Podcast.__init__('Python')

Podcast.__init__('Python')
By Tobias Macey
About this podcast
This is a podcast about the Python programming language, its ecosystem, and its community. We conduct interviews about projects and topics that are of particular interest to people who are interested in and use Python.

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By Michael Kennedy
By Dr. Charles Russell Severance
By Dr. Charles Russell Severance
Latest episodes
Jan. 14, 2018
Summary Your backups are running every day, right? Are you sure? What about that daily report job? We all have scripts that need to be run on a periodic basis and it is easy to forget about them, assuming that they are working properly. Sometimes they fail and in order to know when that happens you need a tool that will let you know so that you can find and fix the problem. Pēteris Caune wrote Healthchecks to be that tool and made it available both as an open source project and a hosted version. In this episode he discusses his motivation for starting the project, the lessons he has learned while managing the hosting for it, and how you can start using it today. Preface Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great. I would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable. When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at podastinit.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app. And now you can deliver your work to your users even faster with the newly upgraded 200 GBit network in all of their datacenters. If you’re tired of cobbling together your deployment pipeline then it’s time to try out GoCD, the open source continuous delivery platform built by the people at ThoughtWorks who wrote the book about it. With GoCD you get complete visibility into the life-cycle of your software from one location. To download it now go to podcatinit.com/gocd. Professional support and enterprise plugins are available for added piece of mind. Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email [email protected]) To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media. Your host as usual is Tobias Macey and today I’m interviewing Pēteris Caune about Healthchecks, a Django app which serves as a watchdog for your cron tasks Interview Introductions How did you get introduced to Python? Can you start by explaining what Healthchecks is and what motivated you to build it? How does Healthchecks compare with other cron monitoring projects such as Cronitor or Dead Man’s Snitch? Your pricing on the hosted service for Healthchecks.io is quite generous so I’m curious how you arrived at that cost structure and whether it has proven to be profitable for you? How is Healthchecks functionality implemented and how has the design evolved since you began working on and using it? What have been some of the most challenging aspects of working on Healthchecks and managing the hosted version? For someone who wants to run their own instance of the service what are the steps and services involved? What are some of the most interesting or unusual uses of Healtchecks that you are aware of? Given that Healthchecks is intended to be used as part of an operations management and alerting system, what are the considerations that users should be aware of when deploying it in a highly available configuration? What improvements or features do you have planned for the future of Healthchecks? Keep In Touch cuu508 on GitHub Blog @cuu508 on Twitter Picks Tobias LG 55UJ6300 Pēteris Zwift TrainerRoad Links Healthchecks.io GitHub Riga Latvia Cross Country Cycling Semantic Web Django Flask Cron Cronitor.io Dead Man’s Snitch IPv6 Load Balancing PostGreSQL MySQL Fabric Ansible Dokku Kubernetes Hetzner CloudFlare PGPool II Streaming Replication Citus Data Website Data Engineering Podcast Interview Heroku Fork the Evolution of healthchecks.io Hosting Setup The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
Jan. 7, 2018
Summary A majority of the work that we do as programmers involves data manipulation in some manner. This can range from large scale collection, aggregation, and statistical analysis across distrbuted systems, or it can be as simple as making a graph in a spreadsheet. In the middle of that range is the general task of ETL (Extract, Transform, and Load) which has its own range of scale. In this episode Romain Dorgueil discusses his experiences building ETL systems and the problems that he routinely encountered that led him to creating Bonobo, a lightweight, easy to use toolkit for data processing in Python 3. He also explains how the system works under the hood, how you can use it for your projects, and what he has planned for the future. Preface Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great. I would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable. When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at podastinit.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app. And now you can deliver your work to your users even faster with the newly upgraded 200 GBit network in all of their datacenters. If you’re tired of cobbling together your deployment pipeline then it’s time to try out GoCD, the open source continuous delivery platform built by the people at ThoughtWorks who wrote the book about it. With GoCD you get complete visibility into the life-cycle of your software from one location. To download it now go to podcatinit.com/gocd. Professional support and enterprise plugins are available for added piece of mind. Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email [email protected]) To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media. Your host as usual is Tobias Macey and today I’m interviewing Romain Dorgueil about Bonobo, a data processing toolkit for modern Python Interview Introductions How did you get introduced to Python? What is Bonobo and what was your motivation for creating it? What is the story behind the name? How does Bonobo differ from projects such as Luigi or Airflow? [RD] After I explain why that’s totally different things, maybe a good follow up would be to ask about differences from other data streaming solutions, like Apache Beam or Spark. How is Bonobo implemented and how has its architecture evolved since you began working on it? What have been some of the most challenging aspects of building and maintaining Bonobo? What are some extensions that you would like to have but don’t have the time to implement? What are some of the most interesting or creative uses of Bonobo that you are aware of? What do you have planned for the future of Bonobo? Keep In Touch Bonobo Project Bonobo ETL Slack GitHub Romain Website @rdorgueil on Twitter hartym on GitHub Picks Tobias Data Skeptic: Quantum Computing Romain Medikit, or how to manage hundreds of projects at the same time, still being able to sleep at night. Rocker, a better builder for docker images. Links Bonobo RedHat Anaconda Installer ETL Pentaho RDC.ETL DAG (Directed Acyclic Graph) Luigi Airflow NamedTuple Jupyter OAuth Graphviz Dask Data Engineering Podcast Dask Interview Selenium Zapier IFTTT (If This Then That) FPGA The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
Dec. 31, 2017
Summary Data mining and visualization are important skills to have in the modern era, regardless of your job responsibilities. In order to make it easier to learn and use these techniques and technologies Blaž Zupan and Janez Demšar, along with many others, have created Orange. In this episode they explain how they built a visual programming interface for creating data analysis and machine learning workflows to simplify the work of gaining insights from the myriad data sources that are available. They discuss the history of the project, how it is built, the challenges that they have faced, and how they plan on growing and improving it in the future. Preface Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great. I would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable. When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at podastinit.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app. And now you can deliver your work to your users even faster with the newly upgraded 200 GBit network in all of their datacenters. If you’re tired of cobbling together your deployment pipeline then it’s time to try out GoCD, the open source continuous delivery platform built by the people at ThoughtWorks who wrote the book about it. With GoCD you get complete visibility into the life-cycle of your software from one location. To download it now go to podcatinit.com/gocd. Professional support and enterprise plugins are available for added piece of mind. Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email [email protected]) To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media. Your host as usual is Tobias Macey and today I’m interviewing Blaž Zupan and Janez Demsar about Orange, a toolbox for interactive machine learning and data visualization in Python Interview Introductions How did you get introduced to Python? What is Orange and what was your motivation for building it? Who is the target audience for this project? How is the graphical interface implemented and what kinds of workflows can be implemented with the visual components? What are some of the most notable or interesting widgets that are available in the catalog? What are the limitations of the graphical interface and what options do user have when they reach those limits? What have been some of the most challenging aspects of building and maintaining Orange? What are some of the most common difficulties that you have seen when users are just getting started with data analysis and machine learning, and how does Orange help overcome those gaps in understanding? What are some of the most interesting or innovative uses of Orange that you are aware of? What are some of the projects or technologies that you consider to be your competition? Under what circumstances would you advise against using Orange? What are some widgets that you would like to see in future versions? What do you have planned for future releases of Orange? Keep In Touch Blaž University Bio @bzupan on Twitter BlazZupan on GitHub Google Scholar Janez University Bio @jademsar on Twitter janezd on GitHub Google Scholar Picks Tobias Data Stories: What’s Going On In This Graph? Blaž How I Built This Janez Advent of Code Links University of Ljubljani Data Explorer Silicon Graphics Visual Programming PyQT Linear Regression t-SNE K-Means TCL/TK Numpy Scikit-Learn SciPy Textable.io RapidMiner Single Cell Genomics Transfer Learning Orange Video Tutorials The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
Dec. 24, 2017
Summary A majority of projects will eventually need some way of managing periodic or long-running tasks outside of the context of the main application. This is where a distributed task queue becomes useful. For many in the Python community the standard option is Celery, though there are other projects to choose from. This week Bogdan Popa explains why he was dissatisfied with the current landscape of task queues and the features that he decided to focus on while building Dramatiq, a new, opinionated distributed task queue for Python 3. He also describes how it is designed, how you can start using it, and what he has planned for the future. Preface Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great. I would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable. When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at podastinit.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app. And now you can deliver your work to your users even faster with the newly upgraded 200 GBit network in all of their datacenters. If you’re tired of cobbling together your deployment pipeline then it’s time to try out GoCD, the open source continuous delivery platform built by the people at ThoughtWorks who wrote the book about it. With GoCD you get complete visibility into the life-cycle of your software from one location. To download it now go to podcatinit.com/gocd. Professional support and enterprise plugins are available for added piece of mind. Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email [email protected]) To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media. Your host as usual is Tobias Macey and today I’m interviewing Bogdan Popa about Dramatiq, a distributed task processing library for Python with a focus on simplicity, reliability and performance Interview Introductions How did you get introduced to Python? What is Dramatiq and what was your motivation for creating it? How does Dramatiq compare to other task queues in Python such as Celery or RQ? How is Dramatiq implemented and how has the internal architecture evolved? What have been some of the most difficult aspects of building Dramatiq? What are some of the features that you are most proud of? For someone who is interested in integrating Dramatiq into an application, can you describe the steps involved and the API? Do you provide any form of migration path or compatibility layer for people who are currently using Celery or RQ? Can you describe the licensing structure for the project and your reasoning? How did you determine the price point for commercial licenses? Have you been successful in selling licenses for commercial use? What are some of the features that you have planned for future releases? Keep In Touch Project Website Personal Website Bogdanp on GitHub @Bogdanp on Twitter Picks Tobias The Anybodies by N.E. Bode Bogdan Pipenv Links Dramatiq LeadPages Lisp Celery RQ Billiard Kombu Google App Engine GAE Task Queue RabbitMQ APScheduler Redis Memcached LRU (Least Recently Used) Middleware Gevent Pika SQS (Amazon Simple Queue Service) Google Cloud PubSub Django API* Bundler Cargo The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
Dec. 17, 2017
Summary Jake Vanderplas is an astronomer by training and a prolific contributor to the Python data science ecosystem. His current role is using Python to teach principles of data analysis and data visualization to students and researchers at the University of Washington. In this episode he discusses how he got started with Python, the challenges of teaching best practices for software engineering and reproducible analysis, and how easy to use tools for data visualization can help democratize access to, and understanding of, data. Preface Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great. I would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable. When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at podastinit.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app. And now you can deliver your work to your users even faster with the newly upgraded 200 GBit network in all of their datacenters. If you’re tired of cobbling together your deployment pipeline then it’s time to try out GoCD, the open source continuous delivery platform built by the people at ThoughtWorks who wrote the book about it. With GoCD you get complete visibility into the life-cycle of your software from one location. To download it now go to podcatinit.com/gocd. Professional support and enterprise plugins are available for added piece of mind. Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email [email protected]) To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media. Your host as usual is Tobias Macey and today I’m interviewing Jake Vanderplas about data science best practices, and applying them to academic sciences Interview Introductions How did you get introduced to Python? How has your astronomy background informed and influenced your current work? In your work at the University of Washington, what are some of the most common difficulties that students face when learning data science? How does that list differ for professional scientists who are learning how to apply data science to their work? Where is the tooling still lacking in terms of enabling consistent and repeatable workflows? One of the projects that you are spending time on now is Altair, which is a library for generating visualizations from Pandas dataframes. How does that work factor into your teaching? What are some of the most novel applications of data science that you have been involved with? What are some of the trends in data analysis that you are most excited for? Keep In Touch Website @jakevdp jakevdp on GitHub Picks Tobias The Redwall Cookbook Jake Kevin M. Kruse White Flight by Kevin Kruse Links UW eScience Institute NumPy SciPy SciPy Conference PyCon Pandas Sloan Digital Sky Survey Spectroscopy Software Carpentry Data Carpentry Git Mercurial Matplotlib Altair Conda Xonsh Jupyter Jupyter Lab Vega Vega-lite Interactive Data Lab D3 Mike Bostock Brian Granger Bokeh Grammar of Graphics ggplot2 Holoviews Wikimedia AstroPy Podcast.__init__ Interview About AstroPy LIGO Wes McKinney Feather The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
Dec. 10, 2017
Summary Kenneth Reitz has contributed many things to the Python community, including projects such as Requests, Pipenv, and Maya. He also started the community written Hitchhiker’s Guide to Python, and serves on the board of the Python Software Foundation. This week he talks about his career in the Python community and digs into some of his current work. Preface Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great. I would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable. When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at podastinit.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app. And now you can deliver your work to your users even faster with the newly upgraded 200 GBit network in all of their datacenters. If you’re tired of cobbling together your deployment pipeline then it’s time to try out GoCD, the open source continuous delivery platform built by the people at ThoughtWorks who wrote the book about it. With GoCD you get complete visibility into the life-cycle of your software from one location. To download it now go to podcatinit.com/gocd. Professional support and enterprise plugins are available for added piece of mind. Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email [email protected]) To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media. Your host as usual is Tobias Macey and today I’m interviewing Kenneth Reitz about his career in Python Interview Introductions How did you get introduced to Python? An overarching theme of your open source projects is the idea of making them “For Humans”. Can you elaborate on how that came to be a focus for you and how that informs the way that you design and write your code? What are the projects that you are most proud of and which do you think have had the biggest impact on the Python community? A: Requests, Hitchhiker’s Guide to Python, and Pipenv (yet to come to full fruition). Which projects have you authored which are relatively unknown but you think people would benefit from using more often? A: Maya: Datetime for Humans, and Records: SQL for Humans. Outside of the code that you write, what are some of your personal missions for the software industry in general and the Python community in particular? A: I consider myself a “spiritual alchemist”, which means “transformation of dark into light”. I seek to do “the great work”, in however in manifests, outside of the programming world, as well as within it. What do you think is the biggest gap in the tool chest for Python developers? A: I seek to fill all the voids that I see, and I’ve done my best to do that to the best of my ability. I think we have a lot of work to do in the area of single-file executable builds (a-la Go). What are your ambitions for future projects? A: At the moment, I have no current plans for future projects, but I’m sure something will come along at some point If you weren’t working with Python what would you be doing instead? A: I’d have a lot less money and I’d be a lot less fufilled. Keep In Touch Website @kennethreitz on Twitter kennethreitz on GitHub Picks Tobias Algorithms to Live By Kenneth The Linux Programming Interface Links Heroku Salesforce PSF Board of Directors Caldera Linux C Pascal Basic Groovy Java PHP Ruby The Design of Everyday Things Requests Hitchhiker’s Guide Pipenv Pipfile The Update Framework Falsehoods Programmer’s Believe About Time PEP20 Py2EXE Cxfreeze Briefcase The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
Dec. 3, 2017
Summary As we rely more on small, distributed processes for building our applications, being able to take advantage of asynchronous I/O is increasingly important for performance. This week Alex Grönholm explains how the Asphalt Framework was created to make it easier to build these network oriented software stacks and the technical challenges that he faced in the process. Preface Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great. I would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable. When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at podastinit.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app. And now you can deliver your work to your users even faster with the newly upgraded 200 GBit network in all of their datacenters. If you’re tired of cobbling together your deployment pipeline then it’s time to try out GoCD, the open source continuous delivery platform built by the people at ThoughtWorks who wrote the book about it. With GoCD you get complete visibility into the life-cycle of your software from one location. To download it now go to podcatinit.com/gocd. Professional support and enterprise plugins are available for added piece of mind. Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email [email protected]) To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media. Your host as usual is Tobias Macey and today I’m interviewing Alex Grönholm about the Asphalt Framework, a Python microframework for network oriented applications Interview Introductions How did you get introduced to Python? What is Asphalt and what was your reason for building it? How does Asphalt compare to Twisted? What are the most challenging parts of writing asynchronous and event-based applications and how does Asphalt help simplify that process? When building an Asphalt application it can be easy to accidentally block an async loop by pulling in third party libraries that don’t support asynchronous execution. What are some of the techniques for identifying and resolving blocking portions of your application? What does the internal architecture of Asphalt look like and how has that evolved from when you first started working on it? What have been some of the most difficult aspects of building and evolving Asphalt? What are some of the most interesting or unexpected uses of Asphalt that you have seen? What are some of the new features or improvements that you have planned for the future of Asphalt? Keep In Touch Gitter IRC GitHub agronholm on GitHub @agronholm on Twitter Picks Tobias Thor: Ragnarok Alex Two Steps From Hell Links Asphalt ERP Asyncio Tornado Twisted SQLAlchemy PEP 550 Sanic WAMP Podcast.init Interview About Crossbar Tee FlexGet APScheduler BitTorrent uvloop Tokio The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
Nov. 25, 2017
Summary The importance of testing your software is widely talked about and well understood. What is not as often discussed is the different types of testing, and how end-to-end tests can benefit your team to ensure proper functioning of your application when it gets released to production. This week Luciano Puccio shares the work that he has done on Golem, a framework for building and executing an automation suite to exercise the entire system from the perspective of the user. He discusses his reasons for creating the project, how he things about testing, and where he plans on taking Golem in the future. Give it a listen and then take it for a test drive. Preface Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great. I would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable. When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at podastinit.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app. And now you can deliver your work to your users even faster with the newly upgraded 200 GBit network in all of their datacenters. If you’re tired of cobbling together your deployment pipeline then it’s time to try out GoCD, the open source continuous delivery platform built by the people at ThoughtWorks who wrote the book about it. With GoCD you get complete visibility into the life-cycle of your software from one location. To download it now go to podcatinit.com/gocd. Professional support and enterprise plugins are available for added piece of mind. Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email [email protected]) To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media. Your host as usual is Tobias Macey and today I’m interviewing Luciano Puccio about Golem, a framework and automation tool for end-to-end testing in Python Interview Introductions How did you get introduced to Python? What is golem and what motivated you to create it? What was your inspiration for the name? Why did you choose to use Python for Golem and if you were to start over today would you make the same choice? For someone who is unfamiliar with the concept, can you describe what end-to-end testing is and the reasons for making it part of their development process? What is the main goal of Golem What does the internal architecture and implementation of Golem look like and how has that evolved from when you first started the project? How does Golem compare to other Python libraries for automated browser testing and what was lacking in the existing solutions when you created it? What are the differences between golem and robot framework? What about projects written in other languages such as protractor? One of the intriguing features of Golem is the web interface for constructing tests. What are the benefits of codeless automation & record-playback functionality? What are some of the most challenging aspects of building and maintaining Golem? It seems that every browser automation library is ultimately a wrapper around Selenium. Why is a wrapper necessary and why haven’t any strong alternatives been created? What are the advantages of making Golem a framework for test automation, rather than a library? What are some of the most interesting or unexpected uses for Golem that you have seen? What do you have planned for the future of Golem? What is the current state of end to end automation and how do you see it evolving in the future? How do you think machine learning and AI will be used in test automation? Keep In Touch lucianopuccio on GitHub @LucianoPuccio on Twitter Picks Tobias Weapons of Math Destruction Links Golem Elementum Pascal Watir JUnit Selenium Page Object Pattern Selenium Grid Sauce Labs py.test Podcast.init Interview About Py.Test Robot Framework Mechanize Acceptance Tests Protractor Webdriver.io Appium The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
Nov. 19, 2017
Summary Do you know what is happening in your production systems right now? If you have a comprehensive metrics platform then the answer is yes. If your answer is no, then this episode is for you. Jason Dixon and Dan Cech, core maintainers of the Graphite project, talk about how graphite is architected to capture your time series data and give you the ability to use it for answering questions. They cover the challenges that have been faced in evolving the project, the strengths that have let it stand the tests of time, and the features that will be coming in future releases. Preface Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great. I would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable. When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at podastinit.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app. And now you can deliver your work to your users even faster with the newly upgraded 200 GBit network in all of their datacenters. If you’re tired of cobbling together your deployment pipeline then it’s time to try out GoCD, the open source continuous delivery platform built by the people at ThoughtWorks who wrote the book about it. With GoCD you get complete visibility into the life-cycle of your software from one location. To download it now go to podcatinit.com/gocd. Professional support and enterprise plugins are available for added piece of mind. Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email [email protected]) To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media. Now is a good time to start planning your conference schedule for 2018. To help you out with that, guest Jason Dixon is offering a $100 discount for Monitorama in Portland, OR on June 4th – 6th and guest Dan Cech is offering a €50 discount to Grafanacon in Amsterdam, Netherlands March 1st and 2nd. There is also still time to get your tickets to PyCascades in Vancouver, BC Canada January 22nd and 23rd. All of the details are in the show notes Your host as usual is Tobias Macey and today I’m interviewing Jason Dixon and Dan Cech about Graphite Interview Introductions How did you get introduced to Python? What is Graphite and how did you each get involved in the project? Why should developers be thinking about collecting and reporting on metrics from their software and systems? How do you think the Graphite project has contributed to or influenced the overall state of the art in systems monitoring? There are a number of different projects that comprise a fully working Graphite deployment. Can you list each of them and describe how they fit together? What are some of the early design choices that have proven to be problematic while trying to evolve the project? What are some of the challenges that you have been faced with while maintaining and improving the various Graphite projects? What will be involved in porting Graphite to run on Python 3? If you were to start the project over would you still use Python? What are the options for scaling Graphite and making it highly available? Given the level of importance to a companies visibility into their systems, what development practices do you use to ensure that Graphite can operate reliably and fail gracefully? What are some of the biggest competitors to Graphite? When is Graphite not the right choice for tracking your system metrics? What are some of the most interesting or unusual uses of Graphite that you are aware of? What are some of the new features and enhancements that are planned for the future of Graphite? Keep In Touch Jason @obfuscurity on Twitter Website obfuscurity on GitHub Dan @dancech on Twitter Website DanCech on GitHub Picks Tobias Archery Jason Rocket League Monitorama $100 Discount (First 100 People) Dan Home Assistant Podcast.init Interview GrafanaCon €50 discount with PODCASTINIT2018 Links Graphite Sensu Monitorama RainTank Grafana Labs Librato GitHub Dyn Telemetry Perl PHP React O’Reilly Graphite Book Time Series RRDTool InfluxDB Adrian Cockcroft NVMe Prometheus CNCF ASAP Smoothing PyCascades The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
Nov. 11, 2017
Summary A relevant and timely recommendation can be a pleasant surprise that will delight your users. Unfortunately it can be difficult to build a system that will produce useful suggestions, which is why this week’s guest, Nicolas Hug, built a library to help with developing and testing collaborative recommendation algorithms. He explains how he took the code he wrote for his PhD thesis and cleaned it up to release as an open source library and his plans for future development on it. Preface Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great. I would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable. When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at podastinit.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app. And now you can deliver your work to your users even faster with the newly upgraded 200 GBit network in all of their datacenters. If you’re tired of cobbling together your deployment pipeline then it’s time to try out GoCD, the open source continuous delivery platform built by the people at ThoughtWorks who wrote the book about it. With GoCD you get complete visibility into the life-cycle of your software from one location. To download it now go to podcatinit.com/gocd. Professional support and enterprise plugins are available for added piece of mind. Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email [email protected]) To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media. Your host as usual is Tobias Macey and today I’m interviewing Nicolas Hug about Surprise, a scikit library for building recommender systems Interview Introductions How did you get introduced to Python? What is Surprise and what was your motivation for creating it? What are the most challenging aspects of building a recommender system and how does Surprise help simplify that process? What are some of the ways that a user or company can bootstrap a recommender system while they accrue data to use a collaborative algorithm? What are some of the ways that a recommender system can be used, outside of the typical ecommerce example? Once an algorithm has been deployed how can a user test the accuracy of the suggestions? How is Surprise implemented and how has it evolved since you first started working on it? What have been the most difficult aspects of building and maintaining Surprise? competitors? What are the attributes of the system that can be modified to improve the relevance of the recommendations that are provided? For someone who wants to use Surprise in their application, what are the steps involved? What are some of the new features or improvements that you have planned for the future of Surprise? Keep In Touch Website @hug_nicolas on Twitter nicolashug on GitHub Picks Tobias Silk profiler for Django Links Surprise Gridsearch Cold Start Problem Content-Based Recommendation Ensemble Learning Spotlight Lightfm Pandas The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA