Have you wondered, how should I package my Python code? You’ve written the application, but now you need to distribute it to the machines it’s intended to run on. It depends on what the code is, the libraries it depends on, and with whom do you want to share it. This week on the show we have Itamar Turner-Trauring, creator of the website pythonspeed.com. We discuss his article “Options for Packaging Your Python Code: Wheels, Conda, Docker, and More,” covering the how of sharing your code.

Itamar also briefly discusses his Python memory profiler named Fil. We talk about his recent PyCon 2020 presentation, “Small Big Data: What to do When Your Data Doesn’t Fit in Memory.” We also cover several of the resources available on his website for data scientists that want to get deeper into Docker.

Course Spotlight: Python Coding Interviews: Tips & Best Practices

In this step-by-step course, you’ll learn how to take your Python coding interview skills to the next level and use Python’s built-in functions and modules to solve problems faster and more easily.

Topics:

00:00:00 – Introduction 00:01:36 – About the naming of pythonspeed.com 00:03:47 – Fil - Python Memory Profiler 00:06:44 – Small Big Data: What to do when your data doesn’t fit in memory - PyCon 2020 00:12:17 – Options for packaging your Python code: Wheels, Conda, Docker, and more 00:15:13 – Python Wheels 00:19:22 – pipx: Install and Run Python Applications in Isolated Environments 00:20:52 – PEX, and friends 00:24:51 – System Package, RPM or DEB 00:29:42 – Conda Packaging and conda-forge 00:36:09 – Video Course Spotlight 00:37:23 – Self-contained executable: PyInstaller, PyOxidizer, Briefcase 00:43:45 – Container image (Docker, Singularity) 00:54:55 – Why alpine may not be the best choice 01:05:28 – Singularity 01:07:50 – What are you excited about in the world of Python? 01:10:40 – What do you want to learn next? 01:13:54 – Thanks and Goodbye

Show Links:

Python => Speed: Ship Better Python Software, Faster Code Without Rules: Helping You Become a Productive Programmer and Get Work/Life Balance Talk Python to Me – Episode #274: Profiling Data Science Code with FIL Fil: A New Python Memory Profiler for Data Scientists and Scientists Small Big Data: What to do When Your Data Doesn’t Fit in Memory - PyCon 2020 Episode 16: Thinking in Pandas: Python Data Analysis the Right Way Options for Packaging Your Python Code: Wheels, Conda, Docker, and More What Are Python Wheels and Why Should You Care?: Real Python article pipx — Install and Run Python Applications in Isolated Environments pex: A Library and Tool for Generating .pex (Python EXecutable) Files WTF is PEX?: Twitter Lightning Talk - YouTube RPM (Red Hat Package Manager): Wikipedia article DEB (Debian Package - file format): Wikipedia article Conda: Package, Dependency and Environment Management for Any Language conda-forge: A community-led collection of recipes, build infrastructure and distributions for the conda package manager PyInstaller: Freezes (packages) Python applications into stand-alone executables PyOxidizer: A utility for producing binaries that embed Python Briefcase: Convert a Python project into a standalone native application Docker: Get Started with Docker Just Enough Docker Packaging: Book Episode 8: Docker + Python for Data Science and Machine Learning Using Alpine can make Python Docker builds 50× slower Introduction to Singularity Docker vs. Singularity for data processing: UIDs and filesystem access Best practices for production-ready Docker packaging: EuroPython 2020 talk EuroPython 2020 Talk: Brian Track Stream - Unedited - Starts @08:33:45 Statistical Rethinking: A Bayesian Course with Examples Bayes Theorem: 3Blue1Brown - YouTube

Level up your Python skills with our expert-led courses:

Python Coding Interviews: Tips & Best Practices Documenting Code in Python How to Publish Your Own Python Package to PyPI

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Options for Packaging Your Python Application: Wheels, Docker, and More

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