Getting Started
TT-Metalium is designed for the needs of non-ML and ML use cases.
The GitHub repository for the project is located here: https://github.com/tenstorrent/tt-metal
Installation and environment setup instructions are in the GitHub repository README: https://github.com/tenstorrent/tt-metal/blob/main/INSTALLING.md
Quick Start Guide
Metalium lets developers run models effortlessly out of the box, engage in lightweight optimizations, and progress to more sophisticated, heavyweight optimizations. The following steps showcase 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 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 read and review the contribution standards.