Getting Started with Forge Demos

This document walks you through how to set up to run demo models using TT-Forge. The following topics are covered:

• Setting up a Front End to Run a Demo
• Running a Demo

NOTE: If you encounter issues, please request assistance on the TT-Forge Issues page.

NOTE: If you plan to do development work, please see the build instructions for the repo you want to work with.

Setting up a Front End to Run a Demo

Requirements: Ubuntu 24.04, Python 3.12.

This section provides instructions for how to set up your frontend so you can run models from the TT-Forge repo.

Before running one of the demos in TT-Forge, you must:

1. Determine which frontend you want to use:

   • TT-XLA - For use with JAX, TensorFlow, PyTorch
   • TT-Forge-ONNX - For use with ONNX and PaddlePaddle

2. Decide what setup you want to use for the frontend:

   • Wheel
   • Docker
   • Build From Source

NOTE: At this time, if you want to use TT-Forge-ONNX, you must use Docker or the build from source option.

3. Follow the installation instructions from the repo for your selected setup method:

   • TT-XLA Wheel
   • TT-XLA Docker
   • TT-Forge-ONNX Docker
   • TT-Forge-ONNX Build From Source

4. Return to this repo and follow the instructions in the Running a Demo section.

Running a Demo

To run a demo, do the following:

1. Clone the TT-Forge repo (alternatively, you can download the script for the model you want to try):

git clone https://github.com/tenstorrent/tt-forge.git

2. Navigate into TT-Forge and run the following command:

git submodule update --init --recursive

3. Navigate to the folder for the frontend you want:
   • TT-XLA Models
   • TT-Forge-ONNX Models

In this walkthrough, the resnet_demo.py from the TT-XLA folder is used.

4. From the main folder in the TT-Forge repository, run the resnet_demo.py script:

export PYTHONPATH=.
python demos/tt-xla/cnn/resnet_demo.py

If all goes well, you should see an image of a cat, and terminal output where the model predicts what the image is and presents a score indicating how confident it is in its prediction.