Metadata-Version: 2.1
Name: pjrt-plugin-tt
Version: 0.2.0.dev20250717
Summary: Tenstorrent PJRT plugin
Home-page: https://github.com/tenstorrent/tt-xla
Author: tt-xla team
Author-email: tt-xla@tenstorrent.com
License: Apache-2.0
Platform: UNKNOWN
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 3
Classifier: Development Status :: 3 - Alpha
Requires-Python: >=3.10, <3.11
Description-Content-Type: text/markdown
Requires-Dist: jax>=0.6.0
Requires-Dist: jaxlib>=0.6.0

[![Tests][tests badge]][tests]
[![Codecov][codecov badge]][codecov]

[Hardware](https://tenstorrent.com/cards/) | [Documentation](docs/src) | [Discord](https://discord.gg/tenstorrent) | [Join Us](https://job-boards.greenhouse.io/tenstorrent?gh_src=22e462047us)

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tt-xla leverages a PJRT interface to integrate JAX (and in the future other frameworks), `tt-mlir` and Tenstorrent hardware. It supports ingestion of JAX models via jit compile, providing a StableHLO (SHLO) graph to `tt-mlir` compiler.

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# Quick Links
- [Getting Started / How to Run a Model](docs/src/getting_started.md)

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# What is this Repo?
The tt-xla repository is primarily used to enable running JAX models on Tenstorrent's AI hardware. It's a backend integration between the JAX ecosystem and Tenstorrent's ML accelerators using the PJRT (Portable JAX Runtime) interface.

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# Current AI Framework Front End Projects
- [tt-forge-fe](https://github.com/tenstorrent/tt-forge-fe)
  - A TVM based graph compiler designed to optimize and transform computational graphs for deep learning models. Supports ingestion of PyTorch, ONNX, TensorFlow, PaddlePaddle and similar ML frameworks via TVM ([tt-tvm](https://github.com/tenstorrent/tt-tvm)).
  - See [docs pages](https://docs.tenstorrent.com/tt-forge-fe/getting-started.html) for an overview and getting started guide.

- [tt-torch](https://github.com/tenstorrent/tt-torch)
  - A MLIR-native, open-source, PyTorch 2.X and torch-mlir based front-end. It provides stableHLO (SHLO) graphs to `tt-mlir`. Supports ingestion of PyTorch models via PT2.X compile and ONNX models via torch-mlir (ONNX->SHLO)
  - See [docs pages](https://docs.tenstorrent.com/tt-torch) for an overview and getting started guide.

- [tt-xla](https://github.com/tenstorrent/tt-xla)
  - Leverages a PJRT interface to integrate JAX (and in the future other frameworks), `tt-mlir` and Tenstorrent hardware. Supports ingestion of JAX models via jit compile, providing StableHLO (SHLO) graph to `tt-mlir` compiler
  - See [docs pages](https://docs.tenstorrent.com/tt-xla/) for an overview and getting started guide.

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# Related Tenstorrent Projects
- [tt-forge](https://github.com/tenstorrent/tt-forge)
- [tt-forge-fe](https://github.com/tenstorrent/tt-forge-fe)
- [tt-torch](https://github.com/tenstorrent/tt-torch)
- [tt-mlir](https://github.com/tenstorrent/tt-mlir)
- [tt-metalium](https://github.com/tenstorrent/tt-metal)
- [tt-tvm](https://github.com/tenstorrent/tt-tvm)

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# Tenstorrent Bounty Program Terms and Conditions
This repo is a part of Tenstorrent’s bounty program. If you are interested in helping to improve tt-forge, please make sure to read the [Tenstorrent Bounty Program Terms and Conditions](https://docs.tenstorrent.com/bounty_terms.html) before heading to the issues tab. Look for the issues that are tagged with both “bounty” and difficulty level!

[codecov]: https://codecov.io/gh/tenstorrent/tt-xla
[tests]: https://github.com/tenstorrent/tt-xla/actions/workflows/on-push.yml?query=branch%3Amain
[codecov badge]: https://codecov.io/gh/tenstorrent/tt-xla/graph/badge.svg?token=XQJ3JVKIRI
[tests badge]: https://github.com/tenstorrent/tt-xla/actions/workflows/on-push.yml/badge.svg?query=branch%3Amain


