commit | a7a1be3d2c6c99a6298b989a56604c78014d80e5 | [log] [tgz] |
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author | Scott Todd <scott.todd0@gmail.com> | Mon Aug 05 15:07:47 2024 -0700 |
committer | GitHub <noreply@github.com> | Mon Aug 05 15:07:47 2024 -0700 |
tree | 32d6eac9ba118606e65cc717e172d1c07a91c1fb | |
parent | 4a1f619d1e712c2f20743a89ee1249730752e141 [diff] |
Move build_and_test_android from ci.yml to pkgci.yml. (#18070) Progress on https://github.com/iree-org/iree/issues/16203 and https://github.com/iree-org/iree/issues/17957. This migrates `.github/workflows/build_and_test_android.yml` to `.github/workflows/pkgci_test_android.yml`. **For now, this only builds for Android, it does not run tests or use real Android devices at all**. The previous workflow * Relied on the "install" directory from a CMake build * Ran on large self-hosted CPU build machines * Built within Docker (using [`build_tools/docker/dockerfiles/android.Dockerfile`](https://github.com/iree-org/iree/blob/main/build_tools/docker/dockerfiles/android.Dockerfile)) * Used GCP/GCS for remote ccache storage * Used GCP/GCS for passing files between jobs * Ran tests on self-hosted lab machines (I think a raspberry pi connected to some physical Android devices) The new workflow * Relies on Python packages produced by pkgci_build_packages * Runs on standard GitHub-hosted runners * Installs dependencies that it needs on-demand (ninja, Android NDK), without using Docker * Uses caches provided by GitHub Actions for ccache storage * Could use Artifacts provided by GitHub Actions for passing files between jobs * Could run tests on self-hosted lab machines or Android emulators I made some attempts at passing files from the build job to a test job but ran into some GitHub Actions debugging that was tricky. Leaving the remaining migration work there to contributors at Google or other parties directly interested in Android CI infrastructure. ci-exactly: build_packages, test_android
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
Package | Release status |
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GitHub release (stable) | |
GitHub release (nightly) | |
Python iree-compiler | |
Python iree-runtime |
Host platform | Build status |
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Linux | |
macOS | |
Windows |
For the full list of workflows see https://iree.dev/developers/general/github-actions/.
See our website for more information.
Community meeting recordings: IREE YouTube channel
IREE is licensed under the terms of the Apache 2.0 License with LLVM Exceptions. See LICENSE for more information.