The Python Podcast.init

By Tobias MaceyTechnology Education

The Python Podcast.init episodes

April 30, 2018Destroy All Software With Gary Bernhardt
Summary
Many developers enter the market from backgrounds that don’t involve a computer science degree, which can lead to blind spots of how to approach certain types of problems. Gary Bernhardt produces screen casts and articles that aim to teach these principles with code to make them approachable and easy to understand. In this episode Gary discusses his views on the state of software education, both in academia and bootcamps, the theoretical concepts that he finds most useful in his work, and some thoughts on how to build better software.
Preface
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 you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 200Gbit network, all controlled by a brand new API you’ve got everything you need to scale up. Go to podcastinit.com/linode to get a $20 credit and launch a new server in under a minute.
Finding a bug in production is never a fun experience, especially when your users find it first. Airbrake error monitoring ensures that you will always be the first to know so you can deploy a fix before anyone is impacted. With open source agents for Python 2 and 3 it’s easy to get started, and the automatic aggregations, contextual information, and deployment tracking ensure that you don’t waste time pinpointing what went wrong. Go to podcastinit.com/airbrake today to sign up and get your first 30 days free, and 50% off 3 months of the Startup plan.
To get worry-free releases download GoCD, the open source continous delivery server built by Thoughworks. You can use their pipeline modeling and value stream map to build, control and monitor every step from commit to deployment in one place. And with their new Kubernetes integration it’s even easier to deploy and scale your build agents. Go to podcastinit.com/gocd to learn more about their professional support services and enterprise add-ons.
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])
Your host as usual is Tobias Macey and today I’m interviewing Gary Bernhardt about teaching and learning Python in the current software landscape
Interview
Introductions
How did you get introduced to Python?
As someone who makes a living from teaching aspects of programming what is your view on the state of software education?
What are some of the ways that we as an industry can improve the experience of new developers?
What are we doing right?

You spend a lot of time exploring some of the fundamental aspects of programming and computation. What are some of the lessons that you have learned which transcend software languages?
Utility of graphs in understanding software
Mechanical sympathy

What are the benefits of ‘from scratch’ tutorials that explore the steps involved in building simple versions of complex topics such as compilers or web frameworks?

Keep In Touch
@garybernhardt on Twitter
garybernhardt on GitHub
Picks
Tobias
Terry Pratchett

Gary
Destroy All Software
Deconstruct Conference
Out Of The Tarpit
Algorithms + Data Structures = Programs by Niklaus Wirth
Dan Grossman Programming Languages Course (click the “Videos” links under “course materials”)
U of W
John Carmack post reconsidering some earlier positions

Links
Wat
Birth and Death of Javascript
Destroy All Software
Deconstruct
Data Structures
Computer Science
Compilers
Programming Bootcamps
Graph Theory
Julia Evans
@b0rk on Twitter

Allen Downey

Jupyter Notebook

Halting Problem

Idris

Visual Basic 3.0

Set Theory

ML Family of Languages

SML, a simple dialect of ML

SML/NJ, a compiler for SML

OCamL, a more modern dialect of ML

F#, an even newer dialect of ML

Clojure, a modern Lisp-like language

Lua Grammar (scroll to the very bottom for the full grammar)

John Carmack

Twitter Thread Explaining Episode Context

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
53 min
April 23, 2018 Scaling Deep Learning Using Polyaxon with Mourad Mourafiq
With libraries such as Tensorflow, PyTorch, scikit-learn, and MXNet being released it is easier than ever to start a deep learning project. Unfortunately, it is still difficult to manage scaling and reproduction of training for these projects. Mourad Mourafiq built Polyaxon on top of Kubernetes to address this shortcoming. In this episode he shares his reasons for starting the project, how it works, and how you can start using it today.
36 min
April 23, 2018Scaling Deep Learning Using Polyaxon with Mourad Mourafiq
Summary
With libraries such as Tensorflow, PyTorch, scikit-learn, and MXNet being released it is easier than ever to start a deep learning project. Unfortunately, it is still difficult to manage scaling and reproduction of training for these projects. Mourad Mourafiq built Polyaxon on top of Kubernetes to address this shortcoming. In this episode he shares his reasons for starting the project, how it works, and how you can start using it today.
Preface
Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
When you’re ready to launch your next app you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 200Gbit network, all controlled by a brand new API you’ve got everything you need to scale up. Go to podcastinit.com/linode to get a $20 credit and launch a new server in under a minute.
Finding a bug in production is never a fun experience, especially when your users find it first. Airbrake error monitoring ensures that you will always be the first to know so you can deploy a fix before anyone is impacted. With open source agents for Python 2 and 3 it’s easy to get started, and the automatic aggregations, contextual information, and deployment tracking ensure that you don’t waste time pinpointing what went wrong. Go to podcastinit.com/airbrake today to sign up and get your first 30 days free, and 50% off 3 months of the Startup plan.
To get worry-free releases download GoCD, the open source continous delivery server built by Thoughworks. You can use their pipeline modeling and value stream map to build, control and monitor every step from commit to deployment in one place. And with their new Kubernetes integration it’s even easier to deploy and scale your build agents. Go to podcastinit.com/gocd to learn more about their professional support services and enterprise add-ons.
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])
Your host as usual is Tobias Macey and today I’m interviewing Mourad Mourafiq about Polyaxon, a platform for building, training and monitoring large scale deep learning applications.
Interview
Introductions
How did you get introduced to Python?
Can you give a quick overview of what Polyaxon is and your motivation for creating it?
What is a typical workflow for building and testing a deep learning application?
How is Polyaxon implemented?
How has the internal architecture evolved since you first started working on it?
What is unique to deep learning workloads that makes it necessary to have a dedicated tool for deploying them?
What does Polyaxon add on top of the existing functionality in Kubernetes?

It can be difficult to build a docker container that holds all of the necessary components for a complex application. What are some tips or best practices for creating containers to be used with Polyaxon?

What are the relative tradeoffs of the various deep learning frameworks that you support?

For someone who is getting started with Polyaxon what does the workflow look like?
What is involved in migrating existing projects to run on Polyaxon?

What have been the most challenging aspects of building Polyaxon?

What are your plans for the future of Polyaxon?

Keep In Touch
Website
@mmourafiq on Twitter
mouradmourafiq on GitHub
Picks
Tobias
Kubernetes
Kubernetes Up And Running
Kelsey Hightower
Food Fight Show With Kelsey Hightower

Mourad
Schopenhauer

Links
Polyaxon
Investment Banking
Luxembourg
Matlab
Text Mining
Tensorflow
Docker
Kubernetes
Deep Learning
Free Deep Learning Textbook

Machine Learning Engineer

Hyperparameters

Continuous Integration

PyTorch

MXNet

Scikit-Learn

Helm

Mesos

Spark

SparkML

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
36 min
April 15, 2018 Electricity Map: Real Time Visibility of Power Generation with Olivier Corradi
One of the biggest issues facing us is the availability of sustainable energy sources. As individuals and energy consumers it is often difficult to understand how we can make informed choices about energy use to reduce our impact on the environment. Electricity Map is a project that provides up to date and historical information about the balance of how the energy we are using is being produced. In this episode Olivier Corradi discusses his motivation for creating Electricity Map, how it is built, and his goals for the project and his other work at Tomorrow Co.
48 min
April 15, 2018Electricity Map: Real Time Visibility of Power Generation with Olivier Corradi
Summary
One of the biggest issues facing us is the availability of sustainable energy sources. As individuals and energy consumers it is often difficult to understand how we can make informed choices about energy use to reduce our impact on the environment. Electricity Map is a project that provides up to date and historical information about the balance of how the energy we are using is being produced. In this episode Olivier Corradi discusses his motivation for creating Electricity Map, how it is built, and his goals for the project and his other work at Tomorrow Co.
Preface
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 you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 200Gbit network, all controlled by a brand new API you’ve got everything you need to scale up. Go to podcastinit.com/linode to get a $20 credit and launch a new server in under a minute.
Finding a bug in production is never a fun experience, especially when your users find it first. Airbrake error monitoring ensures that you will always be the first to know so you can deploy a fix before anyone is impacted. With open source agents for Python 2 and 3 it’s easy to get started, and the automatic aggregations, contextual information, and deployment tracking ensure that you don’t waste time pinpointing what went wrong. Go to podcastinit.com/airbrake today to sign up and get your first 30 days free, and 50% off 3 months of the Startup plan.
To get worry-free releases download GoCD, the open source continous delivery server built by Thoughworks. You can use their pipeline modeling and value stream map to build, control and monitor every step from commit to deployment in one place. And with their new Kubernetes integration it’s even easier to deploy and scale your build agents. Go to podcastinit.com/gocd to learn more about their professional support services and enterprise add-ons.
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])
Your host as usual is Tobias Macey and today I’m interviewing Olivier Corradi about Electricity Map and using Python to analyze data of global power generation
Interview
Introductions
How did you get introduced to Python?
What was your motivation for creating Electricity Map?
How can an average person use or benefit from the information that is available in the map?

What sources are you using to gather the information about how electricity is generated and distributed in various geographic regions?
Is there any standard format in which this data is produced?
What are the biggest difficulties associated with collecting and consuming this data?
How much confidence do you have in the accuracy of the data sources?
Is there any penalty for misrepresenting the fuel consumption or waste generation for a given plant?

Can you describe the architecture of the system and how it has evolved?

What are some of the most interesting uses of the data in your database and API that you are aware of?
How do you measure the impact or effectiveness of the information that you provide through the different interfaces to the data that you have aggregated?

How have you built a community around the project?
How has the community helped in building and growing Electricity Map?

What are some of the most unexpected things that you have learned in the process of building Electricity Map?

What are your plans for the future of Electricity Map?

Keep In Touch
@corradio on Twitter
LinkedIn
corradio on GitHub
Picks
Tobias
Rollerblading

Olivier
Deep Mind AlphaGo Documentary
Consumer’s Guide To Climate Change Impact

Links
Electricity Map
Machine Learning
Youtube
Climate Change
Fossil Fuels
Carbon Intensity
Greenhouse Gas Equivalencies Calculations
Open Data
Electricity Map Project Source
Lignite
Marginal Carbon Intensity
Electricity Map Forecast API
IPCC (Intergovernmental Panel on Climate Change
Redis
D3.js
Spark
Tensorflow
Spatiotemporal Data
MongoDB
Matrix Inversion
PyGRIB
Tomorrow Co.
The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
48 min
April 08, 2018 Building And Growing Nylas with Christine Spang
Email is one of the oldest methods of communication that is still in use on the internet today. Despite many attempts at building a replacement and predictions of its demise we are sending more email now than ever. Recognizing that the venerable inbox is still an important repository of information, Christine Spang co-founded Nylas to integrate your mail with the rest of your tools, rather than just replacing it. In this episode Christine discusses how Nylas is built, how it is being used, and how she has helped to grow a successful business with a strong focus on diversity and inclusion.
44 min
April 08, 2018Building And Growing Nylas with Christine Spang
Summary
Email is one of the oldest methods of communication that is still in use on the internet today. Despite many attempts at building a replacement and predictions of its demise we are sending more email now than ever. Recognizing that the venerable inbox is still an important repository of information, Christine Spang co-founded Nylas to integrate your mail with the rest of your tools, rather than just replacing it. In this episode Christine discusses how Nylas is built, how it is being used, and how she has helped to grow a successful business with a strong focus on diversity and inclusion.
Preface
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 you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 200Gbit network, all controlled by a brand new API you’ve got everything you need to scale up. Go to podcastinit.com/linode to get a $20 credit and launch a new server in under a minute.
Finding a bug in production is never a fun experience, especially when your users find it first. Airbrake error monitoring ensures that you will always be the first to know so you can deploy a fix before anyone is impacted. With open source agents for Python 2 and 3 it’s easy to get started, and the automatic aggregations, contextual information, and deployment tracking ensure that you don’t waste time pinpointing what went wrong. Go to podcastinit.com/airbrake today to sign up and get your first 30 days free, and 50% off 3 months of the Startup plan.
To get worry-free releases download GoCD, the open source continous delivery server built by Thoughworks. You can use their pipeline modeling and value stream map to build, control and monitor every step from commit to deployment in one place. And with their new Kubernetes integration it’s even easier to deploy and scale your build agents. Go to podcastinit.com/gocd to learn more about their professional support services and enterprise add-ons.
Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email [email protected])
To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.
Your host as usual is Tobias Macey and today I’m interviewing Christine Spang about Nylas and the modern era of email
Interview
Introductions
How did you get introduced to Python?
Can you explain what Nylas is and some of its history?
What do you think it is about email as a protocol and a means of communication that has made it so resilient in the face of technological evolution?
What lessons did you learn from your initial offering of the N1 mail client and how has that informed your current focus?
Nylas as a company appears to have a strong focus on diversity and inclusion. Can you speak to how you encourage that type of environment and how it manifests at work?
What are some of the ways that Python is used at Nylas?
Can you share some examples of services that you have written in other languages and why you felt that Python was not the right choice?
What are some of the use cases that Nylas enables?
What are some of the most interesting or innovative uses of the Nylas platform that you have seen?
How do you manage privacy and security in your sync service given the sensitivity of the data that you are handling?
What are some of the biggest challenges that you are currently facing at Nylas?
What do you think will be the future of email?
Keep In Touch
LinkedIn
@spang on Twitter
Website
GitHub
Picks
Tobias
Trello
Christine
Founders For Change
Links
Nylas
MIT
KSplice
Debian
Lisp
REST
Email
N1 Mail Client
Mailspring
Nylas Employee Handbook
Hackbright Academy
Code2040
TextIO
Key Values
IMAP
OAuth
MySQL
Gevent
React
CRM (Customer Relationship Management)
SendGrid
MailGun
MailChimp
GDPR (General Data Protection Regulation)
SOC2
OWASP Top 10
Principle of Least Privilege
The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
44 min
April 01, 2018 Synthetic Data Generation Using Mimesis with Nikita Sobolev
Most applications require data to operate on in order to function, but sometimes that data is hard to come by, so why not just make it up? Mimesis is a library for randomly generating data of different types, such as names, addresses, and credit card numbers, so that you can use it for testing, anonymizing real data, or for placeholders. This week Nikita Sobolev discusses how the project got started, the challenges that it has posed, and how you can use it in your applications.
33 min
April 01, 2018Synthetic Data Generation Using Mimesis with Nikita Sobolev
Summary
Most applications require data to operate on in order to function, but sometimes that data is hard to come by, so why not just make it up? Mimesis is a library for randomly generating data of different types, such as names, addresses, and credit card numbers, so that you can use it for testing, anonymizing real data, or for placeholders. This week Nikita Sobolev discusses how the project got started, the challenges that it has posed, and how you can use it in your applications.
Preface
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 you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to scale up. Go to podcastinit.com/linode to get a $20 credit and launch a new server in under a minute.
To get worry-free releases download GoCD, the open source continous delivery server built by Thoughworks. You can use their pipeline modeling and value stream map to build, control and monitor every step from commit to deployment in one place. Go to podcastinit.com/gocd to learn more about their professional support services and enterprise add-ons.
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])
Your host as usual is Tobias Macey and today I’m interviewing Nikita Sobolev about Mimesis, a library for quickly generating synthetic data
Interview
Introductions
How did you get introduced to Python?
What is mimesis and how does it compare to other projects such as faker and factory_boy?
What was the motivation for creating it?

One of the features that is advertised is the speed of Mimesis. What techniques are used to ensure that the data is generated quickly?

What are the built in mechanisms for generating data?
What options do users have for customizing the types of data that can get generated?

What are some of the most complicated providers to write and maintain?

What are some of the use cases outside of unit or integration tests where Mimesis could be beneficial?
How would you use Mimesis to anonymize data from a production environment to be used for testing?

What are the most challenging aspects of maintaining the Mimesis project?

What are some of the plans that you have for the future of Mimesis?

Keep In Touch
sobolevn on GitHub
@sobolevn on Twitter
Email
Picks
Tobias
Coco

Nikita
I Am A Mediocre Developer

Links
Mimesis
Django
Faker
Factory Boy
Internationalization (I18N)
Unicode
Enum
Pipfile
GeoJSON
Mimesis Cloud
Sanic
GraphQL
Impostor Syndrome
Imposter Syndrome Disclaimer: Add this to all of your projects!
Jacob Kaplan-Moss PyCon Keynote
The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
33 min
March 25, 2018 Luminoth: AI Powered Computer Vision for Python with Joaquin Alori
Making computers identify and understand what they are looking at in digital images is an ongoing challenge. Recent years have seen notable increases in the accuracy and speed of object detection due to deep learning and new applications of neural networks. In order to make it easier for developers to take advantage of these techniques Tryo Labs built Luminoth. In this interview Joaquin Alori explains how how Luminoth works, how it can be used in your projects, and how it compares to API oriented services for computer vision.
22 min