Basic Inference

This guide shows how to run inference on iOS after completing the SDK setup.

Prerequisites

Melange SDK added to your project (iOS Setup)
A compiled model on the Melange Dashboard
Your Personal Key and Model Key

Running Inference

import ZeticMLange

// (1) Load model
// This handles model download (if needed) and Neural Engine context creation
let model = try ZeticMLangeModel(personalKey: PERSONAL_KEY, name: MODEL_NAME)

// (2) Prepare model inputs
// Ensure input shapes match your model's requirement (e.g., Float32 arrays)
let inputs: [Tensor] = [] // Prepare your inputs

// (3) Run Inference
// Executes the fully automated hardware graph.
// No manual delegate configuration or memory syncing required.
let outputs = try model.run(inputs)

Understanding the Flow

Model Download: On first use, the SDK downloads the pre-compiled, hardware-optimized model binary from the Melange CDN. This binary is optimized for Apple Neural Engine.
Neural Engine Context Creation: Melange initializes the Neural Engine and loads the model into NPU memory using zero-copy memory mapping.
Inference Execution: Your input tensor is processed through the NPU-accelerated computation graph, and the output tensor is returned. No data leaves the device.

Always ensure your input tensor shapes exactly match what the model expects. A shape mismatch will throw an error. Check the model's input specification on the Melange Dashboard.

Full Working Example

import ZeticMLange

class ViewController: UIViewController {
    override func viewDidLoad() {
        super.viewDidLoad()

        do {
            // Load model
            let model = try ZeticMLangeModel(personalKey: PERSONAL_KEY, name: "Steve/YOLOv11_comparison")

            // Prepare inputs
            let inputs: [Tensor] = [] // Prepare your inputs

            // Run inference
            let outputs = try model.run(inputs)

            // Process outputs
            for output in outputs {
                // Process each output tensor
            }
        } catch {
            print("Melange error: \(error)")
        }
    }
}

Sample Application

Please refer to the ZETIC Melange Apps repository for complete sample applications and more details.

Next Steps

Advanced Configuration: Inference modes and options
Custom Preprocessing: Implement input preprocessing
Multi-Model Pipelines: Chain models together