Overview - Entrenar - Training & Optimization Library
Description: A comprehensive guide to building neural network training systems with autograd, optimizers, LoRA/QLoRA, and quantization using EXTREME TDD methodology

# Real-Time Training Monitoring

The monitor module provides comprehensive real-time visibility into training runs, implementing Toyota Way principles for quality assurance.

## Features

Component | Purpose | Toyota Way Principle
--- | --- | ---
MetricsCollector | Collect training metrics | Genchi Genbutsu (現地現物)
Dashboard | ASCII terminal visualization | Visual Management
DriftDetector | Anomaly detection | Jidoka (自働化)
AndonSystem | Alert management | Andon (行灯)
ModelLineage | Version tracking | Kaizen (改善)
HanseiAnalyzer | Post-training reports | Hansei (反省)

## Quick Start

    #![allow(unused)]
    fn main() {
    use entrenar::monitor::{MetricsCollector, Metric, Dashboard, HanseiAnalyzer};

    // Create collector
    let mut collector = MetricsCollector::new();

    // During training loop
    for epoch in 0..100 {
        let loss = train_epoch(&model, &data);
        let accuracy = evaluate(&model, &val_data);

        collector.record(Metric::Loss, loss);
        collector.record(Metric::Accuracy, accuracy);
        collector.record(Metric::Epoch, epoch as f64);
    }

    // Generate post-training report
    let analyzer = HanseiAnalyzer::new();
    let report = analyzer.analyze("my-training-run", &collector, duration_secs);
    println!("{}", analyzer.format_report(&report));
    }

## Architecture

    ┌─────────────────────────────────────────────────────────────┐
    │                    Training Loop                             │
    │  ┌─────────┐  ┌─────────┐  ┌─────────┐  ┌─────────┐        │
    │  │ Forward │→│ Backward │→│ Optimize │→│ Metrics │        │
    │  └─────────┘  └─────────┘  └─────────┘  └────┬────┘        │
    └───────────────────────────────────────────────┼─────────────┘
                                                    │
                                                    ▼
    ┌─────────────────────────────────────────────────────────────┐
    │                  MetricsCollector                            │
    │  ┌──────────────────────────────────────────────────────┐  │
    │  │ Welford's Algorithm: O(1) per update                  │  │
    │  │ - Running mean, variance, min, max                    │  │
    │  │ - NaN/Inf detection                                   │  │
    │  └──────────────────────────────────────────────────────┘  │
    └─────────────────────────────────────────────────────────────┘
             │              │              │              │
             ▼              ▼              ▼              ▼
        ┌─────────┐   ┌─────────┐   ┌─────────┐   ┌─────────┐
        │Dashboard│   │  Drift  │   │  Andon  │   │ Hansei  │
        │ (ASCII) │   │Detector │   │ System  │   │Analyzer │
        └─────────┘   └─────────┘   └─────────┘   └─────────┘

## Performance

The monitor module is designed for minimal overhead:

Operation | Performance | Complexity
--- | --- | ---
Record metric | < 1μs | O(1)
Get summary | < 100μs | O(metrics)
Dashboard render | < 100ms | O(history)
Drift detection | < 50μs | O(1)

## Next Steps

- Metrics Collection - Recording training metrics
- Terminal Dashboard - Live visualization
- Drift Detection - Anomaly detection
- Andon Alerting - Stop-the-line on critical failures
- Hansei Reports - Post-training analysis