commit | 9de6fe09a95312d1e9b2cf81f42a6ab398040d7d | [log] [tgz] |
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author | Ben Vanik <benvanik@google.com> | Wed Feb 02 18:05:31 2022 -0800 |
committer | GitHub <noreply@github.com> | Wed Feb 02 18:05:31 2022 -0800 |
tree | 93c2051176a9ca06930ee000e84679decbd33527 | |
parent | 376654fb5a9046fbb5147e0fb02409e459edbcbd [diff] |
Adding some sample flow/stream uses. (#8227) This lets us verify that we can handle ingesting them and acts as an example for someone trying to more directly see the execution model. In the future as we move tiling from flow to the codegen backends this will be simplified and changed to use the public iree dialect instead.
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.