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opensecura / 3p / openxla / iree / 9517472b2194e0f0217486d9e5e065952b6dffab
commit9517472b2194e0f0217486d9e5e065952b6dffab[log] [tgz]
authorScott Todd <scott.todd0@gmail.com>Wed Feb 07 10:27:14 2024 -0800
committerGitHub <noreply@github.com>Wed Feb 07 18:27:14 2024 +0000
tree93723fe1d39ad1715937a106d73a71aa3d628d8c
parent259fe7e5c127816797aa2cd347bb29b183042c1a [diff]
Switch CI to install TF instead of using 'frontends' Docker images. (#16346)

Progress on https://github.com/openxla/iree/issues/16203 and
https://github.com/openxla/iree/issues/15332

At the point where a job is installing a multi-gigabyte Docker image, it
might as well just install Python requirements like TF directly.
Switching to install from pip loses some control over the supply chain
but I think that is fine for these test/benchmark jobs.

Comparing [`build_e2e_test_artifacts`
before](https://github.com/openxla/iree/actions/runs/7815739382/job/21320388848)
to [`build_e2e_test_artifacts`
after](https://github.com/openxla/iree/actions/runs/7818347765/job/21328712850?pr=16346)
(sample size 1):
* Docker fetch time decreased from 1m50s to 30s
  * 'frontends' depended on 'android' so it included the NDK too 😛
* Python setup (including pip install) time increased from 6s to 1m20s

So about the same time taken, just using less cloud storage / infra
complexity.
10 files changed
tree: 93723fe1d39ad1715937a106d73a71aa3d628d8c
  1. .devcontainer/
  2. .github/
  3. build_tools/
  4. compiler/
  5. docs/
  6. experimental/
  7. integrations/
  8. lib/
  9. llvm-external-projects/
  10. runtime/
  11. samples/
  12. tests/
  13. third_party/
  14. tools/
  15. .bazel_to_cmake.cfg.py
  16. .bazelignore
  17. .bazelrc
  18. .bazelversion
  19. .clang-format
  20. .dockerignore
  21. .git-blame-ignore-revs
  22. .gitignore
  23. .gitmodules
  24. .yamllint.yml
  25. AUTHORS
  26. BUILD.bazel
  27. CITATION.cff
  28. CMakeLists.txt
  29. configure_bazel.py
  30. CONTRIBUTING.md
  31. LICENSE
  32. README.md
  33. WORKSPACE
README.md

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.

CI Status IREE Discord Status

Project Status

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!

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

License

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