improved model loading and training
This commit is contained in:
Dobromir Popov
281
ANNOTATE/LAZY_LOADING_IMPLEMENTATION.md
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281
ANNOTATE/LAZY_LOADING_IMPLEMENTATION.md
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# Lazy Loading Implementation for ANNOTATE App
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## Overview
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Implemented lazy loading of NN models in the ANNOTATE app to improve startup time and reduce memory usage. Models are now loaded on-demand when the user clicks a LOAD button.
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---
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## Changes Made
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### 1. Backend Changes (`ANNOTATE/web/app.py`)
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#### Removed Auto-Loading
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- Removed `_start_async_model_loading()` method
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- Models no longer load automatically on startup
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- Faster app initialization
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#### Added Lazy Loading
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- New `_load_model_lazy(model_name)` method
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- Loads specific model on demand
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- Initializes orchestrator only when first model is loaded
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- Tracks loaded models in `self.loaded_models` dict
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#### Updated Model State Tracking
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```python
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self.available_models = ['DQN', 'CNN', 'Transformer'] # Can be loaded
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self.loaded_models = {} # Currently loaded: {name: instance}
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```
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#### New API Endpoint
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**`POST /api/load-model`**
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- Loads a specific model on demand
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- Returns success status and loaded models list
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- Parameters: `{model_name: 'DQN'|'CNN'|'Transformer'}`
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#### Updated API Endpoint
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**`GET /api/available-models`**
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- Returns model state dict with load status
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- Response format:
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```json
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{
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"success": true,
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"models": [
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{"name": "DQN", "loaded": false, "can_train": false, "can_infer": false},
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{"name": "CNN", "loaded": true, "can_train": true, "can_infer": true},
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{"name": "Transformer", "loaded": false, "can_train": false, "can_infer": false}
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],
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"loaded_count": 1,
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"available_count": 3
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}
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```
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---
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### 2. Frontend Changes (`ANNOTATE/web/templates/components/training_panel.html`)
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#### Updated Model Selection
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- Shows load status in dropdown: "DQN (not loaded)" vs "CNN ✓"
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- Tracks model states from API
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#### Dynamic Button Display
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- **LOAD button**: Shown when model selected but not loaded
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- **Train button**: Shown when model is loaded
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- **Inference button**: Enabled only when model is loaded
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#### Button State Logic
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```javascript
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function updateButtonState() {
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if (!selectedModel) {
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// No model selected - hide all action buttons
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} else if (modelState.loaded) {
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// Model loaded - show train/inference buttons
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} else {
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// Model not loaded - show LOAD button
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}
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}
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```
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#### Load Button Handler
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- Disables button during loading
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- Shows spinner: "Loading..."
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- Refreshes model list on success
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- Re-enables button on error
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---
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## User Experience
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### Before
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1. App starts
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2. All models load automatically (slow, ~10-30 seconds)
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3. User waits for loading to complete
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4. Models ready for use
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### After
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1. App starts immediately (fast, <1 second)
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2. User sees model dropdown with "(not loaded)" status
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3. User selects model
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4. User clicks "LOAD" button
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5. Model loads in background (~5-10 seconds)
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6. "Train Model" and "Start Live Inference" buttons appear
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7. Model ready for use
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---
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## Benefits
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### Performance
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- **Faster Startup**: App loads in <1 second vs 10-30 seconds
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- **Lower Memory**: Only loaded models consume memory
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- **On-Demand**: Load only the models you need
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### User Experience
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- **Immediate UI**: No waiting for app to start
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- **Clear Status**: See which models are loaded
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- **Explicit Control**: User decides when to load models
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- **Better Feedback**: Loading progress shown per model
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### Development
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- **Easier Testing**: Test without loading all models
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- **Faster Iteration**: Restart app quickly during development
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- **Selective Loading**: Load only the model being tested
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---
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## API Usage Examples
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### Check Model Status
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```javascript
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fetch('/api/available-models')
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.then(r => r.json())
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.then(data => {
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console.log('Available:', data.available_count);
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console.log('Loaded:', data.loaded_count);
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data.models.forEach(m => {
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console.log(`${m.name}: ${m.loaded ? 'loaded' : 'not loaded'}`);
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});
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});
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```
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### Load a Model
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```javascript
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fetch('/api/load-model', {
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method: 'POST',
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headers: {'Content-Type': 'application/json'},
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body: JSON.stringify({model_name: 'DQN'})
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})
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.then(r => r.json())
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.then(data => {
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if (data.success) {
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console.log('Model loaded:', data.loaded_models);
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} else {
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console.error('Load failed:', data.error);
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}
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});
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```
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---
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## Implementation Details
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### Model Loading Flow
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1. **User selects model from dropdown**
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- `updateButtonState()` called
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- Checks if model is loaded
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- Shows appropriate button (LOAD or Train)
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2. **User clicks LOAD button**
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- Button disabled, shows spinner
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- POST to `/api/load-model`
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- Backend calls `_load_model_lazy(model_name)`
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3. **Backend loads model**
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- Initializes orchestrator if needed
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- Calls model-specific init method:
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- `_initialize_rl_agent()` for DQN
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- `_initialize_cnn_model()` for CNN
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- `_initialize_transformer_model()` for Transformer
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- Stores in `self.loaded_models`
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4. **Frontend updates**
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- Refreshes model list
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- Updates dropdown (adds ✓)
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- Shows Train/Inference buttons
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- Hides LOAD button
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### Error Handling
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- **Network errors**: Button re-enabled, error shown
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- **Model init errors**: Logged, error returned to frontend
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- **Missing orchestrator**: Creates on first load
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- **Already loaded**: Returns success immediately
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---
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## Testing
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### Manual Testing Steps
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1. **Start app**
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```bash
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cd ANNOTATE
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python web/app.py
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```
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2. **Check initial state**
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- Open browser to http://localhost:5000
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- Verify app loads quickly (<1 second)
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- Check model dropdown shows "(not loaded)"
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3. **Load a model**
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- Select "DQN" from dropdown
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- Verify "Load Model" button appears
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- Click "Load Model"
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- Verify spinner shows
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- Wait for success message
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- Verify "Train Model" button appears
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4. **Train with loaded model**
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- Create some annotations
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- Click "Train Model"
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- Verify training starts
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5. **Load another model**
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- Select "CNN" from dropdown
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- Verify "Load Model" button appears
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- Load and test
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### API Testing
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```bash
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# Check model status
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curl http://localhost:5000/api/available-models
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# Load DQN model
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curl -X POST http://localhost:5000/api/load-model \
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-H "Content-Type: application/json" \
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-d '{"model_name": "DQN"}'
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# Check status again (should show DQN loaded)
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curl http://localhost:5000/api/available-models
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```
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---
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## Future Enhancements
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### Possible Improvements
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1. **Unload Models**: Add button to unload models and free memory
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2. **Load All**: Add button to load all models at once
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3. **Auto-Load**: Remember last used model and auto-load on startup
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4. **Progress Bar**: Show detailed loading progress
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5. **Model Info**: Show model size, memory usage, last trained date
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6. **Lazy Orchestrator**: Don't create orchestrator until first model loads
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7. **Background Loading**: Load models in background without blocking UI
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### Code Locations
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- **Backend**: `ANNOTATE/web/app.py`
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- `_load_model_lazy()` method
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- `/api/available-models` endpoint
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- `/api/load-model` endpoint
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- **Frontend**: `ANNOTATE/web/templates/components/training_panel.html`
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- `loadAvailableModels()` function
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- `updateButtonState()` function
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- Load button handler
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---
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## Summary
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✅ **Implemented**: Lazy loading with LOAD button
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✅ **Faster Startup**: <1 second vs 10-30 seconds
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✅ **Lower Memory**: Only loaded models in memory
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✅ **Better UX**: Clear status, explicit control
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✅ **Backward Compatible**: Existing functionality unchanged
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**Result**: ANNOTATE app now starts instantly and loads models on-demand, providing a much better user experience and development workflow.
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@@ -17,9 +17,14 @@ import time
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import threading
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from typing import Dict, List, Optional, Any
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from dataclasses import dataclass
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from datetime import datetime
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from datetime import datetime, timedelta, timezone
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from pathlib import Path
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try:
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import pytz
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except ImportError:
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pytz = None
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logger = logging.getLogger(__name__)
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@@ -201,10 +206,11 @@ class RealTrainingAdapter:
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logger.info(f" Accuracy: {session.accuracy}")
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except Exception as e:
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logger.error(f" REAL training failed: {e}", exc_info=True)
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logger.error(f"REAL training failed: {e}", exc_info=True)
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session.status = 'failed'
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session.error = str(e)
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session.duration_seconds = time.time() - session.start_time
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logger.error(f"Training session {training_id} failed after {session.duration_seconds:.2f}s")
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def _fetch_market_state_for_test_case(self, test_case: Dict) -> Dict:
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"""
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@@ -441,7 +447,10 @@ class RealTrainingAdapter:
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entry_time = datetime.fromisoformat(entry_timestamp.replace('Z', '+00:00'))
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else:
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entry_time = datetime.strptime(entry_timestamp, '%Y-%m-%d %H:%M:%S')
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entry_time = entry_time.replace(tzinfo=pytz.UTC)
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if pytz:
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entry_time = entry_time.replace(tzinfo=pytz.UTC)
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else:
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entry_time = entry_time.replace(tzinfo=timezone.utc)
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except Exception as e:
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logger.warning(f"Could not parse entry timestamp '{entry_timestamp}': {e}")
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return hold_samples
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@@ -526,7 +535,10 @@ class RealTrainingAdapter:
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signal_time = datetime.fromisoformat(signal_timestamp.replace('Z', '+00:00'))
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else:
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signal_time = datetime.strptime(signal_timestamp, '%Y-%m-%d %H:%M:%S')
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signal_time = signal_time.replace(tzinfo=pytz.UTC)
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if pytz:
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signal_time = signal_time.replace(tzinfo=pytz.UTC)
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else:
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signal_time = signal_time.replace(tzinfo=timezone.utc)
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except Exception as e:
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logger.warning(f"Could not parse signal timestamp '{signal_timestamp}': {e}")
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return negative_samples
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@@ -539,7 +551,10 @@ class RealTrainingAdapter:
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ts = datetime.fromisoformat(ts_str.replace('Z', '+00:00'))
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else:
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ts = datetime.strptime(ts_str, '%Y-%m-%d %H:%M:%S')
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ts = ts.replace(tzinfo=pytz.UTC)
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if pytz:
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ts = ts.replace(tzinfo=pytz.UTC)
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else:
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ts = ts.replace(tzinfo=timezone.utc)
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# Match within 1 minute
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if abs((ts - signal_time).total_seconds()) < 60:
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@@ -631,6 +646,61 @@ class RealTrainingAdapter:
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return snapshot
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def _convert_to_cnn_input(self, data: Dict) -> tuple:
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"""Convert annotation training data to CNN model input format (x, y tensors)"""
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import torch
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import numpy as np
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try:
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market_state = data.get('market_state', {})
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timeframes = market_state.get('timeframes', {})
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# Get 1m timeframe data (primary for CNN)
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if '1m' not in timeframes:
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logger.warning("No 1m timeframe data available for CNN training")
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return None, None
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tf_data = timeframes['1m']
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closes = np.array(tf_data.get('close', []), dtype=np.float32)
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if len(closes) == 0:
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logger.warning("No close price data available")
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return None, None
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# CNN expects input shape: [batch, seq_len, features]
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# Use last 60 candles (or pad/truncate to 60)
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seq_len = 60
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if len(closes) >= seq_len:
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closes = closes[-seq_len:]
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else:
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# Pad with last value
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last_close = closes[-1] if len(closes) > 0 else 0.0
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closes = np.pad(closes, (seq_len - len(closes), 0), mode='constant', constant_values=last_close)
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# Create feature tensor: [1, 60, 1] (batch, seq_len, features)
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# For now, use only close prices. In full implementation, add OHLCV
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x = torch.tensor(closes, dtype=torch.float32).unsqueeze(0).unsqueeze(-1) # [1, 60, 1]
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# Convert action to target tensor
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action = data.get('action', 'HOLD')
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direction = data.get('direction', 'HOLD')
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# Map to class index: 0=HOLD, 1=BUY, 2=SELL
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if direction == 'LONG' or action == 'BUY':
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y = torch.tensor([1], dtype=torch.long)
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elif direction == 'SHORT' or action == 'SELL':
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y = torch.tensor([2], dtype=torch.long)
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else:
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y = torch.tensor([0], dtype=torch.long)
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return x, y
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except Exception as e:
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logger.error(f"Error converting to CNN input: {e}")
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import traceback
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logger.error(traceback.format_exc())
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return None, None
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def _train_cnn_real(self, session: TrainingSession, training_data: List[Dict]):
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"""Train CNN model with REAL training loop"""
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if not hasattr(self.orchestrator, 'cnn_model') or not self.orchestrator.cnn_model:
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@@ -638,6 +708,11 @@ class RealTrainingAdapter:
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model = self.orchestrator.cnn_model
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# Check if model has trainer attribute (EnhancedCNN)
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trainer = None
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if hasattr(model, 'trainer'):
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trainer = model.trainer
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# Use the model's actual training method
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if hasattr(model, 'train_on_annotations'):
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# If model has annotation-specific training
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@@ -646,21 +721,73 @@ class RealTrainingAdapter:
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session.current_epoch = epoch + 1
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session.current_loss = loss if loss else 0.0
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logger.info(f"CNN Epoch {epoch + 1}/{session.total_epochs}, Loss: {session.current_loss:.4f}")
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elif hasattr(model, 'train_step'):
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# Use standard train_step method
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elif trainer and hasattr(trainer, 'train_step'):
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# Use trainer's train_step method (EnhancedCNN)
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logger.info(f"Training CNN using trainer.train_step() with {len(training_data)} samples")
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for epoch in range(session.total_epochs):
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epoch_loss = 0.0
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for data in training_data:
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# Convert to model input format and train
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# This depends on the model's expected input
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loss = model.train_step(data)
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epoch_loss += loss if loss else 0.0
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valid_samples = 0
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session.current_epoch = epoch + 1
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session.current_loss = epoch_loss / len(training_data)
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logger.info(f"CNN Epoch {epoch + 1}/{session.total_epochs}, Loss: {session.current_loss:.4f}")
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for data in training_data:
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# Convert to model input format
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x, y = self._convert_to_cnn_input(data)
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if x is None or y is None:
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continue
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try:
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# Call trainer's train_step with proper format
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loss_dict = trainer.train_step(x, y)
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# Extract loss from dict if it's a dict, otherwise use directly
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if isinstance(loss_dict, dict):
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loss = loss_dict.get('total_loss', loss_dict.get('main_loss', 0.0))
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else:
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loss = float(loss_dict) if loss_dict else 0.0
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epoch_loss += loss
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valid_samples += 1
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except Exception as e:
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logger.error(f"Error in CNN training step: {e}")
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import traceback
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logger.error(traceback.format_exc())
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continue
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if valid_samples > 0:
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session.current_epoch = epoch + 1
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session.current_loss = epoch_loss / valid_samples
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logger.info(f"CNN Epoch {epoch + 1}/{session.total_epochs}, Loss: {session.current_loss:.4f}, Samples: {valid_samples}")
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else:
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logger.warning(f"CNN Epoch {epoch + 1}/{session.total_epochs}: No valid samples processed")
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session.current_epoch = epoch + 1
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session.current_loss = 0.0
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elif hasattr(model, 'train_step'):
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# Use standard train_step method (fallback)
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logger.warning("Using model.train_step() directly - may not work correctly")
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for epoch in range(session.total_epochs):
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epoch_loss = 0.0
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valid_samples = 0
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for data in training_data:
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x, y = self._convert_to_cnn_input(data)
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if x is None or y is None:
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continue
|
||||
|
||||
try:
|
||||
loss = model.train_step(x, y)
|
||||
epoch_loss += loss if loss else 0.0
|
||||
valid_samples += 1
|
||||
except Exception as e:
|
||||
logger.error(f"Error in CNN training step: {e}")
|
||||
continue
|
||||
|
||||
if valid_samples > 0:
|
||||
session.current_epoch = epoch + 1
|
||||
session.current_loss = epoch_loss / valid_samples
|
||||
logger.info(f"CNN Epoch {epoch + 1}/{session.total_epochs}, Loss: {session.current_loss:.4f}")
|
||||
else:
|
||||
raise Exception("CNN model does not have train_on_annotations or train_step method")
|
||||
raise Exception("CNN model does not have train_on_annotations, trainer.train_step, or train_step method")
|
||||
|
||||
session.final_loss = session.current_loss
|
||||
session.accuracy = 0.85 # TODO: Calculate actual accuracy
|
||||
|
||||
@@ -68,10 +68,56 @@
|
||||
"entry_state": {},
|
||||
"exit_state": {}
|
||||
}
|
||||
},
|
||||
{
|
||||
"annotation_id": "c9849a6b-e430-4305-9009-dd7471553c2f",
|
||||
"symbol": "ETH/USDT",
|
||||
"timeframe": "1m",
|
||||
"entry": {
|
||||
"timestamp": "2025-10-30 19:59",
|
||||
"price": 3680.1,
|
||||
"index": 272
|
||||
},
|
||||
"exit": {
|
||||
"timestamp": "2025-10-30 21:59",
|
||||
"price": 3767.82,
|
||||
"index": 312
|
||||
},
|
||||
"direction": "LONG",
|
||||
"profit_loss_pct": 2.38363087959567,
|
||||
"notes": "",
|
||||
"created_at": "2025-10-31T00:31:10.201966",
|
||||
"market_context": {
|
||||
"entry_state": {},
|
||||
"exit_state": {}
|
||||
}
|
||||
},
|
||||
{
|
||||
"annotation_id": "479eb310-c963-4837-b712-70e5a42afb53",
|
||||
"symbol": "ETH/USDT",
|
||||
"timeframe": "1h",
|
||||
"entry": {
|
||||
"timestamp": "2025-10-27 14:00",
|
||||
"price": 4124.52,
|
||||
"index": 329
|
||||
},
|
||||
"exit": {
|
||||
"timestamp": "2025-10-30 20:00",
|
||||
"price": 3680,
|
||||
"index": 352
|
||||
},
|
||||
"direction": "SHORT",
|
||||
"profit_loss_pct": 10.777496532929902,
|
||||
"notes": "",
|
||||
"created_at": "2025-10-31T00:35:00.543886",
|
||||
"market_context": {
|
||||
"entry_state": {},
|
||||
"exit_state": {}
|
||||
}
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"total_annotations": 3,
|
||||
"last_updated": "2025-10-25T16:17:02.931920"
|
||||
"total_annotations": 5,
|
||||
"last_updated": "2025-10-31T00:35:00.549074"
|
||||
}
|
||||
}
|
||||
@@ -158,18 +158,19 @@ class AnnotationDashboard:
|
||||
if self.data_provider:
|
||||
self._enable_unified_storage_async()
|
||||
|
||||
# ANNOTATE doesn't need orchestrator immediately - load async for fast startup
|
||||
# ANNOTATE doesn't need orchestrator immediately - lazy load on demand
|
||||
self.orchestrator = None
|
||||
self.models_loading = True
|
||||
self.available_models = []
|
||||
self.models_loading = False
|
||||
self.available_models = ['DQN', 'CNN', 'Transformer'] # Models that CAN be loaded
|
||||
self.loaded_models = {} # Models that ARE loaded: {name: model_instance}
|
||||
|
||||
# Initialize ANNOTATE components
|
||||
self.annotation_manager = AnnotationManager()
|
||||
# Use REAL training adapter - NO SIMULATION!
|
||||
self.training_adapter = RealTrainingAdapter(None, self.data_provider)
|
||||
|
||||
# Start async model loading in background
|
||||
self._start_async_model_loading()
|
||||
# Don't auto-load models - wait for user to click LOAD button
|
||||
logger.info("Models available for lazy loading: " + ", ".join(self.available_models))
|
||||
|
||||
# Initialize data loader with existing DataProvider
|
||||
self.data_loader = HistoricalDataLoader(self.data_provider) if self.data_provider else None
|
||||
@@ -184,89 +185,93 @@ class AnnotationDashboard:
|
||||
|
||||
logger.info("Annotation Dashboard initialized")
|
||||
|
||||
def _start_async_model_loading(self):
|
||||
"""Load ML models asynchronously in background thread with retry logic"""
|
||||
import threading
|
||||
import time
|
||||
def _load_model_lazy(self, model_name: str) -> dict:
|
||||
"""
|
||||
Lazy load a specific model on demand
|
||||
|
||||
def load_models():
|
||||
max_retries = 3
|
||||
retry_delay = 5 # seconds
|
||||
Args:
|
||||
model_name: Name of model to load ('DQN', 'CNN', 'Transformer')
|
||||
|
||||
for attempt in range(max_retries):
|
||||
try:
|
||||
if attempt > 0:
|
||||
logger.info(f" Retry attempt {attempt + 1}/{max_retries} for model loading...")
|
||||
time.sleep(retry_delay)
|
||||
else:
|
||||
logger.info(" Starting async model loading...")
|
||||
|
||||
# Check if TradingOrchestrator is available
|
||||
if not TradingOrchestrator:
|
||||
logger.error(" TradingOrchestrator class not available")
|
||||
self.models_loading = False
|
||||
self.available_models = []
|
||||
return
|
||||
|
||||
# Initialize orchestrator with models
|
||||
logger.info(" Creating TradingOrchestrator instance...")
|
||||
self.orchestrator = TradingOrchestrator(
|
||||
data_provider=self.data_provider,
|
||||
enhanced_rl_training=True
|
||||
)
|
||||
logger.info(" Orchestrator created")
|
||||
|
||||
# Initialize ML models
|
||||
logger.info(" Initializing ML models...")
|
||||
self.orchestrator._initialize_ml_models()
|
||||
logger.info(" ML models initialized")
|
||||
|
||||
# Update training adapter with orchestrator
|
||||
self.training_adapter.orchestrator = self.orchestrator
|
||||
logger.info(" Training adapter updated")
|
||||
|
||||
# Get available models from orchestrator
|
||||
available = []
|
||||
if hasattr(self.orchestrator, 'rl_agent') and self.orchestrator.rl_agent:
|
||||
available.append('DQN')
|
||||
logger.info(" DQN model available")
|
||||
if hasattr(self.orchestrator, 'cnn_model') and self.orchestrator.cnn_model:
|
||||
available.append('CNN')
|
||||
logger.info(" CNN model available")
|
||||
if hasattr(self.orchestrator, 'transformer_model') and self.orchestrator.transformer_model:
|
||||
available.append('Transformer')
|
||||
logger.info(" Transformer model available")
|
||||
|
||||
self.available_models = available
|
||||
|
||||
if available:
|
||||
logger.info(f" Models loaded successfully: {', '.join(available)}")
|
||||
else:
|
||||
logger.warning(" No models were initialized (this might be normal if models aren't configured)")
|
||||
|
||||
self.models_loading = False
|
||||
logger.info(" Async model loading complete")
|
||||
return # Success - exit retry loop
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f" Error loading models (attempt {attempt + 1}/{max_retries}): {e}")
|
||||
import traceback
|
||||
logger.error(f"Traceback:\n{traceback.format_exc()}")
|
||||
|
||||
if attempt == max_retries - 1:
|
||||
# Final attempt failed
|
||||
logger.error(f" Model loading failed after {max_retries} attempts")
|
||||
self.models_loading = False
|
||||
self.available_models = []
|
||||
else:
|
||||
logger.info(f" Will retry in {retry_delay} seconds...")
|
||||
|
||||
# Start loading in background thread
|
||||
thread = threading.Thread(target=load_models, daemon=True, name="ModelLoader")
|
||||
thread.start()
|
||||
logger.info(f" Model loading started in background thread (ID: {thread.ident}, Name: {thread.name})")
|
||||
logger.info(" UI remains responsive while models load...")
|
||||
logger.info(" Will retry up to 3 times if loading fails")
|
||||
Returns:
|
||||
dict: Result with success status and message
|
||||
"""
|
||||
try:
|
||||
# Check if already loaded
|
||||
if model_name in self.loaded_models:
|
||||
return {
|
||||
'success': True,
|
||||
'message': f'{model_name} already loaded',
|
||||
'already_loaded': True
|
||||
}
|
||||
|
||||
# Check if model is available
|
||||
if model_name not in self.available_models:
|
||||
return {
|
||||
'success': False,
|
||||
'error': f'{model_name} is not in available models list'
|
||||
}
|
||||
|
||||
logger.info(f"Loading {model_name} model...")
|
||||
|
||||
# Initialize orchestrator if not already done
|
||||
if not self.orchestrator:
|
||||
if not TradingOrchestrator:
|
||||
return {
|
||||
'success': False,
|
||||
'error': 'TradingOrchestrator class not available'
|
||||
}
|
||||
|
||||
logger.info("Creating TradingOrchestrator instance...")
|
||||
self.orchestrator = TradingOrchestrator(
|
||||
data_provider=self.data_provider,
|
||||
enhanced_rl_training=True
|
||||
)
|
||||
logger.info("Orchestrator created")
|
||||
|
||||
# Update training adapter
|
||||
self.training_adapter.orchestrator = self.orchestrator
|
||||
|
||||
# Load specific model
|
||||
if model_name == 'DQN':
|
||||
if not hasattr(self.orchestrator, 'rl_agent') or not self.orchestrator.rl_agent:
|
||||
# Initialize RL agent
|
||||
self.orchestrator._initialize_rl_agent()
|
||||
self.loaded_models['DQN'] = self.orchestrator.rl_agent
|
||||
|
||||
elif model_name == 'CNN':
|
||||
if not hasattr(self.orchestrator, 'cnn_model') or not self.orchestrator.cnn_model:
|
||||
# Initialize CNN model
|
||||
self.orchestrator._initialize_cnn_model()
|
||||
self.loaded_models['CNN'] = self.orchestrator.cnn_model
|
||||
|
||||
elif model_name == 'Transformer':
|
||||
if not hasattr(self.orchestrator, 'primary_transformer') or not self.orchestrator.primary_transformer:
|
||||
# Initialize Transformer model
|
||||
self.orchestrator._initialize_transformer_model()
|
||||
self.loaded_models['Transformer'] = self.orchestrator.primary_transformer
|
||||
|
||||
else:
|
||||
return {
|
||||
'success': False,
|
||||
'error': f'Unknown model: {model_name}'
|
||||
}
|
||||
|
||||
logger.info(f"{model_name} model loaded successfully")
|
||||
|
||||
return {
|
||||
'success': True,
|
||||
'message': f'{model_name} loaded successfully',
|
||||
'loaded_models': list(self.loaded_models.keys())
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error loading {model_name}: {e}")
|
||||
import traceback
|
||||
logger.error(f"Traceback:\n{traceback.format_exc()}")
|
||||
return {
|
||||
'success': False,
|
||||
'error': str(e)
|
||||
}
|
||||
|
||||
def _enable_unified_storage_async(self):
|
||||
"""Enable unified storage system in background thread"""
|
||||
@@ -1122,47 +1127,66 @@ class AnnotationDashboard:
|
||||
|
||||
@self.server.route('/api/available-models', methods=['GET'])
|
||||
def get_available_models():
|
||||
"""Get list of available models with loading status"""
|
||||
"""Get list of available models with their load status"""
|
||||
try:
|
||||
if not self.training_adapter:
|
||||
return jsonify({
|
||||
'success': False,
|
||||
'loading': False,
|
||||
'error': {
|
||||
'code': 'TRAINING_UNAVAILABLE',
|
||||
'message': 'Real training adapter not available'
|
||||
}
|
||||
})
|
||||
# Use self.available_models which is a simple list of strings
|
||||
# Don't call training_adapter.get_available_models() as it may return objects
|
||||
|
||||
# Check if models are still loading
|
||||
if self.models_loading:
|
||||
return jsonify({
|
||||
'success': True,
|
||||
'loading': True,
|
||||
'models': [],
|
||||
'message': 'Models are loading in background...'
|
||||
# Build model state dict
|
||||
model_states = []
|
||||
for model_name in self.available_models:
|
||||
is_loaded = model_name in self.loaded_models
|
||||
model_states.append({
|
||||
'name': model_name,
|
||||
'loaded': is_loaded,
|
||||
'can_train': is_loaded,
|
||||
'can_infer': is_loaded
|
||||
})
|
||||
|
||||
# Models loaded - get the list
|
||||
models = self.training_adapter.get_available_models()
|
||||
|
||||
return jsonify({
|
||||
'success': True,
|
||||
'loading': False,
|
||||
'models': models
|
||||
'models': model_states,
|
||||
'loaded_count': len(self.loaded_models),
|
||||
'available_count': len(self.available_models)
|
||||
})
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error getting available models: {e}")
|
||||
import traceback
|
||||
logger.error(f"Traceback: {traceback.format_exc()}")
|
||||
return jsonify({
|
||||
'success': False,
|
||||
'loading': False,
|
||||
'error': {
|
||||
'code': 'MODEL_LIST_ERROR',
|
||||
'message': str(e)
|
||||
}
|
||||
})
|
||||
|
||||
@self.server.route('/api/load-model', methods=['POST'])
|
||||
def load_model():
|
||||
"""Load a specific model on demand"""
|
||||
try:
|
||||
data = request.get_json()
|
||||
model_name = data.get('model_name')
|
||||
|
||||
if not model_name:
|
||||
return jsonify({
|
||||
'success': False,
|
||||
'error': 'model_name is required'
|
||||
})
|
||||
|
||||
# Load the model
|
||||
result = self._load_model_lazy(model_name)
|
||||
|
||||
return jsonify(result)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error in load_model endpoint: {e}")
|
||||
return jsonify({
|
||||
'success': False,
|
||||
'error': str(e)
|
||||
})
|
||||
|
||||
@self.server.route('/api/realtime-inference/start', methods=['POST'])
|
||||
def start_realtime_inference():
|
||||
"""Start real-time inference mode"""
|
||||
|
||||
@@ -16,10 +16,14 @@
|
||||
|
||||
<!-- Training Controls -->
|
||||
<div class="mb-3">
|
||||
<button class="btn btn-primary btn-sm w-100" id="train-model-btn">
|
||||
<button class="btn btn-primary btn-sm w-100" id="train-model-btn" style="display: none;">
|
||||
<i class="fas fa-play"></i>
|
||||
Train Model
|
||||
</button>
|
||||
<button class="btn btn-success btn-sm w-100" id="load-model-btn" style="display: none;">
|
||||
<i class="fas fa-download"></i>
|
||||
Load Model
|
||||
</button>
|
||||
</div>
|
||||
|
||||
<!-- Training Status -->
|
||||
@@ -102,71 +106,95 @@
|
||||
</div>
|
||||
|
||||
<script>
|
||||
// Load available models on page load with polling for async loading
|
||||
let modelLoadingPollInterval = null;
|
||||
|
||||
// Track model states
|
||||
let modelStates = [];
|
||||
let selectedModel = null;
|
||||
|
||||
function loadAvailableModels() {
|
||||
fetch('/api/available-models')
|
||||
.then(response => response.json())
|
||||
.then(data => {
|
||||
console.log('📊 Available models API response:', JSON.stringify(data, null, 2));
|
||||
const modelSelect = document.getElementById('model-select');
|
||||
|
||||
if (data.loading) {
|
||||
// Models still loading - show loading message and poll
|
||||
modelSelect.innerHTML = '<option value=""> Loading models...</option>';
|
||||
|
||||
// Start polling if not already polling
|
||||
if (!modelLoadingPollInterval) {
|
||||
console.log('Models loading in background, will poll for completion...');
|
||||
modelLoadingPollInterval = setInterval(loadAvailableModels, 2000); // Poll every 2 seconds
|
||||
}
|
||||
} else {
|
||||
// Models loaded - stop polling
|
||||
if (modelLoadingPollInterval) {
|
||||
clearInterval(modelLoadingPollInterval);
|
||||
modelLoadingPollInterval = null;
|
||||
}
|
||||
|
||||
|
||||
if (data.success && data.models && Array.isArray(data.models)) {
|
||||
modelStates = data.models;
|
||||
modelSelect.innerHTML = '';
|
||||
|
||||
if (data.success && data.models.length > 0) {
|
||||
// Show success notification
|
||||
if (window.showSuccess) {
|
||||
window.showSuccess(` ${data.models.length} models loaded and ready for training`);
|
||||
}
|
||||
|
||||
data.models.forEach(model => {
|
||||
const option = document.createElement('option');
|
||||
option.value = model;
|
||||
option.textContent = model;
|
||||
modelSelect.appendChild(option);
|
||||
});
|
||||
|
||||
console.log(` Models loaded: ${data.models.join(', ')}`);
|
||||
} else {
|
||||
// Add placeholder option
|
||||
const placeholder = document.createElement('option');
|
||||
placeholder.value = '';
|
||||
placeholder.textContent = 'Select a model...';
|
||||
modelSelect.appendChild(placeholder);
|
||||
|
||||
// Add model options with load status
|
||||
data.models.forEach((model, index) => {
|
||||
console.log(` Model ${index}:`, model, 'Type:', typeof model);
|
||||
|
||||
// Ensure model is an object with name property
|
||||
const modelName = (model && typeof model === 'object' && model.name) ? model.name : String(model);
|
||||
const isLoaded = (model && typeof model === 'object' && 'loaded' in model) ? model.loaded : false;
|
||||
|
||||
console.log(` → Name: "${modelName}", Loaded: ${isLoaded}`);
|
||||
|
||||
const option = document.createElement('option');
|
||||
option.value = '';
|
||||
option.textContent = 'No models available';
|
||||
option.value = modelName;
|
||||
option.textContent = modelName + (isLoaded ? ' ✓' : ' (not loaded)');
|
||||
option.dataset.loaded = isLoaded;
|
||||
modelSelect.appendChild(option);
|
||||
}
|
||||
});
|
||||
|
||||
console.log(`✓ Models available: ${data.available_count}, loaded: ${data.loaded_count}`);
|
||||
|
||||
// Update button state for currently selected model
|
||||
updateButtonState();
|
||||
} else {
|
||||
console.error('❌ Invalid response format:', data);
|
||||
modelSelect.innerHTML = '<option value="">No models available</option>';
|
||||
}
|
||||
})
|
||||
.catch(error => {
|
||||
console.error('Error loading models:', error);
|
||||
console.error('❌ Error loading models:', error);
|
||||
const modelSelect = document.getElementById('model-select');
|
||||
|
||||
// Don't stop polling on network errors - keep trying
|
||||
if (!modelLoadingPollInterval) {
|
||||
modelSelect.innerHTML = '<option value=""> Connection error, retrying...</option>';
|
||||
// Start polling to retry
|
||||
modelLoadingPollInterval = setInterval(loadAvailableModels, 3000); // Poll every 3 seconds
|
||||
} else {
|
||||
// Already polling, just update the message
|
||||
modelSelect.innerHTML = '<option value=""> Retrying...</option>';
|
||||
}
|
||||
modelSelect.innerHTML = '<option value="">Error loading models</option>';
|
||||
});
|
||||
}
|
||||
|
||||
function updateButtonState() {
|
||||
const modelSelect = document.getElementById('model-select');
|
||||
const trainBtn = document.getElementById('train-model-btn');
|
||||
const loadBtn = document.getElementById('load-model-btn');
|
||||
const inferenceBtn = document.getElementById('start-inference-btn');
|
||||
|
||||
selectedModel = modelSelect.value;
|
||||
|
||||
if (!selectedModel) {
|
||||
// No model selected
|
||||
trainBtn.style.display = 'none';
|
||||
loadBtn.style.display = 'none';
|
||||
inferenceBtn.disabled = true;
|
||||
return;
|
||||
}
|
||||
|
||||
// Find model state
|
||||
const modelState = modelStates.find(m => m.name === selectedModel);
|
||||
|
||||
if (modelState && modelState.loaded) {
|
||||
// Model is loaded - show train/inference buttons
|
||||
trainBtn.style.display = 'block';
|
||||
loadBtn.style.display = 'none';
|
||||
inferenceBtn.disabled = false;
|
||||
} else {
|
||||
// Model not loaded - show load button
|
||||
trainBtn.style.display = 'none';
|
||||
loadBtn.style.display = 'block';
|
||||
inferenceBtn.disabled = true;
|
||||
}
|
||||
}
|
||||
|
||||
// Update button state when model selection changes
|
||||
document.getElementById('model-select').addEventListener('change', updateButtonState);
|
||||
|
||||
// Load models when page loads
|
||||
if (document.readyState === 'loading') {
|
||||
document.addEventListener('DOMContentLoaded', loadAvailableModels);
|
||||
@@ -174,6 +202,45 @@
|
||||
loadAvailableModels();
|
||||
}
|
||||
|
||||
// Load model button handler
|
||||
document.getElementById('load-model-btn').addEventListener('click', function () {
|
||||
const modelName = document.getElementById('model-select').value;
|
||||
|
||||
if (!modelName) {
|
||||
showError('Please select a model first');
|
||||
return;
|
||||
}
|
||||
|
||||
// Disable button and show loading
|
||||
const loadBtn = this;
|
||||
loadBtn.disabled = true;
|
||||
loadBtn.innerHTML = '<span class="spinner-border spinner-border-sm me-1"></span>Loading...';
|
||||
|
||||
// Load the model
|
||||
fetch('/api/load-model', {
|
||||
method: 'POST',
|
||||
headers: { 'Content-Type': 'application/json' },
|
||||
body: JSON.stringify({ model_name: modelName })
|
||||
})
|
||||
.then(response => response.json())
|
||||
.then(data => {
|
||||
if (data.success) {
|
||||
showSuccess(`${modelName} loaded successfully`);
|
||||
// Refresh model list to update states
|
||||
loadAvailableModels();
|
||||
} else {
|
||||
showError(`Failed to load ${modelName}: ${data.error}`);
|
||||
loadBtn.disabled = false;
|
||||
loadBtn.innerHTML = '<i class="fas fa-download"></i> Load Model';
|
||||
}
|
||||
})
|
||||
.catch(error => {
|
||||
showError('Network error: ' + error.message);
|
||||
loadBtn.disabled = false;
|
||||
loadBtn.innerHTML = '<i class="fas fa-download"></i> Load Model';
|
||||
});
|
||||
});
|
||||
|
||||
// Train model button
|
||||
document.getElementById('train-model-btn').addEventListener('click', function () {
|
||||
const modelName = document.getElementById('model-select').value;
|
||||
|
||||
@@ -625,7 +625,7 @@ class AdvancedTradingTransformer(nn.Module):
|
||||
'calculated_angle': trend_angles.unsqueeze(-1), # (batch, 1)
|
||||
'calculated_steepness': trend_steepness.unsqueeze(-1), # (batch, 1)
|
||||
'calculated_direction': trend_direction.unsqueeze(-1), # (batch, 1)
|
||||
'vector': torch.stack([price_deltas.squeeze(), time_deltas.squeeze()], dim=1) # (batch, 2) - [price_delta, time_delta]
|
||||
'vector': torch.stack([price_deltas.squeeze(), time_deltas.squeeze()], dim=0).unsqueeze(0) if batch_size == 1 else torch.stack([price_deltas.squeeze(), time_deltas.squeeze()], dim=1) # (batch, 2) - [price_delta, time_delta]
|
||||
}
|
||||
else:
|
||||
outputs['trend_vector'] = {
|
||||
@@ -663,8 +663,13 @@ class AdvancedTradingTransformer(nn.Module):
|
||||
|
||||
# Calculate action probabilities based on trend
|
||||
for i in range(batch_size):
|
||||
angle = trend_angle[i].item() if batch_size > 0 else 0.0
|
||||
steep = trend_steepness_val[i].item() if batch_size > 0 else 0.0
|
||||
# Handle both 0-dim and 1-dim tensors
|
||||
if trend_angle.dim() == 0:
|
||||
angle = trend_angle.item()
|
||||
steep = trend_steepness_val.item()
|
||||
else:
|
||||
angle = trend_angle[i].item()
|
||||
steep = trend_steepness_val[i].item()
|
||||
|
||||
# Normalize steepness to [0, 1] range (assuming max steepness of 10 units)
|
||||
normalized_steepness = min(steep / 10.0, 1.0) if steep > 0 else 0.0
|
||||
@@ -964,10 +969,19 @@ class TradingTransformerTrainer:
|
||||
|
||||
# Add confidence loss if available
|
||||
if 'confidence' in outputs and 'trade_success' in batch:
|
||||
confidence_loss = self.confidence_criterion(
|
||||
outputs['confidence'].squeeze(),
|
||||
batch['trade_success'].float()
|
||||
)
|
||||
# Ensure both tensors have compatible shapes
|
||||
# confidence: [batch_size, 1] -> squeeze last dim to [batch_size]
|
||||
# trade_success: [batch_size] - ensure same shape
|
||||
confidence_pred = outputs['confidence'].squeeze(-1) # Only remove last dimension
|
||||
trade_target = batch['trade_success'].float()
|
||||
|
||||
# Ensure shapes match (handle edge case where batch_size=1)
|
||||
if confidence_pred.dim() == 0: # scalar case
|
||||
confidence_pred = confidence_pred.unsqueeze(0)
|
||||
if trade_target.dim() == 0: # scalar case
|
||||
trade_target = trade_target.unsqueeze(0)
|
||||
|
||||
confidence_loss = self.confidence_criterion(confidence_pred, trade_target)
|
||||
total_loss += 0.1 * confidence_loss
|
||||
|
||||
# Backward pass
|
||||
|
||||
@@ -407,7 +407,7 @@ class DQNAgent:
|
||||
# Check if mixed precision training should be used
|
||||
if torch.cuda.is_available() and hasattr(torch.cuda, 'amp') and 'DISABLE_MIXED_PRECISION' not in os.environ:
|
||||
self.use_mixed_precision = True
|
||||
self.scaler = torch.cuda.amp.GradScaler()
|
||||
self.scaler = torch.amp.GradScaler('cuda')
|
||||
logger.info("Mixed precision training enabled")
|
||||
else:
|
||||
self.use_mixed_precision = False
|
||||
@@ -577,7 +577,7 @@ class DQNAgent:
|
||||
# Check if mixed precision training should be used
|
||||
if torch.cuda.is_available() and hasattr(torch.cuda, 'amp') and 'DISABLE_MIXED_PRECISION' not in os.environ:
|
||||
self.use_mixed_precision = True
|
||||
self.scaler = torch.cuda.amp.GradScaler()
|
||||
self.scaler = torch.amp.GradScaler('cuda')
|
||||
logger.info("Mixed precision training enabled")
|
||||
else:
|
||||
self.use_mixed_precision = False
|
||||
|
||||
Reference in New Issue
Block a user