The Python Podcast.init

By Tobias MaceyTechnology Education

The Python Podcast.init episodes

April 29, 2019Probabilistic Modeling In Python (And What That Even Means)
Summary
Most programming is deterministic, relying on concrete logic to determine the way that it operates. However, there are problems that require a way to work with uncertainty. PyMC3 is a library designed for building models to predict the likelihood of certain outcomes. In this episode Thomas Wiecki explains the use cases where Bayesian statistics are necessary, how PyMC3 is designed and implemented, and some great examples of how it is being used in real projects.
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, Dataversity, and the Open Data Science Conference. Go to pythonpodcast.com/conferences to learn more and take advantage of our partner discounts when you register.
Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email [email protected])
To help other people find the show please leave a review on iTunes and tell your friends and co-workers
Join the community in the new Zulip chat workspace at pythonpodcast.com/chat
Your host as usual is Tobias Macey and today I’m interviewing Thomas Wiecki about PyMC3, a project for probabilistic programming in Python
Interview
Introductions
How did you get introduced to Python?
Can you start by explaining what probabilistic programming is?
What is the PyMC3 project and how did you get involved with it?
The opening line for the project README is packed with a slew of terms that are rather opaque to the lay-person. Can you unpack that a bit and discuss some of the ways that PyMC3 is used in real-world projects?
How much knowledge of statistical modeling and Bayesian statistics is necessary to make effective use of PyMC3?
Can you talk through an example use case for PyMC3 to illustrate how you would use it in a project?
How does it compare to the way that you would approach the same problem in a deterministic or frequentist modeling framework?
Can you describe how PyMC3 is implemented?
There are a number of other projects that build on top of PyMC3, what are some that you find particularly interesting or noteworthy?
What do you find to be the most useful features of PyMC3 and what are some areas that you would like to see it improved?
What have been the most interesting/unexpected/challenging lessons that you have learned in the process of building and maintaining PyMC3?
What is in store for the future of PyMC3?
Keep In Touch
PyMC
GitHub
Discourse Forum
Thomas
twiecki on GitHub
@twiecki on Twitter
Website
Picks
Tobias
Fantastic Beasts And Where To Find Them
Fantastic Beasts: The Crimes Of Grindelwald
Thomas
Hyperion by Dan Simmons
The Mind Illuminated
Links
PyMC3
Quantopian
University of Tubingen
MatLab
Probabilistic Modeling
Probability Distribution
A/B Testing
Bayesian Statistics
Beta Distribution
Bernoulli Distribution
P-Value
Hamiltonian Monte Carlo sampling algorithm
Metropolis Hastings Inference Algorithm
Theano
Bayesian Methods For Hackers by Cameron Davidson-Pilon
Bayesian Analysis With Python by Osvaldo Martin
Tensorflow
MXNet deep learning framework
PyTorch
Tensorflow Probability
BAMBI package to build generalized linear models
PMProphet PyMC3 implementation of Facebook’s Prophet for timeseries prediction
Exoplanet
BEAT (Bayesian Earthquake Analysis Tool)
PyMC3 in Google Summer of Code
The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
55 min
April 22, 2019 Exploring Indico: A Full Featured Event Management Platform
Managing an event is rife with inherent complexity that scales as you move from scheduling a meeting to organizing a conference. Indico is a platform built at CERN to handle their efforts to organize events such as the Computing in High Energy Physics (CHEP) conference, and now it has grown to manage booking of meeting rooms. In this episode Adrian Mönnich, core developer on the Indico project, explains how it is architected to facilitate this use case, how it has evolved since its first incarnation two decades ago, and what he has learned while working on it. The Indico platform is definitely a feature rich and mature platform that is worth considering if you are responsible for organizing a conference or need a room booking system for your office.
54 min
April 22, 2019Exploring Indico: A Full Featured Event Management Platform
Summary
Managing an event is rife with inherent complexity that scales as you move from scheduling a meeting to organizing a conference. Indico is a platform built at CERN to handle their efforts to organize events such as the Computing in High Energy Physics (CHEP) conference, and now it has grown to manage booking of meeting rooms. In this episode Adrian Mönnich, core developer on the Indico project, explains how it is architected to facilitate this use case, how it has evolved since its first incarnation two decades ago, and what he has learned while working on it. The Indico platform is definitely a feature rich and mature platform that is worth considering if you are responsible for organizing a conference or need a room booking system for your office.
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!
Bots and automation are taking over whole categories of online interaction. Discover.bot is an online community designed to serve as a platform-agnostic digital space for bot developers and enthusiasts of all skill levels to learn from one another, share their stories, and move the conversation forward together. They regularly publish guides and resources to help you learn about topics such as bot development, using them for business, and the latest in chatbot news. For newcomers to the space they have the Beginners Guide To Bots that will teach you the basics of how bots work, what they can do, and where they are developed and published. To help you choose the right framework and avoid the confusion about which NLU features and platform APIs you will need they have compiled a list of the major options and how they compare. Go to pythonpodcast.com/discoverbot today to get started and thank them for their support of the 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, Dataversity, and the Open Data Science Conference. Go to pythonpodcast.com/conferences to learn more and take advantage of our partner discounts when you register.
Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email [email protected])
To help other people find the show please leave a review on iTunes and tell your friends and co-workers
Join the community in the new Zulip chat workspace at pythonpodcast.com/chat
Your host as usual is Tobias Macey and today I’m interviewing Adrian Mönnich about Indico, the effortless open-source tool for event organisation, archival and collaboration
Interview
Introductions
How did you get introduced to Python?
Can you start by describing what Indico is and how the project got started?
What are some other projects which target a similar use case and what were they lacking that led to Indico being necessary?
Can you talk through an example workflow for setting up and managing an event in Indico?
How does the lifecycle change when working with larger events, such as PyCon?
Can you describe how Indico is architected and how its design has evolved since it was first built?
What are some of the most complex or challenging portions of Indico to implement and maintain?
There are a lot of areas for exercising constraint resolution algorithms. Can you talk through some of the business logic of how that operates?
Most of Indico is highly configurable and flexible. How do you approach managing sane defaults to prevent users getting overwhelmed when onboarding?
What is your approach to testing given how complex the project is?
What are some of the most interesting or unexpected ways that you have seen Indico used?
What are some of the most interesting/unexpected lessons that you have learned in the process of building Indico?
What do you have planned for the future of the project?
Keep In Touch
Indico
Website
GitHub
IRC
Adrian
ThiefMaster on GitHub
Picks
Tobias
Mortal Engines movie
Adrian
Virtual Reality
Portal VR
Links
Indico
Tornado
Podcast Interview
CERN
High Energy Physics
CHEP (Computing in High Energy Physics) conference
ZODB
PostgreSQL
Data Engineering Podcast Interview
SQLAlchemy
Flask
WSGI == Web Server Gateway Interface
Mako Templates
Jinja
ReactJS
Stripe
Paypal
Indico Introduction Video
Reveal.js
Mod_Python
Zope
Doodle
LDAP == Lightweight Directory Access Protocol
Daylight Saving Time
Indico User Guide
Py.Test
Podcast Episode
Selenium
Flask Plugin Engine
CERN Indico Plugins
Linux Plumber’s Conference
Open SUSE
F-Strings
The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
54 min
April 15, 2019 Exploring Python's Internals By Rewriting Them In Rust
The CPython interpreter has been the primary implementation of the Python runtime for over 20 years. In that time other options have been made available for different use cases. The most recent entry to that list is RustPython, written in the memory safe language Rust. One of the added benefits is the option to compile to WebAssembly, offering a browser-native Python runtime. In this episode core maintainers Windel Bouwman and Adam Kelly explain how the project got started, their experience working on it, and the plans for the future. Definitely worth a listen if you are curious about the inner workings of Python and how you can get involved in a relatively new project that is contributing to new options for running your code.
41 min
April 15, 2019Exploring Python's Internals By Rewriting Them In Rust
Summary
The CPython interpreter has been the primary implementation of the Python runtime for over 20 years. In that time other options have been made available for different use cases. The most recent entry to that list is RustPython, written in the memory safe language Rust. One of the added benefits is the option to compile to WebAssembly, offering a browser-native Python runtime. In this episode core maintainers Windel Bouwman and Adam Kelly explain how the project got started, their experience working on it, and the plans for the future. Definitely worth a listen if you are curious about the inner workings of Python and how you can get involved in a relatively new project that is contributing to new options for running your 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, 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, Dataversity, and the Open Data Science Conference. Go to pythonpodcast.com/conferences to learn more and take advantage of our partner discounts when you register.
Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email [email protected])
To help other people find the show please leave a review on iTunes and tell your friends and co-workers
Join the community in the new Zulip chat workspace at pythonpodcast.com/chat
Your host is Tobias Macey and today I’m interviewing Adam Kelly and Windel Bouwman about RusPython, a project to implement a new Python interpreter in Rust
Interview
Introduction
How did you get introduced to Python?
Can you start by explaining what Rust is for anyone who isn’t familiar with it?
How did RustPython got started and what are your goals for the project?
Can you discuss what is involved in implementing a fully compliant Python interpreter?
What are some of the challenges that you face in replicating the capabilities of the CPython interpreter?
Are you attempting to maintain bug parity?
How much of the stdlib needs to be reimplemented?
Can you compare and contrast the benefits of Rust vs C?
Will the end result be compatible with libraries that rely on C extensions such as NumPy?
What is the current state of the project?
What are some of the notable missing features?
Can you talk through your vision of how the WebAssembly support will manifest and the types of applications that it will enable?
How much effort have you put into size optimization for the webassembly target to reduce client-side load time?
Are there any existing options for minification of Python code so that it can be delivered to users with less bandwidth?
What have been some of the most interesting/challenging/unexpected aspects of implementing a Python runtime?
What do you have planned for the future of the project?
What are the risks that you anticipate which could derail the project before it becomes production ready?
Contact Info
Windel
windelbouwman on GitHub
Website
@windelbouwman on Twitter
@[email protected] on Mastodon
Adam
cthulahoops on GitHub
@cthulahoops on Twitter
Picks
Tobias
Oysterhead
Adam
FZF fuzzy finder
Windel
TQDM Python progress bar
Links
RustPython
Windel Presentation EuroPython
Rust
C++
Rust Memory Safety
MicroPython
Podcast Episode
PyPy
Ouroboros – Pure Python standard library
WebAssembly
lalrpop – Rust parser generator
Rust Crates
PickItUp in-browser Python game engine
QuickSilver Game Engine
PEP 441
JIT (Just-In-Time) Compilation
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
41 min
April 08, 2019 Version Control For Your Machine Learning Projects
Version control has become table stakes for any software team, but for machine learning projects there has been no good answer for tracking all of the data that goes into building and training models, and the output of the models themselves. To address that need Dmitry Petrov built the Data Version Control project known as DVC. In this episode he explains how it simplifies communication between data scientists, reduces duplicated effort, and simplifies concerns around reproducing and rebuilding models at different stages of the projects lifecycle. If you work as part of a team that is building machine learning models or other data intensive analysis then make sure to give this a listen and then start using DVC today.
45 min
April 08, 2019Version Control For Your Machine Learning Projects
Summary
Version control has become table stakes for any software team, but for machine learning projects there has been no good answer for tracking all of the data that goes into building and training models, and the output of the models themselves. To address that need Dmitry Petrov built the Data Version Control project known as DVC. In this episode he explains how it simplifies communication between data scientists, reduces duplicated effort, and simplifies concerns around reproducing and rebuilding models at different stages of the projects lifecycle. If you work as part of a team that is building machine learning models or other data intensive analysis then make sure to give this a listen and then start using DVC today.
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!
Bots and automation are taking over whole categories of online interaction. Discover.bot is an online community designed to serve as a platform-agnostic digital space for bot developers and enthusiasts of all skill levels to learn from one another, share their stories, and move the conversation forward together. They regularly publish guides and resources to help you learn about topics such as bot development, using them for business, and the latest in chatbot news. For newcomers to the space they have the Beginners Guide To Bots that will teach you the basics of how bots work, what they can do, and where they are developed and published. To help you choose the right framework and avoid the confusion about which NLU features and platform APIs you will need they have compiled a list of the major options and how they compare. Go to pythonpodcast.com/discoverbot today to get started and thank them for their support of the show.
You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data management. 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, Dataversity, and the Open Data Science Conference. Go to pythonpodcast.com/conferences to learn more and take advantage of our partner discounts when you register.
Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email [email protected])
To help other people find the show please leave a review on iTunes and tell your friends and co-workers
Join the community in the new Zulip chat workspace at pythonpodcast.com/chat
Your host as usual is Tobias Macey and today I’m interviewing Dmitry Petrov about DVC, an open source version control system for machine learning projects
Interview
Introductions
How did you get introduced to Python?
Can you start by explaining what DVC is and how it got started?
How do the needs of machine learning projects differ from other software applications in terms of version control?
Can you walk through the workflow of a project that uses DVC?
What are some of the main ways that it differs from your experience building machine learning projects without DVC?
In addition to the data that is used for training, the code that generates the model, and the end result there are other aspects such as the feature definitions and hyperparameters that are used. Can you discuss how those factor into the final model and any facilities in DVC to track the values used?
In addition to version control for software applications, there are a number of other pieces of tooling that are useful for building and maintaining healthy projects such as linting and unit tests. What are some of the adjacent concerns that should be considered when building machine learning projects?
What types of metrics do you track in DVC and how are they collected?
Are there specific problem domains or model types that require tracking different metric formats?
In the documentation it mentions that the data files live outside of git and can be managed in external storage systems. I’m wondering if there are any plans to integrate with systems such as Quilt or Pachyderm that provide versioning of data natively and what would be involved in adding that support?
What was your motivation for implementing this system in Python?
If you were to start over today what would you do differently?
Being a venture backed startup that is producing open source products, what is the value equation that makes it worthwile for your investors?
What have been some of the most interesting, challenging, or unexpected aspects of building DVC?
What do you have planned for the future of DVC?
Keep In Touch
dmpetrov on GitHub
Blog
@fullstackml on Twitter
LinkedIn
Picks
Tobias
Otter.ai
Dmitry
Go outside and get some fresh air
Links
DVC
Iterative.ai
Linear Regression
Logistic Regression
C++
Perl
Git
Version Control System
Uber Michaelangelo
Domino Data Lab
Git LFS
AUC == Area Under Curve metric for evaluating machine learning model performance
Wes McKinney Interview
PyTorch
Podcast Interview
Tensorflow
TensorBoard
MLFlow
Quilt Data
Data Engineering Podcast Episode
Pachyderm
Data Engineering Podcast Episode
Apache Airflow
Podcast Interview
The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
45 min
April 01, 2019 Building Scalable Ecommerce Sites On Saleor
Ecommerce is an industry that has largely faded into the background due to its ubiquity in recent years. Despite that, there are new trends emerging and room for innovation, which is what the team at Mirumee focuses on. To support their efforts, they build and maintain the open source Saleor framework for Django as a way to make the core concerns of online sales easy and painless. In this episode Mirek Mencel and Patryk Zawadzki discuss the projects that they work on, the current state of the ecommerce industry, how Saleor fits with their technical and business strategy, and their predictions for the near future of digital sales.
59 min
April 01, 2019Building Scalable Ecommerce Sites On Saleor
Summary
Ecommerce is an industry that has largely faded into the background due to its ubiquity in recent years. Despite that, there are new trends emerging and room for innovation, which is what the team at Mirumee focuses on. To support their efforts, they build and maintain the open source Saleor framework for Django as a way to make the core concerns of online sales easy and painless. In this episode Mirek Mencel and Patryk Zawadzki discuss the projects that they work on, the current state of the ecommerce industry, how Saleor fits with their technical and business strategy, and their predictions for the near future of digital sales.
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!
Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email [email protected])
To help other people find the show please leave a review on iTunes and tell your friends and co-workers
Join the community in the new Zulip chat workspace at pythonpodcast.com/chat
Check out the Practical AI podcast from our friends at Changelog Media to learn and stay up to date with what’s happening in AI
You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data management. 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, Dataversity, and the Open Data Science Conference. Go to pythonpodcast.com/conferences to learn more and take advantage of our partner discounts when you register.
Your host as usual is Tobias Macey and today I’m interviewing Mirek Mencel and Patryk Zawadzki about their work at Mirumee, building ecommerce applications in Python, based on their open source framework Saleor
Interview
Introductions
How did you get introduced to Python?
Can you start by describing the types of projects that you work on at Mirumee and how the company got started?
There are a number of libraries and frameworks that you build and maintain. What is your motivation for providing these components freely and how does that play into your overall business strategy?
The most substantial project that you maintain is Saleor. Can you describe what it is and the story behind its creation?
How does it compare to other ecommerce implementations in the Python space?
If someone is agnostic to language and web framework, what would make them choose Saleor over other options that would be available to them?
What are some of the most challenging aspects of building a successful ecommerce platform?
How do the technical needs of an ecommerce site differ as it grows from small to medium and large scale?
Which components of an online store are often overlooked?
One of the common features of ecommerce sites that can drive substantial revenue is a well-built recommender system. What are some best practice strategies that you have discovered during your client work?
What are some projects that you have seen built with Saleor that were particular interesting, innovative, or unexpected?
What are your predictions for the future of the ecommerce industry?
What do you have planned for the future of the Saleor framework and the Mirumee business?
Keep In Touch
Mirumee
Website
Github
@mirumeelabs on Twitter
Mirek
@mirekmencel on Twitter
mirekm on GitHub
Patryk
patrys on GitHub
@patrys on Twitter
Website
Picks
Tobias
Wreck It Ralph: Ralph Breaks The Internet
Mirek
A Guide To The Good Life: The Ancient Art Of Stoic Joy by William B. Irvine
Patryk
Release It: Design And Deploy Production Ready Software by Michael Nygard
Links
Mirumee
Saleor
Django
PHP
Pyramid web framework
Pylons
Magento eCommerce platform
Ecommerce
Satchmo
Satchless
Prices library for handling price data
French National Assembly
Django Oscar
Podcast Interview
David Winterbottom
Ebay
Amazon
Etsy
Shopify
Ariadne GraphQL framework for Python
Graphene GraphQL framework for Python
Podcast Interview
Apollo JavaScript GraphQL framework
PWA == Progressive Web Apps
SKU == Stock Keeping Unit
Collective Intelligence
Elasticsearch
Data Engineering Podcast Interview
A/B Testing
Room Lab store built on Saleor
Augmented Reality
WebGL
Saleor Cloud
ASGI
Podcast Interview
The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
59 min
March 25, 2019 A Quick Python Check-in With Naomi Ceder
Naomi Ceder was fortunate enough to learn Python from Guido himself. Since then she has contributed books, code, and mentorship to the community. Currently she serves as the chair of the board to the Python Software Foundation, leads an engineering team, and has recently completed a new draft of the Quick Python Book. In this episode she shares her story, including a discussion of her experience as a technical author and a detailed account of the role that the PSF plays in supporting and growing the community.
39 min