The Python Podcast.init

By Tobias MaceyTechnology Education

The Python Podcast.init episodes

March 16, 2020Getting A Handle On Portable C Extensions With hpy
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
36 min
March 10, 2020 Open Source Machine Learning On Quantum Computers With Xanadu AI
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.
58 min
March 10, 2020Open Source Machine Learning On Quantum Computers With Xanadu AI
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 [email protected]) 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
58 min
March 02, 2020 The Advanced Python Task Scheduler
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.
34 min
March 02, 2020The Advanced Python Task Scheduler
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
34 min
February 25, 2020 Reducing The Friction Of Embedded Software Development With PlatformIO
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.
47 min
February 25, 2020Reducing The Friction Of Embedded Software Development With PlatformIO
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 [email protected]) 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
47 min
February 18, 2020 APIs, Sustainable Open Source and The Async Web With Tom Christie
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.
44 min
February 18, 2020APIs, Sustainable Open Source and The Async Web With Tom Christie
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 [email protected]) 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
44 min
February 11, 2020 Learning To Program Python By Building Video Games With Arcade
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.
0 min