Docker Build workflow (GACWR/OpenUBA)

The Docker Build workflow from GACWR/OpenUBA, explained and optimized by Latchkey.

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Source: GACWR/OpenUBA.github/workflows/ci-docker.ymlLicense Apache-2.0View source

What it does

This is the Docker Build workflow from the GACWR/OpenUBA 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

workflow (.yml)

name: Docker Build

on:
  push:
    branches: [master, dev/**]
    paths:
      - "docker/**"
      - "core/**"
      - "interface/**"
      - "requirements.txt"
      - "Makefile"
  pull_request:
    branches: [master]

jobs:
  build-backend:
    name: Build backend image
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4

      - name: Build backend Docker image
        run: docker build -f docker/backend.dockerfile -t openuba-backend:ci .

  build-frontend:
    name: Build frontend image
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4

      - name: Build frontend Docker image
        run: docker build -f docker/frontend.dockerfile -t openuba-frontend:ci .

  build-operator:
    name: Build operator image
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4

      - name: Build operator Docker image
        run: docker build -f docker/operator.dockerfile -t openuba-operator:ci .

  build-model-runners:
    name: Build model runner images
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4

      - name: Build base model runner
        run: docker build -f docker/model-runner/Dockerfile.base -t openuba-model-runner:base .

      - name: Build sklearn runner
        run: docker build -f docker/model-runner/Dockerfile.sklearn -t openuba-model-runner:sklearn --build-arg BASE_IMAGE=openuba-model-runner:base .

      - name: Build PyTorch runner
        run: docker build -f docker/model-runner/Dockerfile.pytorch -t openuba-model-runner:pytorch --build-arg BASE_IMAGE=openuba-model-runner:base .

      - name: Build TensorFlow runner
        run: docker build -f docker/model-runner/Dockerfile.tensorflow -t openuba-model-runner:tensorflow --build-arg BASE_IMAGE=openuba-model-runner:base .

      - name: Build NetworkX runner
        run: docker build -f docker/model-runner/Dockerfile.networkx -t openuba-model-runner:networkx --build-arg BASE_IMAGE=openuba-model-runner:base .

The same workflow, on Latchkey

Removes redundant runs and caps runaway jobs. Added and changed lines are highlighted.

name: Docker Build
 
on:
  push:
    branches: [master, dev/**]
    paths:
      - "docker/**"
      - "core/**"
      - "interface/**"
      - "requirements.txt"
      - "Makefile"
  pull_request:
    branches: [master]
 
concurrency:
  group: ${{ github.workflow }}-${{ github.ref }}
  cancel-in-progress: true
 
jobs:
  build-backend:
    timeout-minutes: 30
    name: Build backend image
    runs-on: latchkey-small
    steps:
      - uses: actions/checkout@v4
 
      - name: Build backend Docker image
        run: docker build -f docker/backend.dockerfile -t openuba-backend:ci .
 
  build-frontend:
    timeout-minutes: 30
    name: Build frontend image
    runs-on: latchkey-small
    steps:
      - uses: actions/checkout@v4
 
      - name: Build frontend Docker image
        run: docker build -f docker/frontend.dockerfile -t openuba-frontend:ci .
 
  build-operator:
    timeout-minutes: 30
    name: Build operator image
    runs-on: latchkey-small
    steps:
      - uses: actions/checkout@v4
 
      - name: Build operator Docker image
        run: docker build -f docker/operator.dockerfile -t openuba-operator:ci .
 
  build-model-runners:
    timeout-minutes: 30
    name: Build model runner images
    runs-on: latchkey-small
    steps:
      - uses: actions/checkout@v4
 
      - name: Build base model runner
        run: docker build -f docker/model-runner/Dockerfile.base -t openuba-model-runner:base .
 
      - name: Build sklearn runner
        run: docker build -f docker/model-runner/Dockerfile.sklearn -t openuba-model-runner:sklearn --build-arg BASE_IMAGE=openuba-model-runner:base .
 
      - name: Build PyTorch runner
        run: docker build -f docker/model-runner/Dockerfile.pytorch -t openuba-model-runner:pytorch --build-arg BASE_IMAGE=openuba-model-runner:base .
 
      - name: Build TensorFlow runner
        run: docker build -f docker/model-runner/Dockerfile.tensorflow -t openuba-model-runner:tensorflow --build-arg BASE_IMAGE=openuba-model-runner:base .
 
      - name: Build NetworkX runner
        run: docker build -f docker/model-runner/Dockerfile.networkx -t openuba-model-runner:networkx --build-arg BASE_IMAGE=openuba-model-runner:base .

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 uses latchkey-small; pick the runner size that fits the job.
Cancel superseded runs when a branch or PR gets a newer push.
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:

Container pulls and builds

This workflow runs 4 jobs per trigger. On Latchkey the same minutes cost up to 58% less than GitHub-hosted, with zero queue time.

Actions used in this workflow

actions/checkout