| commit | 065e04a21cfe331f103a5815a4c0a54257b55f55 | [log] [tgz] |
|---|---|---|
| author | Ben Vanik <ben.vanik@gmail.com> | Thu Feb 01 16:29:33 2024 -0800 |
| committer | GitHub <noreply@github.com> | Thu Feb 01 16:29:33 2024 -0800 |
| tree | 47806cb7e49726a06b95d7c4a8b5e6b5532522b9 | |
| parent | 406626bd90a8e8e76946215dbc04efe09eed24f5 [diff] |
Adding support for outputting binary files from tooling. (#16291) Now numpy files are only written if `.npy` extensions are used and otherwise value contents are written directly. Support was added for writing primitive values and HAL or VM buffers by treating them as scalar/blob ndarrays for numpy.
IREE (Intermediate Representation Execution Environment, pronounced as “eerie”) is an MLIR-based end-to-end compiler and runtime that lowers Machine Learning (ML) models to a unified IR that scales up to meet the needs of the datacenter and down to satisfy the constraints and special considerations of mobile and edge deployments.
See our website for project details, user guides, and instructions on building from source.
IREE is still in its early phase. We have settled down on the overarching infrastructure and are actively improving various software components as well as project logistics. It is still quite far from ready for everyday use and is made available without any support at the moment. With that said, we welcome any kind of feedback on any communication channels!
See our website for more information.
IREE is licensed under the terms of the Apache 2.0 License with LLVM Exceptions. See LICENSE for more information.