Ruchy Session Bridge - 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

# Ruchy Session Bridge

The Ruchy session bridge preserves training history from interactive Ruchy sessions, converting them to Entrenar artifacts for reproducibility and archival.

## Feature Flag

Enable the session bridge:

    [dependencies]
    entrenar = { version = "0.2", features = ["ruchy-sessions"] }

## EntrenarSession

Represents a training session with metrics and code history:

    #![allow(unused)]
    fn main() {
    use entrenar::ecosystem::EntrenarSession;

    let session = EntrenarSession::new("sess-001", "LoRA Fine-tuning")
        .with_user("alice")
        .with_architecture("llama-7b")
        .with_dataset("custom-dataset")
        .with_config("batch_size", "32")
        .with_config("learning_rate", "1e-4")
        .with_tag("fine-tuning")
        .with_tag("lora")
        .with_notes("Initial experiment with rank 64");
    }

## SessionMetrics

Track training metrics over time:

    #![allow(unused)]
    fn main() {
    let mut session = EntrenarSession::new("sess-001", "Training");

    // Log metrics
    session.metrics.add_loss(0.5);
    session.metrics.add_loss(0.3);
    session.metrics.add_loss(0.2);

    session.metrics.add_accuracy(75.0);
    session.metrics.add_accuracy(85.0);

    session.metrics.add_lr(0.001);
    session.metrics.add_grad_norm(1.5);

    // Custom metrics
    session.metrics.add_custom("f1_score", 0.82);
    session.metrics.add_custom("bleu", 0.45);

    // Statistics
    println!("Steps: {}", session.metrics.total_steps());
    println!("Final loss: {:?}", session.metrics.final_loss());
    println!("Best loss: {:?}", session.metrics.best_loss());
    println!("Final accuracy: {:?}", session.metrics.final_accuracy());
    println!("Best accuracy: {:?}", session.metrics.best_accuracy());
    }

## Code History

Capture executed code cells:

    #![allow(unused)]
    fn main() {
    use entrenar::ecosystem::CodeCell;

    let cell = CodeCell {
        execution_order: 1,
        source: r#"
    model = load_model("llama-7b")
    trainer = Trainer(model, lr=1e-4)
    trainer.train(epochs=10)
        "#.to_string(),
        output: Some("Training completed. Final loss: 0.2".to_string()),
        timestamp: chrono::Utc::now(),
        duration_ms: Some(45000),
    };

    session.add_code_cell(cell);
    }

## Session Lifecycle

    #![allow(unused)]
    fn main() {
    // Create and track session
    let mut session = EntrenarSession::new("sess-001", "Training")
        .with_user("bob");

    // Log during training
    for epoch in 0..10 {
        let loss = train_epoch();
        session.metrics.add_loss(loss);
    }

    // Check if session has training data
    if session.has_training_data() {
        println!("Recorded {} steps", session.metrics.total_steps());
    }

    // Mark session as ended
    session.end();

    // Get duration
    if let Some(duration) = session.duration() {
        println!("Session lasted {} hours", duration.num_hours());
    }
    }

## Converting from Ruchy

Convert a Ruchy session to EntrenarSession:

    #![allow(unused)]
    fn main() {
    use entrenar::ecosystem::{EntrenarSession, RuchySession};

    // RuchySession comes from the Ruchy crate
    let ruchy_session: RuchySession = /* ... */;

    // Convert to EntrenarSession
    let session: EntrenarSession = ruchy_session.into();

    println!("Session: {}", session.name);
    println!("User: {:?}", session.user);
    println!("Steps: {}", session.metrics.total_steps());
    }

## Converting to Research Artifact

Preserve session as a research artifact:

    #![allow(unused)]
    fn main() {
    use entrenar::ecosystem::session_to_artifact;

    let mut session = EntrenarSession::new("sess-001", "LoRA Experiment")
        .with_user("alice")
        .with_architecture("llama-7b")
        .with_tag("lora")
        .with_tag("fine-tuning");

    session.metrics.add_loss(0.5);
    session.metrics.add_loss(0.2);

    // Convert to artifact
    let artifact = session_to_artifact(&session)?;

    println!("Artifact ID: {}", artifact.id);
    println!("Type: {}", artifact.artifact_type);  // Notebook
    println!("Authors: {:?}", artifact.authors);
    println!("Keywords: {:?}", artifact.keywords);
    println!("Version: {}", artifact.version);  // "1.0.0+steps2"
    }

### Artifact Properties

The conversion:

- Sets artifact type to `Notebook`
- Adds user as author with `Software` and `Investigation` roles
- Generates description from session metrics
- Copies tags as keywords (or defaults to ["training", "experiment", "entrenar"])
- Sets version with step count suffix

## Error Handling

    #![allow(unused)]
    fn main() {
    use entrenar::ecosystem::RuchyBridgeError;

    let session = EntrenarSession::new("empty", "Empty Session");

    match session_to_artifact(&session) {
        Ok(artifact) => println!("Created: {}", artifact.id),
        Err(RuchyBridgeError::NoTrainingHistory) => {
            eprintln!("Session has no training data or code");
        }
        Err(e) => eprintln!("Conversion failed: {}", e),
    }
    }

## Full Workflow Example

    #![allow(unused)]
    fn main() {
    use entrenar::ecosystem::{EntrenarSession, CodeCell, session_to_artifact};
    use entrenar::research::{CitationMetadata, ArchiveDeposit, ZenodoConfig};

    // 1. Create session
    let mut session = EntrenarSession::new("exp-2024-001", "Temperature Ablation Study")
        .with_user("researcher@university.edu")
        .with_architecture("llama-2-7b")
        .with_dataset("alpaca-clean")
        .with_config("temperature", "4.0")
        .with_config("alpha", "0.7")
        .with_tag("distillation")
        .with_tag("ablation");

    // 2. Log training progress
    for epoch in 0..50 {
        let loss = train_epoch();
        session.metrics.add_loss(loss);

        if epoch % 10 == 0 {
            let accuracy = evaluate();
            session.metrics.add_accuracy(accuracy);
        }
    }

    // 3. Capture final code
    session.add_code_cell(CodeCell {
        execution_order: 1,
        source: "# Training code...".to_string(),
        output: Some("Training complete".to_string()),
        timestamp: chrono::Utc::now(),
        duration_ms: Some(3600000),
    });

    // 4. End session
    session.end();

    // 5. Convert to artifact
    let artifact = session_to_artifact(&session)?;

    // 6. Generate citation
    let citation = CitationMetadata::from_artifact(&artifact, 2024);
    println!("{}", citation.to_bibtex());

    // 7. Optionally deposit to archive
    // let deposit = ArchiveDeposit::new(ZenodoConfig::new("your-token"));
    // deposit.prepare(&artifact)?;
    }

## See Also

- Ecosystem Overview
- Research Artifacts
- Academic Research