Deploy tfg-nightly to pypi workflow (tensorflow/graphics)
The Deploy tfg-nightly to pypi workflow from tensorflow/graphics, explained and optimized by Latchkey.
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What it does
This is the Deploy tfg-nightly to pypi workflow from the tensorflow/graphics repository, a real project running GitHub Actions. It is shown here with attribution under its Apache-2.0 license.
Below, Latchkey shows a faster, safer version produced by its optimization engine.
The workflow
# Publishes tfg-nightly
name: Deploy tfg-nightly to pypi
on:
schedule:
# * is a special character in YAML so you have to quote this string
# runs daily at 00:30 am
- cron: '30 0 * * *'
jobs:
deploy:
if: github.repository == 'tensorflow/graphics' # prevents action from running on forks
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v2
- name: Set up Python
uses: actions/setup-python@v2
with:
python-version: 3.8
- name: Install system dependencies
run: |
sudo xargs apt-get update
sudo xargs apt-get -y install < requirements.unix
- name: Install pip requirements
run: |
python -m pip install --upgrade pip
pip install -U -r requirements.txt
pip install -U pytest
pip install -U setuptools wheel
pip install -U twine
- name: Build ops
run: |
bazel build tensorflow_graphics/... --define=BASEDIR=$(pwd) --sandbox_writable_path=$(pwd)
bazel clean --expunge
- name: Run python tests
env:
MESA_GL_VERSION_OVERRIDE: 4.5
MESA_GLSL_VERSION_OVERRIDE: 450
run: |
pytest tensorflow_graphics
- name: Build pip package and install
run: |
python setup.py sdist bdist_wheel --nightly
pip install dist/*.whl
- name: Test install
run: |
cd $(mktemp -d) && python -c 'import tensorflow_graphics as tfg'
- name: Publish to PyPi # https://pypi.org/project/tfg-nightly
env:
TWINE_USERNAME: ${{ secrets.PYPI_USERNAME }}
TWINE_PASSWORD: ${{ secrets.PYPI_PASSWORD }}
run: |
twine upload dist/*
The same workflow, on Latchkey
Estimated ~20% faster on cache hits, plus fewer wasted runs and a safer supply chain. Added and changed lines are highlighted.
# Publishes tfg-nightly name: Deploy tfg-nightly to pypi on: schedule: # * is a special character in YAML so you have to quote this string # runs daily at 00:30 am - cron: '30 0 * * *' jobs: deploy: timeout-minutes: 30 if: github.repository == 'tensorflow/graphics' # prevents action from running on forks runs-on: latchkey-small steps: - uses: actions/checkout@v2 - name: Set up Python uses: actions/setup-python@v2 with: cache: 'pip' python-version: 3.8 - name: Install system dependencies run: | sudo xargs apt-get update sudo xargs apt-get -y install < requirements.unix - name: Install pip requirements run: | python -m pip install --upgrade pip pip install -U -r requirements.txt pip install -U pytest pip install -U setuptools wheel pip install -U twine - name: Build ops run: | bazel build tensorflow_graphics/... --define=BASEDIR=$(pwd) --sandbox_writable_path=$(pwd) bazel clean --expunge - name: Run python tests env: MESA_GL_VERSION_OVERRIDE: 4.5 MESA_GLSL_VERSION_OVERRIDE: 450 run: | pytest tensorflow_graphics - name: Build pip package and install run: | python setup.py sdist bdist_wheel --nightly pip install dist/*.whl - name: Test install run: | cd $(mktemp -d) && python -c 'import tensorflow_graphics as tfg' - name: Publish to PyPi # https://pypi.org/project/tfg-nightly env: TWINE_USERNAME: ${{ secrets.PYPI_USERNAME }} TWINE_PASSWORD: ${{ secrets.PYPI_PASSWORD }} run: | twine upload dist/*
What changed
- Run on Latchkey managed runners with one line (
runs-on), which apply the fixes below automatically and self-heal transient failures. This example useslatchkey-small; pick the runner size that fits the job. - Cache dependency installs on the setup step so they are served from cache.
- Add a job timeout so a hung step cannot burn hours of runner time.
What Latchkey heals here
This workflow has steps that commonly fail on transient issues (network, registries, flaky browsers). On Latchkey managed runners they are detected, retried, and self-healed instead of failing your build:
- Dependency installs
This workflow runs 1 job per trigger. On Latchkey the same minutes cost up to 58% less than GitHub-hosted, with zero queue time.