Toyota Way QA Process - 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

# Toyota Way QA Process

This document defines the 25-point QA checklist used for validating all YAML Mode training scenarios. Based on Toyota Way manufacturing principles.

## Philosophy

Every training run is treated as a manufacturing process where quality is built in, not inspected out:

Principle | Application
--- | ---
Jidoka | Stop on defect (NaN/Inf halts training)
Poka-yoke | Schema validation prevents configuration errors
Genchi Genbutsu | Go and see - observe actual training metrics
Andon | Visual alerts for anomalies
Kaizen | Continuous improvement via experiment tracking

## The 25-Point QA Checklist

For every training scenario, validate these 25 points:

### Category A: Safety & Ethics (5 points)

# | Check | Description
--- | --- | ---
A1 | Human Oversight | Operator is present and `andon` system is active
A2 | Stop-Mechanism | Process halts immediately on critical failure (NaN/Inf)
A3 | Data Privacy | Input data scanned for PII/PHI before ingestion
A4 | Bias Check | Training data distribution verified for demographic parity
A5 | Impact Analysis | Potential downstream harm of model failure assessed

### Category B: Data & Inputs (5 points)

# | Check | Description
--- | --- | ---
B1 | Source Integrity | Input SHA256 hashes match manifest
B2 | Normalization | Input features scaled (0-1 or -1 to 1) correctly
B3 | Splitting | Train/Val/Test split is stratified and leak-free
B4 | Augmentation | Augmentations are deterministic (fixed seed)
B5 | Format | Data types (f32/f16) match hardware capabilities

### Category C: Compute & Resources (5 points)

# | Check | Description
--- | --- | ---
C1 | Resource Cap | Memory usage < 90% of available RAM/VRAM
C2 | Compute Affinity | Process pinned to correct CPU cores/GPU device
C3 | Thermal Safety | System temperatures monitored during run
C4 | Energy Budget | Estimated energy cost < approved budget
C5 | Concurrency | No race conditions in multi-thread/multi-GPU dataloading

### Category D: Process & Training (5 points)

# | Check | Description
--- | --- | ---
D1 | Convergence | Loss curve shows monotonic decrease (smoothed)
D2 | Generalization | Validation loss tracks training loss (no divergence)
D3 | Precision | No underflow/overflow in mixed-precision ops
D4 | Determinism | Global seed produces bit-exact reproduction
D5 | Checkpointing | Atomic writes for model states; no corruption on crash

### Category E: Output & Artifacts (5 points)

# | Check | Description
--- | --- | ---
E1 | Format Validity | Output `.apr` or `.safetensors` passes validator
E2 | Explainability | Saliency maps/attribution generated if required
E3 | Versioning | Artifact tagged with git commit and config hash
E4 | Performance | Inference latency meets SLA (< 100ms etc.)
E5 | Documentation | Run logs and observations archived

## QA Workflow

### 1. Pre-Training

    # Validate configuration (Poka-yoke)
    entrenar validate config.yaml

    # Check data integrity
    entrenar check-data config.yaml --verify-hashes

### 2. During Training

Monitor the terminal dashboard for:

- Loss explosion (Andon alert)
- GPU memory pressure
- Learning rate schedule
- Gradient norms

### 3. Post-Training

    # Validate output artifacts
    entrenar verify-output ./experiments/my-run/

    # Generate QA report
    entrenar qa-report config.yaml --output qa-report.md

## Using the Checklist

### For Each Scenario

1. Run training with the YAML config
2. Complete checklist - mark each of the 25 points
3. Document exceptions - note any deviations
4. Archive results - store logs and checklist

### Example Checklist (YAML-001: MNIST CPU)

    ## QA Checklist: YAML-001 MNIST Baseline CPU

    **Date**: 2025-11-30
    **Operator**: @engineer
    **Config**: examples/yaml/mnist_cpu.yaml

    ### Safety & Ethics
    - [x] A1: Human oversight - operator present
    - [x] A2: Stop mechanism - NaN detection enabled
    - [x] A3: Data privacy - MNIST is public domain
    - [x] A4: Bias check - balanced digit distribution
    - [x] A5: Impact analysis - demo only, no production use

    ### Data & Inputs
    - [x] B1: Source integrity - alimentar verified
    - [x] B2: Normalization - 0-1 scaling applied
    - [x] B3: Splitting - stratified 80/10/10
    - [x] B4: Augmentation - none used
    - [x] B5: Format - float32 on CPU

    ### Compute & Resources
    - [x] C1: Resource cap - 2GB RAM used (<8GB available)
    - [x] C2: Compute affinity - CPU only
    - [x] C3: Thermal safety - N/A for CPU demo
    - [x] C4: Energy budget - minimal
    - [x] C5: Concurrency - single-threaded

    ### Process & Training
    - [x] D1: Convergence - loss decreased monotonically
    - [x] D2: Generalization - val_loss tracked train_loss
    - [x] D3: Precision - float32, no mixed precision
    - [x] D4: Determinism - seed=42 reproducible
    - [x] D5: Checkpointing - epoch checkpoints saved

    ### Output & Artifacts
    - [x] E1: Format validity - safetensors validated
    - [x] E2: Explainability - N/A for demo
    - [x] E3: Versioning - git commit tagged
    - [x] E4: Performance - 50ms inference
    - [x] E5: Documentation - logs archived

    **Result**: PASS (25/25)

## Integration with CI/CD

Add QA gates to your pipeline:

    # .github/workflows/training-qa.yml
    jobs:
      qa-validation:
        steps:
          - name: Validate configs
            run: |
              for f in examples/yaml/*.yaml; do
                entrenar validate "$f"
              done

          - name: Run QA suite
            run: cargo test --test yaml_mode_integration

          - name: Generate QA report
            run: entrenar qa-report --all --output qa-report.md

## References

1. Liker, J. K. (2004). The Toyota Way: 14 Management Principles. McGraw-Hill.
2. Shingo, S. (1986). Zero Quality Control: Source Inspection and the Poka-yoke System.
3. Ohno, T. (1988). Toyota Production System: Beyond Large-Scale Production.
4. Poppendieck, M. & T. (2003). Lean Software Development.

## Next Steps

- YAML Examples Catalog - All 30 example configurations
- YAML Mode Training - Complete schema reference
- Quality Gates - Development quality standards