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android.yml
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name: Android
on:
push:
branches:
- main
- release/*
pull_request:
paths:
- .ci/docker/**
- .github/workflows/android.yml
- build/*android*.sh
- install_requirements.sh
- examples/demo-apps/android/**
- extension/android/**
- extension/module/**
workflow_dispatch:
concurrency:
group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.sha }}-${{ github.event_name == 'workflow_dispatch' }}-${{ github.event_name == 'schedule' }}
cancel-in-progress: true
jobs:
build-llm-demo:
name: build-llm-demo
uses: pytorch/test-infra/.github/workflows/linux_job.yml@main
strategy:
matrix:
tokenizer: [bpe, tiktoken]
with:
runner: linux.2xlarge
docker-image: executorch-ubuntu-22.04-clang12-android
submodules: 'true'
ref: ${{ github.event_name == 'pull_request' && github.event.pull_request.head.sha || github.sha }}
timeout: 90
upload-artifact: android-apps
script: |
set -eux
# The generic Linux job chooses to use base env, not the one setup by the image
CONDA_ENV=$(conda env list --json | jq -r ".envs | .[-1]")
conda activate "${CONDA_ENV}"
PYTHON_EXECUTABLE=python bash .ci/scripts/setup-linux.sh buck2
export ARTIFACTS_DIR_NAME=artifacts-to-be-uploaded
# Build LLM Demo for Android
bash build/build_android_llm_demo.sh ${{ matrix.tokenizer }} ${ARTIFACTS_DIR_NAME}
# Upload artifacts to S3. The artifacts are needed not only by the device farm but also TorchChat
upload-artifacts:
needs: build-llm-demo
runs-on: linux.2xlarge
steps:
- name: Download the artifacts from GitHub
uses: actions/download-artifact@v3
with:
# The name here needs to match the name of the upload-artifact parameter
name: android-apps
path: ${{ runner.temp }}/artifacts/
- name: Verify the artifacts
shell: bash
working-directory: ${{ runner.temp }}/artifacts/
run: |
ls -lah ./
- name: Upload the artifacts to S3
uses: seemethere/upload-artifact-s3@v5
with:
s3-bucket: gha-artifacts
s3-prefix: |
${{ github.repository }}/${{ github.run_id }}/artifact
# NOTE: Consume stale artifacts won't make sense for benchmarking as the goal is always to
# benchmark models as fresh as possible. I'm okay to keep the 14 retention-days for now
# for TorchChat until we have a periodic job can publish it more often. Ideally I want to
# reduce it to <= 2 day, meaning the benchmark job will run daily.
retention-days: 14
if-no-files-found: ignore
path: ${{ runner.temp }}/artifacts/