azaion/ai-training
1
0
Fork 0
mirror of https://github.com/azaion/ai-training.git synced 2026-04-22 12:56:35 +00:00
Code Issues Packages Projects Releases Wiki Activity

Refactor constants management to use Pydantic BaseModel for configuration

Browse Source 
- Replaced module-level path variables in constants.py with a structured Pydantic Config class.
- Updated all relevant modules (train.py, augmentation.py, exports.py, dataset-visualiser.py, manual_run.py) to access paths through the new config structure.
- Fixed bugs related to image processing and model saving.
- Enhanced test infrastructure to accommodate the new configuration approach.

This refactor improves code maintainability and clarity by centralizing configuration management.
This commit is contained in:
Oleksandr Bezdieniezhnykh
2026-03-27 18:18:30 +02:00
parent b68c07b540
commit 142c6c4de8
106 changed files with 5706 additions and 654 deletions
Download Patch File Download Diff File Expand all files Collapse all files
_docs/02_document/tests/performance-tests.md
+33
View File
@@ -0,0 +1,33 @@
# Performance Test Scenarios
## PT-AUG-01: Augmentation throughput
- **Input**: 10 images from fixture dataset
- **Action**: Run `augment_annotations()`, measure wall time
- **Expected**: Completes within 60 seconds (10 images × 8 outputs = 80 files)
- **Traces**: Restriction: Augmentation runs continuously
- **Note**: Threshold is generous; actual performance depends on CPU
## PT-AUG-02: Parallel augmentation speedup
- **Input**: 10 images from fixture dataset
- **Action**: Run with ThreadPoolExecutor vs sequential, compare times
- **Expected**: Parallel is ≥ 1.5× faster than sequential
- **Traces**: AC: Parallelized per-image processing
## PT-DSF-01: Dataset formation throughput
- **Input**: 100 images + labels
- **Action**: Run `form_dataset()`, measure wall time
- **Expected**: Completes within 30 seconds
- **Traces**: Restriction: Dataset formation before training
## PT-ENC-01: Encryption throughput
- **Input**: 10MB random bytes
- **Action**: Encrypt + decrypt roundtrip, measure wall time
- **Expected**: Completes within 5 seconds
- **Traces**: AC: Model encryption feasible for large models
## PT-INF-01: ONNX inference latency (single image)
- **Input**: 1 preprocessed image + ONNX model
- **Action**: Run single inference, measure wall time
- **Expected**: Completes within 10 seconds on CPU (no GPU requirement for test)
- **Traces**: AC: Inference capability
- **Note**: Production uses GPU; CPU is slower but validates correctness