[alex_sf]

Python is a rough language to be productive in. It's a great scratchpad, but dynamic typing, exceptions/poor error handling, and a horrifying deployment and dependency system make me reach for something like Go in any case where I need something to be even vaguely reliable.

The more ML I do, the more disappointed I get.

[ajitid]

Ever tried gluing Go with either Python or JavaScript? I'm interested in learning what libraries are there to glue them and how complicated and slow they could be.

[noam_k]

I've used gopy[0] recently to access a go library in Python. It surprisingly Just Worked, but I was disappointed by some performance issues, like converting lists to slices.

[0] https://github.com/go-python/gopy

[jerf]

The gulf between the world Python was written to address and the world we live in continues to grow. Python's native memory layout is actively hostile to any sort of performance on modern systems, so it's going to continue to have the problem that interacting with a faster runtime will require a ton of copying.

I've never been impressed by the claim that you can just drop in other faster languages into Python; the costs of communication are so staggeringly high that you have to write around it to have any gain, and by the time you've moved effectively your entire task into the underlying faster language, Python's gain is often quite minimal. NumPy works to some extent because it can fire off huge tasks from a single Python call, a lot of less numerical code can't do that, and I think the ML community still generally underestimates and fails to understand how much performance they leave on the floor in the Python portions of their code, at least based on my interaction with them.

[xapata]

To some degree, it's about familiarity with your tools. And different tools are optimized for different tasks.

Besides, aren't you deploying Docker containers, anyway?

[alex_sf]

I do, absolutely. But it seems like an exceptionally rare practice for most Python code, at least in the ML space, which puts me back at the start.

[xapata]

Many ML practitioners aren't software engineers. I don't expect that cohort (non-engineers) would manage a deployment well in any language.

[INTPenis]

That's just habitual. Anyone coming from Python could say the same things about another language. The typing never bothered me because you learn to work with it. But there is of course type hinting now, which I barely use.

[alex_sf]

I don't think so. I was a Python dev for years, and plenty of other languages don't have the issues Python does. It was a great learning language when I started, but I think most folks that have only used Python just don't realize what they are missing.

[INTPenis]

Can you give me an example from your recent memory?

I've been coding since the 90s, ASP, PHP, JS, C, Perl, I transitioned from Perl to Python back in 2012-2013. Dabbled in Go when it first came out because I was a Plan9 fanatic and recognized some of the source files, but never went further than tutorials.

Honestly I find very little wrong with the Python ecosystem, except the general insecurity of using package managers. But that applies to most package management, it's a social/infosec issue that Fedora has mitigated fairly well, if you want role models.

The languages that I found most annoying, as a user and developer, were C, Javascript, Typescript and Ruby.

[IshKebab]

I agree. Add in the fact that you will have to do awkward FFI to some faster language... It's better just to use that language in the first place.