- Introduction
- First 5 Things To-Do
- Model Demos
- Documentation
- FAQ & Troubleshooting
- Communication
- Contributing
The TT-Buda software stack can compile AI/ML models from several different frameworks such as PyTorch and Tensorflow, and execute them in many different ways on Tenstorrent hardware.
Note on terminology:
TT-Buda is the official Tenstorrent AI/ML compiler stack and PyBuda is the Python interface for TT-Buda. PyBuda allows users to access and utilize TT-Buda's features directly from Python. This includes directly importing model architectures and weights from PyTorch, TensorFlow, ONNX, and TFLite.
For a simple, 5-step, starting guide on learning the basics of TT-Buda please visit first_5_steps.
In that directory, you will find the following user guides:
1_install_ttbuda.md-> Installation guide for TT-Buda2_running_nlp_models.ipynb-> Run your first NLP model with TT-Buda3_running_cv_models.ipynb-> Run your first CNN model with TT-Buda4_batched_inputs.ipynb-> Learn how to run with batched inputs and how to benchmark models on TT-Buda5_serving_tt_models.ipynb-> Use FastAPI to host a model running on Tenstorrent hardware to build custom APIs
For additional example code for running some popular AI/ML models, please visit model_demos.
Please refer to the official documentation website.
We keep a running FAQ & troubleshoot guide to help you quickly resolve some of the most common issues at FAQ & Troubleshooting.
If you would like to formally propose a new feature, report a bug, or have issues with permissions, please file through GitHub issues.
Please access the Discord community forum for updates, tips, live troubleshooting support, and more!
We are excited to move our development to the public, open-source domain. However, we are not adequately staffed to review contributions in an expedient and manageable time frame at this time. In the meantime, please review the contributor's guide for more information about contribution standards.