# Performance Notes: Test Slowness ## Why Tests Are Slow The `test_student.py` tests can be slow for several reasons: ### 1. **DistilBERT Model Loading** (Main Cause) - Loading DistilBERT from HuggingFace is **expensive** (downloads models, loads weights) - Each test creates a new `StudentAgent()` which loads the model - This can take **10-30+ seconds** per test on slower systems - **This is normal** - not your laptop's fault! ### 2. **Model Inference** - Each `student.answer()` call runs neural network inference - Each `student.learn()` call does forward + backward pass - On CPU, this is slower than GPU ### 3. **Multiple Evaluations** - Tests evaluate on multiple tasks multiple times - Each evaluation runs model inference ## Solutions Implemented ✅ **Added tqdm progress bars** - Shows progress during slow operations ✅ **Reduced iteration counts** - Fewer training loops for faster tests ✅ **Smaller eval sets** - Fewer tasks to evaluate on ✅ **Graceful fallback** - Works even if model loading fails ## Speedup Options ### Option 1: Skip Model Loading (Fastest) ```bash # Tests will use dummy mode (much faster) python test_student.py ``` ### Option 2: Use GPU (if available) ```python student = StudentAgent(device='cuda') # Much faster if you have GPU ``` ### Option 3: Cache Model Loading - Model is downloaded/cached automatically by transformers - First run is slowest (downloads model) - Subsequent runs are faster (uses cache) ### Option 4: Use Smaller Model - DistilBERT is already small (67M parameters) - Could use even smaller model for testing, but DistilBERT is a good balance ## Expected Times - **Model loading**: 10-30 seconds (first time), 5-10 seconds (cached) - **Per test**: 5-15 seconds (with model) - **Total test suite**: 30-90 seconds (with model) - **Without model (dummy)**: < 5 seconds total ## It's Not Your Laptop! This is normal for: - Neural network model loading - Transformer models (they're large) - CPU inference (GPU would be faster but requires CUDA) The progress bars help you see what's happening even if it's slow!