commit | c1c7b48209af286cf24b16c6dc973b910f6e10cf | [log] [tgz] |
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author | Han-Chung Wang <hanchung@google.com> | Fri Jan 13 15:05:37 2023 -0800 |
committer | GitHub <noreply@github.com> | Fri Jan 13 15:05:37 2023 -0800 |
tree | 8af89df5d24039319370ab64a94d87f0e3f5e5b7 | |
parent | 273225105ade2463f9d99048cb191f1fd04c76a0 [diff] |
Integrate llvm-project at 73d94b191613 and bump dependencies. (#11833) - llvm-project: 73d94b191613 - mlir-hlo: af0f386317f085186cb0f3050aae58f9584e373e - tensorflow: 1647373b24f7fee65a555f13abb1aca97ea17f3b cherry-pick llvm-project commits: - 71c837f6d7d74e44f156ac60a47b1198727ba90f
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.