The Python Podcast.init

By Tobias MaceyTechnology Education

The Python Podcast.init episodes

April 20, 2020Build The Next Generation Of Python Web Applications With FastAPI
Summary
Python has an embarrasment of riches when it comes to web frameworks, each with their own particular strengths. FastAPI is a new entrant that has been quickly gaining popularity as a performant and easy to use toolchain for building RESTful web services. In this episode Sebastián Ramirez shares the story of the frustrations that led him to create a new framework, how he put in the extra effort to make the developer experience as smooth and painless as possible, and how he embraces extensability with lightweight dependency injection and a straightforward plugin interface. If you are starting a new web application today then FastAPI should be at the top of your list.
Announcements
Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, node balancers, a 40 Gbit/s public network, fast object storage, and a brand new managed Kubernetes platform, all controlled by a convenient API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they’ve got dedicated CPU and GPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!
Your host as usual is Tobias Macey and today I’m interviewing Sebastián Ramirez about FastAPI, a framework for building production ready APIs in Python 3
Interview
Introductions
How did you get introduced to Python?
Can you start by describing what FastAPI is?
What are the main frustrations that you ran into with other frameworks that motivated you to create an entirely new one?
What are some of the main use cases that FastAPI is designed for?
Many web frameworks focus on managing the end-to-end functionality of a website, including the UI. Why did you focus on just API capabilities?
What are the benefits of building an API only framework?
If you wanted to integrate a presentation layer, what would be involved in that effort?
What API formats does FastAPI support?
What would be involved in adding support for additional specifications such as GraphQL or JSON-LD?
There are a huge number of web frameworks available just in the Python ecosystem. How does FastAPI fit into that landscape and why might someone choose it over the other options?
Can you share your design philosophy for the project?
What are your main sources of inspiration for the framework?
You have also built the Typer CLI library which you refer to as the little sibling of FastAPI. How have your experiences building these two projects influenced their counterpart’s evolution?
What are the benefits of incorporating type annotations into a web framework and in what ways do they manifest in its functionality?
What is the workflow for a developer building a complex application in FastAPI?
Can you describe how FastAPI itself is architected and how its design has evolved since you first began working on it?
What are the extension points that are available for someone to build plugins for FastAPI?
What are some of the challenges that you have faced in building an async framework that is leveraging the new ASGI specification?
What are some sharp edges that users should keep an eye out for?
What are some unique or underutilized features of FastAPI that users might not be aware of?
What are some of the most interesting, unexpected, or innovative ways that you have seen FastAPI used?
When is FastAPI the wrong choice?
What are some of the most interesting, unexpected, or challenging lessons that you have learned in the process of building and maintaining FastAPI?
What do you have planned for the future of the project?
Keep In Touch
@tiangolo on Twitter.
@tiangolo on GitHub.
Picks
Tobias
Once Upon A Time TV Show
Sebastián
Cloud Atlas Movie
Isaac Asimov’s robot short stories
Python devtools debug function
async compatible requests with HTTPX
RescueTime for automatic time tracking
Joplin for Notes
Closing Announcements
Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.
Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
If you’ve learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story.
To help other people find the show please leave a review on iTunes and tell your friends and co-workers
Join the community in the new Zulip chat workspace at pythonpodcast.com/chat
Links
FastAPI
Typer
Typer CLI
FastAPI Alternatives, Inspiration and Comparisons
Explosion’s spaCy
Explosion’s Prodigy
Starlette
Pydantic
Uvicorn
Hypercorn
fastapi-utils
Class Based Views
GrahQL Ariadne
Coronavirus Tracker API
Terminals from browser: termpair
XPublish
Uber’s Ludwig
Netflix Dispatch
Colombia
Berlin Germany
Explosion AI
Python Type Annotations
Django Rest Framework
Flask
Swagger/OpenAPI
Sanic
NodeJS
JSON Schema
OAuth2
Swagger UI
ReDoc
React
VueJS
Angular
REST == REpresentational State Transfer
JSON-LD
Go Language
Hug API framework
Click CLI Framework
Flask Blueprints
Tom Christie
Podcast Interview
Dependency Injection
ASGI
Podcast Episode
WSGI
Thread Local Variables
Context Vars
OAUTH2 Scopes
PipX
XArray
JAM Stack
NextJS
Hugo
GatsbyJS
FastAPI Project Templates
The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
59 min
April 14, 2020 Distributed Computing In Python Made Easy With Ray
Distributed computing is a powerful tool for increasing the speed and performance of your applications, but it is also a complex and difficult undertaking. While performing research for his PhD, Robert Nishihara ran up against this reality. Rather than cobbling together another single purpose system, he built what ultimately became Ray to make scaling Python projects to multiple cores and across machines easy. In this episode he explains how Ray allows you to scale your code easily, how to use it in your own projects, and his ambitions to power the next wave of distributed systems at Anyscale. If you are running into scaling limitations in your Python projects for machine learning, scientific computing, or anything else, then give this a listen and then try it out!
41 min
April 14, 2020Distributed Computing In Python Made Easy With Ray
Summary
Distributed computing is a powerful tool for increasing the speed and performance of your applications, but it is also a complex and difficult undertaking. While performing research for his PhD, Robert Nishihara ran up against this reality. Rather than cobbling together another single purpose system, he built what ultimately became Ray to make scaling Python projects to multiple cores and across machines easy. In this episode he explains how Ray allows you to scale your code easily, how to use it in your own projects, and his ambitions to power the next wave of distributed systems at Anyscale. If you are running into scaling limitations in your Python projects for machine learning, scientific computing, or anything else, then give this a listen and then try it out!
Announcements
Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, node balancers, a 40 Gbit/s public network, fast object storage, and a brand new managed Kubernetes platform, all controlled by a convenient API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they’ve got dedicated CPU and GPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!
Your host as usual is Tobias Macey and today I’m interviewing Robert Nishihara about Ray, a framework for building and running distributed applications and machine learning
Interview
Introductions
How did you get introduced to Python?
Can you start by describing what Ray is and how the project got started?
How did the environment of the RISE lab factor into the early design and development of Ray?
What are some of the main use cases that you were initially targeting with Ray?
Now that it has been publicly available for some time, what are some of the ways that it is being used which you didn’t originally anticipate?
What are the limitations for the types of workloads that can be run with Ray, or any edge cases that developers should be aware of?
For someone who is building on top of ray, what is involved in either converting an existing application to take advantage of Ray’s parallelism, or creating a greenfield project with it?
Can you describe how Ray itself is implemented and how it has evolved since you first began working on it?
How does the clustering and task distriubtion mechanism in Ray work?
How does the increased parallelism that Ray offers help with machine learning workloads?
Are there any types of ML/AI that are easier to do in this context?
What are some of the additional layers or libraries that have been built on top of the functionality of Ray?
What are some of the most interesting, challenging, or complex aspects of building and maintaining Ray?
You and your co-founders recently announced the formation of Anyscale to support the future development of Ray. What is your business model and how are you approaching the governance of Ray and its ecosystem?
What are some of the most interesting or unexpected projects that you have seen built with Ray?
What are some cases where Ray is the wrong choice?
What do you have planned for the future of Ray and Anyscale?
Keep In Touch
Website
@robertnishihara on Twitter
robertnishihara on GitHub
Picks
Tobias
D&D Castle Ravenloft board game
One Deck Dungeon
Robert
The Everything Store
Closing Announcements
Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.
Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
If you’ve learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story.
To help other people find the show please leave a review on iTunes and tell your friends and co-workers
Join the community in the new Zulip chat workspace at pythonpodcast.com/chat
Links
Ray
Anyscale
UC Berkeley
RISELab
MATLAB
Deep Learning
Theano
Tensorflow
PyTorch
Podcast Episode
Philip Moritz
Reinforcement Learning
Hyperparameter Tuning
IPython Parallel
AMPLab
Apache Spark
Data Engineering Podcast Episode
Actor Model
Horovod(?)
Flink
Data Engineering Podcast Episode
Spark Streaming
Dask
Data Engineering Podcast Episode
gRPC
Tune
Rust
C++
C
Apache Arrow
Wes McKinney
Podcast Interview
DataBricks
MongoDB
Elastic
Data Engineering Podcast Episode
Confluent
Embarassingly Parallel
Ant Financial
Flame Graph
The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
41 min
April 07, 2020 Building The Seq Language For Bioinformatics
Bioinformatics is a complex and computationally demanding domain. The intuitive syntax of Python and extensive set of libraries make it a great language for bioinformatics projects, but it is hampered by the need for computational efficiency. Ariya Shajii created the Seq language to bridge the divide between the performance of languages like C and C++ and the ecosystem of Python with built-in support for commonly used genomics algorithms. In this episode he describes his motivation for creating a new language, how it is implemented, and how it is being used in the life sciences. If you are interested in experimenting with sequencing data then give this a listen and then give Seq a try!
37 min
April 07, 2020Building The Seq Language For Bioinformatics
Summary
Bioinformatics is a complex and computationally demanding domain. The intuitive syntax of Python and extensive set of libraries make it a great language for bioinformatics projects, but it is hampered by the need for computational efficiency. Ariya Shajii created the Seq language to bridge the divide between the performance of languages like C and C++ and the ecosystem of Python with built-in support for commonly used genomics algorithms. In this episode he describes his motivation for creating a new language, how it is implemented, and how it is being used in the life sciences. If you are interested in experimenting with sequencing data then give this a listen and then give Seq a try!
Announcements
Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, node balancers, a 40 Gbit/s public network, fast object storage, and a brand new managed Kubernetes platform, all controlled by a convenient API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they’ve got dedicated CPU and GPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!
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 great conferences. And now, the events are coming to you, with no travel necessary! We have partnered with organizations such as ODSC, and Data Council. Upcoming events include the Observe 20/20 virtual conference on April 6th and ODSC East which has also gone virtual starting April 16th. Go to pythonpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today.
Your host as usual is Tobias Macey and today I’m interviewing Ariya Shajii about Seq, a programming language built for bioinformatics and inspired by Python
Interview
Introductions
How did you get introduced to Python?
Can you start by describing what Seq is and your motivation for creating it?
What was lacking in other languages or libraries for your use case that is made easier by creating a custom language?
If someone is already working in Python, possibly using BioPython, what might motivate them to consider migrating their work to Seq?
Can you give an impression of the scope and nature of the tasks or projects that a biologist or geneticist might build with Seq?
What was your process for identifying and prioritizing features and algorithms that would be beneficial to the target audience?
For someone using Seq can you describe their workflow and how it might differ from performing the same task in Python?
How is Seq implemented?
What are some of the features that are included to simplify the work of bioinformatics?
What was your process of designing the language and runtime?
How has the scope or direction of the project evolved since it was first conceived?
What impact do you anticipate Seq having on the domain of bioinformatics and genomics?
What have you found to be the most interesting, unexpected, and/or challenging aspects of building a language for this problem domain?
What is in store for the future of Seq?
Keep In Touch
arshajii on GitHub
Website
Picks
Tobias
Board Games
Labyrinth Boardgame
Board Game Geek
Ariya
Breakthrough 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
Join the community in the new Zulip chat workspace at pythonpodcast.com/chat
Links
Seq
MIT CSAIL
Bioinformatics
LLVM
Intermediate Representation
MatLab
Moore’s Law
BioPython
Smith Waterman Algorithm
Hamming Distance
Pattern Matching in Functional Programming
SIMD == Single Instruction Multiple Data
Computational Genomics
Phylogenetics
Sequence Read Archive public data set
Google Cloud Life Sciences
The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
37 min
March 30, 2020 An Open Source Toolchain For Natural Language Processing From Explosion AI
The state of the art in natural language processing is a constantly moving target. With the rise of deep learning, previously cutting edge techniques have given way to robust language models. Through it all the team at Explosion AI have built a strong presence with the trifecta of SpaCy, Thinc, and Prodigy to support fast and flexible data labeling to feed deep learning models and performant and scalable text processing. In this episode founder and open source author Matthew Honnibal shares his experience growing a business around cutting edge open source libraries for the machine learning developent process.
52 min
March 30, 2020An Open Source Toolchain For Natural Language Processing From Explosion AI
Summary
The state of the art in natural language processing is a constantly moving target. With the rise of deep learning, previously cutting edge techniques have given way to robust language models. Through it all the team at Explosion AI have built a strong presence with the trifecta of SpaCy, Thinc, and Prodigy to support fast and flexible data labeling to feed deep learning models and performant and scalable text processing. In this episode founder and open source author Matthew Honnibal shares his experience growing a business around cutting edge open source libraries for the machine learning developent process.
Announcements
Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, node balancers, a 40 Gbit/s public network, fast object storage, and a brand new managed Kubernetes platform, all controlled by a convenient API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they’ve got dedicated CPU and GPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!
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 great conferences. And now, the events are coming to you, with no travel necessary! We have partnered with organizations such as ODSC, and Data Council. Upcoming events include the Observe 20/20 virtual conference on April 6th and ODSC East which has also gone virtual starting April 16th. Go to pythonpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today.
Your host as usual is Tobias Macey and today I’m interviewing Matthew Honnibal about the Thinc and Prodigy tools and an update on SpaCy
Interview
Introductions
How did you get introduced to Python?
Can you start by giving an overview of your mission at Explosion?
We spoke previously about your work on SpaCy. What has changed in the past 3 1/2 years?
How have recent innovations in language models such as BERT and GPT-2 influenced the direction or implementation of the project?
When I last looked SpaCy only supported English and German, but you have added several new languages. What are the most challenging aspects of building the additional models?
What would be required for supporting symbolic or right-to-left languages?
How has the ecosystem for language processing in Python shifted or evolved since you first introduced SpaCy?
Another project that you have released is Prodigy to support labelling of datasets. Can you talk through the motivation for creating it and describe the workflow for someone using it?
What was lacking in the other annotation tools that you have worked with that you are trying to solve for in Prodigy?
What are some of the most challenging or problematic aspects of labelling data sets for use in machine learning projects?
What is a typical scale of data that can be reasonably handled by an individual or small team working with Prodigy?
At what point do you find that it makes sense to use a labeling service rather than generating the labels yourself?
Your most recent project is Thinc for building and using deep learning models. What was the motivation for creating it and what problem does it solve in the ecosystem?
How does its design and usage compare to other deep learning frameworks such as PyTorch and Tensorflow?
How does it compare to projects such as Keras that abstract across those frameworks?
How do the SpaCy, Prodigy, and Thinc libraries work together?
What are some of the biggest challenges that you are facing in building open source tools to meet the needs of data scientists and machine learning engineers?
What are some of the most interesting or impressive projects that you have seen built with the tools your team is creating?
What do you have planned for the future of Explosion, SpaCy, Prodigy, and Thinc?
Keep In Touch
LinkedIn
@honnibal on Twitter
honnibal on GitHub
Picks
Tobias
Onward movie
Matthew
Coronavirus Preparedness
Ray
Closing Announcements
Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.
Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
If you’ve learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story.
To help other people find the show please leave a review on iTunes and tell your friends and co-workers
Join the community in the new Zulip chat workspace at pythonpodcast.com/chat
Links
Explosion AI
SpaCy
Podcast Episode
Thinc
Prodigy
Natural Language Processing
Perl
NLTK
GPU == Graphics Processing Unit
TPU == Tensor Processing Unit
Transfer Learning
Airflow
Luigi
Perceptron
PyTorch
Tensorflow
Functional Programming
MxNet
Keras
Cuda
C Language
Continuous Integration
Blackstone
Allen AI Institute
SciSpaCy
Holmes
Sense2Vec
FastAPI
The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
52 min
March 23, 2020 A Flexible Open Source ERP Framework To Run Your Business
Running a successful business requires some method of organizing the information about all of the processes and activity that take place. Tryton is an open source, modular ERP framework that is built for the flexibility needed to fit your organization, rather than requiring you to model your workflows to match the software. In this episode core developers Nicolas Évrard and Cédric Krier are joined by avid user Jonathan Levy to discuss the history of the project, how it is being used, and the myriad ways that you can adapt it to suit your needs. If you are struggling to keep a consistent view of your business and ensure that all of the necessary workflows are being observed then listen now and give Tryton a try.
1 hr 8 min
March 23, 2020A Flexible Open Source ERP Framework To Run Your Business
Summary
Running a successful business requires some method of organizing the information about all of the processes and activity that take place. Tryton is an open source, modular ERP framework that is built for the flexibility needed to fit your organization, rather than requiring you to model your workflows to match the software. In this episode core developers Nicolas Évrard and Cédric Krier are joined by avid user Jonathan Levy to discuss the history of the project, how it is being used, and the myriad ways that you can adapt it to suit your needs. If you are struggling to keep a consistent view of your business and ensure that all of the necessary workflows are being observed then listen now and give Tryton a try.
Announcements
Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, node balancers, a 40 Gbit/s public network, fast object storage, and a brand new managed Kubernetes platform, all controlled by a convenient API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they’ve got dedicated CPU and GPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!
You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Corinium Global Intelligence, ODSC, and Data Council. Go to pythonpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today.
Your host as usual is Tobias Macey and today I’m interviewing Nicolas Évrard, Cédric Krier, and Jonathan Levy about Tryton
Interview
Introductions
How did you get introduced to Python?
Can you start by describing what Tryton is and how it got started?
What kinds of businesses is Tryton most suited to?
What kinds of businesses is Tryton not a good fit for?
Within a business, who are the primary users of Tryton?
Can you talk through a typical workflow for interacting with Tryton?
What are some of the most complex or challenging aspects of modeling a business while maintaining a high degree of customizability?
Can you describe how Tryton is architected and how its design has evolved since it was first started?
If you were to start over today, what would you do differently?
There are a number of plugins for Tryton. What kinds of functionality can be customized using the available interfaces?
What is the process for building a custom module for Tryton?
How do you manage sustainability of the Tryton project?
Given the criticality of the Tryton platform, how do you approach ongoing stability and security of the project?
What is involved in deploying and maintaining an installation of Tryton?
What are some of the most interesting, innovative, or unexpected ways that you have seen Tryton used?
What is in store for the future of Tryton?
Keep In Touch
Nicolas
nicoe on GitHub
@nicoe on Twitter
Cédric
@cedrickrier on Twitter
cedk on GitHub
Jonathan
LinkedIn
Picks
Tobias
Audio Books
Audible free trial (Affiliate Link)
Overdrive – ebooks and audiobooks from your local library
Public Domain Audiobooks
Nicolas
Civilization VI
FreeCiv
The 3 Body Problem
Cédric
Valérian and Laureline
Jonathan
Roil.com
Links
Tryton
B2CK
Tryton Foundation
Advocate Consulting Legal Group
Scheme
Lisp
Belgium
EuroPython Conference
Plone
Zope
VBA (Visual Basic for Applications)
Django
Odoo
ERP == Enterprise Resource Planning
Small/Medium Enterprise (SME)
GTK (Gnome ToolKit)
3-Tier Application
Cookiecutter
Tryton Module Cookiecutter
Tryton Repository
Docker
GNU Health
Nereid
The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
1 hr 8 min
March 16, 2020 Getting A Handle On Portable C Extensions With hpy
One of the driving factors of Python's success is the ability for developers to integrate with performant languages such as C and C++. The challenge is that the interface for those extensions is specific to the main implementation of the language. This contributes to difficulties in building alternative runtimes that can support important packages such as NumPy. To address this situation a team of developers are working to create the hpy project, a new interface for extension developers that is standardized and provides a uniform target for multiple runtimes. In this episode Antonio Cuni discusses the motivations for creating hpy, how it benefits the whole ecosystem, and ways to contribute to the effort. This is an exciting development that has the potential to unlock a new wave of innovation in the ways that you can run your Python code.
36 min