List of videos

"Fighting the controls: tragedy and madness for programmers and pilots [EuroPython 2017 - Talk - 2017-07-13 - PyCharm Room] [Rimini, Italy] Damn it, this can’t be happening! As programmers, we find ourselves time and again spiralling down into tighter loops of desperate troubleshooting, fighting the controls of our machinery and descending into what feels like a kind of madness. Later, when it’s all over, we realise that the clues we needed to recover the situation were staring us in the face all along, but we somehow couldn’t even see them. There’s a reason for this: the nature of debugging for programmers means that it quickly tips us into these states, and then very effectively keeps us there. In programming we have worked hard to improve some aspects of programmers’ work, creating methodologies, development frameworks, paradigms, practices and thinking deeply about how to solve the problems of producing good code. We have done very little work to improve the way we debug our code, The good news is that although programmers have not developed very adequate strategies or techniques for mitigating the risks that debugging draws us into, other industries, and in particular aviation, have. We can learn from their lessons without paying their price. 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://ep2017.europython.eu/en/speaker-release-agreement/

"Scripting across hosts with Chopsticks [EuroPython 2017 - Talk - 2017-07-13 - PythonAnywhere Room] [Rimini, Italy] Chopsticks lets your import and run Python code on remote Unix hosts over SSH. It works with no code deployment and no preinstalled software other than Python and SSH. It has built-in support for running code on many hosts in parallel. Unlike Fabric or Ansible, Chopsticks not opinionated about the structure of the code you run, allowing it to be used for orchestration, auditing, diagnostics, monitoring probes, and more Also unlike these, Chopsticks is not wedded to SSH, so it can transparently work on Docker containers over pipes, local subprocesses - and in future, sudo? Daniel will demonstrate Chopsticks in action, walk through the API, and explain the three clever tricks that Chopsticks is built on. 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://ep2017.europython.eu/en/speaker-release-agreement/

"How Booking.com serves Deep Learning model predictions [EuroPython 2017 - Talk - 2017-07-13 - Anfiteatro 1] [Rimini, Italy] With so many machine learning frameworks and libraries available, writing a model isn’t a bottleneck anymore while putting your models in production is still a challenge. In this talk, you will learn how we deploy the python deep learning models in production at Booking.com. Topics will include: Deep Learning model training in Docker containers Automated retraining of models Deployment of models using Kubernetes Serving model predictions in containerized environment Optimising serving predictions for latency and throughput 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://ep2017.europython.eu/en/speaker-release-agreement/

"Inspect (Or Gadget) [EuroPython 2017 - Talk - 2017-07-13 - Anfiteatro 2] [Rimini, Italy] Introspection is often seen as a bad coding practice and as such a gadget. Nevertheless, the Python Standard Library provides different tools (among them the 'inspect' library) to easily identify a generator, recover the source code of a function or get a function signature. We propose to spend 1/2 hour to dig into what introspection has to offer to developers, to see what tools are available, what you can get out of them and some useful use cases that we met in our practice at Criteo. 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://ep2017.europython.eu/en/speaker-release-agreement/

"Explaining behavior of Machine Learning models with eli5 library [EuroPython 2017 - Talk - 2017-07-13 - Anfiteatro 2] [Rimini, Italy] ML estimators don't have to be black boxes. Interpretability has many benefits: it is easier to debug interpretable models, humans trust decisions of such models more. In this talk I’ll give an overview of ML models interpretation and debugging techniques. I’ll cover linear models, decision trees, tree ensembles, arbitrary classifiers using LIME algorithm. The talk focus is on explanation algorithms, because it is important to be aware of pitfalls and limitations of the explanation method to be able to interpret an explanation correctly. I’ll also show how to use these techniques in practice, to debug and explain behavior of estimators from Python ML libraries like scikit-learn and xgboost using open-source eli5 library: https://github.com/TeamHG-Memex/eli5 . Attendees will get both practical and theoretical understanding of these explanation methods. Target audience is ML practitioners who want to 1) get a better quality from their ML pipelines - understanding of why a wrong decision happens is often a first step to improve the quality of an ML solution; 2) explain ML model behavior to clients or stakeholders - inspectable ML pipelines are easier to “sell” to a client; humans trust such models more because they can check if an explanation is consistent with their domain knowledge or gut feeling, understand better shortcomings of the solution and make a more informed decision as a result. 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://ep2017.europython.eu/en/speaker-release-agreement/

"AI on a Pi [EuroPython 2017 - Talk - 2017-07-13 - Anfiteatro 1] [Rimini, Italy] In recent months, Artificial Intelligence has become the hottest topic in the IT industry. In this session, we’ll first explain how Deep Learning — a subset of AI — differs from traditional Machine Learning and how it can help you solve complex problems such as computer vision or natural language processing. Then, we’ll show you how to start writing Deep Learning applications in Python thanks to MXNet, a popular library for Deep Learning for both CPUs and GPUs. We'll also see how to use pre-trained models and we'll load one on a Raspberry Pi equipped with a camera. Finally, we’ll show random objects to the Pi…and listen to what it thinks the objects are, thanks to the text-to-speech capabilities of Amazon Polly. 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://ep2017.europython.eu/en/speaker-release-agreement/

"Developing elegant workflows in Python code with Apache Airflow [EuroPython 2017 - Talk - 2017-07-13 - Anfiteatro 1] [Rimini, Italy] Every time a new batch of data comes in, you start a set of tasks. Some tasks can run in parallel, some must run in a sequence, perhaps on a number of different machines. That's a workflow. Did you ever draw a block diagram of your workflow? Imagine you could bring that diagram to life and actually run it as it looks on the whiteboard. With Airflow you can just about do that. http://airflow.apache.org Apache Airflow is an open-source Python tool for orchestrating data processing pipelines. In each workflow tasks are arranged into a directed acyclic graph (DAG). Shape of this graph decides the overall logic of the workflow. A DAG can have many branches and you can decide which of them to follow and which to skip at execution time. This creates a resilient design because each task can be retried multiple times if an error occurs. Airflow can even be stopped entirely and running workflows will resume by restarting the last unfinished task. Logs for each task are stored separately and are easily accessible through a friendly web UI. In my talk I will go over basic Airflow concepts and through examples demonstrate how easy it is to define your own workflows in Python code. We'll also go over ways to extend Airflow by adding custom task operators, sensors and plugins. 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://ep2017.europython.eu/en/speaker-release-agreement/

"Cloud Native Python in Kubernetes [EuroPython 2017 - Talk - 2017-07-13 - PyCharm Room] [Rimini, Italy] Serverside applications are more and more likely to need to run in dynamic cloud environments where they can automatically scale as required. One rightfully popular approach is to run the application as a Docker container inside a Kubernetes cluster, giving you a lot of operational benefits thanks to the Kubernetes folks. For the most part it is rather easy to make your Python application work inside a Docker container. But there are a number of common patterns one can follow to save time by delegating more things to the runtime environment. Furthermore you can start adding a few simple non-intrusive features to your application which will help improve the application live-cycle in the cluster, ensuring smooth hand-over when migrating the container to different nodes or scaling it up or down. This talk will quickly cover the basics of Kubernetes and will then start from a simple program and will discuss the steps to take to make it behave well in this environment. Starting with the basics steps you can rely on the runtime for, covering logging and all the way to supporting the service life-cycle, health checking and monitoring in a Kubernetes environment. You will see that building a cloud-native application is not very hard and something you can gradually introduce. 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://ep2017.europython.eu/en/speaker-release-agreement/

"Fixture factories for faster end-to-end tests [EuroPython 2017 - Talk - 2017-07-13 - Anfiteatro 2] [Rimini, Italy] When developing and maintaining many different services, unit testing is not enough to make sure your code works in production. By now, many teams doing SOA (service-oriented architectures) have a set of end-to-end tests that cover critical workflows to make sure these work. For these tests, all of the utilized services need to have the proper test fixture data in their datastores. This often leads to developers having to deal with raw datastore data (like JSON or SQL) for these tests, making the authoring of those tests very slow, tedious, and error-prone. This talk is going to discuss several approaches we tried at Yelp to generating these fixture data in a quicker, developer-friendly and more correct way. The main part of the talk will be a deep-dive into what fixture factories are, how to implement them and how to integrate them with pytest, the leading Python testing framework. I'll show you several other benefits this approach has over writing raw fixture data and how this leads to more maintainable and easier to adapt code. We'll also explore how you can then run your tests in parallel, cutting down runtime drastically. 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://ep2017.europython.eu/en/speaker-release-agreement/