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    News

    The Python Podcast.__init__

    The podcast about Python and the people who make it great

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    Copyright: © 2023 Boundless Notions, LLC.

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    Latest Episodes:
    Distributed Computing In Python Made Easy With Ray Apr 14, 2020
    Show notes

    Summary

    Distributed computing is a powerful tool for increasing the speed and performance of your applications, but it is also a complex and difficult undertaking. While performing research for his PhD, Robert Nishihara ran up against this reality. Rather than cobbling together another single purpose system, he built what ultimately became Ray to make scaling Python projects to multiple cores and across machines easy. In this episode he explains how Ray allows you to scale your code easily, how to use it in your own projects, and his ambitions to power the next wave of distributed systems at Anyscale. If you are running into scaling limitations in your Python projects for machine learning, scientific computing, or anything else, then give this a listen and then try it out!

    Announcements

    • Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
    • When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, node balancers, a 40 Gbit/s public network, fast object storage, and a brand new managed Kubernetes platform, all controlled by a convenient API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they’ve got dedicated CPU and GPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!
    • Your host as usual is Tobias Macey and today I’m interviewing Robert Nishihara about Ray, a framework for building and running distributed applications and machine learning

    Interview

    • Introductions
    • How did you get introduced to Python?
    • Can you start by describing what Ray is and how the project got started?
      • How did the environment of the RISE lab factor into the early design and development of Ray?
    • What are some of the main use cases that you were initially targeting with Ray?
      • Now that it has been publicly available for some time, what are some of the ways that it is being used which you didn’t originally anticipate?
    • What are the limitations for the types of workloads that can be run with Ray, or any edge cases that developers should be aware of?
    • For someone who is building on top of ray, what is involved in either converting an existing application to take advantage of Ray’s parallelism, or creating a greenfield project with it?
    • Can you describe how Ray itself is implemented and how it has evolved since you first began working on it?
    • How does the clustering and task distriubtion mechanism in Ray work?
    • How does the increased parallelism that Ray offers help with machine learning workloads?
      • Are there any types of ML/AI that are easier to do in this context?
    • What are some of the additional layers or libraries that have been built on top of the functionality of Ray?
    • What are some of the most interesting, challenging, or complex aspects of building and maintaining Ray?
    • You and your co-founders recently announced the formation of Anyscale to support the future development of Ray. What is your business model and how are you approaching the governance of Ray and its ecosystem?
    • What are some of the most interesting or unexpected projects that you have seen built with Ray?
    • What are some cases where Ray is the wrong choice?
    • What do you have planned for the future of Ray and Anyscale?

    Keep In Touch

    • Website
    • @robertnishihara on Twitter
    • robertnishihara on GitHub

    Picks

    • Tobias
      • D&D Castle Ravenloft board game
      • One Deck Dungeon
    • Robert
      • The Everything Store

    Closing Announcements

    • Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.
    • Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
    • If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.
    • To help other people find the show please leave a review on iTunes and tell your friends and co-workers
    • Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

    Links

    • Ray
    • Anyscale
    • UC Berkeley
    • RISELab
    • MATLAB
    • Deep Learning
    • Theano
    • Tensorflow
    • PyTorch
      • Podcast Episode
    • Philip Moritz
    • Reinforcement Learning
    • Hyperparameter Tuning
    • IPython Parallel
    • AMPLab
    • Apache Spark
      • Data Engineering Podcast Episode
    • Actor Model
    • Horovod(?)
    • Flink
      • Data Engineering Podcast Episode
    • Spark Streaming
    • Dask
      • Data Engineering Podcast Episode
    • gRPC
    • Tune
    • Rust
    • C++
    • C
    • Apache Arrow
    • Wes McKinney
      • Podcast Interview
    • DataBricks
    • MongoDB
    • Elastic
      • Data Engineering Podcast Episode
    • Confluent
    • Embarassingly Parallel
    • Ant Financial
    • Flame Graph

    The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

    Building The Seq Language For Bioinformatics Apr 07, 2020
    Show notes

    Summary

    Bioinformatics is a complex and computationally demanding domain. The intuitive syntax of Python and extensive set of libraries make it a great language for bioinformatics projects, but it is hampered by the need for computational efficiency. Ariya Shajii created the Seq language to bridge the divide between the performance of languages like C and C++ and the ecosystem of Python with built-in support for commonly used genomics algorithms. In this episode he describes his motivation for creating a new language, how it is implemented, and how it is being used in the life sciences. If you are interested in experimenting with sequencing data then give this a listen and then give Seq a try!

    Announcements

    • Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
    • When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, node balancers, a 40 Gbit/s public network, fast object storage, and a brand new managed Kubernetes platform, all controlled by a convenient API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they’ve got dedicated CPU and GPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!
    • You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on great conferences. And now, the events are coming to you, with no travel necessary! We have partnered with organizations such as ODSC, and Data Council. Upcoming events include the Observe 20/20 virtual conference on April 6th and ODSC East which has also gone virtual starting April 16th. Go to pythonpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today.
    • Your host as usual is Tobias Macey and today I’m interviewing Ariya Shajii about Seq, a programming language built for bioinformatics and inspired by Python

    Interview

    • Introductions
    • How did you get introduced to Python?
    • Can you start by describing what Seq is and your motivation for creating it?
      • What was lacking in other languages or libraries for your use case that is made easier by creating a custom language?
      • If someone is already working in Python, possibly using BioPython, what might motivate them to consider migrating their work to Seq?
    • Can you give an impression of the scope and nature of the tasks or projects that a biologist or geneticist might build with Seq?
    • What was your process for identifying and prioritizing features and algorithms that would be beneficial to the target audience?
    • For someone using Seq can you describe their workflow and how it might differ from performing the same task in Python?
    • How is Seq implemented?
      • What are some of the features that are included to simplify the work of bioinformatics?
      • What was your process of designing the language and runtime?
      • How has the scope or direction of the project evolved since it was first conceived?
    • What impact do you anticipate Seq having on the domain of bioinformatics and genomics?
    • What have you found to be the most interesting, unexpected, and/or challenging aspects of building a language for this problem domain?
    • What is in store for the future of Seq?

    Keep In Touch

    • arshajii on GitHub
    • Website

    Picks

    • Tobias
      • Board Games
      • Labyrinth Boardgame
      • Board Game Geek
    • Ariya
      • Breakthrough documentary

    Closing Announcements

    • Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.
    • Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
    • If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.
    • To help other people find the show please leave a review on iTunes and tell your friends and co-workers
    • Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

    Links

    • Seq
    • MIT CSAIL
    • Bioinformatics
    • LLVM
    • Intermediate Representation
    • MatLab
    • Moore’s Law
    • BioPython
    • Smith Waterman Algorithm
    • Hamming Distance
    • Pattern Matching in Functional Programming
    • SIMD == Single Instruction Multiple Data
    • Computational Genomics
    • Phylogenetics
    • Sequence Read Archive public data set
    • Google Cloud Life Sciences

    The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

    An Open Source Toolchain For Natural Language Processing From Explosion AI Mar 30, 2020
    Show notes

    Summary

    The state of the art in natural language processing is a constantly moving target. With the rise of deep learning, previously cutting edge techniques have given way to robust language models. Through it all the team at Explosion AI have built a strong presence with the trifecta of SpaCy, Thinc, and Prodigy to support fast and flexible data labeling to feed deep learning models and performant and scalable text processing. In this episode founder and open source author Matthew Honnibal shares his experience growing a business around cutting edge open source libraries for the machine learning developent process.

    Announcements

    • Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
    • When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, node balancers, a 40 Gbit/s public network, fast object storage, and a brand new managed Kubernetes platform, all controlled by a convenient API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they’ve got dedicated CPU and GPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!
    • You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on great conferences. And now, the events are coming to you, with no travel necessary! We have partnered with organizations such as ODSC, and Data Council. Upcoming events include the Observe 20/20 virtual conference on April 6th and ODSC East which has also gone virtual starting April 16th. Go to pythonpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today.
    • Your host as usual is Tobias Macey and today I’m interviewing Matthew Honnibal about the Thinc and Prodigy tools and an update on SpaCy

    Interview

    • Introductions
    • How did you get introduced to Python?
    • Can you start by giving an overview of your mission at Explosion?
    • We spoke previously about your work on SpaCy. What has changed in the past 3 1/2 years?
      • How have recent innovations in language models such as BERT and GPT-2 influenced the direction or implementation of the project?
    • When I last looked SpaCy only supported English and German, but you have added several new languages. What are the most challenging aspects of building the additional models?
      • What would be required for supporting symbolic or right-to-left languages?
    • How has the ecosystem for language processing in Python shifted or evolved since you first introduced SpaCy?
    • Another project that you have released is Prodigy to support labelling of datasets. Can you talk through the motivation for creating it and describe the workflow for someone using it?
      • What was lacking in the other annotation tools that you have worked with that you are trying to solve for in Prodigy?
    • What are some of the most challenging or problematic aspects of labelling data sets for use in machine learning projects?
      • What is a typical scale of data that can be reasonably handled by an individual or small team working with Prodigy?
        • At what point do you find that it makes sense to use a labeling service rather than generating the labels yourself?
    • Your most recent project is Thinc for building and using deep learning models. What was the motivation for creating it and what problem does it solve in the ecosystem?
      • How does its design and usage compare to other deep learning frameworks such as PyTorch and Tensorflow?
      • How does it compare to projects such as Keras that abstract across those frameworks?
    • How do the SpaCy, Prodigy, and Thinc libraries work together?
    • What are some of the biggest challenges that you are facing in building open source tools to meet the needs of data scientists and machine learning engineers?
    • What are some of the most interesting or impressive projects that you have seen built with the tools your team is creating?
    • What do you have planned for the future of Explosion, SpaCy, Prodigy, and Thinc?

    Keep In Touch

    • LinkedIn
    • @honnibal on Twitter
    • honnibal on GitHub

    Picks

    • Tobias
      • Onward movie
    • Matthew
      • Coronavirus Preparedness
      • Ray

    Closing Announcements

    • Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.
    • Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
    • If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.
    • To help other people find the show please leave a review on iTunes and tell your friends and co-workers
    • Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

    Links

    • Explosion AI
    • SpaCy
      • Podcast Episode
    • Thinc
    • Prodigy
    • Natural Language Processing
    • Perl
    • NLTK
    • GPU == Graphics Processing Unit
    • TPU == Tensor Processing Unit
    • Transfer Learning
    • Airflow
    • Luigi
    • Perceptron
    • PyTorch
    • Tensorflow
    • Functional Programming
    • MxNet
    • Keras
    • Cuda
    • C Language
    • Continuous Integration
    • Blackstone
    • Allen AI Institute
    • SciSpaCy
    • Holmes
    • Sense2Vec
    • FastAPI

    The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

    A Flexible Open Source ERP Framework To Run Your Business Mar 23, 2020
    Show notes

    Summary

    Running a successful business requires some method of organizing the information about all of the processes and activity that take place. Tryton is an open source, modular ERP framework that is built for the flexibility needed to fit your organization, rather than requiring you to model your workflows to match the software. In this episode core developers Nicolas Évrard and Cédric Krier are joined by avid user Jonathan Levy to discuss the history of the project, how it is being used, and the myriad ways that you can adapt it to suit your needs. If you are struggling to keep a consistent view of your business and ensure that all of the necessary workflows are being observed then listen now and give Tryton a try.

    Announcements

    • Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
    • When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, node balancers, a 40 Gbit/s public network, fast object storage, and a brand new managed Kubernetes platform, all controlled by a convenient API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they’ve got dedicated CPU and GPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!
    • You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Corinium Global Intelligence, ODSC, and Data Council. Go to pythonpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today.
    • Your host as usual is Tobias Macey and today I’m interviewing Nicolas Évrard, Cédric Krier, and Jonathan Levy about Tryton

    Interview

    • Introductions
    • How did you get introduced to Python?
    • Can you start by describing what Tryton is and how it got started?
    • What kinds of businesses is Tryton most suited to?
      • What kinds of businesses is Tryton not a good fit for?
    • Within a business, who are the primary users of Tryton?
    • Can you talk through a typical workflow for interacting with Tryton?
    • What are some of the most complex or challenging aspects of modeling a business while maintaining a high degree of customizability?
    • Can you describe how Tryton is architected and how its design has evolved since it was first started?
      • If you were to start over today, what would you do differently?
    • There are a number of plugins for Tryton. What kinds of functionality can be customized using the available interfaces?
      • What is the process for building a custom module for Tryton?
    • How do you manage sustainability of the Tryton project?
    • Given the criticality of the Tryton platform, how do you approach ongoing stability and security of the project?
    • What is involved in deploying and maintaining an installation of Tryton?
    • What are some of the most interesting, innovative, or unexpected ways that you have seen Tryton used?
    • What is in store for the future of Tryton?

    Keep In Touch

    • Nicolas
      • nicoe on GitHub
      • @nicoe on Twitter
    • Cédric
      • @cedrickrier on Twitter
      • cedk on GitHub
    • Jonathan
      • LinkedIn

    Picks

    • Tobias
      • Audio Books
        • Audible free trial (Affiliate Link)
        • Overdrive – ebooks and audiobooks from your local library
        • Public Domain Audiobooks
    • Nicolas
      • Civilization VI
      • FreeCiv
      • The 3 Body Problem
    • Cédric
      • Valérian and Laureline
    • Jonathan
      • Roil.com

    Links

    • Tryton
    • B2CK
    • Tryton Foundation
    • Advocate Consulting Legal Group
    • Scheme
    • Lisp
    • Belgium
    • EuroPython Conference
    • Plone
    • Zope
    • VBA (Visual Basic for Applications)
    • Django
    • Odoo
    • ERP == Enterprise Resource Planning
    • Small/Medium Enterprise (SME)
    • GTK (Gnome ToolKit)
    • 3-Tier Application
    • Cookiecutter
    • Tryton Module Cookiecutter
    • Tryton Repository
    • Docker
    • GNU Health
    • Nereid

    The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

    Getting A Handle On Portable C Extensions With hpy Mar 16, 2020
    Show notes

    Summary

    One of the driving factors of Python’s success is the ability for developers to integrate with performant languages such as C and C++. The challenge is that the interface for those extensions is specific to the main implementation of the language. This contributes to difficulties in building alternative runtimes that can support important packages such as NumPy. To address this situation a team of developers are working to create the hpy project, a new interface for extension developers that is standardized and provides a uniform target for multiple runtimes. In this episode Antonio Cuni discusses the motivations for creating hpy, how it benefits the whole ecosystem, and ways to contribute to the effort. This is an exciting development that has the potential to unlock a new wave of innovation in the ways that you can run your Python code.

    Announcements

    • Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
    • When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, node balancers, a 40 Gbit/s public network, fast object storage, and a brand new managed Kubernetes platform, all controlled by a convenient API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they’ve got dedicated CPU and GPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!
    • As a developer, maintaining a state of flow is key to your productivity. Don’t let something as simple as the wrong function ruin your day. Kite is the smartest completions engine available for Python, featuring a machine learning model trained by the brightest stars of GitHub. Featuring ranked suggestions sorted by relevance, offering up to full lines of code, and a programming copilot that offers up the documentation you need right when you need it. Get Kite for free today at getkite.com with integrations for top editors, including Atom, VS Code, PyCharm, Spyder, Vim, and Sublime.
    • You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Corinium Global Intelligence, ODSC, and Data Council. Upcoming events include the Software Architecture Conference in NYC, Strata Data in San Jose, and PyCon US in Pittsburgh. Go to pythonpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today.
    • Your host as usual is Tobias Macey and today I’m interviewing Antonio Cuni about hpy, a project aiming to reimagine the C API for Python

    Interview

    • Introductions
    • How did you get introduced to Python?
    • Can you start by describing what the hpy project is and how it got started?
      • What are the goals for the project?
      • Who else is involved?
    • How much engagement have you had with CPython core contributors or the steering council?
    • Who are the consumers of the current C API for the CPython implementation?
      • What are some of the pain points or shortcomings for those consumers?
      • What impact does that have for users of a given library that leverages C extensions?
    • Can you talk through the structure of the hpy project?
      • What are some of the design challenges that you are facing for determining the external API?
      • What is involved in integrating the hpy interface into alternate runtimes such as PyPy or RustPython?
    • What is the potential or observed performance impact for libraries that currently rely on the existing C API?
    • How has the vision and scope of this project been updated as you have gotten further along in the implementation?
    • What are the downstream impacts that you anticipate in projects such as PyPy and Cython?
    • What have you found to be the most challenging or contentious aspects of implementing hpy so far?
    • What are some of the most interesting/unexpected/useful lessons that you have learned while working on hpy?
    • What do you have planned for the near to medium term for hpy?

    Keep In Touch

    • antocuni on GitHub
    • Website
    • @antocuni on Twitter

    Picks

    • Tobias
      • Poetry
    • Antonio
      • Collapse: How Societies Choose To Fail Or Succeed by Jared Diamond

    Links

    • hpy
    • PyPy
    • Alex Martelli
      • Podcast Interview
    • Python C Extensions
    • EuroPython
    • Victor Stinner
    • Cython
      • Podcast Episode
    • Armin Rigo
    • NumPy
    • ultrajson
    • GIL == Global Interpreter Lock
    • RustPython
      • Podcast Episode
    • GraalPython
    • hpy-rust

    The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

    Open Source Machine Learning On Quantum Computers With Xanadu AI Mar 10, 2020
    Show notes

    Summary

    Quantum computers promise the ability to execute calculations at speeds several orders of magnitude faster than what we are used to. Machine learning and artificial intelligence algorithms require fast computation to churn through complex data sets. At Xanadu AI they are building libraries to bring these two worlds together. In this episode Josh Izaac shares his work on the Strawberry Fields and Penny Lane projects that provide both high and low level interfaces to quantum hardware for machine learning and deep neural networks. If you are itching to get your hands on the coolest combination of technologies, then listen now and then try it out for yourself.

    Announcements

    • Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
    • When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, node balancers, a 40 Gbit/s public network, fast object storage, and a brand new managed Kubernetes platform, all controlled by a convenient API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they’ve got dedicated CPU and GPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!
    • As a developer, maintaining a state of flow is key to your productivity. Don’t let something as simple as the wrong function ruin your day. Kite is the smartest completions engine available for Python, featuring a machine learning model trained by the brightest stars of GitHub. Featuring ranked suggestions sorted by relevance, offering up to full lines of code, and a programming copilot that offers up the documentation you need right when you need it. Get Kite for free today at getkite.com with integrations for top editors, including Atom, VS Code, PyCharm, Spyder, Vim, and Sublime.
    • You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Corinium Global Intelligence, ODSC, and Data Council. Upcoming events include the Software Architecture Conference in NYC, Strata Data in San Jose, and PyCon US in Pittsburgh. Go to pythonpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today.
    • Your host as usual is Tobias Macey and today I’m interviewing Josh Izaac about how the work that he is doing at Xanadu AI to make it easier to build applications for quantum processors

    Interview

    • Introductions
    • How did you get introduced to Python?
    • Can you start by describing what you are working on at Xanadu AI?
      • How do the specifics of your quantum hardware influence the way in which developers need to build their algorithms? (e.g. as compared to DWave)
    • What are some of the underlying principles that developers need to understand in order to take full advantage of the capabilities provided by quantum processors?
    • Can you outline the different components and libraries that you are building to simplify the work of building machine learning/AI projects for quantum processors?
      • What’s the story behind all of the Beatles references?
      • How do the different libraries fit together?
    • What are some of the workloads and use cases that you and your customers are focused on?
    • What are some of the most challenging aspects of designing a library that is accessible to developers while being able to take advantage of the underlying hardware?
    • How does the workflow for machine learning on quantum computers differ from what is being done in classical environments?
      • Given the magnitude of computational power and data processing that can be achieved in a quantum processor it seems that there is a potential for small bugs to have disproportionately large impacts. How can developers identify and mitigate potential sources of error in their algorithms?
    • For someone who is building an application or algorithm to be executed on a Xanadu processor, what does their workflow look like?
      • What are some of the common errors or misconceptions that you have seen in customer code?
    • Can you describe the design and implementation of the Penny Lane and Strawberry Fields libraries and how they have evolved since you first began working on them?
    • What are some of the most ambitious or exciting use cases for quantum systems that you have seen?
    • How are you using the computational capabilities of your platform to feed back into the research and design of successive generations of hardware?
    • What are some useful heuristics for determining whether it is worthwhile to build for a quantum processor rather than leveraging classical hardware?
    • What are some of the most interesting/unexpected/useful lessons that you have learned while working on quantum algorithms and the libraries to support them?
    • What is in store for the future of the Xanadu software ecosystem?
    • What are your predictions for the near to medium term of quantum computing?

    Keep In Touch

    • josh146 on GitHub
    • Website
    • LinkedIn

    Picks

    • Tobias
      • Knives Out movie
    • Josh
      • Baking Sourdough Bread

    Closing Announcements

    • Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.
    • Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
    • If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.
    • To help other people find the show please leave a review on iTunes and tell your friends and co-workers
    • Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

    Links

    • Xanadu AI
    • Strawberry Fields
    • PennyLane
    • Quantum Physics
    • ASIC == Application Specific Integrated Circuit
    • FPGA == Field Programmable Gate Array
    • GPU == Graphics Processing Unit
    • Quantum Photonics
    • Qubit
    • Trapped Ions
    • Quantum Optics
    • Coherent Light
    • Heisenberg’s Uncertainty Principle
    • Wave/Particle Duality
    • Continuous Variable Quantum Computation
    • NetworkX
    • Tensorflow
    • The Walrus
    • Rigetti Computing
    • PyTorch
      • Podcast Episode
    • The Walrus Operator (Assignment Expressions)
    • Fortran
    • NumPy
    • SciPy
    • IPython
      • Podcast Episode
    • Jax
    • Quantum Machine Learning
    • Xanadu User Discussion Forum

    The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

    The Advanced Python Task Scheduler Mar 02, 2020
    Show notes

    Summary

    Most long-running programs have a need for executing periodic tasks. APScheduler is a mature and open source library that provides all of the features that you need in a task scheduler. In this episode the author, Alex Grönholm, explains how it works, why he created it, and how you can use it in your own applications. He also digs into his plans for the next major release and the forces that are shaping the improved feature set. Spare yourself the pain of triggering events at just the right time and let APScheduler do it for you.

    Announcements

    • Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
    • When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, node balancers, a 40 Gbit/s public network, and a brand new managed Kubernetes platform, all controlled by a convenient API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they’ve got dedicated CPU and GPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!
    • You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Corinium Global Intelligence, ODSC, and Data Council. Upcoming events include the Software Architecture Conference in NYC, Strata Data in San Jose, and PyCon US in Pittsburgh. Go to pythonpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today.
    • Your host as usual is Tobias Macey and today I’m interviewing Alex Grönholm about APScheduler, a library for scheduling tasks in your Python projects

    Interview

    • Introductions
    • How did you get introduced to Python?
    • Can you start by describing what APScheduler is and the main use cases that APScheduler is designed for?
      • What was your movitvation for creating it?
    • What is the workflow for integrating APScheduler into an application?
      • In the documentation it says not to run more than one instance of the scheduler, what are some strategies for scaling schedulers?
    • What are some common architectures for applications that take advantage of APScheduler?
      • What are some potential pitfalls that developers should be aware of?
    • Can you describe how APScheduler is implemented and how its design has evolved since you first began working on it?
      • What have you found to be the most complex or challenging aspects of building or using a scheduling framework?
    • What are some of the most interesting/innovative/unexpected ways that you have seen APScheduler used?
    • What are some of the features or capabilities that you have consciously left out?
      • What design strategies or features of APScheduler are often overlooked or underappreciated?
    • What are some of the most useful or interesting lessons that you have learned while building and maintaining APScheduler?
    • When is APScheduler the wrong choice for managing task execution?
    • What do you have planned for the future of the project?

    Keep In Touch

    • agronholm on GitHub

    Picks

    • Tobias
      • The Data Exchange Podcast
    • Alex
      • Tenacity

    Links

    • APScheduler
    • PHP
    • Java
    • ECMAScript
    • Celery
    • ERP == Enterprise Resource Planning
    • Cron Daemon
    • RPyC
    • Zookeeper
      • Data Engineering Podcast Episode
    • RethinkDB
    • Daylight Saving Time
    • Falsehoods Programmers Believe About Time
    • PyTZ
    • Celery Beats
    • Asphalt Framework
      • Podcast Episode
    • AnyIO
    • Twisted
      • Podcast Episode
    • Py2EXE
    • PyInstaller

    The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

    Reducing The Friction Of Embedded Software Development With PlatformIO Feb 25, 2020
    Show notes

    Summary

    Embedded software development is a challenging endeavor due to a fragmented ecosystem of tools. Ivan Kravets experienced the pain of programming for different hardware platforms when embroiled in a home automation project. As a result he built the PlatformIO ecosystem to reduce the friction encountered by engineers working with multiple microcontroller architectures. In this episode he describes the complexities associated with targeting multiple platforms, the tools that PlatformIO offers to simplify the workflow, and how it fits into the development process. If you are feeling the pain of working with different editing environments and build toolchains for various microcontroller vendors then give this interview a listen and then try it out for yourself.

    Announcements

    • Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
    • When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, node balancers, a 40 Gbit/s public network, and a brand new managed Kubernetes platform, all controlled by a convenient API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they’ve got dedicated CPU and GPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!
    • You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Corinium Global Intelligence, ODSC, and Data Council. Upcoming events include the Strata Data in San Jose, and PyCon US in Pittsburgh. Go to pythonpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today.
    • Your host as usual is Tobias Macey and today I’m interviewing Ivan Kravets about PlatformIO, an open source ecosystem for IoT development including a cross-platform IDE, unified debugger, remote unit testing, and firmware updates.

    Interview

    • Introductions
    • How did you get introduced to Python?
    • Can you start by describing what PlatformIO is?
      • What was your motivation for creating it?
      • What are the aspects of embedded development that keep you interested and engaged in this space?
    • What are some of the types of projects that someone might use PlatformIO to build?
    • What are some of the common challenges that a developer might encounter when working on embedded systems?
      • What are the additional complexities that get introduced as more hardware targets get added to a project?
    • What is the workflow for someone using PlatformIO for embedded systems development?
    • What are the different elements of PlatformIO and how do they simplify the work of building embedded systems projects?
    • How is PlatformIO implemented and how has the system design evolved since you first began working on it?
      • What was your reason for selecting Python as the implementation language?
      • If you were to start over today what would you do differently?
    • How has the embedded hardware and software landscape changed since you first started work on PlatformIO?
      • How has that impacted your product direction?
    • How do developers handle testing and validation of their applications?
    • How does PlatformIO help with updating deployed devices with new firmware?
    • What have been some of the most interesting/unexpected/innovative projects that you have seen built with PlatformIO?
    • What have been some of the most interesting/unexpected/challenging aspects of building and maintaining PlatformIO?
    • How are you approaching sustainability of the project and business?
    • What do you have planned for the future of PlatformIO?

    Keep In Touch

    • LinkedIn
    • Website
    • ivankravets on GitHub
    • @ikravets on Twitter

    Picks

    • Tobias
      • UMass Amherst Making Electricity From Thin Air
    • Ivan
      • Don’t focus on the money side of your project, just focus on building a great product.

    Closing Announcements

    • Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.
    • Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
    • If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.
    • To help other people find the show please leave a review on iTunes and tell your friends and co-workers
    • Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

    Links

    • PlatformIO
    • Ukraine
    • Home Automation
    • Home Assistant
      • Podcast Episode
    • Twisted
      • Podcast Episode
    • Zigbee Radio
    • Serial I/O
    • RS-232
    • ARM CPU Architecture
    • RISC-V
    • AVR Microcontrollers
    • Arduino
    • Texas Instruments Launchpad
    • Eclipse IDE
    • MCU == MicroController Unit
    • VSCode
      • PlatformIO Extension
    • SCons
    • Make
    • Raspberry Pi
    • ESP8266
    • Marlin 3D Printer Firmware
    • ESP Home
    • Zephyr Realtime Operating System
    • Western Digital

    The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

    APIs, Sustainable Open Source and The Async Web With Tom Christie Feb 18, 2020
    Show notes

    Summary

    Tom Christie is probably best known as the creator of Django REST Framework, but his contributions to the state the web in Python extend well beyond that. In this episode he shares his story of getting involved in web development, his work on various projects to power the asynchronous web in Python, and his efforts to make his open source contributions sustainable. This was an excellent conversation about the state of asynchronous frameworks for Python and the challenges of making a career out of open source.

    Announcements

    • Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
    • When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, node balancers, a 40 Gbit/s public network, and a brand new managed Kubernetes platform, all controlled by a convenient API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they’ve got dedicated CPU and GPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!
    • You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Corinium Global Intelligence, ODSC, and Data Council. Upcoming events include the Software Architecture Conference in NYC, Strata Data in San Jose, and PyCon US in Pittsburgh. Go to pythonpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today.
    • Your host as usual is Tobias Macey and today I’m interviewing Tom Christie about the Encode organization and the work he is doing to drive the state of the art in async for Python

    Interview

    • Introductions
    • How did you get introduced to Python?
    • Can you start by describing what the Encode organization is and how it came to be?
      • What are some of the other approaches to funding and sustainability that you have tried in the past?
      • What are the benefits to the developers provided by an organization which you were unable to achieve through those other means?
      • What benefits are realized by your sponsors as compared to other funding arrangements?
    • What projects are part of the Encode organization?
    • How do you determine fund allocation for projects and participants in the organization?
    • What is the process for becoming a member of the Encode organization and what benefits and responsibilities does that entail?
    • A large number of the projects that are part of the organization are focused on various aspects of asynchronous programming in Python. Is that intentional, or just an accident of your own focus and network?
    • For those who are familiar with Python web programming in the context of WSGI, what are some of the practices that they need to unlearn in an async world, and what are some new capabilities that they should be aware of?
    • Beyond Encode and your recent work on projects such as Starlette you are also well known as the creator of Django Rest Framework. How has your experience building and growing that project influenced your current focus on a technical, community, and professional level?
    • Now that Python 2 is officially unsupported and asynchronous capabilities are part of the core language, what future directions do you foresee for the community and ecosystem?
      • What are some areas of potential focus that you think are worth more attention and energy?
    • What do you have planned for the future of Encode, your own projects, and your overall engagement with the Python ecosystem?

    Keep In Touch

    • Website
    • tomchristie on Github
    • @_tomchristie on Twitter

    Picks

    • Tobias
      • Maleficent: Mistress of Evil
      • Abominable
    • Tom
      • The Lobster
      • The Master And His Emissary by Ian McGilchrist

    Closing Announcements

    • Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.
    • Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
    • If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.
    • To help other people find the show please leave a review on iTunes and tell your friends and co-workers
    • Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

    Links

    • Encode
    • Django Rest Framework
    • Starlette
    • Zope
    • Django
    • Django Piston
    • Django Tastypie
    • Andrew Godwin
    • ASGI
    • Django Channels
      • Podcast Episode
    • Flask
    • Pyramid
    • Sentry
      • Podcast Episode
    • Tidelift
    • Uvicorn
    • HTTPX
    • Tidelift
    • Open Collective
    • Stripe
    • Github Sponsors
    • Python Software Foundation
      • Podcast Episode
    • Firebase
    • Databases
    • ORM
    • HTTP3

    The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

    Learning To Program Python By Building Video Games With Arcade Feb 11, 2020
    Show notes

    Summary

    Video games have been a vehicle for learning to program since the early days of computing. Continuing in that tradition, Paul Craven created the Arcade library as a modern alternative to PyGame for use in his classroom. In this episode he explains his motivations for starting a new framework for video game development, his view on the benefits of games in computer education, and how his students and the broader community are using it to build interesting and creative projects. If you are looking for a way to get new programmers engaged, or just want to experiment with building your own games, then this is the conversation for you. Give it a listen and then give Arcade a try for yourself.

    Announcements

    • Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
    • When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they just launched dedicated CPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!
    • You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Corinium Global Intelligence, ODSC, and Data Council. Upcoming events include the Software Architecture Conference in NYC, Strata Data in San Jose, and PyCon US in Pittsburgh. Go to pythonpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today.
    • Your host as usual is Tobias Macey and today I’m interviewing Paul Craven about Arcade, an easy-to-learn Python library for creating 2D video games

    Interview

    • Introductions
    • How did you get introduced to Python?
    • Can you start by describing what Arcade is?
      • What inspired you to begin working on it?
    • Who is your primary audience?
    • As an educator, what have you found to be most effective about using games as a vehicle for teaching programming?
      • What elements of programming or computer science do you have difficulty in addressing within the context of a video game?
      • For someone who wants to move on from working on games to something like web development or data analytics, what elements of software design and structure are easily translated to other domains?
    • Can you describe how Arcade is implemented and how the architecture has evolved since you first began working on it?
      • If you were to start over today, what would you do differently?
    • What have you found to be the most interesting/unexpected/challenging aspects of building and maintaining Arcade?
    • What are some of the most interesting/innovative/unexpected ways that you have seen Arcade used?
    • When is Arcade the wrong platform, or at what point does someone need to move on from Arcade?
    • What do you have planned for the future of Arcade?

    Keep In Touch

    • @professorcraven on Twitter
    • pvcraven on GitHub
    • Faculty Page

    Picks

    • Tobias
      • Ori And The Blind Forest
    • Paul
      • Fahrenheit 451 by Ray Bradbury
        • “Mistakes can be profited by Man, when i was young I showed my ignorance in people’s faces. They beat me with sticks. By the time I was forty my blunt instrument had been honed to a fine cutting point for me. If you hide your ignorance, no one will hit you and you’ll never learn.”

    Closing Announcements

    • Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.
    • Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
    • If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@podcastinit.com) with your story.
    • To help other people find the show please leave a review on iTunes and tell your friends and co-workers
    • Join the community in the new Zulip chat workspace at pythonpodcast.com/chat

    Links

    • Arcade
    • Simpson College
    • PyGame
    • SDL
    • OpenGL
    • Unity
    • Unreal Engine
    • GoDot
    • Automate The Boring Stuff With Python
    • Minesweeper
    • Pyglet
    • Spatial Hashing
    • Tiled Map Editor
    • Python Type Hints
    • F Strings
    • Data Classes
    • PyMunk
    • FFMPEG
    • PyWeek
      • Podcast Episode
    • Python Discord
    • Arcade Enhancement Requests

    The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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