Scaling Python for AI: Implementing Ray Clusters on Nordic Infrastructure

Date: January 29, 2024

Stop trying to force multiprocessing.Pool to do things it wasn't designed for. If you are managing Python workloads for data processing or machine learning in 2024, you have likely hit the wall: the Global Interpreter Lock (GIL) is strangling your throughput, and your 64GB RAM workstation is swapping to disk.

The standard industry knee-jerk reaction is "throw it in Kubernetes." But for many teams, K8s is engineering overhead you don't need just to parallelize a Pandas dataframe or fine-tune a Mistral 7B model. Enter Ray. It abstracts away the cluster management, letting you treat a fleet of servers as a single pool of Python resources.

However, distributed computing is unforgiving. Network latency between nodes can turn a linear speedup into a bottleneck. Data sovereignty laws (specifically Schrems II and GDPR) make sending Nordic customer data to US-managed GPU clouds a legal minefield. This is where bare-metal performance on local infrastructure becomes critical.

The Architecture: Why Ray on Bare-Metal VDS?

Ray operates on a Head-Worker architecture. The Head node runs the Global Control Store (GCS) via Redis. If the latency between your Head and Workers fluctuates, the GCS heartbeat fails, and Ray kills the worker. I've seen entire training runs abort after 40 hours because a "cloud" provider had noisy neighbors stealing CPU cycles.

To mitigate this, we rely on dedicated resources. At CoolVDS, we use KVM virtualization. Unlike containers sharing a kernel, KVM ensures your Ray scheduler gets the CPU time it expects. When you are pushing tensors across nodes, you also need high throughput.

1. The Prerequisite Environment

We assume you are running Ubuntu 22.04 LTS or Debian 12. Python 3.10 is the sweet spot for Ray 2.9.0 (current stable as of Jan '24).

First, optimize the OS for high-throughput networking. Default Linux settings are conservative. Increase the read/write buffers.

2. Installing Ray

On all nodes (Head and Workers), install the standard package. We include the "default" extras to get the dashboard.

Deploying the Head Node

The Head node coordinates the cluster. On your primary CoolVDS instance (ideally one with High-Performance NVMe to handle object spilling), start the ray process.

Pro Tip: Never expose the Ray dashboard (port 8265) or the Redis port (6379) to the public internet. Ray has no built-in authentication by default. Bind to internal IPs or use a VPN/VPC. If you must access it remotely, tunnel it.

Command to start Head:

You should see output indicating the node is alive. It will provide a tokenized command for workers to join.

Connecting Worker Nodes

Provision your secondary instances. Since CoolVDS offers low latency to the NIX (Norwegian Internet Exchange), cross-node communication remains snappy even if nodes are in different racks. Run the join command provided by the head node:

Verifying the Mesh

Back on the head node, run a Python script to verify the cluster sees all cores.

import ray

# Connect to the local cluster
ray.init(address='auto')

@ray.remote
def get_hostname():
    import socket
    import time
    time.sleep(0.1)
    return socket.gethostname()

# Fire off 100 tasks
futures = [get_hostname.remote() for _ in range(100)]
results = ray.get(futures)

from collections import Counter
print(Counter(results))

If you configured your CoolVDS instances correctly, you will see a distribution of hostnames corresponding to your VPS fleet. If you see timeouts here, check your firewall (`ufw`). You need to allow TCP traffic on ports 10001-10050 for object transfer.

Real-World Scenario: Distributed Data Processing

Let's tackle a real problem: processing a massive log dataset for a Norwegian e-commerce platform compliant with Datatilsynet auditing. We need to anonymize logs in parallel before archiving.

Without Ray, you load the file chunk by chunk. With Ray, we load the dataset into the object store (distributed shared memory).

import ray
import pandas as pd
import numpy as np

ray.init(address="auto")

# Define the actor to hold state if needed, or just a task for pure functional processing
@ray.remote
def process_log_chunk(file_path):
    # Simulate high IO read from NVMe
    df = pd.read_csv(file_path)
    
    # Anonymize user IPs (GDPR requirement)
    df['ip_address'] = df['ip_address'].apply(lambda x: "xxx.xxx.xxx.xxx")
    
    return df.to_dict()

# Assume we have 50 log files split across our NVMe storage
log_files = [f"/mnt/data/logs/2024-01-{i}.csv" for i in range(1, 51)]

# Launch tasks across the CoolVDS cluster
futures = [process_log_chunk.remote(f) for f in log_files]

# Blocking call to retrieve results when ready
results = ray.get(futures)

print(f"Processed {len(results)} files across the cluster.")

The magic here is Object Spilling. If the dataframe is too large for the RAM of one worker, Ray spills it to disk. This is where CoolVDS's NVMe storage shines. Standard SATA SSDs or network-attached block storage (ceph) often choke here, causing the "Object Store Full" error. On local NVMe, the spill/restore is nearly invisible.

Systemd: Making it Production Ready

Do not run Ray inside a screen or tmux session for production. Create a Systemd unit file to ensure the Ray daemon rises from the dead if the server reboots.

[Unit]
Description=Ray Head Node
After=network.target

[Service]
Type=simple
User=ubuntu
WorkingDirectory=/home/ubuntu
ExecStart=/home/ubuntu/venv/bin/ray start --head --port=6379 --dashboard-host=0.0.0.0 --block
Restart=always
RestartSec=5
LimitNOFILE=65536

[Install]
WantedBy=multi-user.target

Enable it with:

The Latency & Compliance Factor

Why host this in Norway? Aside from the obvious latency benefits for Nordic users, the legal landscape in 2024 demands strict control over data processing locations. When you use Ray to process user data, that data resides in the RAM of your worker nodes.

If you spin up instances on a US hyperscaler, even in a "European Region," you often contend with the CLOUD Act. Hosting on CoolVDS guarantees that your hardware—and the RAM chips holding your sensitive vectors—physically resides in our Norwegian datacenters. You maintain full sovereignty.

Furthermore, tuning ray.init() object store memory limits is crucial on VPS environments to prevent OOM killers:

Conclusion

Ray turns a collection of Linux servers into a unified supercomputer. It removes the need for complex orchestration tools for pure Python workloads. However, the software is only as good as the hardware underneath. High IOPS, low latency networking, and rock-solid virtualization are prerequisites, not luxuries.

If you are building the next generation of AI applications or data pipelines in the Nordics, don't let slow I/O kill your performance. Deploy a test cluster on CoolVDS today and watch your processing times drop.