🦀 TuskLang Rust Machine Learning Integration

"We don't bow to any king" - Rust Edition

Master machine learning and AI integration with TuskLang Rust. From model serving to real-time inference, from data pipelines to intelligent configuration - build intelligent, adaptive systems with Rust and TuskLang.

🤖 ML & AI Foundation

Model Serving with Rust

use tusklang_rust::{parse, Parser};
use tract_onnx::prelude::*;
use std::sync::Arc;
#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    // Load ONNX model
    let model = tract_onnx::onnx()
        .model_for_path("models/model.onnx")?
        .with_input_fact(0, InferenceFact::dt_shape(f32::datum_type(), tvec!(1, 4)))?
        .into_optimized()?
        .into_runnable()?;
    // Example input
    let input = tract_ndarray::arr2(&[[5.1f32, 3.5, 1.4, 0.2]]);
    let result = model.run(tvec!(input.into_dyn()))?;
    let output: Vec<f32> = result[0].to_array_view::<f32>()?.iter().cloned().collect();
    println!("Model output: {:?}", output);
    Ok(())
}

Integrating ML Inference in TSK Config

[ml]
model_path: "models/model.onnx"
input_shape: [1, 4]
output_shape: [1, 3]
[prediction]
input: [5.1, 3.5, 1.4, 0.2]
result: @ml.infer($model_path, $input)

Rust ML Ecosystem

- tract: ONNX/TensorFlow inference - linfa: Classical ML (SVM, KMeans, etc.) - tch-rs: PyTorch bindings - ndarray: N-dimensional arrays - smartcore: Data science toolkit

Example: Linfa for Classification

use linfa::prelude::*;
use linfa_datasets;
use linfa_logistic::LogisticRegression;
fn main() {
    // Load Iris dataset
    let (train, _test) = linfa_datasets::iris();
    let model = LogisticRegression::default().fit(&train).unwrap();
    let pred = model.predict(&train);
    println!("Predictions: {:?}", pred);
}

ML-Driven Configuration

[feature_flags]
use_ai: true
[ai]
model: "models/feature_selector.onnx"
selected_features: @ml.infer($model, $input_data)
[config]
feature_set: $selected_features

Real-Time Inference API (Actix-web)

use actix_web::{post, web, App, HttpServer, HttpResponse, Responder};
use tract_onnx::prelude::*;
use serde::Deserialize;
#[derive(Deserialize)]
struct PredictRequest {
    input: Vec<f32>,
}
#[post("/predict")]
async fn predict(req: web::Json<PredictRequest>, data: web::Data<RunnableModel<TypedFact, Box<dyn TypedOp>>>) -> impl Responder {
    let input = tract_ndarray::Array2::from_shape_vec((1, req.input.len()), req.input.clone()).unwrap();
    let result = data.run(tvec!(input.into_dyn())).unwrap();
    let output: Vec<f32> = result[0].to_array_view::<f32>().unwrap().iter().cloned().collect();
    HttpResponse::Ok().json(output)
}
#[actix_web::main]
async fn main() -> std::io::Result<()> {
    let model = tract_onnx::onnx()
        .model_for_path("models/model.onnx").unwrap()
        .into_optimized().unwrap()
        .into_runnable().unwrap();
    let data = web::Data::new(model);
    HttpServer::new(move || App::new().app_data(data.clone()).service(predict))
        .bind("0.0.0.0:8080")?
        .run()
        .await
}

ML @ Operator in TuskLang

[ai]
model: "models/price_predictor.onnx"
input: [3.2, 1.5, 0.7, 2.1]
predicted_price: @ml.infer($model, $input)

Model Versioning and A/B Testing

[ml]
model_v1: "models/model_v1.onnx"
model_v2: "models/model_v2.onnx"
use_v2: @feature_flag("ml_v2_enabled")
active_model: @if($use_v2, $model_v2, $model_v1)
[prediction]
input: [2.5, 3.1, 1.2, 0.8]
result: @ml.infer($active_model, $input)

Security and Performance

- Run inference in sandboxed processes - Limit input size and validate data - Use model caching for performance - Monitor inference latency and errors

Testing ML Integration

#[tokio::test]
async fn test_ml_integration() {
    let model = tract_onnx::onnx()
        .model_for_path("models/model.onnx").unwrap()
        .into_optimized().unwrap()
        .into_runnable().unwrap();
    let input = tract_ndarray::arr2(&[[5.1f32, 3.5, 1.4, 0.2]]);
    let result = model.run(tvec!(input.into_dyn())).unwrap();
    let output: Vec<f32> = result[0].to_array_view::<f32>().unwrap().iter().cloned().collect();
    assert!(!output.is_empty());
}

🎯 What You've Learned

1. Model serving - ONNX, PyTorch, and Linfa integration 2. TSK-driven inference - ML @ operators and config-driven AI 3. Real-time inference APIs - Actix-web and async Rust 4. Model versioning - A/B testing and feature flags 5. Security and performance - Sandboxing, validation, and monitoring

🚀 Next Steps

1. Integrate ML into your TSK configs 2. Build real-time inference APIs 3. Monitor and optimize model performance 4. Experiment with Linfa and tract 5. Add ML-driven features to your Rust apps

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You now have complete machine learning integration mastery with TuskLang Rust! From model serving to real-time inference, from data pipelines to intelligent configuration - TuskLang gives you the tools to build intelligent, adaptive systems with Rust.