Alright, let me tell you about this thing I was working on, this “ray kai” stuff. It wasn’t really a formal project name, more like what I called it in my head, you know? Mostly messing around with the Ray framework and trying to build this specific little piece, let’s call it the ‘Kai’ module.
So, the whole thing started because I had this data processing job that was just taking forever. Like, run it overnight and pray it finishes by morning kinda slow. I’d heard whispers about Ray, how it could make Python stuff run faster by spreading it across multiple cores, or even multiple machines. Sounded good, right? Anything to stop staring at that progress bar.
Getting Started with Ray
First step, obviously, was getting Ray installed. That part was easy enough, just a `pip install ray`. Then I started looking at the examples. You know how documentation is – sometimes it clicks, sometimes it feels like reading a different language. Ray’s examples were okay, showed the basic `@*` decorator thing. Seemed simple enough on the surface.
I spent maybe a day just playing with their basic tutorials. Making dummy functions, adding the decorator, seeing if they ran in parallel. It kinda worked. Felt a bit like magic, seeing my CPU usage spike across all cores for simple tasks. Okay, I thought, maybe this isn’t just hype.
Building the ‘Kai’ Module
Now, the real challenge: taking my actual slow code, the ‘Kai’ part, and making it work with Ray. This ‘Kai’ thing involved reading a bunch of files, doing some calculations on each, and then combining the results. Classic scenario for parallel processing, or so I thought.
My first attempt was just slapping `@*` on my main processing function. Yeah, didn’t work. Turns out, you gotta think about how your data moves around. My function relied on some global state, big objects in memory. Ray doesn’t like that much when you distribute things. It needs tasks to be more self-contained.
So, began the refactoring. This was the tedious part. Breaking down the ‘Kai’ logic:
- Identify the truly independent parts (processing single files).
- Figure out how to pass data efficiently (Ray’s object store seemed relevant here, but also confusing).
- Rewrite the main function to submit these smaller tasks to Ray.
- Add a final step to gather all the results.
This took way longer than I expected. Lots of trial and error. Debugging distributed code is a pain, man. Your errors aren’t always straightforward. Sometimes tasks just failed silently, or the whole thing would hang. Spent quite a few late nights just adding print statements everywhere, trying to figure out where things went wrong.
Why Bother? The Backstory
You might wonder why I was putting myself through this. Well, this wasn’t just for fun. This slow process was blocking other stuff. People were waiting for these results, and the pressure was mounting. My boss wasn’t exactly breathing down my neck, but you could feel it, you know? That constant “is it done yet?” vibe. Plus, honestly, it just bugged me that it was so inefficient. It felt sloppy. I wanted to fix it, make it better. It became a bit of a personal challenge.
There was this one week where the process failed two nights in a row, and we missed a deadline. Wasn’t catastrophic, but it was embarrassing. That’s when I really decided I had to try something drastic like Ray. The old ways just weren’t cutting it anymore.
Did It Work? Well… Kinda.
After a lot more tweaking, head-scratching, and probably too much coffee, I got a version running. The ‘ray kai’ setup. And… it was faster! Definitely faster. Not like, 100x faster instantly, but maybe 3-4x faster on my machine. Significant enough to make a difference.
But it wasn’t perfect. It used a ton more memory, which I had to manage carefully. And sometimes, for reasons I still haven’t fully nailed down, it would randomly be slower than the old version. Maybe network hiccups, maybe the Ray scheduler doing something weird? Who knows. Distributed systems, right? They add complexity.
So, the current status is, we use the ‘ray kai’ version cautiously. It runs faster most of the time, which is good. But I have to keep an eye on it. It’s not the magic bullet I initially hoped for, but it’s an improvement. Learned a lot, though. Mostly learned that making things parallel isn’t just about adding a decorator. It forces you to really understand your code and your data flow. And yeah, learned that debugging distributed stuff requires a whole new level of patience.
