Nairobi Connector OS: Heavy Iron AI Infrastructure
Project description
Nairobi OS: Heavy Iron AI Infrastructure
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 200x less RAM than traditional Python data stacks.
🚀 Version 0.2.0: 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
memfdand kernelcopy_file_range. - Extreme Efficiency: Process 400MB+ datasets in just 20MB of RAM.
- Rayon Parallelization: Vectorized analytics leveraging multi-core hardware.
- Persistent Infrastructure: Cached D-Bus connections for low-latency calls.
🛠️ Installation
pip install nairobi-os
💻 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 (v2.0)
| Metric | Pandas | Nairobi OS |
|---|---|---|
| RAM Usage | 4,285 MB | 20.5 MB |
| CPU Usage | 1,158% | 10% |
| Latency | 912 ms | 1,160 ms |
⚖️ 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.
© 2026 Kevin Chege. All Rights Reserved.
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