commit | ffda498426c69ba686a5b431afcc01c15face96f | [log] [tgz] |
---|---|---|
author | Scott Todd <scotttodd@google.com> | Mon Jun 07 11:50:55 2021 -0700 |
committer | GitHub <noreply@github.com> | Mon Jun 07 11:50:55 2021 -0700 |
tree | df1953ddbc4decc2e056f0f1d54aca0973db8bc8 | |
parent | 0bafed8a63007cbbafa521d31a0aae75a89161bf [diff] |
Add a minimal sample using variables from TF -> C API. (#6104) I'm planning on using this as a template for future samples as part of https://github.com/google/iree/issues/5222 (showing dynamic shapes, flow control, etc.). Some guiding principles: * Samples should be written as external (out of tree) applications would be * No `iree_cc_binary`, `iree_bytecode_module`, or other CMake helpers * Only use parts of the public C API * Samples should show _an_ entire flow, but designate clear points where users could branch off on their own * The included Colab notebook provides both a .mlir file and a .vmfb for VMVX, along with instructions for compiling the .mlir file separately for other backends * The notebook also shows how to use the Python runtime API, so users could stop there if they aren't interested in the C API * These samples should be _minimal_ * No complex tensor types, ops, or layers - just scalars
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!
IREE is licensed under the terms of the Apache 2.0 License with LLVM Exceptions. See LICENSE for more information.