E2E Test Environment

Overview

System under test: Azaion.Detections — FastAPI HTTP service exposing POST /detect, POST /detect/{media_id}, GET /detect/stream, GET /health Consumer app purpose: Standalone test runner that exercises the detection service through its public HTTP/SSE interfaces, validating end-to-end use cases without access to internals.

Docker Environment

Services

Service	Image / Build	Purpose	Ports
detections	Build from repo root (setup.py + Cython compile, uvicorn entrypoint)	System under test — the detection microservice	8000:8000
mock-loader	Custom lightweight HTTP stub (Python/Node)	Mock of the Loader service — serves ONNX model files, accepts TensorRT uploads	8080:8080
mock-annotations	Custom lightweight HTTP stub (Python/Node)	Mock of the Annotations service — accepts detection results, provides token refresh	8081:8081
e2e-consumer	Build from `e2e/` directory	Black-box test runner (pytest)	—

GPU Configuration

For tests requiring TensorRT (GPU path):

Deploy detections with runtime: nvidia and NVIDIA_VISIBLE_DEVICES=all
The test suite has two profiles: gpu (TensorRT tests) and cpu (ONNX fallback tests)
CPU-only tests run without GPU runtime, verifying ONNX fallback behavior

Networks

Network	Services	Purpose
e2e-net	all	Isolated test network — all service-to-service communication via hostnames

Volumes

Volume	Mounted to	Purpose
test-models	mock-loader:/models	Pre-built ONNX model file for test inference
test-media	e2e-consumer:/media	Sample images and video files for detection requests
test-classes	detections:/app/classes.json	classes.json with 19 detection classes
test-results	e2e-consumer:/results	CSV test report output

docker-compose structure

services:
  mock-loader:
    build: ./e2e/mocks/loader
    ports: ["8080:8080"]
    volumes:
      - test-models:/models
    networks: [e2e-net]

  mock-annotations:
    build: ./e2e/mocks/annotations
    ports: ["8081:8081"]
    networks: [e2e-net]

  detections:
    build:
      context: .
      dockerfile: Dockerfile
    ports: ["8000:8000"]
    environment:
      - LOADER_URL=http://mock-loader:8080
      - ANNOTATIONS_URL=http://mock-annotations:8081
    volumes:
      - test-classes:/app/classes.json
    depends_on:
      - mock-loader
      - mock-annotations
    networks: [e2e-net]
    # GPU profile adds: runtime: nvidia

  e2e-consumer:
    build: ./e2e
    volumes:
      - test-media:/media
      - test-results:/results
    depends_on:
      - detections
    networks: [e2e-net]
    command: pytest --csv=/results/report.csv

volumes:
  test-models:
  test-media:
  test-classes:
  test-results:

networks:
  e2e-net:

Consumer Application

Tech stack: Python 3, pytest, requests, sseclient-py Entry point: pytest --csv=/results/report.csv

Communication with system under test

Interface	Protocol	Endpoint	Authentication
Health check	HTTP GET	`http://detections:8000/health`	None
Single image detect	HTTP POST (multipart)	`http://detections:8000/detect`	None
Media detect	HTTP POST (JSON)	`http://detections:8000/detect/{media_id}`	Bearer JWT + x-refresh-token headers
SSE stream	HTTP GET (SSE)	`http://detections:8000/detect/stream`	None

What the consumer does NOT have access to

No direct import of Cython modules (inference, annotation, engines)
No direct access to the detections service filesystem or Logs/ directory
No shared memory with the detections process
No direct calls to mock-loader or mock-annotations (except for test setup/teardown verification)

CI/CD Integration

When to run: On PR merge to dev, nightly scheduled run Pipeline stage: After unit tests, before deployment Gate behavior: Block merge if any functional test fails; non-functional failures are warnings Timeout: 15 minutes for CPU profile, 30 minutes for GPU profile

Reporting

Format: CSV Columns: Test ID, Test Name, Execution Time (ms), Result (PASS/FAIL/SKIP), Error Message (if FAIL) Output path: /results/report.csv (mounted volume → ./e2e-results/report.csv on host)

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