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swift-mlx

Build and train neural networks with lazy evaluation and automatic differentiation on Apple Silicon.

2.0k|301|Updated Dec 12, 2023
Search Tags:#swift#mlx#gpu#metal#machine-learning#automatic-differentiation#apple-silicon
One-click install
npx skills add https://github.com/ml-explore/mlx-swift --skill swift-mlx
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: swift-mlx
Source: https://github.com/ml-explore/mlx-swift/tree/main/skills/mlx-swift
Command: npx skills add https://github.com/ml-explore/mlx-swift --skill swift-mlx

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

MLX Swift provides a high-performance machine learning framework for Apple Silicon, enabling developers to implement, train, and deploy ML models with lazy evaluation and automatic differentiation for rapid experimentation.

Core Features & Use Cases

  • NumPy-like array operations with lazy evaluation and unified memory
  • Automatic differentiation and gradient-based training workflows
  • Modular ML stack (MLX, MLXNN, MLXOptimizers) with support for Metal kernels
  • Easy experimentation on macOS and iOS, with performance-friendly design
  • Use cases include building neural networks, custom kernels, and performance-tuned ML pipelines

Quick Start

Create a minimal Swift project, import MLX, and run a simple MLXArray operation to verify the setup.

Frequently Asked Questions about swift-mlx

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

FAQPage Schema
How do I run machine learning workflows on Apple Silicon with GPU acceleration?▼

Machine learning on Apple Silicon uses MLX Swift to provide lazy evaluation and automatic differentiation, enabling high-performance neural network training and array computations with Metal-compatible GPU execution.

What is lazy evaluation in MLX Swift for Apple Silicon?▼

Lazy evaluation in MLX Swift defers array computations until explicitly requested, optimizing memory usage and performance on Apple Silicon hardware by leveraging unified memory architecture.

Can I build and train neural networks on iOS using Swift?▼

Yes, you can build and train neural networks on iOS using MLX Swift modules like MLXNN and MLXOptimizers, which support gradient-based training workflows and custom Metal kernels.

Do I need Metal-compatible GPU hardware to use MLX Swift?▼

Yes, MLX Swift requires Apple Silicon hardware with Metal-compatible GPU execution to run machine learning workflows, as the framework is specifically designed for macOS and iOS performance.

How do I set up automatic differentiation for gradient-based training in Swift?▼

Automatic differentiation in MLX Swift is built into the framework, allowing gradient-based training workflows to be implemented directly in Swift using MLXOptimizers for neural network optimization.

What are the limitations of using MLX Swift for machine learning?▼

MLX Swift is limited to Apple Silicon hardware and requires Swift tooling with Metal-compatible GPU execution, making it unsuitable for non-Apple platforms or systems without unified memory architecture.

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