Why we’re writing machine learning infrastructure in Go, not Python
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Why we’re writing machine learning infrastructure in Go, not Python
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Re: Why we’re writing machine learning infrastructure in Go, not Python
#2All the stuff that requires speed is written in a language that compiles directly to machine code while the machine learning libraries are all python based. That seems standard, no?
Re: Why we’re writing machine learning infrastructure in Go, not Python
#3Why not Swift? (I think I know the answer).
Concurrency in Swift is not yet a solved problem, but libdispatch is quite workable (although not "elegant, out of the box" per the article).
With the work being done in Swift for TensorFlow [0], I'd imagine in a year or two both the infrastructure and the ML portions of a product like Cortex could be written in a single language.
Re: Why we’re writing machine learning infrastructure in Go, not Python
#4All the stuff that requires speed is written in a language that compiles directly to machine code while the machine learning libraries are all python based. That seems standard, no?
I think that happens, but I don't know about standard. It is pretty obvious and natural. However, a lot of code is written in Python and it can be hard to move ML teams to use other tools. Many of them aren't great at programming because their background is stats/math so it can be hard to move critical code to more performant solutions without resistance from the teams.
Re: Why we’re writing machine learning infrastructure in Go, not Python
#5Why not Swift? (I think I know the answer). Concurrency in Swift is not yet a solved problem, but libdispatch is quite workable (although not "elegant, out of the box" per the article). With the work being done in Swift for TensorFlow [0], I'd imagine in a year or two both the infrastructure and the ML portions of a product like Cortex could be written in a single language. [0] - https://www.tensorflow.org/swift
Swift is an interesting choice, one we haven't explored in depth. Out of curiosity, have you done any work with Swift for Tensorflow/what has your experience been?
Re: Why we’re writing machine learning infrastructure in Go, not Python
#6> Making all of these overlapping API calls in a performative, reliable way is a challenge.
Pythons asyncio is pretty hard to beat. For non-cpu intensive tasks, I find it a pleasure to work with. Goroutines can still have race conditions.
> Originally, we wrote the CLI in Python, but trying to distribute it across platforms proved to be too difficult
Sure, I get that go can cross-compile. But what makes python hard? Python works on every platform, and distributing is just a "pip install" and "pip install -u" Surely thats easier than "Download the correct binary for the platform, unzip it, change permissions, add it to your path, then do it all over again for every update"
I was the original author of the awseb cli and we found that pip install was significantly less of a hurdle than a go binary and decided to do it in Python instead. If a user on windows has a hard time installing python and pip, telling them to drop a binary and change their path isnt going to be any easier.
Re: Why we’re writing machine learning infrastructure in Go, not Python
#7I'm a bit green on infrastructure & deployment but I don't quite get this. If your ML algorithm code is still Python how does deployment with Go make that much difference? It sounds like you're not replacing the Python ML code so why is this such a big deal?
Re: Why we’re writing machine learning infrastructure in Go, not Python
#8> Making all of these overlapping API calls in a performative, reliable way is a challenge. Pythons asyncio is pretty hard to beat. For non-cpu intensive tasks, I find it a pleasure to work with. Goroutines can still have race conditions. > Originally, we wrote the CLI in Python, but trying to distribute it across platforms proved to be too difficult Sure, I get that go can cross-compile. But what makes python hard?…
Not a fair comparison. Go has go install.
Re: Why we’re writing machine learning infrastructure in Go, not Python
#9> Making all of these overlapping API calls in a performative, reliable way is a challenge. Pythons asyncio is pretty hard to beat. For non-cpu intensive tasks, I find it a pleasure to work with. Goroutines can still have race conditions. > Originally, we wrote the CLI in Python, but trying to distribute it across platforms proved to be too difficult Sure, I get that go can cross-compile. But what makes python hard?…
"... is just a "pip install..."
I was yelled at a few times for not using package and environment management tools (Conda, etc). So, when working with Python it is not just a "pip install" anymore
Re: Why we’re writing machine learning infrastructure in Go, not Python
#10"in the land of the blind, the one-eyed man is king"
Golang is probably a step up from Python, but it's just that. There are a lot of issues with Golang. From the top of my head, lack of decent error handling (if err !=nil { return nil,err} ) or lack of decent polymorphism are the most annoying. There's a github repo dedicated to what's bugging people:
https://github.com/ksimka/go-is-not-good