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opensecura / 3p / openxla / iree / d1a65a7cc08bac4a2a2159402f09e8ea34b9fc83
commitd1a65a7cc08bac4a2a2159402f09e8ea34b9fc83[log] [tgz]
authorStella Laurenzo <laurenzo@google.com>Wed Mar 08 19:41:11 2023 -0800
committerStella Laurenzo <laurenzo@google.com>Thu Mar 09 11:55:28 2023 -0800
tree52e41290cdf09aa172f45ba42fcff43e758f1738
parent6bc40847d6f9677d1a52062118e5e4367db691dd [diff]
Extract and improve script to download CUDA toolkit components.

Previous to this, we were fetching a sample from an NVIDIA github repo and using CMake scripting to use it to download an appropriate SDK. This patch:

* Forks the parse_redist.py sample locally into third_party/nvidia_sdk_download.
* Fixes a number of things in parse_redist.py to make it more robust, remove warnings, and eliminate the dependency on the Python requests package.
* Removes the 'requests' package from all requirements files as it is no longer needed.
* Adds a fetch_cuda_toolkit.py which duplicates the behavior that was open coded in CMake scripting.
* Updates the build_tools/third_party/cuda/CMakeLists.txt to use the new script instead of its internal approach.

In a follow-on, I will use this script on the Bazel side to make it auto-fetch the CUDA SDK as needed as well.
5 files changed
tree: 52e41290cdf09aa172f45ba42fcff43e758f1738
  1. .github/
  2. benchmarks/
  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. .bazelignore
  16. .bazelrc
  17. .bazelversion
  18. .clang-format
  19. .dockerignore
  20. .gitignore
  21. .gitmodules
  22. .pylintrc
  23. .style.yapf
  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

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

  • 2021-06-09: IREE Runtime Design Tech Talk (recording and slides)
  • 2020-08-20: IREE CodeGen: MLIR Open Design Meeting Presentation (recording and slides)
  • 2020-03-18: Interactive HAL IR Walkthrough (recording)
  • 2020-01-31: End-to-end MLIR Workflow in IREE: MLIR Open Design Meeting Presentation (recording and slides)

License

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