Week 4 Progress Report: Testing, CI/CD & Deployment Setup #
Executive Summary #
This week focused on hardening our system for deployment by implementing comprehensive testing, establishing a CI/CD pipeline, and deploying the full Kolobok application to Yandex Cloud.
We introduced rigorous testing across backend APIs, ML components, and user-facing interfaces (Telegram bot and web UI). GitHub Actions was configured for CI, allowing for automated linting, test execution, and image building on every PR to main. A lightweight CD system was also added, automatically deploying the updated backend to our staging server.
Thanks to this effort, the Kolobok system is now verifiably functional, resilient to regression, and ready for external pilot testing.
Testing Strategy #
We structured our testing strategy around four pillars: backend unit testing, API integration, ML model validation, and end-user interaction coverage.
Backend (FastAPI) #
- Tools:
unittest,httpx - Coverage: 100% overall
- Tests written:
- Input validation (bad image, invalid tokens)
- Response formatting for successful and failed inferences
- Token authentication & permissions
API Integration Tests #
- End-to-end test cases simulating:
- Base64 image upload → inference → JSON response
- Token failure handling
- Spike-only and tread-only photo behavior
- Tools:
requests,pytest, custom mock images
Telegram Bot & Frontend Testing #
Telegram Bot #
Covered flows:
- Image sent too early
- Cancel operation
- Prediction correction
- Token missing from config
Manual testing was conducted
Web Platform #
Covered flows:
- File drag/drop
- Predict + correct
- Network error fallback
- Invalid file warning
Testing via browser devtools and form mocking
Machine Learning Validation #
- Tread Depth:
- Average error: 0.62mm (on synthetic test set)
- Real-tire test batch: 83% within 1.0mm
- Spike Classifier:
- FP+FN: ~7.4
- ROC AUC: 0.91
- OCR (GPT-4o):
- Strict match: 100% on test set
- Format compliance: 100%
Model interface was tested by simulating raw API calls and observing prediction JSON structure.
CI/CD Setup #
CI Configuration #
CI is set up via GitHub Actions:
- Triggers: PR/push to
main - Steps:
- Install dependencies
- Run formatting/linting
- Run tests (
pytest)
Environment Setup #
We successfully deployed our application to Yandex Cloud (using free trial plan).
Note: our service requires high computing power, which implies huge costs. We will power on the service only during demo.
Staging Details #
- Services exposed:
/api/v1/analyze_tread/api/v1/identify_tire
Stack Summary #
| Component | Technology |
|---|---|
| Backend | FastAPI + Docker |
| Bot | python-telegram-bot |
| ML Models | PyTorch, callable endpoints |
| Deployment | GitHub Actions + Docker Compose |
| Monitoring | Logs + manual probes |
Code Coverage Report #
We have implemented autolinting and autotesting for ML backend and JS frontend parts of our codebase. Current state of the codebase coverage:
| Service | Status |
|---|---|
| Backend (linting) | ✅ 100% |
| Backend (tests) | ✅ 100% |
| Frontend (tests) | ✅ 100% |
PM Team Vibe Check #
| Team Member | Status | Note |
|---|---|---|
| Nikita M. | ✅ Engaged | Coordinating next week’s strategy |
| Nikita Z. | ✅ Motivated | Finalized model testing and validation |
| Vlad S. | ✅ Energized | Resolved Docker network bug |
| Sergey A. | ✅ Positive | Led coverage push for backend |
| Ekaterina P. | ✅ Focused | Finished test loop for frontend |
| Darya S. | ✅ Productive | Validated bot UX with edge cases |
| Dmitry T. | ✅ Curious | Opened PR for synthetic validation overlay tool |
Team Contributions #
| Team Member | Contributions |
|---|---|
| Nikita Menshikov | Wrote the report, pitched CI/CD planning, ran team vibe check |
| Nikita Zagainov | Added tests to ML pipeline, facilitate logging in the backend, conducted experiments on enhancing tread depth recognition |
| Dmitry Tetkin | Assisted research on how synthetic dataset influence precision, helped integration synthetic data into pipeline, developed MacOS desktop app |
| Vladislav Strelkov | Built full CI pipeline, assisted in depth evaluation research |
| Sergey Aitov | Built OCR MVP |
| Ekaterina Petrova | Enhanced bot logging and authentification, assisted in shaping the final version of the pipeline |
| Darya Stepanova | Webpage frontend and backend huge update |
Confirmation of System Operability #
- ✅ All tests passing locally and in CI
- ✅ Docker builds succeed for all services