"Four months ago, Adnan Khan and I exploited a critical CI/CD vulnerability in PyTorch, one of the world’s leading ML platforms. Used by titans like Google, Meta, Boeing, and Lockheed Martin, PyTorch is a major target for hackers and nation-states alike.
Thankfully, we exploited this vulnerability before the bad guys.
Here is how we did it."
This tutorial walks you through how to package a simple Python project. It will show you how to add the necessary files and structure to create the package, how to build the package, and how to upload it to the Python Package Index (PyPI).
I don't agree with much of this, but I recognise it's a valid position. To quote from the TL;DR at the end:
"Capping dependencies has long term negative effects, especially for libraries, and should never be taken lightly. A library is not installed in isolation; it has to live with other libraries in a shared environment. Only add a cap if a dependency is known to be incompatible or there is a high (>75%) chance of it being incompatible in its next release. Do not cap by default - capping dependencies makes your software incompatible with other libraries that also have strict lower limits on dependencies, and limits future fixes. Anyone can fix a missing cap, but users cannot fix an over restrictive cap causing solver errors. It also encourages hiding issues until they become harder to fix, it does not scale to larger systems, it limits your ability to access security and bugfix updates, and some tools (Poetry) force these bad decisions on your downstream users if you make them. Never cap Python, it is fundamentally broken at the moment. Also, even packing capping has negative consequences that can produce unexpected solves."