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A retargetable MLIR-based machine learning compiler and runtime toolkit.

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IREE: Intermediate Representation Execution Environment

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 Discord Status pre-commit OpenSSF Best Practices

Project news

Project status

Release status

Releases notes are published on GitHub releases.

Package Release status
GitHub release (stable) GitHub Release
GitHub release (nightly) GitHub Release
Python iree-base-compiler PyPI version
Python iree-base-runtime PyPI version

Build status

CI PkgCI

Nightly build status

Operating system Build status
Linux CI - Linux arm64 clang
macOS CI - macOS x64 clang
Windows CI - Windows x64 MSVC

For the full list of workflows see https://iree.dev/developers/general/github-actions/.

Communication channels

Related project channels

  • MLIR topic within LLVM Discourse: IREE is enabled by and heavily relies on MLIR. IREE sometimes is referred to in certain MLIR discussions. Useful if you are also interested in MLIR evolution.

Architecture overview

IREE Architecture IREE Architecture

See our website for more information.

Presentations and talks

Community meeting recordings: IREE YouTube channel

Date Title Recording Slides
2021-06-09 IREE Runtime Design Tech Talk recording slides
2020-08-20 IREE CodeGen (MLIR Open Design Meeting) recording slides
2020-03-18 Interactive HAL IR Walkthrough recording
2020-01-31 End-to-end MLIR Workflow in IREE (MLIR Open Design Meeting) recording slides

License

IREE is licensed under the terms of the Apache 2.0 License with LLVM Exceptions. See LICENSE for more information.

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A retargetable MLIR-based machine learning compiler and runtime toolkit.

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  • C++ 40.9%
  • MLIR 27.7%
  • C 20.4%
  • Python 3.7%
  • CMake 3.5%
  • Starlark 2.4%
  • Other 1.4%