Getting Started

TT-Metalium is designed with the needs for non-ML and ML use cases.

The GitHub page for the project is located here: https://github.com/tenstorrent/tt-metal

Installation and environment setup instructions are in the GitHub repository front-page README: https://github.com/tenstorrent/tt-metal/blob/main/INSTALLING.md

Quick Start Guide

Metalium provides developers to do more than running models, facilitating a transition from running models effortlessly out of the box, engaging in lightweight optimizations, and progressing into more sophisticated, heavyweight optimizations. This series of steps serves as an illustrative example, showcasing the available tools for optimizing performance on Tenstorrent hardware.

1. Install and Build

Install and build the project by following the instructions in the installation guide.

2. Beginner Metalium Usage: DRAM Loopback

Try creating a basic kernel example that uses the L1 and DRAM memory structures of the Tenstorrent device.

3. Beginner Metalium Usage: Eltwise Binary Kernel

Augment your loopback example an additional kernel that will use the compute engine of the Tensix core to add values in two buffers.

4. Beginner Metalium Usage: Single-core Matrix Multiplication Kernel

Use TT-Metalium to define your own matrix multiplication kernels. Refer to our simpler single-core example as a starting point.

5. Advanced Metalium Usage: Multi-core Matrix Multiplication Kernel

Explore expert-level usage by building on the previous example to create a multi-core implementation.

Where to go from here

If you’re an ML developer and looking for a simpler Python API to build models, take a look at our higher-level API TT-NN.

If you’re an internal TT-Metalium developer, please now read and review the contribution standards.