Okay, so I’ve been hearing about this “Ray Kai” thing for a while now, and I finally decided to give it a shot. I wasn’t really sure what to expect, but I’m always up for trying something new, especially if it promises to speed up my Python code. Here’s how it went down, from start to finish.
Getting Started
First things first, I needed to get Ray installed. It seemed pretty straightforward. I just fired up my terminal and typed:
pip install ray
I waited. It took a bit, pulling down a bunch of packages. I guess that’s expected, it’s a whole framework, not just some tiny library.
Trying it Out
Once the installation was done, I wanted to see this thing in action. I grabbed a simple example I found online. Nothing fancy, just something to see if it actually worked. It looked something like this:
import ray
import time
def my_function(x):
*(1)
return x x
futures = [my_*(i) for i in range(4)]
print(*(futures))
I saved it as a Python file (test_*, or something like that) and ran it. It feels a bit weird with `@*`, but it did the job.
My Own Test
Okay, the basic example worked, but that’s not really how I do things. I had this old Python script lying around, the kind of thing that takes forever to run. Perfect candidate for a speed boost. It’s a loop doing many *, I started by adding that import ray at the top and at the beginning. It seemed I had to tell the scrip when to shut down Ray, so I added at the end.
Then, the tricky part. I had this big function that did most of the heavy lifting. I wrapped it with that decorator. My thinking was, “Let’s make this thing run in parallel.”
The original script just called the function in a loop. I changed that. Instead of calling the function directly, I used function_*(arguments). This, apparently, doesn’t run the function right away. It just creates something called a “future,” which is like a promise that the result will be available… eventually.
So, I collected all these “futures” in a list. Then, I used *(list_of_futures) to actually get the results. This is where the magic happens, I guess. Ray figures out how to run all those function calls in parallel, using all the cores on my machine.
The Results (and some head-scratching)
I ran the modified script, and… wow! It was noticeably faster. Like, way faster. I was pretty stoked! It really cut down the processing time.
But it wasn’t all smooth sailing. I ran into a few bumps along the way. There are some limitations, especially about large data. I had to adjust how I was passing data around to make it work efficiently with Ray.
Wrapping Up
So, that’s my Ray Kai adventure so far. It definitely wasn’t a perfect, one-click solution, but the speedup was totally worth the effort. I’m still learning the ropes, figuring out the best ways to structure my code to take full advantage of it. I’m going to keep experimenting with it. There’s a lot more to explore, I’m sure. But for now, I’m pretty happy with the results.
