Beginner Issues

This document lists beginner‑friendly issues intended for new contributors joining the Atriva AI Inference (OpenVINO) project. These are specifically chosen to help new grads understand the structure, codebase, and workflow with minimal complexity.

Each issue includes:

• Goal
• Location in repo
• Expected difficulty
• Skills learned

1. Add Comments to Test Scripts

Goal: Improve readability of /tests/test_image_classification.py and /tests/test_detection.py.

Skills: Python, reading inference code.

2. Improve Logging in Inference API

Goal: Add INFO logs when model loads, preprocess runs, and inference completes.

Skills: Python logging, tracing pipelines.

3. Add Model Validation Check

Goal: Modify model loader to verify model.xml and model.bin exist before loading.

Skills: Python error handling.

4. Add Simple Timing Utility

Goal: Create a small utility to measure inference latency inside the API.

Skills: Python decorators or time module.

5. Add a New Test Script for Segmentation

Goal: Add /tests/test_segmentation.py with basic inference flow.

Skills: Understanding inference classes.

6. Add Docstrings to Core Modules

Goal: Add docstrings to inference_engine.py and model_loader.py.

Skills: Documentation, Python readability.

7. Add Error Handling for Unsupported Model Types

Goal: If user loads a non-IR model, the system should return a clear error.

Skills: Validation, Python exceptions.

8. Create Basic Unit Test for Model Loader

Goal: Add a simple test to verify model loads in CI or local test.

Skills: pytest basics.

9. Add Example of Custom Model Path in Tests

Goal: Update test script to show how to load a model from /models/custom.

Skills: Model paths, filesystem.

10. Clean Up requirements.txt

Goal: Remove unused packages and pin minimum versions.

Skills: Package management.