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coreml-ref

Document Core ML APIs for model lifecycles, device management, and tensor operations.

Updated Dec 23, 2025
Search Tags:#deployment#swift#tensors#coremltools#coreml#mlmodel#mlcompute
One-click install
npx skills add https://github.com/pradeepmouli/swift-template --skill coreml-ref-pradeepmouli
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: coreml-ref
Source: https://github.com/pradeepmouli/swift-template/tree/main/.agents/skills/axiom-ios-ml/coreml-ref
Command: npx skills add https://github.com/pradeepmouli/swift-template --skill coreml-ref-pradeepmouli

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Core ML developers need authoritative guidance on MLModel lifecycle management, device availability, and tensor operations to build efficient ML-powered apps on Apple platforms.

Core Features & Use Cases

  • MLModel lifecycle management: loading, configuration, and caching strategies.
  • Compute device discovery and selection for optimal performance across devices.
  • MLTensor operations and multi-function model handling for advanced ML workloads.
  • Core ML tools integration (coremltools) for model conversion and deployment workflows.
  • Deployment targets and performance profiling guidance for robust app releases.

Quick Start

Review the MLModel loading and device availability sections to implement Core ML in your app.

Frequently Asked Questions about coreml-ref

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

FAQPage Schema
How do I manage the MLModel lifecycle in iOS and macOS apps?▼

Manage the MLModel lifecycle by loading, configuring, and caching models within your iOS and macOS apps. Core ML APIs provide structured strategies to handle model states and optimize performance during app execution. (Duplicate removed)

How do I select compute devices for Core ML tensor operations?▼

Select compute devices for Core ML tensor operations by discovering and evaluating MLCompute device availability. This ensures optimal performance by matching multi-function model workloads to the appropriate hardware. (Duplicate removed)

Can I use coremltools to convert and deploy models in Swift?▼

Yes, you can use coremltools to convert and deploy models for Swift. The integration supports model conversion workflows and defines deployment targets to ensure robust app releases across Apple platforms. (Duplicate removed)

What is the best way to profile Core ML performance across devices?▼

Profile Core ML performance by utilizing built-in performance profiling guidance across various compute devices. This approach identifies bottlenecks in MLModel loading and MLTensor operations to ensure robust app releases. (Duplicate removed)

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