The Python Podcast.init

By Tobias MaceyTechnology Education

The Python Podcast.init episodes

March 25, 2018Luminoth: AI Powered Computer Vision for Python with Joaquin Alori
Summary
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 Joaquín 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.
Introduction
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.
For complete visibility into your application stack, deployment tracking, and powerful alerting, DataDog has got you covered. With their monitoring, metrics, and log collection agent, including extensive integrations and distributed tracing, you’ll have everything you need to find and fix bugs in no time. Go to podcastinit.com/datadog today to start your free 14 day trial and get a sweet new T-Shirt.
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 Joaquín Alori about Luminoth, a deep learning toolkit for computer vision in Python
Interview
Introductions
How did you get introduced to Python?
What is Luminoth and what was your motivation for creating it?
Computer vision has been a focus of AI research for decades. How do current approaches with deep learning compare to previous generations of tooling?
What are some of the most difficult problems in visual processing that still need to be solved?
What are the limitations of Luminoth for building a computer vision application and how do they differ from the capabilities of something built with a prior generation of tooling such as OpenCV?
For someone who is interested in using Luminoth in their project what is the current workflow?
How do the capabilities of Luminoth compare with some of the various service based options such as Rekognition for Amazon or the Cloud Vision API from Google?
What are some of the motivations for using Luminoth in place of these services?

What are some of the highest priority features that you are focusing on implementing in Luminoth?

When is Luminoth the wrong choice for a computer vision application and what are some of the strongest alternatives at the moment?

Keep In Touch
@JoaquinAlori on Twitter
LinkedIn
Picks
Tobias
PyCon US

Joaquin
3Blue1Brown

Links
Luminoth
Luminoth Release Announcement
Tryo Labs
Uruguay
Industrial Engineering
Manufacturing Engineering
Elon Musk
Artificial Intelligence
Deep Learning
Neural Networks
Object Detection
Image Segmentation
Convolutional Neural Network
Recurrent Neural Network
Back Propagation
Geoff Hinton
Capsule Networks
Generative Adversarial Networks
SVM (Support Vector Machine)
Haar Classifiers
OpenCV
Drones
GPU (Graphics Processing Unit)
Rekognition
Cloud Vision API
TensorFlow Object Detection API
Sonnet
DeepMind
Caffe
The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
22 min
March 18, 2018 Thonny: The IDE For Beginning Programmers with Aivar Annamaa
Learning to program is a rewarding pursuit, but is often challenging. One of the roadblocks on the way to proficiency is getting a development environment installed and configured. In order to simplify that process Aivar Annamaa built Thonny, a Python IDE designed for beginning programmers. In this episode he discusses his initial motivations for starting Thonny and how it helps newcomers to Python learn and understand how to write software.
30 min
March 18, 2018Thonny: The IDE For Beginning Programmers with Aivar Annamaa
Summary
Learning to program is a rewarding pursuit, but is often challenging. One of the roadblocks on the way to proficiency is getting a development environment installed and configured. In order to simplify that process Aivar Annamaa built Thonny, a Python IDE designed for beginning programmers. In this episode he discusses his initial motivations for starting Thonny and how it helps newcomers to Python learn and understand how to write 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 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.
For complete visibility into your application stack, deployment tracking, and powerful alerting, DataDog has got you covered. With their monitoring, metrics, and log collection agent, including extensive integrations and distributed tracing, you’ll have everything you need to find and fix bugs in no time. Go to podcastinit.com/datadog today to start your free 14 day trial and get a sweet new T-Shirt.
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 podcastinit.com to subscribe to the show, sign up for the newsletter, and read the show notes.
Your host as usual is Tobias Macey and today I’m interviewing Aivar Annamaa about Thonny, a Python IDE for beginning programmers
Interview
Introductions
How did you get introduced to Python?
What was your motivation for building an IDE focused on beginning programmers?
What are the features of Thonny that make it easier for users to understand what is happening in their programs?
What have you found to be the types of issues that users most frequently struggle with and how does Thonny help overcome those gaps in understanding?
What kinds of tutorials or supporting material have you found to be the most useful for teaching students the principles that they need to be able to take advantage of the environment that Thonny provides?
How is Thonny built and what have been the most challenging aspects of writing an IDE in Python?
What are some of the interface design choices that you have made to avoid confusing or overwhelming beginning users?
Once a user becomes more proficient in Python is there a point where it no longer makes sense to continue using Thonny for development?
I noticed that Thonny has an plugin architecture and there is an extension for interacting with the BBC micro:bit. What are some of the other types of extensions that you would like to see built for Thonny?
Keep In Touch
Aivar
@aivarannamaa on Twitter
aivarannamaa on GitHub
Google Scholar Page

Thonny
Website
Forum
@thonnyide on Twitter
Source repository and wiki

Picks
Tobias
Data Engineering Podcast
Kubo and the Two Strings

Aivar
MicroPython
Podcast.__init__ Interview
How to Talk So Kids Will Listen & Listen So Kids Will Talk

Links
Thonny
University of Tartu
Estonia
Recursion
TKinter
Aivar Estonian Textbook
Pascal
MyPy
Podcast.__init__ Interview

BBC Micro:bit

Version Control

GitHub

GitLab

Elm Compiler Messages

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
30 min
March 12, 2018 Keeping The Beets with Adrian Sampson
Maintaining a consistent taxonomy for your music library is a challenging and time consuming endeavor. Eventually you end up with a mess of folders and files with inconsistent names and missing metadata. Beets is built to solve this problem by programmatically managing the tags and directory structure for all of your music files and providing a fast lookup when you are trying to find that perfect song to play. Adrian Sampson began the project because he was trying to clean up his own music collection and in this episode he discusses how the project was built, how streaming media is affecting our relationship to digital music, and how he envisions Beets position in the ecosystem in the future.
40 min
March 12, 2018Keeping The Beets with Adrian Sampson
Summary
Maintaining a consistent taxonomy for your music library is a challenging and time consuming endeavor. Eventually you end up with a mess of folders and files with inconsistent names and missing metadata. Beets is built to solve this problem by programmatically managing the tags and directory structure for all of your music files and providing a fast lookup when you are trying to find that perfect song to play. Adrian Sampson began the project because he was trying to clean up his own music collection and in this episode he discusses how the project was built, how streaming media is affecting our relationship to digital music, and how he envisions Beets position in the ecosystem in the future.
Preface
Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
I would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable.
When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at podastinit.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app. And now you can deliver your work to your users even faster with the newly upgraded 200 GBit network in all of their datacenters.
If you’re tired of cobbling together your deployment pipeline then it’s time to try out GoCD, the open source continuous delivery platform built by the people at ThoughtWorks who wrote the book about it. With GoCD you get complete visibility into the life-cycle of your software from one location. To download it now go to podcatinit.com/gocd. Professional support and enterprise plugins are available for added piece of mind.
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 Adrian Sampson about Beets, the swiss army knife for managing your music library.
Interview
Introductions
How did you get introduced to Python?
What is Beets and what was your reason for creating it?
What was your reason for using Python and if you were to start over today would you make the same choice?

If I have a directory with inconsistent naming conventions, poor organization, and some random folders full of mixed MP3 files how can Beets help me and what does the workflow look like?

How is Beets architected to allow for interactively processing a large volume of media files and how has the design evolved over the time that you have been working on it?

What are your thoughts on the current trend toward streaming music services replacing local media files?

What have been some of the most challenging aspects of building Beets?

What are some of the most interesting uses for Beets that you have seen?

What are some of the other projects for managing a music library and how does Beets compare to them?

Are there any features that you have planned for the future of Beets, or any new functionality that you would like to see contributed?

Keep In Touch
sampsyo on GitHub
Website
@samps on Twitter
Picks
Tobias
Mozart’s Requiem
Wikipedia
YouTube
Gov’t Mule
Darkest Hour

Adrian
Spiralizer
Spiralized Beats With Pesto

Links
Beets
SQLite
Mutagen
ID3 Tags
Musicbrainz
Bandcamp
Free Music Archive
Cornell
AcoustID
Chromaprint
Musicbrainz Picard
iTunes
Spotify
Amazon Music
DLNA
UPnP
AURA
The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
40 min
March 04, 2018 Salabim: Logistics Simulation with Ruud van der Ham
Determining the best way to manage the capacity and flow of goods through a system is a complicated issue and can be exceedingly expensive to get wrong. Rather than experimenting with the physical objects to determine the optimal algorithm for managing the logistics of everything from global shipping lanes to your local bank, it is better to do that analysis in a simulation. Ruud van der Ham has been working in this area for the majority of his professional life at the Dutch port of Rotterdam. Using his acquired domain knowledge he wrote Salabim as a library to assist others in writing detailed simulations of their own and make logistical analysis of real world systems accessible to anyone with a Python interpreter.
52 min
March 04, 2018Salabim: Logistics Simulation with Ruud van der Ham
Summary
Determining the best way to manage the capacity and flow of goods through a system is a complicated issue and can be exceedingly expensive to get wrong. Rather than experimenting with the physical objects to determine the optimal algorithm for managing the logistics of everything from global shipping lanes to your local bank, it is better to do that analysis in a simulation. Ruud van der Ham has been working in this area for the majority of his professional life at the Dutch port of Rotterdam. Using his acquired domain knowledge he wrote Salabim as a library to assist others in writing detailed simulations of their own and make logistical analysis of real world systems accessible to anyone with a Python interpreter.
Preface
Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
I would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable.
When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at podastinit.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app. And now you can deliver your work to your users even faster with the newly upgraded 200 GBit network in all of their datacenters.
If you’re tired of cobbling together your deployment pipeline then it’s time to try out GoCD, the open source continuous delivery platform built by the people at ThoughtWorks who wrote the book about it. With GoCD you get complete visibility into the life-cycle of your software from one location. To download it now go to podcatinit.com/gocd. Professional support and enterprise plugins are available for added piece of mind.
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 Ruud van der Ham about Salabim, a Python library for conducting discrete event simulations
Interview
Introductions
How did you get introduced to Python?
Can you start by explaining what Discrete Event Simulation is and how Salabim helps with that?
Can you explain how you chose the name?

What was your motivation for creating Salabim and how does it compare to other tools for discrete event simulation?

How does discrete event simulation compare with state machines?

How is Salabim implemented and how has the design evolved over the time that you have been working on it?

I understand that you have done a majority of Salabim was written on an iPad. Can you speak about why you have chosen that as your development environment and your experience working in that manner?

What are some examples of the types of models that you can model with Salabim?
What would an implementation of one of these models look like for someone using Salabim?

What options does a user have to verify the accuracy of a simulation created with Salabim?

One of the nice aspects of Salabim is the fact that it provides a visual output as a simulation runs. Can you describe the workflow for someone who wants to use Salabim for modeling and visualizing a system?

At what point does a system become too complex to encapsulate in a simulation and what techniques can you use to modularize it to make a simulation useful?

When is Salabim not the right tool to use and what would you suggest for people who find themselves in that situation?

What have been some of the most complicated or difficult aspects of building and maintaining Salabim?

What are some of the new features or improvements that you have planned for the future of Salabim?

Keep In Touch
Email
Picks
Tobias
Cuisinart Burr Mill Coffee Grinder

Ruud
Pythonista (Python for iOS)
Python Notes for Professionals
Fluent Python by Luciano Ramalho

Links
Salabim
GitHub

Dining Philosophers Animation

Elevator Animation

Rotterdam

Discrete Event Simulation

Container Terminal Automation

Basic

Algol

Pascal

Operations Research

Continuous Simulation

Simula

Coroutines

SymPy

Another DES in Python: SimPy

DES in Julia: SimJulia

DES in R: Simmer

DES in Delphi/Pascal: Tomas

Pillow

PyPy

Delphi

PyGame

PyQT

TkInter

Inspect Module

OpenCV

Blender

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
52 min
February 26, 2018 Laboratory: Safer Refactoring with Joe Alcorn
Every piece of software that has been around long enough ends up with some piece of it that needs to be redesigned and refactored. Often the code that needs to be updated is part of the critical path through the system, increasing the risks associated with any change. One way around this problem is to compare the results of the new code against the existing logic to ensure that you aren't introducing regressions. This week Joe Alcorn shares his work on Laboratory, how the engineers at GitHub inspired him to create it as an analog to the Scientist gem, and how he is using it for his day job.
22 min
February 26, 2018Laboratory: Safer Refactoring with Joe Alcorn
Summary
Every piece of software that has been around long enough ends up with some piece of it that needs to be redesigned and refactored. Often the code that needs to be updated is part of the critical path through the system, increasing the risks associated with any change. One way around this problem is to compare the results of the new code against the existing logic to ensure that you aren’t introducing regressions. This week Joe Alcorn shares his work on Laboratory, how the engineers at GitHub inspired him to create it as an analog to the Scientist gem, and how he is using it for his day job.
Preface
Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
I would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable.
When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at podastinit.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app. And now you can deliver your work to your users even faster with the newly upgraded 200 GBit network in all of their datacenters.
If you’re tired of cobbling together your deployment pipeline then it’s time to try out GoCD, the open source continuous delivery platform built by the people at ThoughtWorks who wrote the book about it. With GoCD you get complete visibility into the life-cycle of your software from one location. To download it now go to podcatinit.com/gocd. Professional support and enterprise plugins are available for added piece of mind.
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.
A brief announcement before we start the show:
If you work with data or want to learn more about how the projects you have heard about on the show get used in the real world then join me at the Open Data Science Conference in Boston from May 1st through the 4th. It has become one of the largest events for data scientists, data engineers, and data driven businesses to get together and learn how to be more effective. To save 60% off your tickets go to podcastinit.com/odsc-east-2018 and register.

Your host as usual is Tobias Macey and today I’m interviewing Joe Alcorn about using Laboratory as a safety net for your refactoring.

Interview
Introductions
How did you get introduced to Python?
Can you start be explaining what Laboratory is and what motivated you to start the project?
How much of the design and implementation were directly inspired by the Scientist project from GitHub and how much of it did you have to figure out from scratch due to differences in the target languages?
What have been some of the most challenging aspects of building and maintaining Laboratory, and have you had any opportunities to use it on itself?
For someone who would like to use Laboratory in their project, what does the workflow look like and what potential pitfalls should they watch out for?
In the documentation you mention that portions of code that perform I/O and create side effects should be avoided. Have you found any strategies to allow for stubbing out the external interactions while still executing the rest of the logic?
How do you keep track of the results for active experiments and what sort of reporting is available?
What are some examples of the types of routines that would be good candidates for conducting an experiment?
What are some of the most complicated or difficult pieces of code that you have refactored with the help of Laboratory?
Given the fact that Laboratory is intended to be run in production and provide a certain measure of safety, what methods do you use to ensure that users of the library will not suffer from a drastic increase in overhead or unintended aberrations in the functionality of their software?
Are there any new features or improvements that you have planned for future releases of Laboratory?
Keep In Touch
joealcorn on GitHub
Website
Picks
Tobias
Chronicles of Narnia

Joe
Why We Sleep: Unlocking The Power of Sleep and Dreams by Matthew Walker, PhD

Links
Marvel App
GitHub: Move Fast and Fix Things
GitHub Scientist: Measure Twice, Cut Over Once
Scientist
Laboratory
Sure Footed Refactoring
Graphite
StatsD
The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
22 min
February 18, 2018 Software Architecture For Developers with Neal Ford
Whether it is intentional or accidental, every piece of software has an existing architecture. In this episode Neal Ford discusses the role of a software architect, methods for improving the design of your projects, pitfalls to avoid, and provides some resources for continuing to learn about how to design and build successful systems.
51 min