I recently had a client come to me with a massive data processing script. They’d just dropped a small fortune on a 64-core Threadripper workstation, but their script was still taking over an hour to finish. When I looked at the task manager, I saw one core pinned at 100% while the other 63 were basically taking a nap. It was a total waste of silicon. They needed to learn Distributed Computing with Ray, but they didn’t even know where to start.
My first thought was the obvious one: just use the standard Python multiprocessing library. I spent an afternoon refactoring their code into pools and workers. It worked, sort of. But the boilerplate was a nightmare, and if one process crashed, the whole thing became a zombie. Plus, the moment they’d need to move this to a cloud cluster, I’d have to rewrite it all over again. It’s a common trap for devs who want to fix slow Python code fast without looking at the long-term maintenance costs.
Mastering Distributed Computing with Ray
That’s when I pulled out Ray. If you haven’t used it, Ray is a framework designed to scale Python applications from a single laptop to a massive cluster with almost zero friction. It handles the scheduling, the data serialization, and the process management so you don’t have to. You can find the full details in the Ray Core official documentation, but here is how I fixed that client’s mess.
Instead of manual process management, you use decorators. You mark a function as “remote,” and Ray takes care of the rest. Here’s a simplified version of what we did to handle a heavy CPU-bound task like counting primes across a huge range.
import math
import time
import ray
# Initializing the local cluster
ray.init()
@ray.remote
def bbioon_is_prime_task(n: int) -> bool:
if n < 2: return False
for i in range(2, int(math.isqrt(n)) + 1):
if n % i == 0:
return False
return True
@ray.remote
def bbioon_process_range(start: int, end: int) -> int:
count = 0
for i in range(start, end):
# We're calling the function locally here for simplicity in the demo
if i > 1:
is_p = True
for j in range(2, int(math.isqrt(i)) + 1):
if i % j == 0:
is_p = False
break
if is_p:
count += 1
return count
if __name__ == "__main__":
A, B = 10_000_000, 15_000_000
num_cpus = int(ray.cluster_resources().get("CPU", 1))
step = (B - A) // num_cpus
# Launching remote tasks
futures = [bbioon_process_range.remote(A + i * step, A + (i + 1) * step) for i in range(num_cpus)]
# Gathering results
results = ray.get(futures)
print(f"Total primes: {sum(results)}")By using Distributed Computing with Ray, we cut the execution time from 45 minutes down to about 2 minutes. The code is actually cleaner than the original single-threaded version. For those coming from a data science background, this is far more robust than basic threading, similar to how we master reliable agents by letting the framework handle the heavy lifting.
Using Actors for Shared State
The real kicker is when you need to share state. Regular global variables don’t work in distributed systems because every process has its own memory space. Ray solves this with “Actors”—classes decorated with @ray.remote. I used this to create a global progress tracker for the client so they could see the status of all 64 cores in real-time. This is a much better approach than the one described in many a distributed processing guide you’ll find online.
Another thing I love? The dashboard. You just run your script, and Ray spins up a web interface at localhost:8265. You can see exactly which cores are busy, how much memory is being used, and if any tasks are hanging. It’s a lifesaver when you’re building your first distributed application and things start acting weird.
The Reality of Scaling
Look, I’ve been doing this for 14 years. I’ve seen people throw expensive hardware at bad code more times than I can count. Distributed Computing with Ray isn’t just about speed; it’s about writing code that’s ready to grow. Whether you’re running on a laptop or a 100-node cluster, the API stays exactly the same. That’s the kind of pragmatism I live for.
This stuff gets complicated fast. If you’re tired of debugging someone else’s mess and just want your site or your data pipeline to actually work, drop me a line. I’ve probably seen it before and I can help you get it sorted without the fluff.
Are you still pinning single cores and wondering why your bill is so high? Maybe it’s time to refactor. Catch you in part 2 where we take this to the cloud.
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