tell me more about metal

Generated on 12/5/2024

1 search

Metal is a powerful graphics and compute API designed and optimized for Apple devices. It is widely used in various applications, including machine learning and game development, to leverage the capabilities of Apple GPUs.

Machine Learning with Metal

Metal provides a backend for popular machine learning frameworks like TensorFlow, PyTorch, and JAX, enabling efficient training on Apple GPUs. The Metal Performance Shaders (MPS) Graph framework is used to construct and run general-purpose compute graphs, offering low-level control over GPU synchronization and memory. This is particularly useful for applications that require integration with other GPU tasks.

  • JAX Metal Backend: Supports features like just-in-time compilation, a NumPy-like interface, and mixed precision training. It is optimized for Apple silicon and supports distributed training for large projects. Recent updates include support for the Pfloat 16 data type and advanced array indexing.

  • PyTorch and TensorFlow: Both frameworks benefit from Metal's capabilities, with features like custom operations and profiling to improve network performance.

For more details on training machine learning models on Apple GPUs, you can refer to the session Train your machine learning and AI models on Apple GPUs.

Game Development with Metal

Metal is also crucial in game development, providing advanced graphics and compute features. It supports shader conversion and resource residency, allowing developers to efficiently port and optimize games for Apple platforms.

  • MetalFX Upscaler: A feature that enhances graphics rendering, making it easier to display high-quality visuals on screen.

  • Game Porting: Developers can use Metal to port advanced games to macOS and iOS, taking advantage of the unified memory architecture and other Apple-specific optimizations.

For insights into using Metal for game development, you can explore the session Port advanced games to Apple platforms.

VisionOS and Metal

In the context of VisionOS, Metal is used to render content in mixed immersion environments, blurring the line between the real world and digital content. This involves using compositor services and ARKit for rendering and tracking.

For more information on rendering with Metal in VisionOS, see the session Render Metal with passthrough in visionOS.

These sessions provide a comprehensive overview of how Metal is utilized across different domains, showcasing its versatility and power on Apple platforms.

Train your machine learning and AI models on Apple GPUs

Train your machine learning and AI models on Apple GPUs

Learn how to train your models on Apple Silicon with Metal for PyTorch, JAX and TensorFlow. Take advantage of new attention operations and quantization support for improved transformer model performance on your devices.

Port advanced games to Apple platforms

Port advanced games to Apple platforms

Discover how simple it can be to reach players on Apple platforms worldwide. We’ll show you how to evaluate your Windows executable on Apple silicon, start your game port with code samples, convert your shader code to Metal, and bring your game to Mac, iPhone, and iPad. Explore enhanced Metal tools that understand HLSL shaders to validate, debug, and profile your ported shaders on Metal.

Accelerate machine learning with Metal

Accelerate machine learning with Metal

Learn how to accelerate your machine learning transformer models with new features in Metal Performance Shaders Graph. We’ll also cover how to improve your model’s compute bandwidth and quality, and visualize it in the all new MPSGraph viewer.

Render Metal with passthrough in visionOS

Render Metal with passthrough in visionOS

Get ready to extend your Metal experiences for visionOS. Learn best practices for integrating your rendered content with people’s physical environments with passthrough. Find out how to position rendered content to match the physical world, reduce latency with trackable anchor prediction, and more.

Explore machine learning on Apple platforms

Explore machine learning on Apple platforms

Get started with an overview of machine learning frameworks on Apple platforms. Whether you’re implementing your first ML model, or an ML expert, we’ll offer guidance to help you select the right framework for your app’s needs.