mirror of
https://github.com/azaion/detections.git
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Update .gitignore and refine documentation for execution environment
- Added Cython generated files to .gitignore to prevent unnecessary tracking. - Updated paths in `inference.c` and `coreml_engine.c` to reflect the correct virtual environment. - Revised the execution environment documentation to clarify hardware dependency checks and local execution instructions, ensuring accurate guidance for users. - Removed outdated Docker suitability checks and streamlined the assessment process for test execution environments.
This commit is contained in:
Oleksandr Bezdieniezhnykh
@@ -32,36 +32,13 @@ Check in order — first match wins:
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If no runner detected → report failure and ask user to specify.
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#### Docker Suitability Check
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#### Execution Environment Check
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Docker is the preferred test environment. Before using it, verify no constraints prevent easy Docker execution:
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1. Check `_docs/02_document/tests/environment.md` for a "Test Execution" decision (if the test-spec skill already assessed this, follow that decision)
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2. If no prior decision exists, check for disqualifying factors:
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- Hardware bindings: GPU, MPS, CUDA, TPU, FPGA, sensors, cameras, serial devices, host-level drivers
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- Host dependencies: licensed software, OS-specific services, kernel modules, proprietary SDKs
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- Data/volume constraints: large files (> 100MB) impractical to copy into a container
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- Network/environment: host networking, VPN, specific DNS/firewall rules
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- Performance: Docker overhead would invalidate benchmarks or latency measurements
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3. If any disqualifying factor found → fall back to local test runner. Present to user using Choose format:
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```
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══════════════════════════════════════
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DECISION REQUIRED: Docker is preferred but factors
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preventing easy Docker execution detected
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══════════════════════════════════════
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Factors detected:
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- [list factors]
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══════════════════════════════════════
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A) Run tests locally (recommended)
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B) Run tests in Docker anyway
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══════════════════════════════════════
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Recommendation: A — detected constraints prevent
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easy Docker execution
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══════════════════════════════════════
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```
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4. If no disqualifying factors → use Docker (preferred default)
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1. Check `_docs/02_document/tests/environment.md` for a "Test Execution" section. If the test-spec skill already assessed hardware dependencies and recorded a decision (local / docker / both), **follow that decision**.
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2. If the "Test Execution" section says **local** → run tests directly on host (no Docker).
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3. If the "Test Execution" section says **docker** → use Docker (docker-compose).
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4. If the "Test Execution" section says **both** → run local first, then Docker (or vice versa), and merge results.
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5. If no prior decision exists → fall back to the hardware-dependency detection logic from the test-spec skill's "Hardware-Dependency & Execution Environment Assessment" section. Ask the user if hardware indicators are found.
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### 2. Run Tests
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@@ -209,7 +209,7 @@ Based on all acquired data, acceptance_criteria, and restrictions, form detailed
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- [ ] Expected results use comparison methods from `.cursor/skills/test-spec/templates/expected-results.md`
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- [ ] Positive and negative scenarios are balanced
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- [ ] Consumer app has no direct access to system internals
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- [ ] Test environment matches project constraints (see Docker Suitability Assessment below)
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- [ ] Test environment matches project constraints (see Hardware-Dependency & Execution Environment Assessment below)
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- [ ] External dependencies have mock/stub services defined
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- [ ] Traceability matrix has no uncovered AC or restrictions
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@@ -337,43 +337,80 @@ When coverage ≥ 70% and all remaining tests have validated data AND quantifiab
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---
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### Docker Suitability Assessment (BLOCKING — runs before Phase 4)
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### Hardware-Dependency & Execution Environment Assessment (BLOCKING — runs before Phase 4)
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Docker is the **preferred** test execution environment (reproducibility, isolation, CI parity). Before generating scripts, check whether the project has any constraints that prevent easy Docker usage.
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Docker is the **preferred** test execution environment (reproducibility, isolation, CI parity). However, hardware-dependent projects may require local execution to exercise the real code paths. This assessment determines the right execution strategy by scanning both documentation and source code.
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**Disqualifying factors** (any one is sufficient to fall back to local):
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- Hardware bindings: GPU, MPS, TPU, FPGA, accelerators, sensors, cameras, serial devices, host-level drivers (CUDA, Metal, OpenCL, etc.)
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- Host dependencies: licensed software, OS-specific services, kernel modules, proprietary SDKs not installable in a container
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- Data/volume constraints: large files (> 100MB) that would be impractical to copy into a container, databases that must run on the host
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- Network/environment: tests that require host networking, VPN access, or specific DNS/firewall rules
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- Performance: Docker overhead would invalidate benchmarks or latency-sensitive measurements
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#### Step 1 — Documentation scan
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**Assessment steps**:
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1. Scan project source, config files, and dependencies for indicators of the factors above
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2. Check `TESTS_OUTPUT_DIR/environment.md` for environment requirements
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3. Check `_docs/00_problem/restrictions.md` and `_docs/01_solution/solution.md` for constraints
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Check the following files for mentions of hardware-specific requirements:
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**Decision**:
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- If ANY disqualifying factor is found → recommend **local test execution** as fallback. Present to user using Choose format:
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| File | Look for |
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|------|----------|
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| `_docs/00_problem/restrictions.md` | Platform requirements, hardware constraints, OS-specific features |
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| `_docs/01_solution/solution.md` | Engine selection logic, platform-dependent paths, hardware acceleration |
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| `_docs/02_document/architecture.md` | Component diagrams showing hardware layers, engine adapters |
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| `_docs/02_document/components/*/description.md` | Per-component hardware mentions |
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| `TESTS_OUTPUT_DIR/environment.md` | Existing environment decisions |
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#### Step 2 — Code scan
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Search the project source for indicators of hardware dependence. The project is **hardware-dependent** if ANY of the following are found:
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| Category | Code indicators (imports, APIs, config) |
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|----------|-----------------------------------------|
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| GPU / CUDA | `import pycuda`, `import tensorrt`, `import pynvml`, `torch.cuda`, `nvidia-smi`, `CUDA_VISIBLE_DEVICES`, `runtime: nvidia` |
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| Apple Neural Engine / CoreML | `import coremltools`, `CoreML`, `MLModel`, `ComputeUnit`, `MPS`, `sys.platform == "darwin"`, `platform.machine() == "arm64"` |
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| OpenCL / Vulkan | `import pyopencl`, `clCreateContext`, vulkan headers |
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| TPU / FPGA | `import tensorflow.distribute.TPUStrategy`, FPGA bitstream loaders |
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| Sensors / Cameras | `import cv2.VideoCapture(0)` (device index), serial port access, GPIO, V4L2 |
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| OS-specific services | Kernel modules (`modprobe`), host-level drivers, platform-gated code (`sys.platform` branches selecting different backends) |
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Also check dependency files (`requirements.txt`, `setup.py`, `pyproject.toml`, `Cargo.toml`, `*.csproj`) for hardware-specific packages.
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#### Step 3 — Classify the project
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Based on Steps 1–2, classify the project:
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- **Not hardware-dependent**: no indicators found → use Docker (preferred default), skip to "Record the decision" below
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- **Hardware-dependent**: one or more indicators found → proceed to Step 4
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#### Step 4 — Present execution environment choice
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Present the findings and ask the user using Choose format:
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```
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══════════════════════════════════════
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DECISION REQUIRED: Test execution environment
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══════════════════════════════════════
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Docker is preferred, but factors preventing easy
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Docker execution detected:
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- [list factors found]
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Hardware dependencies detected:
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- [list each indicator found, with file:line]
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══════════════════════════════════════
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A) Local execution (recommended)
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B) Docker execution (constraints may cause issues)
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Running in Docker means these hardware code paths
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are NOT exercised — Docker uses a Linux VM where
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[specific hardware, e.g. CoreML / CUDA] is unavailable.
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The system would fall back to [fallback engine/path].
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══════════════════════════════════════
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Recommendation: A — detected constraints prevent
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easy Docker execution
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A) Local execution only (tests the real hardware path)
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B) Docker execution only (tests the fallback path)
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C) Both local and Docker (tests both paths, requires
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two test runs — recommended for CI with heterogeneous
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runners)
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══════════════════════════════════════
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Recommendation: [A, B, or C] — [reason]
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══════════════════════════════════════
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```
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- If NO disqualifying factors → use Docker (preferred default)
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- Record the decision in `TESTS_OUTPUT_DIR/environment.md` under a "Test Execution" section
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#### Step 5 — Record the decision
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Write or update a **"Test Execution"** section in `TESTS_OUTPUT_DIR/environment.md` with:
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1. **Decision**: local / docker / both
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2. **Hardware dependencies found**: list with file references
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3. **Execution instructions** per chosen mode:
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- **Local mode**: prerequisites (OS, SDK, hardware), how to start services, how to run the test runner, environment variables
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- **Docker mode**: docker-compose profile/command, required images, how results are collected
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- **Both mode**: instructions for each, plus guidance on which CI runner type runs which mode
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---
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@@ -4,6 +4,9 @@
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*~
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Thumbs.db
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# Cython generated
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*.c
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# Python
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__pycache__/
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*.py[cod]
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@@ -1590,3 +1590,180 @@
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[00:21:21 INFO] init AI...
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[00:21:21 INFO] init AI...
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[00:21:21 INFO] init AI...
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[00:41:54 INFO] init AI...
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[00:41:54 INFO] Downloading
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[00:41:57 INFO] CoreML model: 1280x1280
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[00:41:57 INFO] Enabled
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[00:42:05 INFO] init AI...
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[00:42:05 INFO] init AI...
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[00:42:05 INFO] run inference on ./fixtures/image_small.jpg...
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[00:42:06 INFO] init AI...
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[00:42:06 INFO] run inference on ./fixtures/video_test01.mp4...
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[00:42:06 ERROR] Failed to open video: ./fixtures/video_test01.mp4
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[00:45:22 INFO] init AI...
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[00:45:22 INFO] Downloading
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[00:45:24 INFO] CoreML model: 1280x1280
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[00:45:24 INFO] Enabled
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[00:45:31 INFO] init AI...
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[00:45:31 INFO] init AI...
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[00:45:31 INFO] run inference on /Users/obezdienie001/dev/azaion/suite/detections/e2e/fixtures/image_small.jpg...
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[00:45:31 INFO] ground sampling distance: 0.3059895833333333
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[00:45:31 INFO] Initial ann: image_small_000000: class: 0 77.0% (0.47, 0.21) (0.14, 0.19)
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[00:45:31 INFO] Removed (53.80277931690216 42.89022199809551) > 8. class: ArmorVehicle
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[00:45:32 INFO] init AI...
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[00:45:32 INFO] run inference on /Users/obezdienie001/dev/azaion/suite/detections/e2e/fixtures/video_test01.mp4...
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[00:45:32 INFO] Video: 200 frames, 25.0 fps, 2560x1440
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[00:45:32 INFO] Video batch 1: frame 4/200 (2%)
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[00:45:32 INFO] Video batch 2: frame 8/200 (4%)
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[00:45:32 INFO] Video batch 3: frame 12/200 (6%)
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[00:45:32 INFO] Video batch 4: frame 16/200 (8%)
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[00:45:32 INFO] Video batch 4: 1 detections from postprocess
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[00:45:32 INFO] Video frame video_test01_000006: 1 dets, valid=True
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[00:45:32 INFO] Video batch 5: frame 20/200 (10%)
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[00:45:33 INFO] Video batch 5: 1 detections from postprocess
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[00:45:33 INFO] Video frame video_test01_000007: 1 dets, valid=True
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[00:45:33 INFO] Video batch 6: frame 24/200 (12%)
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[00:45:33 INFO] Video batch 6: 1 detections from postprocess
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[00:45:33 INFO] Video frame video_test01_000009: 1 dets, valid=True
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[00:45:33 INFO] Video batch 7: frame 28/200 (14%)
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[00:45:33 INFO] Video batch 7: 1 detections from postprocess
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[00:45:33 INFO] Video frame video_test01_000010: 1 dets, valid=True
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[00:45:33 INFO] Video batch 8: frame 32/200 (16%)
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[00:45:33 INFO] Video batch 8: 1 detections from postprocess
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[00:45:33 INFO] Video frame video_test01_000012: 1 dets, valid=True
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[00:45:33 INFO] Video batch 9: frame 36/200 (18%)
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[00:45:33 INFO] Video batch 9: 1 detections from postprocess
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[00:45:33 INFO] Video frame video_test01_000014: 1 dets, valid=True
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[00:45:33 INFO] Video batch 10: frame 40/200 (20%)
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[00:45:33 INFO] Video batch 10: 1 detections from postprocess
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[00:45:33 INFO] Video frame video_test01_000015: 1 dets, valid=True
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[00:45:33 INFO] Video batch 11: frame 44/200 (22%)
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[00:45:33 INFO] Video batch 11: 1 detections from postprocess
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[00:45:33 INFO] Video frame video_test01_000017: 1 dets, valid=True
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[00:45:33 INFO] Video batch 12: frame 48/200 (24%)
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[00:45:33 INFO] Video batch 12: 1 detections from postprocess
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[00:45:33 INFO] Video frame video_test01_000018: 1 dets, valid=True
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[00:45:33 INFO] Video batch 13: frame 52/200 (26%)
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[00:45:34 INFO] Video batch 13: 1 detections from postprocess
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[00:45:34 INFO] Video frame video_test01_000020: 1 dets, valid=True
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[00:45:34 INFO] Video batch 14: frame 56/200 (28%)
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[00:45:34 INFO] Video batch 14: 1 detections from postprocess
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[00:45:34 INFO] Video frame video_test01_000022: 1 dets, valid=True
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[00:45:34 INFO] Video batch 15: frame 60/200 (30%)
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[00:45:34 INFO] Video batch 15: 1 detections from postprocess
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[00:45:34 INFO] Video frame video_test01_000023: 1 dets, valid=True
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[00:45:34 INFO] Video batch 16: frame 64/200 (32%)
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[00:45:34 INFO] Video batch 16: 1 detections from postprocess
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[00:45:34 INFO] Video frame video_test01_000025: 1 dets, valid=True
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[00:45:34 INFO] Video batch 17: frame 68/200 (34%)
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[00:45:34 INFO] Video batch 17: 1 detections from postprocess
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[00:45:34 INFO] Video frame video_test01_000026: 1 dets, valid=True
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[00:45:34 INFO] Video batch 18: frame 72/200 (36%)
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[00:45:34 INFO] Video batch 18: 1 detections from postprocess
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[00:45:34 INFO] Video frame video_test01_000028: 1 dets, valid=True
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[00:45:34 INFO] Video batch 19: frame 76/200 (38%)
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[00:45:34 INFO] Video batch 19: 1 detections from postprocess
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[00:45:34 INFO] Video frame video_test01_000030: 1 dets, valid=True
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[00:45:34 INFO] Video batch 20: frame 80/200 (40%)
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[00:45:34 INFO] Video batch 20: 1 detections from postprocess
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[00:45:34 INFO] Video frame video_test01_000031: 1 dets, valid=True
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[00:45:34 INFO] Video batch 21: frame 84/200 (42%)
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[00:45:35 INFO] Video batch 21: 1 detections from postprocess
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[00:45:35 INFO] Video frame video_test01_000033: 1 dets, valid=True
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[00:45:35 INFO] Video batch 22: frame 88/200 (44%)
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[00:45:35 INFO] Video batch 22: 1 detections from postprocess
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[00:45:35 INFO] Video frame video_test01_000034: 1 dets, valid=True
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[00:45:35 INFO] Video batch 23: frame 92/200 (46%)
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[00:45:35 INFO] Video batch 24: frame 96/200 (48%)
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[00:45:35 INFO] Video batch 25: frame 100/200 (50%)
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[00:45:35 INFO] Video batch 26: frame 104/200 (52%)
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[00:45:35 INFO] Video batch 26: 1 detections from postprocess
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[00:45:35 INFO] Video frame video_test01_000041: 1 dets, valid=True
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[00:45:35 INFO] Video batch 27: frame 108/200 (54%)
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[00:45:35 INFO] Video batch 27: 1 detections from postprocess
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[00:45:35 INFO] Video frame video_test01_000042: 1 dets, valid=True
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[00:45:35 INFO] Video batch 28: frame 112/200 (56%)
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[00:45:35 INFO] Video batch 28: 1 detections from postprocess
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[00:45:35 INFO] Video frame video_test01_000044: 1 dets, valid=True
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[00:45:35 INFO] Video batch 29: frame 116/200 (58%)
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[00:45:35 INFO] Video batch 29: 1 detections from postprocess
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[00:45:35 INFO] Video frame video_test01_000046: 1 dets, valid=True
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[00:45:36 INFO] Video batch 30: frame 120/200 (60%)
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[00:45:36 INFO] Video batch 30: 1 detections from postprocess
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[00:45:36 INFO] Video frame video_test01_000047: 1 dets, valid=True
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[00:45:36 INFO] Video batch 31: frame 124/200 (62%)
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[00:45:36 INFO] Video batch 31: 1 detections from postprocess
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[00:45:36 INFO] Video frame video_test01_000049: 1 dets, valid=True
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[00:45:36 INFO] Video batch 32: frame 128/200 (64%)
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[00:45:36 INFO] Video batch 32: 1 detections from postprocess
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[00:45:36 INFO] Video frame video_test01_000050: 1 dets, valid=True
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[00:45:36 INFO] Video batch 33: frame 132/200 (66%)
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[00:45:36 INFO] Video batch 33: 1 detections from postprocess
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[00:45:36 INFO] Video frame video_test01_000052: 1 dets, valid=True
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[00:45:36 INFO] Video batch 34: frame 136/200 (68%)
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[00:45:36 INFO] Video batch 34: 1 detections from postprocess
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[00:45:36 INFO] Video frame video_test01_000054: 1 dets, valid=True
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[00:45:36 INFO] Video batch 35: frame 140/200 (70%)
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[00:45:36 INFO] Video batch 35: 1 detections from postprocess
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[00:45:36 INFO] Video frame video_test01_000055: 1 dets, valid=True
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[00:45:36 INFO] Video batch 36: frame 144/200 (72%)
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[00:45:36 INFO] Video batch 36: 1 detections from postprocess
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[00:45:36 INFO] Video frame video_test01_000057: 1 dets, valid=True
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[00:45:36 INFO] Video batch 37: frame 148/200 (74%)
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[00:45:36 INFO] Video batch 38: frame 152/200 (76%)
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[00:45:37 INFO] Video batch 39: frame 156/200 (78%)
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[00:45:37 INFO] Video batch 40: frame 160/200 (80%)
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[00:45:37 INFO] Video batch 41: frame 164/200 (82%)
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[00:45:37 INFO] Video batch 42: frame 168/200 (84%)
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[00:45:37 INFO] Video batch 42: 1 detections from postprocess
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[00:45:37 INFO] Video frame video_test01_000066: 1 dets, valid=True
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[00:45:37 INFO] Video batch 43: frame 172/200 (86%)
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[00:45:37 INFO] Video batch 43: 1 detections from postprocess
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[00:45:37 INFO] Video frame video_test01_000068: 1 dets, valid=True
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[00:45:37 INFO] Video batch 44: frame 176/200 (88%)
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[00:45:37 INFO] Video batch 45: frame 180/200 (90%)
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[00:45:37 INFO] Video batch 46: frame 184/200 (92%)
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[00:45:37 INFO] Video batch 46: 1 detections from postprocess
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[00:45:37 INFO] Video frame video_test01_000073: 1 dets, valid=True
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[00:45:38 INFO] Video batch 47: frame 188/200 (94%)
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[00:45:38 INFO] Video batch 47: 1 detections from postprocess
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[00:45:38 INFO] Video frame video_test01_000074: 1 dets, valid=True
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[00:45:38 INFO] Video batch 48: frame 192/200 (96%)
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[00:45:38 INFO] Video batch 48: 1 detections from postprocess
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[00:45:38 INFO] Video frame video_test01_000076: 1 dets, valid=True
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[00:45:38 INFO] Video batch 49: frame 196/200 (98%)
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[00:45:38 INFO] Video batch 49: 1 detections from postprocess
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[00:45:38 INFO] Video frame video_test01_000078: 1 dets, valid=True
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[00:45:38 INFO] Video batch 50: frame 200/200 (100%)
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[00:45:38 INFO] Video batch 50: 1 detections from postprocess
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[00:45:38 INFO] Video frame video_test01_000079: 1 dets, valid=True
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[00:45:38 INFO] Video done: 200 frames read, 50 batches processed
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[00:45:38 INFO] init AI...
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[00:45:38 INFO] run inference on /Users/obezdienie001/dev/azaion/suite/detections/e2e/fixtures/image_small.jpg...
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||||
[00:45:38 INFO] ground sampling distance: 0.3059895833333333
|
||||
[00:45:38 INFO] init AI...
|
||||
[00:45:38 INFO] Initial ann: image_small_000000: class: 0 77.0% (0.47, 0.21) (0.14, 0.19)
|
||||
[00:45:38 INFO] Removed (53.80277931690216 42.89022199809551) > 8. class: ArmorVehicle
|
||||
[00:45:38 INFO] init AI...
|
||||
[00:45:38 INFO] init AI...
|
||||
[00:45:38 INFO] init AI...
|
||||
[00:45:39 INFO] init AI...
|
||||
[00:45:39 INFO] init AI...
|
||||
[00:45:39 INFO] init AI...
|
||||
[00:45:39 INFO] init AI...
|
||||
[00:45:39 INFO] init AI...
|
||||
[00:45:39 INFO] init AI...
|
||||
[00:45:39 INFO] init AI...
|
||||
[00:45:39 INFO] init AI...
|
||||
[00:45:39 INFO] init AI...
|
||||
[00:45:40 INFO] init AI...
|
||||
[00:45:40 INFO] init AI...
|
||||
[00:45:40 INFO] init AI...
|
||||
[00:45:40 INFO] init AI...
|
||||
[00:45:40 INFO] init AI...
|
||||
[00:45:40 INFO] init AI...
|
||||
[00:45:40 INFO] init AI...
|
||||
[00:53:38 INFO] init AI...
|
||||
[00:53:38 INFO] Downloading
|
||||
[00:53:41 INFO] CoreML model: 1280x1280
|
||||
[00:53:41 INFO] Enabled
|
||||
|
||||
@@ -5,104 +5,106 @@
|
||||
**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 black-box use cases without access to internals.
|
||||
|
||||
## Docker Environment
|
||||
## Test Execution
|
||||
|
||||
### Services
|
||||
**Decision**: local
|
||||
|
||||
| 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) | — |
|
||||
### Hardware Dependencies Found
|
||||
|
||||
### GPU Configuration
|
||||
| # | Category | Indicator | File | Detail |
|
||||
|---|----------|-----------|------|--------|
|
||||
| 1 | Apple CoreML | `import coremltools`, `ct.models.MLModel`, `ct.ComputeUnit.ALL` | `engines/coreml_engine.pyx:15-22` | CoreML engine requires macOS + Apple Silicon for Neural Engine / GPU inference |
|
||||
| 2 | Apple platform gate | `sys.platform == "darwin"`, `platform.machine() == "arm64"` | `engines/__init__.py:29` | Engine auto-selection branches on macOS arm64 |
|
||||
| 3 | NVIDIA GPU / CUDA | `import pynvml`, `nvmlDeviceGetCudaComputeCapability` | `engines/__init__.py:7-14` | TensorRT engine requires NVIDIA GPU with compute capability ≥ 6.1 |
|
||||
| 4 | TensorRT | `from engines.tensorrt_engine import TensorRTEngine` | `engines/__init__.py:43` | TensorRT runtime only available on Linux with NVIDIA drivers |
|
||||
| 5 | Cython compilation | `setup.py build_ext --inplace` | `Dockerfile:8`, `setup.py` | Cython modules must be compiled natively for the host architecture |
|
||||
|
||||
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
|
||||
### Rationale
|
||||
|
||||
### Networks
|
||||
The detection service uses a polymorphic engine factory (`engines/__init__.py:EngineClass`) that auto-selects the inference backend at startup:
|
||||
|
||||
| Network | Services | Purpose |
|
||||
|---------|----------|---------|
|
||||
| e2e-net | all | Isolated test network — all service-to-service communication via hostnames |
|
||||
1. **TensorRT** — if NVIDIA GPU with CUDA compute ≥ 6.1 is detected
|
||||
2. **CoreML** — if macOS arm64 with `coremltools` installed
|
||||
3. **ONNX** — CPU-only fallback
|
||||
|
||||
### Volumes
|
||||
Running in Docker on macOS means a Linux VM where neither CoreML nor TensorRT is available. The service falls back to the ONNX CPU engine — which is a valid but different code path. To test the actual engine used in the target deployment environment, tests must run locally.
|
||||
|
||||
| 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 |
|
||||
### Local Execution Instructions
|
||||
|
||||
### docker-compose structure
|
||||
#### Prerequisites
|
||||
|
||||
```yaml
|
||||
services:
|
||||
mock-loader:
|
||||
build: ./e2e/mocks/loader
|
||||
ports: ["8080:8080"]
|
||||
volumes:
|
||||
- test-models:/models
|
||||
networks: [e2e-net]
|
||||
- macOS arm64 (Apple Silicon) with `coremltools` installed, OR Linux with NVIDIA GPU + TensorRT for the GPU path
|
||||
- Python 3.11 with Cython modules compiled (`python setup.py build_ext --inplace`)
|
||||
- `flask` and `gunicorn` installed (for mock services)
|
||||
- `pytest`, `requests`, `sseclient-py`, `pytest-csv`, `pytest-timeout` installed (for test runner)
|
||||
|
||||
mock-annotations:
|
||||
build: ./e2e/mocks/annotations
|
||||
ports: ["8081:8081"]
|
||||
networks: [e2e-net]
|
||||
#### 1. Start mock services
|
||||
|
||||
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
|
||||
Open two terminal tabs and start the mocks. The mock-loader serves ONNX/CoreML model files from `e2e/fixtures/models/` and the mock-annotations accepts detection result posts.
|
||||
|
||||
e2e-consumer:
|
||||
build: ./e2e
|
||||
volumes:
|
||||
- test-media:/media
|
||||
- test-results:/results
|
||||
depends_on:
|
||||
- detections
|
||||
networks: [e2e-net]
|
||||
command: pytest --csv=/results/report.csv
|
||||
```bash
|
||||
# Terminal 1 — mock-loader (port 8080)
|
||||
cd e2e/mocks/loader
|
||||
MODELS_ROOT=../../fixtures gunicorn -b 0.0.0.0:8080 -w 1 --timeout 120 app:app
|
||||
|
||||
volumes:
|
||||
test-models:
|
||||
test-media:
|
||||
test-classes:
|
||||
test-results:
|
||||
|
||||
networks:
|
||||
e2e-net:
|
||||
# Terminal 2 — mock-annotations (port 8081)
|
||||
cd e2e/mocks/annotations
|
||||
gunicorn -b 0.0.0.0:8081 -w 1 --timeout 120 app:app
|
||||
```
|
||||
|
||||
#### 2. Start the detection service
|
||||
|
||||
```bash
|
||||
# Terminal 3 — detections (port 8080 by default, override to 8000 to avoid mock conflict)
|
||||
LOADER_URL=http://localhost:8080 \
|
||||
ANNOTATIONS_URL=http://localhost:8081 \
|
||||
python -m uvicorn main:app --host 0.0.0.0 --port 8000
|
||||
```
|
||||
|
||||
#### 3. Run tests
|
||||
|
||||
```bash
|
||||
# Terminal 4 — test runner
|
||||
cd e2e
|
||||
BASE_URL=http://localhost:8000 \
|
||||
MOCK_LOADER_URL=http://localhost:8080 \
|
||||
MOCK_ANNOTATIONS_URL=http://localhost:8081 \
|
||||
MEDIA_DIR=./fixtures \
|
||||
pytest tests/ -v --csv=./results/report.csv --timeout=300
|
||||
```
|
||||
|
||||
#### Environment Variables
|
||||
|
||||
| Variable | Default | Purpose |
|
||||
|----------|---------|---------|
|
||||
| `BASE_URL` | `http://detections:8080` | Detection service URL (override to `http://localhost:8000` for local) |
|
||||
| `MOCK_LOADER_URL` | `http://mock-loader:8080` | Mock loader URL (override to `http://localhost:8080` for local) |
|
||||
| `MOCK_ANNOTATIONS_URL` | `http://mock-annotations:8081` | Mock annotations URL (override to `http://localhost:8081` for local) |
|
||||
| `MEDIA_DIR` | `/media` | Path to test media fixtures (override to `./fixtures` for local) |
|
||||
| `LOADER_URL` | `http://loader:8080` | Loader URL for detections service |
|
||||
| `ANNOTATIONS_URL` | `http://annotations:8080` | Annotations URL for detections service |
|
||||
|
||||
## Services
|
||||
|
||||
| Service | Role | Local Port | Startup Command |
|
||||
|---------|------|------------|-----------------|
|
||||
| detections | System under test | 8000 | `uvicorn main:app --port 8000` |
|
||||
| mock-loader | Serves model files, accepts engine uploads | 8080 | `gunicorn -b 0.0.0.0:8080 app:app` |
|
||||
| mock-annotations | Accepts detection results, provides token refresh | 8081 | `gunicorn -b 0.0.0.0:8081 app:app` |
|
||||
|
||||
## Consumer Application
|
||||
|
||||
**Tech stack**: Python 3, pytest, requests, sseclient-py
|
||||
**Entry point**: `pytest --csv=/results/report.csv`
|
||||
**Entry point**: `pytest tests/ -v --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 |
|
||||
| Health check | HTTP GET | `{BASE_URL}/health` | None |
|
||||
| Single image detect | HTTP POST (multipart) | `{BASE_URL}/detect` | None |
|
||||
| Media detect | HTTP POST (JSON) | `{BASE_URL}/detect/{media_id}` | Bearer JWT + x-refresh-token headers |
|
||||
| SSE stream | HTTP GET (SSE) | `{BASE_URL}/detect/stream` | None |
|
||||
|
||||
### What the consumer does NOT have access to
|
||||
|
||||
@@ -117,9 +119,10 @@ networks:
|
||||
**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
|
||||
**Runner requirement**: macOS arm64 self-hosted runner (for CoreML path) or Linux with NVIDIA GPU (for TensorRT path)
|
||||
|
||||
## 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)
|
||||
**Output path**: `e2e/results/report.csv`
|
||||
|
||||
@@ -46,7 +46,3 @@ def test_ft_n_03_loader_error_mode_detect_does_not_500(
|
||||
assert r.status_code != 500
|
||||
|
||||
|
||||
@pytest.mark.skip(reason="Requires separate Docker profile without classes.json")
|
||||
@pytest.mark.cpu
|
||||
def test_ft_n_05_missing_classes_json_prevents_normal_operation():
|
||||
pass
|
||||
|
||||
@@ -1,14 +1,9 @@
|
||||
import json
|
||||
import os
|
||||
import threading
|
||||
import time
|
||||
import uuid
|
||||
from concurrent.futures import ThreadPoolExecutor
|
||||
|
||||
import pytest
|
||||
|
||||
_MEDIA = os.environ.get("MEDIA_DIR", "/media")
|
||||
|
||||
|
||||
def _percentile_ms(sorted_ms, p):
|
||||
n = len(sorted_ms)
|
||||
@@ -119,59 +114,3 @@ def test_nft_perf_03_tiling_overhead_large_image(
|
||||
assert large_ms > small_ms - 500.0
|
||||
|
||||
|
||||
@pytest.mark.skip(reason="video perf covered by test_ft_p09_sse_event_delivery")
|
||||
@pytest.mark.slow
|
||||
@pytest.mark.timeout(300)
|
||||
def test_nft_perf_04_video_frame_rate_sse(
|
||||
warm_engine,
|
||||
http_client,
|
||||
jwt_token,
|
||||
sse_client_factory,
|
||||
):
|
||||
media_id = f"perf-sse-{uuid.uuid4().hex}"
|
||||
body = {
|
||||
"probability_threshold": 0.25,
|
||||
"paths": [f"{_MEDIA}/video_test01.mp4"],
|
||||
"frame_period_recognition": 4,
|
||||
"frame_recognition_seconds": 2,
|
||||
}
|
||||
headers = {"Authorization": f"Bearer {jwt_token}"}
|
||||
stamps = []
|
||||
thread_exc = []
|
||||
done = threading.Event()
|
||||
|
||||
def _listen():
|
||||
try:
|
||||
with sse_client_factory() as sse:
|
||||
time.sleep(0.3)
|
||||
for event in sse.events():
|
||||
if not event.data or not str(event.data).strip():
|
||||
continue
|
||||
data = json.loads(event.data)
|
||||
if data.get("mediaId") != media_id:
|
||||
continue
|
||||
stamps.append(time.monotonic())
|
||||
if (
|
||||
data.get("mediaStatus") == "AIProcessed"
|
||||
and data.get("mediaPercent") == 100
|
||||
):
|
||||
break
|
||||
except BaseException as e:
|
||||
thread_exc.append(e)
|
||||
finally:
|
||||
done.set()
|
||||
|
||||
th = threading.Thread(target=_listen, daemon=True)
|
||||
th.start()
|
||||
time.sleep(0.5)
|
||||
r = http_client.post(f"/detect/{media_id}", json=body, headers=headers)
|
||||
assert r.status_code == 200
|
||||
ok = done.wait(timeout=290)
|
||||
assert ok
|
||||
th.join(timeout=5)
|
||||
assert not thread_exc
|
||||
assert len(stamps) >= 2
|
||||
span = stamps[-1] - stamps[0]
|
||||
assert span <= 290.0
|
||||
gaps = [stamps[i + 1] - stamps[i] for i in range(len(stamps) - 1)]
|
||||
assert max(gaps) <= 30.0
|
||||
|
||||
@@ -1,27 +1,7 @@
|
||||
import json
|
||||
import os
|
||||
import threading
|
||||
import time
|
||||
import uuid
|
||||
|
||||
import pytest
|
||||
import requests
|
||||
|
||||
_DETECT_TIMEOUT = 60
|
||||
_MEDIA = os.environ.get("MEDIA_DIR", "/media")
|
||||
|
||||
|
||||
def _ai_config_video() -> dict:
|
||||
return {
|
||||
"probability_threshold": 0.25,
|
||||
"tracking_intersection_threshold": 0.6,
|
||||
"altitude": 400,
|
||||
"focal_length": 24,
|
||||
"sensor_width": 23.5,
|
||||
"paths": [f"{_MEDIA}/video_test01.mp4"],
|
||||
"frame_period_recognition": 4,
|
||||
"frame_recognition_seconds": 2,
|
||||
}
|
||||
|
||||
|
||||
def test_ft_n_06_loader_unreachable_during_init_health(
|
||||
@@ -44,62 +24,6 @@ def test_ft_n_06_loader_unreachable_during_init_health(
|
||||
assert d.get("errorMessage") is None
|
||||
|
||||
|
||||
@pytest.mark.skip(reason="video resilience covered by test_ft_p09_sse_event_delivery")
|
||||
@pytest.mark.slow
|
||||
@pytest.mark.timeout(300)
|
||||
def test_ft_n_07_annotations_unreachable_detection_continues(
|
||||
warm_engine,
|
||||
http_client,
|
||||
jwt_token,
|
||||
mock_annotations_url,
|
||||
sse_client_factory,
|
||||
):
|
||||
requests.post(
|
||||
f"{mock_annotations_url}/mock/config", json={"mode": "error"}, timeout=10
|
||||
).raise_for_status()
|
||||
media_id = f"res-n07-{uuid.uuid4().hex}"
|
||||
body = _ai_config_video()
|
||||
headers = {"Authorization": f"Bearer {jwt_token}"}
|
||||
collected = []
|
||||
thread_exc = []
|
||||
done = threading.Event()
|
||||
|
||||
def _listen():
|
||||
try:
|
||||
with sse_client_factory() as sse:
|
||||
time.sleep(0.3)
|
||||
for event in sse.events():
|
||||
if not event.data or not str(event.data).strip():
|
||||
continue
|
||||
data = json.loads(event.data)
|
||||
if data.get("mediaId") != media_id:
|
||||
continue
|
||||
collected.append(data)
|
||||
if (
|
||||
data.get("mediaStatus") == "AIProcessed"
|
||||
and data.get("mediaPercent") == 100
|
||||
):
|
||||
break
|
||||
except BaseException as e:
|
||||
thread_exc.append(e)
|
||||
finally:
|
||||
done.set()
|
||||
|
||||
th = threading.Thread(target=_listen, daemon=True)
|
||||
th.start()
|
||||
time.sleep(0.5)
|
||||
pr = http_client.post(f"/detect/{media_id}", json=body, headers=headers)
|
||||
assert pr.status_code == 200
|
||||
ok = done.wait(timeout=290)
|
||||
assert ok
|
||||
th.join(timeout=5)
|
||||
assert not thread_exc
|
||||
assert any(
|
||||
e.get("mediaStatus") == "AIProcessed" and e.get("mediaPercent") == 100
|
||||
for e in collected
|
||||
)
|
||||
|
||||
|
||||
def test_nft_res_01_loader_outage_after_init(
|
||||
warm_engine, http_client, mock_loader_url, image_small
|
||||
):
|
||||
@@ -117,62 +41,6 @@ def test_nft_res_01_loader_outage_after_init(
|
||||
assert hd.get("errorMessage") is None
|
||||
|
||||
|
||||
@pytest.mark.skip(reason="Single video run — covered by test_ft_p09_sse_event_delivery")
|
||||
@pytest.mark.slow
|
||||
@pytest.mark.timeout(300)
|
||||
def test_nft_res_02_annotations_outage_during_async_detection(
|
||||
warm_engine,
|
||||
http_client,
|
||||
jwt_token,
|
||||
mock_annotations_url,
|
||||
sse_client_factory,
|
||||
):
|
||||
media_id = f"res-n02-{uuid.uuid4().hex}"
|
||||
body = _ai_config_video()
|
||||
headers = {"Authorization": f"Bearer {jwt_token}"}
|
||||
collected = []
|
||||
thread_exc = []
|
||||
done = threading.Event()
|
||||
|
||||
def _listen():
|
||||
try:
|
||||
with sse_client_factory() as sse:
|
||||
time.sleep(0.3)
|
||||
for event in sse.events():
|
||||
if not event.data or not str(event.data).strip():
|
||||
continue
|
||||
data = json.loads(event.data)
|
||||
if data.get("mediaId") != media_id:
|
||||
continue
|
||||
collected.append(data)
|
||||
if (
|
||||
data.get("mediaStatus") == "AIProcessed"
|
||||
and data.get("mediaPercent") == 100
|
||||
):
|
||||
break
|
||||
except BaseException as e:
|
||||
thread_exc.append(e)
|
||||
finally:
|
||||
done.set()
|
||||
|
||||
th = threading.Thread(target=_listen, daemon=True)
|
||||
th.start()
|
||||
time.sleep(0.5)
|
||||
pr = http_client.post(f"/detect/{media_id}", json=body, headers=headers)
|
||||
assert pr.status_code == 200
|
||||
requests.post(
|
||||
f"{mock_annotations_url}/mock/config", json={"mode": "error"}, timeout=10
|
||||
).raise_for_status()
|
||||
ok = done.wait(timeout=290)
|
||||
assert ok
|
||||
th.join(timeout=5)
|
||||
assert not thread_exc
|
||||
assert any(
|
||||
e.get("mediaStatus") == "AIProcessed" and e.get("mediaPercent") == 100
|
||||
for e in collected
|
||||
)
|
||||
|
||||
|
||||
def test_nft_res_03_transient_loader_first_fail(
|
||||
mock_loader_url, http_client, image_small
|
||||
):
|
||||
@@ -188,8 +56,3 @@ def test_nft_res_03_transient_loader_first_fail(
|
||||
assert r1.status_code != 500
|
||||
|
||||
|
||||
@pytest.mark.skip(
|
||||
reason="Requires docker compose restart capability not available in e2e-runner"
|
||||
)
|
||||
def test_nft_res_04_service_restart():
|
||||
pass
|
||||
|
||||
@@ -1,78 +1,10 @@
|
||||
import json
|
||||
import re
|
||||
import threading
|
||||
import time
|
||||
import uuid
|
||||
from datetime import datetime
|
||||
from pathlib import Path
|
||||
|
||||
import pytest
|
||||
|
||||
|
||||
def _video_ai_body(video_path: str) -> dict:
|
||||
return {
|
||||
"probability_threshold": 0.25,
|
||||
"tracking_intersection_threshold": 0.6,
|
||||
"altitude": 400,
|
||||
"focal_length": 24,
|
||||
"sensor_width": 23.5,
|
||||
"paths": [video_path],
|
||||
"frame_period_recognition": 4,
|
||||
"frame_recognition_seconds": 2,
|
||||
}
|
||||
|
||||
|
||||
@pytest.mark.skip(reason="Single video run — covered by test_ft_p09_sse_event_delivery")
|
||||
@pytest.mark.slow
|
||||
@pytest.mark.timeout(300)
|
||||
def test_ft_n_08_nft_res_lim_02_sse_queue_bounded_best_effort(
|
||||
warm_engine,
|
||||
http_client,
|
||||
jwt_token,
|
||||
video_short_path,
|
||||
sse_client_factory,
|
||||
):
|
||||
media_id = f"rlim-sse-{uuid.uuid4().hex}"
|
||||
body = _video_ai_body(video_short_path)
|
||||
headers = {"Authorization": f"Bearer {jwt_token}"}
|
||||
collected: list[dict] = []
|
||||
thread_exc: list[BaseException] = []
|
||||
done = threading.Event()
|
||||
|
||||
def _listen():
|
||||
try:
|
||||
with sse_client_factory() as sse:
|
||||
time.sleep(0.3)
|
||||
for event in sse.events():
|
||||
if not event.data or not str(event.data).strip():
|
||||
continue
|
||||
data = json.loads(event.data)
|
||||
if data.get("mediaId") != media_id:
|
||||
continue
|
||||
collected.append(data)
|
||||
if (
|
||||
data.get("mediaStatus") == "AIProcessed"
|
||||
and data.get("mediaPercent") == 100
|
||||
):
|
||||
break
|
||||
except BaseException as e:
|
||||
thread_exc.append(e)
|
||||
finally:
|
||||
done.set()
|
||||
|
||||
th = threading.Thread(target=_listen, daemon=True)
|
||||
th.start()
|
||||
time.sleep(0.5)
|
||||
r = http_client.post(f"/detect/{media_id}", json=body, headers=headers)
|
||||
assert r.status_code == 200
|
||||
assert done.wait(timeout=290)
|
||||
th.join(timeout=5)
|
||||
assert not thread_exc, thread_exc
|
||||
assert collected
|
||||
assert collected[-1].get("mediaStatus") == "AIProcessed"
|
||||
|
||||
|
||||
|
||||
@pytest.mark.slow
|
||||
@pytest.mark.timeout(120)
|
||||
def test_nft_res_lim_03_max_detections_per_frame(
|
||||
|
||||
@@ -4,7 +4,7 @@
|
||||
{
|
||||
"distutils": {
|
||||
"include_dirs": [
|
||||
"/Users/obezdienie001/dev/azaion/suite/detections/.venv-e2e/lib/python3.13/site-packages/numpy/_core/include"
|
||||
"/Users/obezdienie001/dev/azaion/suite/detections/.venv/lib/python3.13/site-packages/numpy/_core/include"
|
||||
],
|
||||
"name": "engines.coreml_engine",
|
||||
"sources": [
|
||||
|
||||
@@ -4,7 +4,7 @@
|
||||
{
|
||||
"distutils": {
|
||||
"include_dirs": [
|
||||
"/Users/obezdienie001/dev/azaion/suite/detections/.venv-e2e/lib/python3.13/site-packages/numpy/_core/include"
|
||||
"/Users/obezdienie001/dev/azaion/suite/detections/.venv/lib/python3.13/site-packages/numpy/_core/include"
|
||||
],
|
||||
"name": "engines.inference_engine",
|
||||
"sources": [
|
||||
|
||||
+1
-1
@@ -4,7 +4,7 @@
|
||||
{
|
||||
"distutils": {
|
||||
"include_dirs": [
|
||||
"/Users/obezdienie001/dev/azaion/suite/detections/.venv-e2e/lib/python3.13/site-packages/numpy/_core/include"
|
||||
"/Users/obezdienie001/dev/azaion/suite/detections/.venv/lib/python3.13/site-packages/numpy/_core/include"
|
||||
],
|
||||
"name": "inference",
|
||||
"sources": [
|
||||
|
||||
Reference in New Issue
Block a user