximally empower data scientists to get their work into production without the need for a dedicated MLOps team. The talk will cover our ML stack as it exists in production today, and will touch on our application of a number of technologies and techniques, including: - AWS SageMaker - Airflow - Docker - Cookiecutter - Property-based testing - Jsonschema - Linting - Slack integration - Model artifacts and diagnostics - Automated deployments and rollbacks - Healthchecks - Autoscaling - DBT At the end of the session, attendees should expect to leave with new insights that they can apply immediately to their own ML systems and infrastructure, as well as a better understanding of how to minimize engineering and ops overhead, in the real world, across data science teams of any size and composition. License: This video is licensed under the CC BY-NC-SA 3.0 license: https://creativecommons.org/licenses/by-nc-sa/3.0/ Please see our speaker release agreement for details: https://ep2020.europython.eu/events/speaker-release-agreement/ "

"Train. Serve. Deploy! Story of a NLP Model ft. PyTorch, Docker, Uwsgi and Nginx EuroPython 2020 - Talk - 2020-07-23 - Parrot Data Science Online By Shreya Khurana Natural language processing has seen leaps of technology progress with Machine Learning becoming the norm of solving the major problems in this area, with Machine translation being one of the major problems in this area. Neural machine translation systems are now used to convert sentences or phrases from one language to another, or in general, for sequence to sequence modeling. In this talk, we’ll be covering the steps from scratch to preprocess, train and serve a NMT model using PyTorch. While building a highly accurate model is a prerequisite to getting good quality translations, often in industry, we also need to make sure we can serve the model to customers without getting timeouts or delays. The practice of serving models requires creating a web app to get client requests and process them in a way the model would understand. For this, we’ll use the various components of the application server environment - Flask, Docker, uwsgi and nginx. This talk is suitable for audience who is working in general with ML models and want to learn how to serve them or working specifically with NMT and want to learn about some quick prototyping tips. Prerequisites: Audience should be comfortable with the basic ML terminology and procedure of training models. NLP knowledge will be good, but is not a necessity as the focus will be on quick prototyping in production. By the end of the talk, the audience will have: - Learnt how to preprocess data for NLP systems - Learnt how to quickly prototype and train a translation model - Learnt how to create a web app for the NLP model using Flask - Learnt how to containerize a pytorch model using Docker - Learnt how to serve the model as an app using uwsgi, nginx and Outline: Introduction to translation systems, machine translation framework ML Modelling - Preprocessing data - Training - Generating new translations Serving and prototyping - Flask app - Docker container br / - Nginx + uwsgi + supervisord configurations - Putting it all together Good practices Q/A (optional?) License: This video is licensed under the CC BY-NC-SA 3.0 license: https://creativecommons.org/licenses/by-nc-sa/3.0/ Please see our speaker release agreement for details: https://ep2020.europython.eu/events/speaker-release-agreement/ "

"A Brief History of Jupyter Notebooks EuroPython 2020 - Talk - 2020-07-23 - Parrot Data Science Online By William Horton The Jupyter Notebook: many Python users love it, many other Python users love to hate it. But where did it come from? How did we come to have a tool that combines code execution, visualization, Markdown, and more? In this talk, we will dive into the development of the Jupyter Notebook and the older ideas that it built upon. To start, we will look at tools that popularized the “computational notebook” interface. In 1988, Mathematica introduced this interface to the scientific community. In the 90s, tools like Maple competed with Mathematica to provide the best scientific programming environment. The early 2000s saw the rise in popularity of open-source scientific tools in Python, including IPython, leading to IPython Notebook and then Jupyter. Turning to the present, we look at the expanding ecosystem beyond the Notebook. JupyterLab provides a richer programming environment. Voilà and Binder give users better options for sharing their notebooks. And increased language support has led to Jupyter being a tool not only for Julia, Python, and R, but for dozens of other languages. Finally: what is still to come? JupyerLab 2.0 promises even greater IDE-like capabilities, while IDEs increase their own Notebook support. Projects like Deepnote and CoCalc promise real-time collaboration on top of the Notebook interface. And the frustrations of working with Git are the source of a growing number of possible solutions. These efforts point us toward what the Jupyter Notebook could become. License: This video is licensed under the CC BY-NC-SA 3.0 license: https://creativecommons.org/licenses/by-nc-sa/3.0/ Please see our speaker release agreement for details: https://ep2020.europython.eu/events/speaker-release-agreement/ "

"Pluggable Architecture EuroPython 2020 - Talk - 2020-07-23 - Ni Online By Aly Sivji Applications and libraries with a pluggable architecture allow developers to add custom functionality. Plugins can customize user interfaces, create new workflows, and interface with legacy systems. Designing a plugin is often difficult - documentation is sparse, outdated, or non-existent. You end up diving into a unfamiliar codebase to figure out what to do. This talk examines Pluggable Architecture by creating a custom plugin system: we will design an interface, think about where to hook in custom behavior, and discuss testing techniques. Understanding these principles will enable us to write custom plugins for third-party libraries. Extend the functionality of your favourite library without touching existing code! License: This video is licensed under the CC BY-NC-SA 3.0 license: https://creativecommons.org/licenses/by-nc-sa/3.0/ Please see our speaker release agreement for details: https://ep2020.europython.eu/events/speaker-release-agreement/ "

"Roadmap to an Open Source Artificial Pancreas & Diabetes monitoring with Flask EuroPython 2020 - Talk - 2020-07-23 - Ni Online By Diana Rodriguez Manrique Open Source has crossed frontiers and is widely used to solve problems in the health & science fields. In this talk we’ll learn about the many moving parts of continuous glucose monitoring for type 1 Diabetes as part of the open-source artificial pancreas project and building a notifier for the most used OSS continuous glucose monitoring dashboard: Nightscout. License: This video is licensed under the CC BY-NC-SA 3.0 license: https://creativecommons.org/licenses/by-nc-sa/3.0/ Please see our speaker release agreement for details: https://ep2020.europython.eu/events/speaker-release-agreement/ "

"Detecting and Analyzing Solar Panels in Switzerland using Aerial Imagery EuroPython 2020 - Talk - 2020-07-23 - Parrot Data Science Online By Adrian Meyer License: This video is licensed under the CC BY-NC-SA 3.0 license: https://creativecommons.org/licenses/by-nc-sa/3.0/ Please see our speaker release agreement for details: https://ep2020.europython.eu/events/speaker-release-agreement/ "

"Real Time Stream Processing for Machine Learning at Massive Scale EuroPython 2020 - Talk - 2020-07-23 - Parrot Data Science Online By Alejandro Saucedo This talk will provide a practical insight on how to build scalable data streaming machine learning pipelines to process large datasets in real time using Python and popular frameworks such as Kafka, SpaCy and Seldon. We will be covering a case study performing automated content moderation on Reddit comments in real time. Our dataset will consist of 200k reddit comments from /r/science, 50,000 of which have been removed by moderators. We will be handling the stream data in a Kubernetes cluster, and the stream processing will be handled using the stream processing library Kafka. We will be running the end-to-end pipeline in Kubernetes with various components legeraging SKLearn, SpaCy and Seldon. We will then dive into fundamental concepts on stream processing such as windows, watermarking and checkponting, and we will show how to use each of these frameworks to build complex data streaming pipelines that can perform real time processing at scale by building, deploying and monitoring a machine learning model which will process production incoming data.. Finally we will show best practices when using these frameworks, as well as a high level overview of tools that can be used for monitoring in-depth. License: This video is licensed under the CC BY-NC-SA 3.0 license: https://creativecommons.org/licenses/by-nc-sa/3.0/ Please see our speaker release agreement for details: https://ep2020.europython.eu/events/speaker-release-agreement/ "