Overview

SCARCITY is an online-first framework for scarcity-aware deep learning. It provides a complete runtime for adaptive, resource-efficient machine learning with real-time performance feedback and dynamic optimization.

The core library implements a sophisticated multi-layered architecture designed for: 1. Federated Learning: Training across distributed, private nodes. 2. Online Inference: Learning from streaming data in real-time. 3. Adaptive Resource Management: Scaling compute based on device health.

Key Features

• Multi-Path Inference Engine (MPIE) Online bandit-based path exploration with UCB/Thompson sampling. Automatically finds the best calculation path.

• Federated Learning Decentralized model aggregation with differential privacy preservation. Learn from data without seeing it.

• Meta-Learning Cross-domain adaptation using online Reptile optimization. Transfer knowledge between different environments.

• Dynamic Resource Governance (DRG) Adaptive resource allocation based on system telemetry. If CPU usage spikes, the model shrinks.

• Real-time Simulation Agent-based modeling with 3D visualization to stress-test policies.

• Stream Processing Continuous data ingestion with backpressure control (PI-Controller).

• Event-Driven Architecture Asynchronous pub/sub communication fabric for non-blocking operations.

Version Information

• Version: 1.0.0
• Author: Omega Makena
• License: MIT (See LICENSE file)