Metadata-Version: 2.4
Name: pjrt-plugin-tt
Version: 1.2.0.dev20260522002921
Summary: commit=d119398c822f4125d74cb0106aa69f8d9e55a013, tt-mlir-commit=2bd67018499ffa0ed9a0aee507325d75a8e46b84, tt-metal-commit=c5ebc6351098dfb68ce913eedcc20ee5abd1509f, built-date=2026-05-21, build-type=release
Home-page: https://github.com/tenstorrent/tt-xla
Author: tt-xla team
Author-email: tt-xla@tenstorrent.com
License: Apache-2.0
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 3
Classifier: Development Status :: 3 - Alpha
Requires-Python: >=3.12, <3.13
Description-Content-Type: text/markdown
Requires-Dist: jax==0.7.1
Requires-Dist: jaxlib==0.7.1
Requires-Dist: requests
Requires-Dist: torch @ https://download.pytorch.org/whl/cpu/torch-2.10.0%2Bcpu-cp312-cp312-manylinux_2_28_x86_64.whl
Requires-Dist: loguru
Requires-Dist: torch-xla @ https://pypi.eng.aws.tenstorrent.com/torch-xla/torch_xla-2.9.0%2Bgit5c82b10-cp312-cp312-linux_x86_64.whl
Requires-Dist: nanobind==2.10.2
Requires-Dist: networkx==3.6.1
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Requires-Dist: seaborn==0.13.2
Requires-Dist: pyyaml==6.0.3
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Requires-Dist: pandas==3.0.0
Dynamic: author
Dynamic: author-email
Dynamic: classifier
Dynamic: description
Dynamic: description-content-type
Dynamic: home-page
Dynamic: license
Dynamic: requires-dist
Dynamic: requires-python
Dynamic: summary

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[Hardware](https://tenstorrent.com/cards/) | [Documentation](https://docs.tenstorrent.com/tt-xla/) | [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 PyTorch models through PyTorch/XLA and 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/source/getting_started.md)

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# What is This Repo?
The TT-XLA repository is primarily used to enable running PyTorch and 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-XLA](https://github.com/tenstorrent/tt-xla)
  - TT-XLA is the primary frontend for running PyTorch and JAX models. It 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 StableHLO (SHLO) graph to TT-MLIR compiler. TT-XLA can be used for single and multi-chip projects.
  - See the [TT-XLA docs pages](https://docs.tenstorrent.com/tt-xla) for an overview and getting started guide.

- [TT-Forge-ONNX](https://github.com/tenstorrent/tt-forge-onnx)
  - A TVM based graph compiler designed to optimize and transform computational graphs for deep learning models. Supports ingestion of ONNX, TensorFlow, PaddlePaddle and similar ML frameworks via TVM ([TT-TVM](https://github.com/tenstorrent/tt-tvm)). It also supports ingestion of PyTorch, however it is recommended that you use TT-XLA. TT-Forge-ONNX does not support multi-chip configurations; it is for single-chip projects only.
  - See the [TT-Forge-ONNX docs pages](https://docs.tenstorrent.com/tt-forge-onnx/getting_started.html) for an overview and getting started guide.

- [TT-Torch](https://github.com/tenstorrent/tt-torch) - (deprecated)
  - 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 the [TT-Torch docs pages](https://docs.tenstorrent.com/tt-torch) for an overview and getting started guide.

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# Related Tenstorrent Projects
- [TT-XLA](https://github.com/tenstorrent/tt-xla) - (single and multi-chip) For use with PyTorch, JAX, and TensorFlow
- [TT-Forge-ONNX](https://github.com/tenstorrent/tt-forge-onnx) - (single chip only) For use with ONNX and PaddlePaddle, it also runs PyTorch, however it is recommended to use TT-XLA for PyTorch
- [TT-MLIR](https://github.com/tenstorrent/tt-mlir) - Open source compiler framework for compiling and optimizing machine learning models for Tenstorrent hardware
- [TT-Metal](https://github.com/tenstorrent/tt-metal) - Low-level programming model, enabling kernel development for Tenstorrent hardware
- [TT-TVM](https://github.com/tenstorrent/tt-tvm) - A compiler stack for deep learning systems designed to close the gap between the productivity-focused deep learning frameworks, and the performance and efficiency-focused hardware backends
- [TT-Torch](https://github.com/tenstorrent/tt-torch) - (Deprecated) Previously for use with PyTorch. It is recommended that you use TT-XLA for PyTorch.

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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
[deepwiki]: https://deepwiki.com/tenstorrent/tt-xla
[deepwiki badge]: https://deepwiki.com/badge.svg
