List of videos

Code and data go together like tomato and basil; not many applications work without moving data in some way. As our applications modernise and evolve to become more event-driven, the requirements for data are changing. In this session we will explore Apache Kafka, a data streaming platform, to enable reliable real-time data integration for your applications. We will look at the types of problems that Kafka is best at solving, and show how to use it in your own applications. Whether you have a new application or are looking to upgrade an existing one, this session includes advice on adding Kafka using the Python libraries and includes code examples (with bonus discussion of pizza toppings) to use. With Kafka in place, many things are possible so this session also introduces Kafka Connect, a selection of pre-built connectors that you can use to route events between systems and integrate with other tools. This session is recommended for engineers and architects whose applications are ready for next-level data abilities. Notebook link: https://github.com/aiven/kafka-python-notebooks Slides: https://speakerdeck.com/ftisiot/event-driven-applications-apache-kafka-and-python

Scalene is a high-performance CPU and memory profiler for Python that does a number of things that other Python profilers do not and cannot do. It runs orders of magnitude faster than other profilers while delivering far more detailed information. This talk will present case studies of using Scalene, and describe some of the technical advances that make it work. Slides: https://www.cs.umass.edu/~emery/scalene-pycon2021.pdf

I consider myself relatively new to the open source world; my first open source contribution was in 2016. Since then, I’ve continued to actively contribute to open source and specifically to core Python and Python libraries. Pretty soon I found myself being given commit rights to other people’s open source projects. It’s been quite a journey. Being a new open source contributor has its own challenges, and being a new open source maintainer brings another set of unique challenges. In this talk, I will share my journey and the things I’ve learned along the way, and some advice for other aspiring open source maintainers and contributors. Slides: https://speakerdeck.com/mariatta/oops-i-became-an-open-source-maintainer

You trained a ML model, now what? The model needs to be deployed for online serving and offline processing. This talk walks through the journey of deploying your ML models in production. I will cover common deployment patterns backed by concrete use cases which are drawn from 100+ user interviews for Ray and Ray Serve. Lastly, I will cover how we built Ray Serve, a scalable model serving framework, from these learnings. Slides: https://drive.google.com/file/d/1iLY5Tw7Sq3Hik4Fy0LXRnclyfXBbsJUj/view?usp=sharing

In this talk you'll see an update to the Pyjion project, a JIT compiler for CPython byte-code. This project was started 5 years ago but stopped after making no gains in performance. Recent changes to CPython have made optimisations more viable, so now it has been restarted and is showing big performance gains vs. standard CPython with 100% compatibility. Many attempts have been made to build a general purpose JIT for Python and few have succeeded. Is it worth it and what are the gains to be made? This talk will cover the design ideas of a JIT for CPython, optimisations, and future potential. Website: https://pyjion.readthedocs.io Source code: https://GitHub.com/tonybaloney/pyjion Book: https://realpython.com/products/cpython-internals-book/

Las imágenes multiespectrales contienen información útil pero su procesamiento puede presentar pérdida de datos valiosos, por ello se propone un método de restauración basado en GAN que genera resultados visualmente aceptables a partir de imágenes multiespectrales con corrupciones de tamaño y ubicación aleatoria. Diapositivas: https://drive.google.com/file/d/1SR7kFPduibOtFwwBE9w-XfFi1N9lTEg7/view?usp=sharing

OpenTelemetry nace de la fusión de OpenTracing y OpenCensus, dos proyectos similares que brindan un conjunto de APIs para tracing distribuido y métricas. El tracing distribuido nos permite monitorear sistemas distribuidos recolectando información de monitoreo que es generada por los diferentes componentes. Para generar esta información, los desarrolladores instrumentan las aplicaciones y librerías introduciendo pequeñas porciones de código que envían información a un sistema colector que analiza y ensambla todos los datos para dar una visión general de las interacciones de los diferentes componentes del sistema. OpenTelemetry también provee un mecanismo de instrumentación automática que permite generar información de tracing en aplicaciones que no incluyen soporte para OpenTelemetry. Este mecanismo se basa en el hecho que la mayoría de aplicaciones usan un conjunto de librerías estándar que está instrumentado con OpenTelemetry. Esta charla explica el concepto de tracing distribuido, brinda una visión general de OpenTelemetry, presenta una demostración de una aplicación simple y como el sistema de instrumentación automática puede ser usado en aplicaciones que no tienen soporte nativo para OpenTelemetry. Diapositivas: https://tinyurl.com/pyconus-2021

Entendiendo en nuestro idioma la filosofía detrás de Python, poniendo en contexto cada uno de los principios que conforman el "Zen" del lenguaje. Diapositivas: https://objectstorage.us-ashburn-1.oraclecloud.com/p/yXzK_sUn7Qag9ihL7kGHv8KXlcRYONIkWXgnwptCf-4LRZdIG3zvIIrmsBYTb2ds/n/idzlqojegvvo/b/pycon21/o/ElZenDePython_Pycon21.pdf

Esta presentación cubrirá nuestro uso previo de un único repositorio de GitHub, sus ventajas y desventajas y nuestras experiencias durante la migración a GitHub Classroom y su impacto positivo en el aprendizaje de nuestros estudiantes de Python.