The Python Podcast.init

By Tobias MaceyTechnology Education

The Python Podcast.init episodes

June 27, 2022Design Real-World Objects In Python With CadQuery
Summary
Virtually everything that you interact with on a daily basis and many other things that make modern life possible were designed and modeled in software called CAD or Computer-Aided Design. These programs are advanced suites with graphical editing environments tailored to domain experts in areas such as mechanical engineering, electrical engineering, architecture, etc. While the UI-driven workflow is more accessible, it isn’t scalable which opens the door to code-driven workflows. In this episode Jeremy Wright discusses the design, uses, and benefits of the CadQuery framework for building 3D CAD models entirely in Python.
Announcements
Hello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.
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 their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. And now you can launch a managed MySQL, Postgres, or Mongo database cluster in minutes to keep your critical data safe with automated backups and failover. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!
So now your modern data stack is set up. How is everyone going to find the data they need, and understand it? Select Star is a data discovery platform that automatically analyzes & documents your data. For every table in Select Star, you can find out where the data originated, which dashboards are built on top of it, who’s using it in the company, and how they’re using it, all the way down to the SQL queries. Best of all, it’s simple to set up, and easy for both engineering and operations teams to use. With Select Star’s data catalog, a single source of truth for your data is built in minutes, even across thousands of datasets. Try it out for free and double the length of your free trial today at pythonpodcast.com/selectstar. You’ll also get a swag package when you continue on a paid plan.
Need to automate your Python code in the cloud? Want to avoid the hassle of setting up and maintaining infrastructure? Shipyard is the premier orchestration platform built to help you quickly launch, monitor, and share python workflows in a matter of minutes with 0 changes to your code. Shipyard provides powerful features like webhooks, error-handling, monitoring, automatic containerization, syncing with Github, and more. Plus, it comes with over 70 open-source, low-code templates to help you quickly build solutions with the tools you already use. Go to dataengineeringpodcast.com/shipyard to get started automating with a free developer plan today!
Your host as usual is Tobias Macey and today I’m interviewing Jeremy Wright about CadQuery, an easy-to-use Python module for building parametric 3D CAD models
Interview
Introductions
How did you get introduced to Python?
Can you start by explaining what CAD is and some of the real-world applications of it?
Can you describe what CadQuery is and the story behind it?
How did you get involved with it and what keeps you motivated?
What are the different methods that are in common use for building CAD models?
Are there approaches that are more common for models used in different industries?
What was missing in other projects for programmatically generating CAD models that motivated you to build CadQuery?
Can you describe how the CadQuery library is implemented?
How have the design and goals of the project changed or evolved since you started working on it?
How would you characterize the rate of change/evolution in the CAD ecosystem, and how has that factored into your work on CadQuery?
How did you approach the process of API design?
How do you balance accessibility for non-professionals with domain-related nomenclature?
Can you describe some example workflows for going from idea to finished product with CadQuery?
How are you using CadQuery in your own work?
What are the most interesting, innovative, or unexpected ways that you have seen CadQuery used?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on CadQuery?
When is CadQuery the wrong choice?
What do you have planned for the future of CadQuery?
Keep In Touch
Discord
Twitter
GitHub
GitLab
Picks
Tobias
Doctor Strange: In The Multiverse of Madness
Jeremy
Star Trek: Strange New Worlds
Closing Announcements
Thank you for listening! Don’t forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. The Machine Learning Podcast helps you go from idea to production with machine learning.
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
Links
CadQuery
CAD == Computer Assisted Design
3D Printer
Jeremy’s CNC Router
jQuery
Blender
Fusion 360
Open Cascade (OCCT)
Fluent API
FreeCAD
KiCAD
Semblage
cq-editor
jupyter-cadquery
cq-kit
FX Bricks
Voxels
cq_warehouse
The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
46 min
June 15, 2022Intelligent Dependency Resolution For Optimal Compatibility And Security With Project Thoth
Summary
Building any software project is going to require relying on dependencies that you and your team didn’t write or maintain, and many of those will have dependencies of their own. This has led to a wide variety of potential and actual issues ranging from developer ergonomics to application security. In order to provide a higher degree of confidence in the optimal combinations of direct and transitive dependencies a team at Red Hat started Project Thoth. In this episode Fridolín Pokorný explains how the Thoth resolver uses multiple signals to find the best combination of dependency versions to ensure compatibility and avoid known security issues.
Announcements
Hello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.
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 their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. And now you can launch a managed MySQL, Postgres, or Mongo database cluster in minutes to keep your critical data safe with automated backups and failover. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!
Need to automate your Python code in the cloud? Want to avoid the hassle of setting up and maintaining infrastructure? Shipyard is the premier orchestration platform built to help you quickly launch, monitor, and share python workflows in a matter of minutes with 0 changes to your code. Shipyard provides powerful features like webhooks, error-handling, monitoring, automatic containerization, syncing with Github, and more. Plus, it comes with over 70 open-source, low-code templates to help you quickly build solutions with the tools you already use. Go to dataengineeringpodcast.com/shipyard to get started automating with a free developer plan today!
Your host as usual is Tobias Macey and today I’m interviewing Fridolín Pokorný about Project Thoth, a resolver service that computes the optimal combination of versions for your dependencies
Interview
Introductions
How did you get introduced to Python?
Can you describe what Project Thoth is and the story behind it?
What are some examples of the types of problems that can be introduced by mismanaged dependency versions?
The Python ecosystem has seen a number of dependency management tools introduced recently. What are the capabilities that Thoth offers that make it stand out?
How does it compare to e.g. pip, Poetry, pip-tools, etc.?
How do those other tools approach resolution of dependencies?
Can you describe how Thoth is implemented?
How have the scope and design of the project evolved since it was started?
What are the sources of information that it relies on for generating the possible solution space?
What are the algorithms that it relies on for finding an optimal combination of packages?
Can you describe how Thoth fits into the workflow of a developer while selecting a set of dependencies and keeping them up to date over the life of a project?
What are the opportunities for expanding Thoth’s application to other language ecosystems?
What are the interfaces available for extending or integrating with Thoth?
What are the most interesting, innovative, or unexpected ways that you have seen Thoth used?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on Thoth?
When is Thoth the wrong choice?
What do you have planned for the future of Thoth?
Keep In Touch
LinkedIn
Website
Picks
Tobias
Brass Against
Fridolin
micropipenv
Links
Redhat
Emerging Technologies Group
Project Thoth
Thamos CLI
PyPA Advisory Database
Project2Vec
Thoth Prescriptions
Thoth: Egyptian God
The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
32 min
May 30, 2022Take A Deep Dive On How Code Completion Works And How To Customize It
Summary
Most developers have encountered code completion systems and rely on them as part of their daily work. They allow you to stay in the flow of programming, but have you ever stopped to think about how they work? In this episode Meredydd Luff takes us behind the scenes to dig into the mechanics of code completion engines and how you can customize them to fit your particular use case.
Announcements
Hello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.
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 the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. 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 Meredydd Luff about how code completion works and what it takes to build your own
Interview
Introductions
How did you get introduced to Python?
Most programmers are familiar with the idea of code completion, but can you just give the elevator pitch to get us all on the same page?
You gave a presentation recently at PyCon about how to build a code completion system. What was your approach to identifying what fundamental concepts needed to be addressed and how to fit that lesson into the available time?
In the presentation you mentioned that you had built a more full-featured completion engine into Anvil. Can you describe what possessed you to build your own code completion tool?
What are the core components required to build a completion engine?
What are the benefits that can be realized by customizing the completion engine for a given language or task?
Can you describe the feature set and implementation details of the full-fledged completion engine that is available in Anvil?
Beyond the toy example, there are a number of considerations to address if you want to make the completion engine "production grade". Can you talk through some of the obvious edge cases and how to solve for them? (e.g. handling parsing of incomplete code)
What are the inputs that you use to build up the list of candidate tokens for completion?
Once you have a functioning baseline for offering completions, what are some of the signals that you hook into for ranking suggestions?
In your presentation you leaned on the machinery available in the Python standard library. What are some of the ways that you might think about generalizing across languages vs. coupling to a given language?
What design/architectural advice do you have for compartmentalizing logic in a full-featured completion engine?
What are some of the complexities that become a factor when you are trying to scale across an entire code base?
Beyond just being able to parse and process a body of code, there is also the question of integrating with the development environment. What are some of the challenges that get introduced when trying to access the appropriate set(s) of files and code through the editor interface(s)?
What are the most interesting, innovative, or unexpected ways that you have seen code completion applied to developer experience?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on code completion for Anvil?
When is code completion more effort than it’s worth?
What do you have planned for the future of the Anvil code completion functionality?
Keep In Touch
LinkedIn
meredydd on GitHub
@meredydd on Twitter
Picks
Tobias
"Weird Al" Yankovic
Meredydd
TimescaleDB
Data Engineering Podcast Episode
Promscale
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
Links
PyCon presentation about building a completion engine
Anvil
Podcast Episode
Nano
Language Server Protocol
Jedi
Podcast Episode
Skulpt
Parser
Abstract Syntax Tree
OpenAPI
GitHub Copilot
Halting Problem
Parser Generator
Python Language Grammar Definition
Lezer Parser Generator
Tree-sitter
PyScript
Grafana Tempo Tracing Service
The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
1 hr 1 min
May 24, 2022Hunting Black Swans With Bees: Catching Up With The Inimitable Russell Keith-Magee
Summary
Russell Keith-Magee is an accomplished engineer and a fixture of the Python community. His work on the Beeware suite of projects is one of the most ambitious undertakings in the ecosystem and unfailingly forward-looking. With his recent transition to working for Anaconda he is now able to dedicate his full focus to the effort. In this episode he reflects on the journey that he has taken so far, how Beeware is helping to address some of the threats to Python’s long term viability, and how he envisions its future in light of the recent release of PyScript, an in-browser runtime for Python.
Announcements
Hello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.
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 the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. 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 Russell Keith-Magee about the latest status of the Beeware project, the state of Python’s black swans, and how the PyScript project ties into his ambitions for world domination
Interview
Introductions
How did you get introduced to Python?
For anyone who hasn’t been graced with the BeeWare vision, can you give the elevator pitch of what it is and why it matters?
At PyCon US 2019 you presented a keynote about the various potential threats to the Python language community and its future viability. With the clarity of 3 years hindsight, how has the landscape shifted?
What is PyScript and how does it fit into the venn diagram of BeeWare’s objectives and the portents of black swan events (and what is your involvement with it)?
How does it differ from the dozens of other "Python in the browser" and "Python transpiled to Javascript" projects that have sprouted over the years?
Now that you have been granted the opportunity to dedicate your full attention to BeeWare and build a team to support it, what new potential does that unlock?
What are the current areas of focus/challenges that you are spending your time on for the BeeWare project?
What are some of the efforts in the BeeWare suite that proved to be dead-ends?
What are the most interesting, innovative, or unexpected ways that you have seen the BeeWare suite/PyScript used?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on BeeWare?
When is BeeWare the wrong choice?
What do you have planned for the future of BeeWare/PyScript/Python/world domination?
Keep In Touch
LinkedIn
Website
@freakboy3742 on Twitter
Picks
Tobias
Joby Gorillapod
Russell
PyScript
The Great TV Show
Links
Black Swans Episode
BeeWare Episode
BeeWare
Django
Cordova
Black Swan
Apple II
Altair
Briefcase
Web Assembly (WASM)
Gary Bernhardt
PyScript
Pyodide
Toga
Kotlin
Swift
Gaffer Tape
Repl.it
Brython
Transcrypt
Python Anywhere
Batavia
Anaconda
Conda
Voc
Maestral
Eddington GUI
The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
57 min
May 16, 2022Take Control Of Your Digital Photos By Running Your Own Smart Library Manager With LibrePhotos
Summary
Digital cameras and the widespread availability of smartphones has allowed us all to generate massive libraries of personal photographs. Unfortunately, now we are all left to our own devices of how to manage them. While cloud services such as iPhotos and Google Photos are convenient, they aren’t always affordable and they put your pictures under the control of large companies with their own agendas. LibrePhotos is an open source and self-hosted alternative to these services that puts you in control of your digital memories. In this episode the maintainer of LibrePhotos, Niaz Faridani-Rad, explains how he got involved with the project, the capabilities that it offers for managing your image library, and how to get your own instance set up to take back control of your pictures.
Announcements
Hello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.
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 the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!
This episode is sponsored by Mergify. It’s an amazing tool to make you and your team way more productive with GitHub. Mergify is all about leveling up your pull requests with useful features that eliminate busy work. Automatic merges allow you define the conditions for acceptance and Mergify will take care of merging the pull request as soon as it’s ready. Automatic updates take care of merging your pull requests serially on top of each other, so there is no way to introduce a regression. With a merge queue you can merge your urgent pull request first, organize your Prs as you wish and Mergify will merge them in that order. Mergify’s backports feature will even copy the pull request into another branch once the pull request has been merged, shipping your bug fixes on multiple branches automatically. By saving time you and your team can focus on projects that matter. Mergify is coordinated with any CI and fully integrated into GitHub. They have a Startup Program that offers a 12 months credit to leverage Mergify (up to $21,000 of value). Start saving time; visit pythonpodcast.com/mergify today to sign up for a demo and get started! Or just click the link in the show notes.
Your host as usual is Tobias Macey and today I’m interviewing Niaz Faridani-Rad about LibrePhotos, an open source, self-hosted application for managing your personal photo collection
Interview
Introductions
How did you get introduced to Python?
Can you describe what LibrePhotos is and the story behind it?
What are the core objectives of the project?
What kind of users are you focused on?
What are some of the major features of LibrePhotos?
There are a number of open source and commercial options for different photo oriented use cases. What are the main capabilities that influence someone’s decision to use one over the other?
Many people’s baseline expectations will be around services such as Google Photos or iPhotos. What are some of the challenges that you face in trying to provide a comparable experience?
One of the features that users rely on with these services is backup/disaster recovery of their photo library. What is the recommended approach for users of LibrePhotos?
Can you describe how LibrePhotos is architected?
How have the design and goals evolved since you first started working on it?
How have recent advances in machine learning algorithms and related tooling improved the availability and quality of advanced features in LibrePhotos?
How much improvement of accuracy in face/object recognition do you see as users invest in cataloging and organizing their collections?
Is there a minimum quantity of images/iindividual people that are necessary to start using the ML powered features?
What kinds of storage locations are supported?
What are the interfaces available for extending/enhancing/integrating with LibrePhotos?
What are the most interesting, innovative, or unexpected ways that you have seen LibrePhotos used?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on LibrePhotos?
When is LibrePhotos the wrong choice?
What do you have planned for the future of LibrePhotos?
Keep In Touch
derneuere on GitHub
@der_neuere on Twitter
Website
LinkedIn
Picks
Tobias
Uncharted movie
Niaz
Steam Deck
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
Links
LibrePhotos
Self-hosted Sub-Reddit
OwnPhotos
Google Photos
Google Takeout
Digikam
x265
HEIC Files
RAW Image Format
ImageMagick
Panorama Photograph
Lytro light field cameras
rq asynchronous task library
Typescript
Redux Toolkit
MobileNet v3
DLib
ARM Processor
Docker Compose
LibrePhotos Comparison Page
The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
46 min
May 10, 2022Making Investment Data Easy To Access And Analyze With The OpenBB Terminal
Summary
Investing effectively is largely a game of information access and analysis. This can involve a substantial amount of research and time spent on finding, validating, and acquiring different information sources. In order to reduce the barrier to entry and provide a powerful framework for amateur and professional investors alike Didier Rodrigues Lopes created the OpenBB Terminal. In this episode he explains how a pandemic project that started as an experiment has led to him founding a new company and dedicating his time to growing and improving the project and its community.
Announcements
Hello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.
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 the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. 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 Didier Rodrigues Lopes about the OpenBB Terminal, a modern Python-based integrated environment for investment research
Interview
Introductions
How did you get introduced to Python?
Can you describe what OpenBB is and the story behind it?
What is the problem that you are trying to address by creating the OpenBB project and providing it as open source?
What are some of the use cases where someone might need to use this project?
The elephant in the room for financial data research is the Bloomberg Terminal. What are the other tools or services available for that purpose?
What are the differentiating features of the OpenBB Terminal?
Can you describe how the OpenBB Terminal is implemented?
How have the design and goals/scope of the project changed since you started working on it?
Can you describe a typical workflow for someone who is using the OpenBB Terminal?
How have you approached the user experience design, and what are you optimizing for?
What kinds of utilities do you offer beyond raw data access?
What are some examples of data sources that you rely on?
What is involved in integrating a new data source?
What are the extension points and integration capabilities for expanding the functionality of the tool?
What are the most interesting, innovative, or unexpected ways that you have seen OpenBB Terminal used?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on OpenBB Terminal?
When is OpenBB Terminal the wrong choice?
What do you have planned for the future of OpenBB Terminal?
Keep In Touch
DidierRLopes on GitHub
LinkedIn
@didier_lopes on Twitter
Picks
Tobias
Vikings: Valhalla show on Netflix
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
Links
OpenBB
Matlab
Papermill
Bloomberg Terminal
Robinhood
Coinbase
The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
48 min
May 02, 2022Accelerate Your Machine Learning Experimentation With Automatic Checkpoints Using FLOR
Summary
The experimentation phase of building a machine learning model requires a lot of trial and error. One of the limiting factors of how many experiments you can try is the length of time required to train the model which can be on the order of days or weeks. To reduce the time required to test different iterations Rolando Garcia Sanchez created FLOR which is a library that automatically checkpoints training epochs and instruments your code so that you can bypass early training cycles when you want to explore a different path in your algorithm. In this episode he explains how the tool works to speed up your experimentation phase and how to get started with it.
Announcements
Hello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.
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 the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. 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 Rolando Garcia about FLOR, a suite of machine learning tools for hindsight logging that lets you speed up model experimentation by checkpointing training data
Interview
Introductions
How did you get introduced to Python?
Can you describe what FLOR is and the story behind it?
What is the core problem that you are trying to solve for with FLOR?
What are the fundamental challenges in model training and experimentation that make it necessary?
How do machine learning reasearchers and engineers address this problem in the absence of something like FLOR?
Can you describe how FLOR is implemented?
What were the core engineering problems that you had to solve for while building it?
What is the workflow for integrating FLOR into your model development process?
What information are you capturing in the log structures and epoch checkpoints?
How does FLOR use that data to prime the model training to a given state when backtracking and trying a different approach?
How does the presence of FLOR change the costs of ML experimentation and what is the long-range impact of that shift?
Once a model has been trained and optimized, what is the long-term utility of FLOR?
What are the opportunities for supporting e.g. Horovod for distributed training of large models or with large datasets?
What does the maintenance process for research-oriented OSS projects look like?
What are the most interesting, innovative, or unexpected ways that you have seen FLOR used?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on FLOR?
When is FLOR the wrong choice?
What do you have planned for the future of FLOR?
Keep In Touch
rlnsanz on GitHub
@rogarcia_sanz on Twitter
Picks
Tobias
The Batman
Rolando
Severance
GitHub Codespaces
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
Links
FLOR
UC Berkeley
Joe Hellerstein
MLOps
Data Engineering Podcast Episode
RISE Lab
AMP Lab
Clipper Model Serving
Ground Data Context Service
Context: The Missing Piece Of The Machine Learning Lifecycle
Airflow
Copy on write
ASTor
Green Tree Snakes: Python AST Documentation
MLFlow
Amazon Sagemaker
Cloudpickle
Horovod
Podcast Episode
Ray Anyscale
PyTorch
Tensorflow
The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
47 min
April 25, 2022Automatically Enforce Software Structures With Powerful Code Modifications Powered By LibCST
Summary
Programmers love to automate tedious processes, including refactoring your code. In order to support the creation of code modifications for your Python projects Jimmy Lai created LibCST. It provides a richly typed and high level API for creating and manipulating concrete syntax trees of your source code. In this episode Jimmy Lai and Zsolt Dollenstein explain how it works, some of the linting and automatic code modification utilities that you can build with it and how to get started with using it to maintain your own Python projects.
Announcements
Hello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.
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 the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. 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 Zsolt Dollenstein and Jimmy Lai about LibCST, a concrete syntax tree parser and serializer library for Python
Interview
Introductions
How did you get introduced to Python?
Can you describe what LibCST is and the story behind it?
How does a concrete syntax tree differ from an abstract syntax tree?
What are some of the situations where the preservation of the exact structure is necessary?
There are a few other libraries in Python for creating concrete syntax trees. What was missing in the available options that made it necessary to create LibCST?
What are the use cases that LibCST is focused on supporting
Can you describe how LibCST is implemented?
How have the design and goals of the project changed or evolved since you started working on it?
How might I use LibCST for something like restructuring a set of modules to move a function definition while maintaining proper imports?
How do the capabilities of LibCST for codemodding compare to the Rope framework?
What are some other workflows that someone might build with LibCST?
What are some of the ways that LibCST is being used in your own work?
What are the most interesting, innovative, or unexpected ways that you have seen LibCST used?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on LibCST?
When is LibCST the wrong choice?
What do you have planned for the future of LibCST?
Keep In Touch
Zsolt
zsol on GitHub
LinkedIn
Jimmy
jimmylai on GitHub
LinkedIn
Picks
Tobias
Osprey Manta Backpack
Zsolt
Autotransform
Glean
Jimmy
Paying down technical debt
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
Links
LibCST
Carta
lib2to3
Abstract Syntax Tree
Concrete Syntax Tree
Pyre
Parso
Cython
Podcast Episode
mypyc
Rope
Flake8
Podcast Episode
Pylint
ESLint
Fixit
MonkeyType
Podcast Episode
The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
57 min
April 19, 2022Cloud Native Networking For Developers With The Gloo Platform
Summary
Communication is a fundamental requirement for any program or application. As the friction involved in deploying code has gone down, the motivation for architecting your system as microservices goes up. This shifts the communication patterns in your software from function calls to network calls. In this episode Idit Levine explains how the Gloo platform that she and her team at Solo have created makes it easier for you to configure and monitor the network topologies for your microservice environments. She also discusses what developers need to know about networking in cloud native environments and how a combination of API gateways and service mesh technologies allow you to more rapidly iterate on your systems.
Announcements
Hello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.
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 the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. 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 Idit Levine about what developers need to know about service-oriented networking and her work at Solo on the Gloo project
Interview
Introductions
How did you get introduced to Python?
Can you describe what Solo is and the story behind it?
How much should developers need to know about the ways that their applications and services are communicating?
What is the current state of networking for applications across physical, cloud, and containerized environments?
How do service mesh features influence the architectural decisions that software teams make while building their applications?
What operational capabilities do they unlock?
What are the aspects of application networking that are simplified or enhanced by service mesh platforms?
In what ways has service mesh introduced new complexity to operating software systems?
How can developers mirror the network topologies for production environments while working on new features?
What are the most interesting, innovative, or unexpected ways that you have seen Gloo used?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on Gloo?
When is Gloo the wrong choice?
What do you have planned for the future of Gloo?
Keep In Touch
LinkedIn
@Idit_Levine on Twitter
Picks
Tobias
Shadow and Bone on Netflix
Idit
Elizabeth Holmes HBO 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 [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
Links
Solo
Computational Biology
Microservices
Kubernetes
Service Mesh
Istio
LinkerD
Envoy Proxy
API Gateway
CRD == Custom Resource Definition
Gloo Edge
Bazel Build System
GraphQL
mTLS
GitOps
Dagger
WASM == Web Assembly
Kubernetes Gateway API
Consul Connect
eBPF
The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
51 min
April 11, 2022Accelerate And Simplify Cloud Native Development For Kubernetes Environments With Gefyra
Summary
Cloud native architectures have been gaining prominence for the past few years due to the rising popularity of Kubernetes. This introduces new complications to development workflows due to the need to integrate with multiple services as you build new components for your production systems. In order to reduce the friction involved in developing applications for cloud native environments Michael Schilonka created Gefyra. In this episode he explains how it connects your local machine to a running Kubernetes environment so that you can rapidly iterate on your software in the context of the whole system. He also shares how the Django Hurricane plugin lets your applications work closely with the Kubernetes process model.
Announcements
Hello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.
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 the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!
So now your modern data stack is set up. How is everyone going to find the data they need, and understand it? Select Star is a data discovery platform that automatically analyzes & documents your data. For every table in Select Star, you can find out where the data originated, which dashboards are built on top of it, who’s using it in the company, and how they’re using it, all the way down to the SQL queries. Best of all, it’s simple to set up, and easy for both engineering and operations teams to use. With Select Star’s data catalog, a single source of truth for your data is built in minutes, even across thousands of datasets. Try it out for free and double the length of your free trial today at pythonpodcast.com/selectstar. You’ll also get a swag package when you continue on a paid plan.
Your host as usual is Tobias Macey and today I’m interviewing Michael Schilonka about Gefyra and what is involved with developing applications for Kubernetes environments
Interview
Introductions
How did you get introduced to Python?
Can you describe what Gefyra is and the story behind it?
What are the challenges that Kubernetes introduces to the development process?
What are some of the strategies that developers might use for developing and testing applications that are deployed to Kubernetes environments?
What are the use cases that Gefyra is focused on enabling?
What are some of the other tools or platforms that Gefyra might replace or supplement?
What are the services that need to be present in the K8s cluster to enable Gefyra’s functionality?
Can you describe how Gefyra is implemented?
How have the design and goals of the project changed since you first started working on it?
What is the process for getting Gefyra set up between a K8s cluster and a developer’s laptop?
Can you describe what the developer’s workflow looks like when using Gefyra?
How do you avoid collisions/resource contention among a team of developers who are working on the same project?
What are some of the ways that developing for Kubernetes influences the architectural and design decisions for a project?
What are some of the additional practices or systems that you have found to be beneficial for accelerating development in cloud-native environments?
What are the most interesting, innovative, or unexpected ways that you have seen Gefyra used?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on Gefyra?
When is Gefyra the wrong choice?
What do you have planned for the future of Gefyra?
Keep In Touch
LinkedIn
Schille on GitHub
Picks
Tobias
kubernetes.el – Kubernetes interface for Emacs
Michael
It’s fermentation friday, perfect for baking a sourdough bread or brewing beer
Two of my favorit YouTube channels Kurzgesagt – In a Nutshell and LockPickingLawyer
For entrepreneurial spirits: Reddit community research with (GummySearch)[https://gummysearch.com/]?utm_source=rss&utm_medium=rss
Links
Kopf framework
PyOxidizer
Tuna
Wireguard-go
https://k3d.io/?utm_source=rss&utm_medium=rss
kind
Django Hurricane
Blueshoe
Django
Kubernetes
K3d
Telepresence
Unikube
Sidecar Pattern
Docker-compose
Kubernetes Patterns book
O’Reilly Platform
Amazon (affiliate link)
CodeZero
CoreDNS
Nginx
Cookiecutter
Tornado
Podcast Episode
uWSGI
Podcast Episode
12 Factor App
Pycloak
Keycloak
Kubernetes Operator
Kubernetes CRD (Custom Resource Definition
The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
39 min