So, I spent some time messing around with this thing I called “Ray Kai”. Just my name for trying to get Ray to handle a specific, kinda quirky data job I had.
Getting Ray itself up and running wasn’t too bad, actually. Installed it, ran the basic `ray start –head` command, felt pretty good. Seemed simple enough on the surface. The docs had these neat little examples, you know? Run a function remotely, piece of cake.
Putting the ‘Kai’ into Ray
Okay, the “Kai” part. This was basically a bunch of custom Python code I wrote ages ago. It chews through these weirdly formatted log files, pulls out specific patterns, does some calculations. It’s slow when run normally, so I thought, “Hey, Ray’s perfect for this! Parallel processing!”
First step was just trying to wrap my old function with `@*`. Seemed logical. Just make it a Ray task. And that’s where things started getting… interesting.
Turns out, my “Kai” code had all sorts of baggage. It relied on some global variables I’d forgotten about. It also used a couple of old libraries that didn’t play nice when serialized by Ray’s backend, Pickle. Got errors left and right. Stuff about objects not being serializable. Total headache.
Hitting the Wall
I spent a good while just trying to fix the serialization issues. Refactoring the code, trying to make everything self-contained within the function. It felt like I was wrestling with it more than actually getting any speed benefits.
- Tried breaking the function into smaller pieces.
- Tried explicitly passing data instead of relying on globals.
- Even looked into using different serialization libraries with Ray, but that seemed like opening another can of worms.
The thing is, Ray promises simplicity, but when your own code isn’t perfectly clean or designed for distributed systems from the start, you pay a price. It highlights all the messy bits you got away with before.
Figuring it Out, Sort Of
Eventually, I had to simplify my “Kai” logic quite a bit. Ripped out some of the less critical calculations. Focused only on the core pattern matching part that really needed the speed boost. Made sure each task operated on its own chunk of data, independently.
I used `*()` to get the big data files into Ray’s object store first, then passed references to the remote tasks. That seemed to help avoid some data transfer bottlenecks I hit earlier. Instead of sending data with the task call, the task just fetched it when needed.
So, did it work? Yeah, kinda. The stripped-down version ran much faster across multiple cores. Ray did its job distributing the tasks. But it wasn’t the magic bullet I first imagined. I had to fundamentally change my original “Kai” process to make it fit Ray’s way of doing things.
It was a good learning experience, for sure. Showed me that just slapping `@*` on any old code isn’t the answer. You gotta think about how the code works, its dependencies, its state. Distributed computing makes you face those things head-on. So, “Ray Kai” ended up being more about fixing “Kai” than just using Ray.
