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

# MLOps Overview

Entrenar provides a comprehensive MLOps toolkit for production machine learning workflows, following Toyota Production System principles.

## Toyota Way Principles

Each component implements specific TPS principles:

Component | Toyota Principle | Description
--- | --- | ---
Preflight Validation | Jidoka (自働化) | Built-in quality through automatic defect detection
Experiment Tracking | Genchi Genbutsu | Go and see - understand through observation
Model Registry | Kanban | Visual workflow management for model stages
GPU Monitoring | Andon | Visual alerting system for issues
HPO | Kaizen | Continuous improvement through optimization
Differential Privacy | Jidoka | Protect quality of privacy guarantees

## Components

### Experiment Tracking

Track experiments, runs, metrics, and artifacts with SQLite-based storage:

    #![allow(unused)]
    fn main() {
    use entrenar::storage::{SqliteBackend, ExperimentStorage, RunStatus};

    let mut backend = SqliteBackend::open("experiments.db")?;
    let exp_id = backend.create_experiment("my-experiment", None)?;
    let run_id = backend.create_run(&exp_id)?;
    backend.start_run(&run_id)?;
    backend.log_metric(&run_id, "loss", 0, 0.5)?;
    backend.complete_run(&run_id, RunStatus::Success)?;
    }

### Preflight Validation

Catch data issues before training starts (30-50% of failures):

    #![allow(unused)]
    fn main() {
    use entrenar::storage::{Preflight, PreflightCheck};

    let preflight = Preflight::standard()
        .add_check(PreflightCheck::min_samples(1000))
        .add_check(PreflightCheck::disk_space_mb(10240));

    let results = preflight.run(&data);
    if !results.all_passed() {
        eprintln!("{}", results.report());
    }
    }

### Model Registry

Manage model lifecycle with staging workflows:

    #![allow(unused)]
    fn main() {
    use entrenar::storage::{InMemoryRegistry, ModelRegistry, ModelStage};

    let mut registry = InMemoryRegistry::new();
    registry.register_model("my-model", 1, metrics, artifact_path)?;
    registry.transition_stage("my-model", 1, ModelStage::Staging)?;
    registry.transition_stage("my-model", 1, ModelStage::Production)?;
    }

### Hyperparameter Optimization

Bayesian optimization with TPE sampler:

    #![allow(unused)]
    fn main() {
    use entrenar::optim::hpo::{BayesianOptimizer, SearchSpace, ParameterDef};

    let space = SearchSpace::new()
        .add("lr", ParameterDef::LogUniform(1e-5, 1e-2))
        .add("hidden_dim", ParameterDef::Discrete(vec![64, 128, 256, 512]));

    let optimizer = BayesianOptimizer::new(space, 50);
    }

### Differential Privacy

Privacy-preserving training with DP-SGD:

    #![allow(unused)]
    fn main() {
    use entrenar::optim::dp::{DPOptimizer, PrivacyEngine};

    let engine = PrivacyEngine::new()
        .with_noise_multiplier(1.0)
        .with_max_grad_norm(1.0)
        .with_target_epsilon(1.0)
        .with_target_delta(1e-5);

    let dp_optimizer = DPOptimizer::new(base_optimizer, engine);
    }

### GPU Monitoring

Real-time GPU metrics with Andon alerting:

    #![allow(unused)]
    fn main() {
    use entrenar::monitor::gpu::{GpuMonitor, AndonSystem, GpuAlert};

    let monitor = GpuMonitor::new()?;
    let metrics = monitor.collect_metrics()?;

    let andon = AndonSystem::default();
    let alerts = andon.check(&metrics);
    for alert in alerts {
        eprintln!("ALERT: {}", alert.message());
    }
    }

### REST API Server

HTTP API for remote experiment tracking:

    #![allow(unused)]
    fn main() {
    use entrenar::server::{TrackingServer, ServerConfig};

    let config = ServerConfig::default().with_port(5000);
    let server = TrackingServer::new(config);
    server.run().await?;
    }

Endpoints:

- `GET /health` - Health check
- `POST /api/v1/experiments` - Create experiment
- `POST /api/v1/runs` - Create run
- `POST /api/v1/runs/{id}/metrics` - Log metrics

### Cloud Storage

Store artifacts in S3, Azure Blob, or GCS:

    #![allow(unused)]
    fn main() {
    use entrenar::storage::{S3Config, BackendConfig, LocalBackend};

    // Local storage
    let backend = LocalBackend::new("./artifacts");

    // S3 configuration
    let s3_config = S3Config {
        bucket: "my-bucket".to_string(),
        region: Some("us-east-1".to_string()),
        endpoint: None,
        access_key: None,
        secret_key: None,
    };
    }

### LLM Evaluation

Evaluate LLM outputs for quality:

    #![allow(unused)]
    fn main() {
    use entrenar::monitor::llm::{InMemoryLLMEvaluator, LLMEvaluator};

    let mut evaluator = InMemoryLLMEvaluator::new();
    let result = evaluator.evaluate_response(
        &run_id,
        "What is machine learning?",
        "Machine learning is...",
        Some("ML is a subset of AI...")
    )?;

    println!("Relevance: {}", result.relevance);
    println!("Coherence: {}", result.coherence);
    println!("Groundedness: {}", result.groundedness);
    }

## Cargo Run Examples

    # Start REST API server
    cargo run --features server -- server --port 5000

    # Run with experiment tracking
    cargo run --example experiment_tracking

    # Run with GPU monitoring
    cargo run --example gpu_monitor

    # Run HPO sweep
    cargo run --example hpo_sweep

## See Also

- Experiment Tracking
- Preflight Validation
- Model Registry
- Hyperparameter Optimization
- Differential Privacy
- GPU Monitoring
- REST API
- Cloud Storage
- LLM Evaluation