Publish CI images to GitHub Container Registry

Author: ekrov

.github/workflows/ci.yml

@@ -0,0 +1,72 @@
+name: CI
+
+on:
+  push:
+    branches: [main]
+  pull_request:
+    branches: [main]
+
+permissions:
+  contents: read
+
+jobs:
+  quality:
+    name: Lint and test
+    runs-on: ubuntu-latest
+    timeout-minutes: 20
+    steps:
+      - name: Check out repository
+        uses: actions/checkout@v5
+
+      - name: Set up Python 3.11
+        uses: actions/setup-python@v6
+        with:
+          python-version: "3.11"
+          cache: pip
+          cache-dependency-path: pyproject.toml
+
+      - name: Install CI dependencies
+        run: |
+          python -m pip install --upgrade pip
+          python -m pip install --editable ".[ci]"
+
+      - name: Run Ruff
+        run: python -m ruff check src tests scripts spark airflow/dags --select E4,E7,E9,F
+
+      - name: Run tests
+        run: python -m pytest -q
+
+  docker:
+    name: Build Docker image
+    needs: quality
+    runs-on: ubuntu-latest
+    timeout-minutes: 20
+    permissions:
+      contents: read
+      packages: write
+    steps:
+      - name: Check out repository
+        uses: actions/checkout@v5
+
+      - name: Set up Docker Buildx
+        uses: docker/setup-buildx-action@v3
+
+      - name: Log in to GitHub Container Registry
+        if: github.event_name == 'push' && github.ref == 'refs/heads/main'
+        uses: docker/login-action@v4
+        with:
+          registry: ghcr.io
+          username: ${{ github.actor }}
+          password: ${{ secrets.GITHUB_TOKEN }}
+
+      - name: Build API image
+        uses: docker/build-push-action@v6
+        with:
+          context: .
+          file: ./Dockerfile
+          push: ${{ github.event_name == 'push' && github.ref == 'refs/heads/main' }}
+          tags: |
+            ghcr.io/ekrov/factoryvision-mlops:${{ github.sha }}
+            ghcr.io/ekrov/factoryvision-mlops:latest
+          cache-from: type=gha
+          cache-to: type=gha,mode=max

README.md

@@ -1,5 +1,7 @@
 # FactoryVision
 
+[![CI](https://github.com/ekrov/factoryvision-mlops/actions/workflows/ci.yml/badge.svg)](https://github.com/ekrov/factoryvision-mlops/actions/workflows/ci.yml)
+
 ## Production-Grade Surface Defect Segmentation & MLOps Platform
 
 FactoryVision is an end-to-end industrial visual-inspection system for detecting and segmenting manufacturing surface defects. The project covers the complete machine-learning lifecycle:
@@ -204,7 +206,7 @@ clamp_min(
 
 ### CI/CD and deployment
 
-- Add a GitHub Actions pipeline for linting, tests, and Docker builds.
+- The [GitHub Actions CI workflow](.github/workflows/ci.yml) runs on pushes to `main` and pull requests targeting `main`. It installs Python 3.11, runs Ruff and `pytest`, and builds the API Docker image after the quality checks pass. Successful pushes to `main` are authenticated with the built-in GitHub token and published to `ghcr.io/ekrov/factoryvision-mlops` with both the commit SHA and `latest` tags.
 - Push successful images to GitHub Container Registry.
 - Deploy the FastAPI container to Azure Container Apps or another simple Azure target.
 - Document secrets, environment variables, load-test results, and rollback procedures.

pyproject.toml

@@ -38,6 +38,10 @@ dev = [
     "jupyter==1.1.1",
     "pytest==9.0.3",
 ]
+ci = [
+    "pytest==9.0.3",
+    "ruff==0.16.0",
+]
 
 [tool.setuptools.packages.find]
 where = ["src"]

scripts/register_best_model.py

@@ -8,7 +8,6 @@
 from typing import Any
 
 import mlflow
-import numpy as np
 import torch
 from mlflow.models import infer_signature
 from mlflow.tracking import MlflowClient

src/factoryvision/training/train.py

@@ -18,7 +18,7 @@
 import cv2
 import pandas as pd
 import torch
-from torch import Tensor, nn
+from torch import nn
 from torch.optim import Adam
 from torch.utils.data import DataLoader, WeightedRandomSampler
 

tests/test_storage.py

@@ -6,7 +6,7 @@
 
 from sqlalchemy import create_engine
 
-from factoryvision.api.inference import InferenceConfig, Prediction
+from factoryvision.api.inference import Prediction
 from factoryvision.api.schemas import BoundingBox
 from factoryvision.storage.database import initialize_database
 from factoryvision.storage.repository import PredictionRepository, image_id_from_bytes