apple-ml

What problem does it solve?

This Skill provides essential patterns and best practices for developing and deploying Machine Learning models efficiently on Apple Silicon (M-series) hardware, addressing the absence of CUDA and unique memory management characteristics.

Core Features & Use Cases

• PyTorch MPS Optimization: Guides on device selection, memory management, synchronization, and fallback mechanisms for PyTorch on Apple Silicon.
• MLX Integration: Details on using MLX for ML tasks, including memory monitoring, lazy evaluation, gradient accumulation, and checkpointing.
• Performance Tuning: Strategies for auto batch size tuning and performance profiling specific to Apple's hardware.
• Use Case: When developing a new PyTorch model intended to run on a MacBook Pro, this Skill ensures you correctly configure MPS, manage memory to avoid OOM errors, and leverage MLX for specific tasks where it offers advantages.

Quick Start

Use the apple-ml skill to optimize PyTorch code for Apple Silicon by ensuring MPS is correctly configured and memory is managed efficiently.