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tt-xla leverages [PJRT](https://github.com/openxla/xla/tree/main/xla/pjrt/c#pjrt---uniform-device-api)interface to simplify the integration of Tenstorrent hardware and [compiler](https://github.com/tenstorrent/tt-mlir)with machine learning frameworks. Please see [this](https://opensource.googleblog.com/2023/05/pjrt-simplifying-ml-hardware-and-framework-integration.html) blog post for more information about PJRT project. This project is a fork of [iree-pjrt](https://github.com/stellaraccident/iree-pjrt).
tt-xla leverages [PJRT](https://github.com/openxla/xla/tree/main/xla/pjrt/c#pjrt---uniform-device-api) to integrate JAX (and in the future other frameworks), [tt-mlir](https://github.com/tenstorrent/tt-mlir)and Tenstorrent hardware. Please see [this](https://opensource.googleblog.com/2023/05/pjrt-simplifying-ml-hardware-and-framework-integration.html) blog post for more information about PJRT project. This project is a fork of [iree-pjrt](https://github.com/stellaraccident/iree-pjrt).
**Note:** Currently only Tenstorrent `nebula` boards are supported and `galaxy` boards are not yet supported.
