• #391 A weak episode

  • Jul 9 2024
  • Length: 26 mins
  • Podcast

#391 A weak episode By cover art

#391 A weak episode

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  • Topics covered in this episode: Vendorize packages from PyPIA Guide to Python's Weak References Using weakref ModuleMaking Time SpeakHow Should You Test Your Machine Learning Project? A Beginner’s GuideExtrasJokeWatch on YouTube About the show Sponsored by Code Comments, an original podcast from RedHat: pythonbytes.fm/code-comments Connect with the hosts Michael: @mkennedy@fosstodon.orgBrian: @brianokken@fosstodon.orgShow: @pythonbytes@fosstodon.org Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Tuesdays at 10am PT. Older video versions available there too. Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Michael #1: Vendorize packages from PyPI Allows pure-Python dependencies to be vendorized: that is, the Python source of the dependency is copied into your own package.Best used for small, pure-Python dependencies Brian #2: A Guide to Python's Weak References Using weakref Module Martin HeinzVery cool discussion of weakrefQuick garbage collection intro, and how references and weak references are used.Using weak references to build data structures. Example of two kinds of treesImplementing the Observer patternHow logging and OrderedDict use weak references Michael #3: Making Time Speak by Prayson, a former guest and friend of the showTranslating time into human-friendly spoken expressionsExample: clock("11:15") # 'quarter past eleven' Features Convert time into spoken expressions in various languages.Easy-to-use API with a simple and intuitive design.Pure Python implementation with no external dependencies.Extensible architecture for adding support for additional languages using the plugin design pattern. Brian #4: How Should You Test Your Machine Learning Project? A Beginner’s Guide François PorcherUsing pytest and pytest-cov for testing machine learning projectsLots of pieces can and should be tested just as normal functions. Example of testing a clean_text(text: str) -> str functionTest larger chunks with canned input and expected output. Example test_tokenize_text()Using fixtures for larger reusable components in testing Example fixture: bert_tokenizer() with pretrained dataChecking coverage Extras Michael: Twilio Authy Hack Google Authenticator is the only option? Really?Bitwarden to the rescueRequires (?) an update to their app, whose release notes (v26.1.0) only say “Bug fixes”Introducing Docs in Proton Drive This is what I called on Mozilla to do in “Unsolicited Advice for Mozilla and Firefox” But Proton got there firstEarly bird ending for Code in a Castle course Joke: I Lied
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