commit | 05ec88df628c5aeda03263de68d56e35ac194794 | [log] [tgz] |
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author | Scott Todd <scotttodd@google.com> | Fri Jul 14 13:09:58 2023 -0700 |
committer | GitHub <noreply@github.com> | Fri Jul 14 13:09:58 2023 -0700 |
tree | c94a643eb7224bf2e30a9d7de7eb34aeb9c758fb | |
parent | 90499ddd1c2873a09156393585e8602722cdca8f [diff] |
Link to website overview slides in docs/README.md. (#14400) I presented these slides at an internal team meeting and may repeat the presentation for the broader community if there is interest and a time slot. The slides should be world-readable (except where `docs.google.com` is blocked). I tried exporting to .pdf but don't want to commit a 1.3MB file to the repo (lots of screenshots, even with good compression).
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.