pex VS cloudpickle

We get (very) close to cross-environment reproducible builds for Python with https://github.com/pantsbuild/pex (via Pants). For instance, we build Linux x86-64 artifacts that run on AWS Lambda, and can build them natively on ARM macOS.

This is not raw requirements.txt, but isn’t too far off: Pants/PEX can consume one to produce a hash-pinned lock file.

You should look into pex, or it’s parent build system pants. A PEX (Python EXecutable) file can package up all your code including dependencies and run on another machine of similar OS with just an available compatible interpreter.

For well-supported libraries, pip-audit might do the trick. Where I've worked, we have used a central build system with library version enforcement. The build system produces a deployable archive, like PEX or similar. Rock-solid tests and sandbox validation environments provide good paths for version upgrades. Restricting libraries to a small set, making sure those repos remain actively developed, performing audits and centralizing builds has helped organizations I've worked in keep on top of potential security issues.

Don't know if I agree about the goto thing, but there are actually a number of options now for delivering varying degrees of self-contained Python executable.

When I evaluated the landscape a few years ago, I settled on PEX [1] as the solution that happened to fit my use-case the best— it uses a system-provided Python + stdlib, but otherwise brings everything (including compiled modules) with it in a self-extracting executable. Other popular options include pyinstaller and cx_freeze, which have different tradeoffs as far as size, speed, convenience, etc.

[1]: https://github.com/pantsbuild/pex

Somewhat related, I had a devil of a time a little bit ago trying to ship a small Python app as a fully standalone environment runnable on "any Linux" (but for practical purposes, Ubuntu 16.04, 18.04, and 20.04). It turns out that if you don't want to use pip, and you don't want to build separate bundles for different OSes and Python versions, it can be surprisingly tricky to get this right. Just bundling the whole interpreter doesn't work either because it's tied to a particular stdlib which is then linked to specific versions of a bunch of system dependencies, so if you go that route, you basically end up taking an entire rootfs/container with you.

After evaluating a number of different solutions, I ended up being quite happy with pex: https://github.com/pantsbuild/pex

It basically bundles up the wheels for whatever your workspace needs, and then ships them in an archive with a bootstrap script that can recreate that environment on your target. But critically, it natively supports the idea of targeting multiple OS and Python versions, you just explicitly tell it which ones to include, eg:

    --platform=manylinux2014_x86_64-cp-38-cp38   # 16.04

I believe you just pass objects instead, like you would in OOP, and take the hit of pickling and unpickling them every time.

If you really want to pass lambdas, you can use a third party library to pickle them

https://github.com/cloudpipe/cloudpickle

Yes, this is not great.

That was my understanding as well but then I found this package -- cloudpickle which seems to serialize both data and functionality?

It used in production for several months now. We use it to train data science models. The main goal was to make multiprocessing code easier. It actually works great with multiprocessing, especially if you use a library able to serialize lambda functions such as https://github.com/cloudpipe/cloudpickle . I have yet to write a tutorial on how to use multiprocessing and cloudpickle to distribute work to all the worker processes. Thanks for letting me know about pfun. I've never heard of it. Having a look at its documentation, our goals seem to be very close. The features I wanted above all where: