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coreml

Integrate Core ML models into iOS apps for on-device inference.

Updated Mar 31, 2026
Search Tags:#ios#inference#model-deployment#coreml#mlmodel#mlcomputeplan#mltensor
One-click install
npx skills add https://github.com/DFly7/iOS-FastAPI-Supabase-AI --skill coreml-dfly7
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: coreml
Source: https://github.com/DFly7/iOS-FastAPI-Supabase-AI/tree/main/.agents/skills/coreml
Command: npx skills add https://github.com/DFly7/iOS-FastAPI-Supabase-AI --skill coreml-dfly7

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Provide reliable patterns and best practices to load, configure, run, and manage Core ML models in iOS apps so developers can deliver fast, memory-efficient on-device inference without blocking the UI or misusing device accelerators.

Core Features & Use Cases

  • Async model loading and caching for runtime-downloaded models and persisted compiled artifacts to avoid repeated expensive compilation.
  • Compute unit configuration and profiling guidance to choose between CPU, GPU, and Neural Engine and inspect MLComputePlan for per-operation dispatch.
  • Prediction patterns including auto-generated model classes, MLFeatureProvider, batch inference, and stateful predictions for sequence models.
  • Data interoperability with MLTensor and MLMultiArray for advanced pre/post-processing and efficient zero-copy transfers.
  • Vision and pipeline integration for image models, multi-model pipelines, and recommendations for deployment strategies (bundle, ODR, background assets).
  • Memory and lifecycle management including unloading on background, actor-based model managers, and testing and instrumentation best practices.

Quick Start

Load the compiled model from the app bundle or application support, create an MLFeatureProvider with the image pixel buffer, and run an async prediction to return the top label and confidence.

Frequently Asked Questions about coreml

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I run on-device inference with a Core ML model in my iOS app?▼

Run on-device inference by loading a compiled mlmodel from the app bundle, creating an MLFeatureProvider with input data, and executing an async prediction to return results without blocking the UI.

What is the best way to manage Core ML model lifecycle and memory in Swift?▼

Manage the Core ML model lifecycle using actor-based model managers to handle async loading, cache compiled artifacts, and unload models on background to ensure memory-efficient on-device inference.

Can I configure Core ML compute units to use the Neural Engine and inspect operation dispatch?▼

Yes, configure Core ML compute units to choose between CPU, GPU, and Neural Engine, and inspect MLComputePlan to profile per-operation dispatch and optimize on-device inference performance.

How do I handle data interoperability with MLTensor and MLMultiArray for Core ML predictions?▼

Handle data interoperability by using MLTensor and MLMultiArray for advanced pre-processing and post-processing, enabling efficient zero-copy data transfers during Core ML on-device inference.

Does Core ML support stateful predictions and batch inference for sequence models?▼

Yes, Core ML supports stateful predictions for sequence models and batch inference using auto-generated model classes and MLFeatureProvider to process multiple inputs efficiently.

How do I integrate Vision pipelines with Core ML models for image processing?▼

Integrate Vision with Core ML by loading image models, creating multi-model pipelines, and utilizing Vision integration to process image pixel buffers and return top labels with confidence scores.

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