commit | 776d7e6072fd7ca42cab03fd68e9a04f5aeacebb | [log] [tgz] |
---|---|---|
author | Marius Brehler <marius.brehler@iml.fraunhofer.de> | Tue Dec 21 22:50:52 2021 +0100 |
committer | GitHub <noreply@github.com> | Tue Dec 21 13:50:52 2021 -0800 |
tree | 72e7d5bd8dd83468bb366d532624315aab787194 | |
parent | 4b775e9b5501b94df4b05503102e7fe544aed35b [diff] |
Allow to turn of error on uninitialized submodules (#7911) Makes the error on uninitialized submodules optional. With this, external projects that use IREE are no longer forced to initialize all submodules. E.g. if a project only uses the runtime and does not reply on the TensorFlow integration, these submodules don't have to be cloned at all. This is especially convenient in CI pipelines.
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.