nairobi-os 0.3.0

Nairobi OS: Heavy Iron Data Science Infrastructure with Lagos Vision (Hardware-Accelerated Zero-Copy Rendering)

Nairobi OS: Heavy Iron AI Infrastructure

[!IMPORTANT] Linux & WSL2 Only. We do not compromise on kernel physics.

Author: Kevin Chege. Location: Nairobi
License: PolyForm Noncommercial License 1.0.0

Nairobi OS is a high-performance data science operating system primitive designed for extreme resource efficiency. It enables memory-constrained environments (Edge, Containers, Serverless) to process large-scale datasets with hardware-accelerated ingestion and vectorized Rust analytics.

🚀 Version 0.2.1: The Fused Strike

This release introduces the Fused Analytics Pipeline, allowing ingestion, statistical distillation, and correlation to happen in a single, high-speed D-Bus round trip.

Key Features:

• Fused Pipeline: nairobi_os.data.pipeline() for maximum throughput.
• Zero-Copy Ingestion: Powered by memfd, io_uring, and kernel copy_file_range.
• Extreme Speed: Ingest 450MB+ datasets 12x faster than standard Pandas.
• Rayon Parallelization: Vectorized analytics leveraging multi-core hardware saturation.
• Persistent Infrastructure: Cached D-Bus connections for low-latency calls.

🛠️ Installation

pip install nairobi-os==0.2.1

💻 Quick Start

import nairobi_os
import json

# Ignite the refinery daemon
nairobi_os.start_refinery()

# Run a fused analytics strike
# (Ingest + Mean/StdDev/Skew/Kurtosis + Pearson Correlation)
result_json = nairobi_os.data.pipeline(
    "data.csv", 
    "target_column", 
    "col1,col2" # Correlation pair
)

result = json.loads(result_json)
print(f"Mean: {result['mean']}")
print(f"Correlation: {result['pearson']}")

# Shutdown refinery
nairobi_os.stop_refinery()

📊 Performance Benchmark (v0.2.1)

Metric	Pandas (Unoptimized)	Nairobi OS	Speedup
Ingestion Latency	6.38s	0.52s	12.2x
Statistical Distillation	8.79s	1.59s	5.5x
Total Pipeline	6.42s	2.29s	2.8x

⚖️ Licensing

This project is licensed under the PolyForm Noncommercial License 1.0.0. It is free for personal, educational, and research use. For commercial inquiries, please contact the author.

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© 2026 Kevin Chege. All Rights Reserved.