These examples demonstrate the power and flexibility of MAX. They include:
A collection of sample programs written in the Mojo programming language.
The MAX Graph API provides a powerful framework for staging computational graphs to be run on GPUs, CPUs, and more. Each operation in one of these graphs is defined in Mojo, an easy-to-use language for writing high-performance code.
The examples here illustrate how to construct custom graph operations in Mojo that run on GPUs and CPUs, as well as how to build computational graphs that contain and run them on different hardware architectures.
In addition to placing custom Mojo functions within a computational graph, the MAX Driver API can handle direct compilation of GPU functions written in Mojo and can dispatch them onto the GPU. This is a programming model that may be familiar to those who have worked with CUDA or similar GPGPU frameworks.
These examples show how to compile and run Mojo functions, from simple to complex, on an available GPU. Note that a MAX-compatible GPU will be necessary to build and run these.
MAX has the power to accelerate existing PyTorch and ONNX models directly, and provides Python, Mojo, and C APIs for this. These examples showcase common models from these frameworks and how to run them even faster via MAX.
Jupyter notebooks that showcase PyTorch and ONNX models being accelerated through MAX.