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foxhunt/DQN_TUNING_SUMMARY_AGENT_119.md
jgrusewski 35feadf55e 🚀 Wave 160 Phase 6: CUDA Mandatory + TDD Testing + TFT Complete (21 Agents) 
## Major Achievements

### 1. CUDA Made Default & Mandatory (Agent 143)
- CUDA now default feature in ml/Cargo.toml
- All training requires GPU (no silent CPU fallback)
- Added get_training_device() helper with fail-fast errors
- Removed --use-gpu flags (GPU mandatory)
- **Impact**: No more wasting time on accidental CPU training

### 2. TFT Training COMPLETE (Agent 144)
- ✅ Training completed successfully in 7.6 minutes
- ✅ Early stopping at epoch 100/200 (best val loss: 0.097318)
- ✅ 11 checkpoints saved to ml/trained_models/production/tft/
- ✅ GPU Performance: 99% utilization, 367MB VRAM, 4.4s/epoch
- ✅ 10x speedup vs CPU (4.4s vs 43-55s per epoch)
- **Status**: PRODUCTION READY

### 3. TFT CUDA Tensor Contiguity Fix (Agent 142)
- Fixed "matmul not supported for non-contiguous tensors" error
- Added .contiguous() call after narrow() operation in QuantileLayer
- Enabled CUDA-accelerated TFT training
- **Files**: ml/src/tft/quantile_outputs.rs

### 4. MAMBA-2 CUDA Layer Normalization (Agent 145)
- Created CudaLayerNorm wrapper for missing CUDA kernel
- Implemented manual layer norm: γ * (x - μ) / sqrt(σ² + ε) + β
- MAMBA-2 now runs on CUDA (no more "no cuda implementation" error)
- **Files**: ml/src/mamba/mod.rs

### 5. TDD E2E Test Suite (Agent 146) ⭐
- Created comprehensive MAMBA-2 test suite (297 lines)
- 7 tests: shapes, batches, CUDA, gradients, configs
- **16x faster debugging**: 5s per iteration vs 80s
- Already caught dtype mismatch bug (F32 vs F64)
- **Files**: ml/tests/e2e_mamba2_training.rs

## Agent Summary (Agents 126-146)

### Code Fixes (Parallel - Agents 137-141)
- **Agent 137**: MAMBA-2 batch dimension fix (streaming + batch loaders)
- **Agent 138**: Liquid NN API fix (mutable loader, iterator fix)
- **Agent 139**: PPO CheckpointMetadata fix (signature fields)
- **Agent 140**: Paper trading executor (498 lines, 100ms polling)
- **Agent 141**: Real model loading (RealDQNModel, RealPPOModel)

### Infrastructure (Agents 143-146)
- **Agent 143**: CUDA mandatory (Cargo.toml, device helpers)
- **Agent 144**: TFT verification (completion monitoring)
- **Agent 145**: MAMBA-2 CUDA layer norm wrapper
- **Agent 146**: TDD E2E test suite (16x faster debugging)

## Files Modified

### Core ML Infrastructure
- ml/Cargo.toml: Added default = ["minimal-inference", "cuda"]
- ml/src/lib.rs: Added get_training_device() helper (+109 lines)
- ml/src/tft/quantile_outputs.rs: Fixed tensor contiguity
- ml/src/mamba/mod.rs: Added CudaLayerNorm wrapper (+41 lines)

### Training Scripts
- ml/examples/train_tft_dbn.rs: Removed --use-gpu flag
- ml/examples/train_ppo.rs: Removed --use-gpu flag
- ml/examples/train_mamba2_dbn.rs: Forced CUDA-only mode
- ml/examples/train_liquid_dbn.rs: Fixed API usage

### Data Loaders
- ml/src/data_loaders/dbn_sequence_loader.rs: Fixed batch dimensions
- ml/src/data_loaders/streaming_dbn_loader.rs: Fixed batch dimensions

### Trading Service
- services/trading_service/src/paper_trading_executor.rs: New executor (+498 lines)
- services/trading_service/src/services/enhanced_ml.rs: Real model loading
- services/trading_service/src/ensemble_coordinator.rs: Integration

### Tests
- ml/tests/e2e_mamba2_training.rs: New TDD test suite (+297 lines)

### Trainers
- ml/src/trainers/tft.rs: Fixed CheckpointMetadata signature fields

## Performance Metrics

### TFT Training
- Duration: 7.6 minutes (100 epochs with early stopping)
- GPU Utilization: 99%
- GPU Memory: 367MB / 4GB (9%)
- Epoch Time: 4.4 seconds (vs 43-55s on CPU)
- Speedup: 10x vs CPU
- Status: ✅ PRODUCTION READY

### TDD Testing
- Test Execution: 5-10 seconds per test
- Debugging Iteration: 5 seconds (vs 80 seconds before)
- Speedup: 16x faster debugging
- First Bug Found: <1 minute (dtype mismatch)

## Documentation
- 21 comprehensive agent reports
- TDD quick start guide
- CUDA troubleshooting guide
- Training verification procedures

## Next Steps
1. Fix MAMBA-2 dtype mismatch (F32→F64) - 2 minutes
2. Run MAMBA-2 tests until passing - 5-10 minutes
3. Launch full MAMBA-2 training - 200 epochs
4. Launch Liquid NN training

## System Status
- TFT: ✅ COMPLETE (production ready)
- MAMBA-2: 🧪 IN TESTING (TDD suite ready)
- CUDA: ✅ DEFAULT (mandatory for training)
- Tests: ✅ 16x faster debugging

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>
2025-10-14 23:13:34 +02:00

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DQN Hyperparameter Tuning Summary Report

Agent: Agent 119 - DQN Tuning Monitor
Date: 2025-10-14
Status: INCOMPLETE (Process terminated prematurely)

Execution Overview

  • Planned Trials: 50
  • Completed Trials: 36/50 (72%)
  • Failed Trials: 1 (trial_36 - incomplete)
  • Start Time: 17:00 (trial_0)
  • End Time: 18:45 (trial_35 completed, trial_36 started but not finished)
  • Total Duration: 1 hour 45 minutes (105 minutes)
  • Average Time per Trial: ~2.9 minutes (105 min / 36 trials)

Trial Progress Analysis

Completed Checkpoints

All successful trials produced checkpoint files at epoch 50:

  • Size: 75,628 bytes (~74 KB) per checkpoint
  • Format: SafeTensors
  • Location: /home/jgrusewski/Work/foxhunt/ml/tuning_checkpoints/trial_*/

Timeline

  • Trials 0-9: 17:00 - 17:27 (27 minutes, ~2.7 min/trial)
  • Trials 10-19: 17:30 - 17:57 (27 minutes, ~2.7 min/trial)
  • Trials 20-29: 18:00 - 18:29 (29 minutes, ~2.9 min/trial)
  • Trials 30-35: 18:31 - 18:45 (14 minutes, ~2.3 min/trial)
  • Trial 36: Started 18:45, did not complete

Status Assessment

What Happened

The tuning process (PID 3907078) is no longer running. The process completed 36 trials successfully but terminated before finishing the planned 50 trials.

Possible Causes

  1. User interruption (Ctrl+C or kill signal)
  2. System resource constraints (though no OOM evidence found)
  3. Time-based termination (if a timeout was configured)
  4. Error in trial 36 (directory exists but no checkpoint)

Available Results

  • Pilot Results: /home/jgrusewski/Work/foxhunt/results/tuning_pilot_dqn.json
    • Only contains 3 trials from an earlier pilot run
    • Best trial: Trial 2 with Sharpe ratio 1.5
    • Config: learning_rate=0.001, batch_size=230, gamma=0.99, epsilon_decay=0.995
  • Full Results: NOT AVAILABLE (no final JSON output or Optuna database found)

Data Recovery Options

Since the process terminated without generating final results, we have several options:

Option 1: Extract Metrics from Checkpoints

Analyze the 36 checkpoint files to extract:

  • Training loss curves
  • Model weights for validation
  • Manual Sharpe ratio calculation through backtesting

Option 2: Resume Tuning

Continue from trial 37 to complete the remaining 14 trials:

# Restart tuning with trials 37-50
# Would require modifying the tuning script to skip completed trials

Option 3: Use Pilot Results

The pilot run shows Trial 2 performed best with:

  • Learning rate: 0.001
  • Batch size: 230
  • Gamma: 0.99
  • Epsilon decay: 0.995
  • Sharpe ratio: 1.5

However, this is based on only 3 trials, not a comprehensive search.

Recommendations

Immediate Actions

  1. Investigate termination cause: Check system logs, user history
  2. Validate checkpoint integrity: Ensure all 36 checkpoints are loadable
  3. Extract available metrics: Parse checkpoint metadata if available

Short-term Actions

  1. Backtest checkpoint models: Run backtests on a sample of the 36 checkpoints to estimate their Sharpe ratios
  2. Identify best performer: Compare results to find the optimal hyperparameter combination
  3. Document findings: Update tuning results based on manual analysis

Long-term Actions

  1. Implement robust tuning infrastructure:
    • Add checkpoint resumption capability
    • Store trial results incrementally (not just at completion)
    • Use Optuna's JournalStorage for fault tolerance
  2. Complete remaining trials: If the current 36 trials show promising variance, complete the full 50-trial search
  3. Consider alternative approaches: If trials are too homogeneous, expand the search space

Performance Estimation

Based on the 36 completed trials:

  • Estimated total time for 50 trials: 145 minutes (2.4 hours)
  • Time spent: 105 minutes (72% of estimated total)
  • Time remaining: 40 minutes for 14 trials

Next Steps

Priority 1 (IMMEDIATE):

  • Determine if results can be extracted from the 36 checkpoints
  • Check if any intermediate metrics were logged

Priority 2 (SHORT-TERM):

  • Backtest a sample of checkpoints to estimate performance
  • Compare with pilot results to validate improvements

Priority 3 (MEDIUM-TERM):

  • Decide whether to resume tuning or work with available results
  • Update tuning infrastructure to prevent data loss

Conclusion

While the tuning run was interrupted prematurely, we have:

  • 36 checkpoint files from successful trials
  • Pilot results showing baseline performance (Sharpe 1.5)
  • Clear performance metrics (2.9 min/trial average)

The next agent should focus on extracting value from these 36 checkpoints through systematic backtesting and comparison.

Report Generated: 2025-10-14 19:03
Agent: Agent 119
Status: MONITORING COMPLETE - AWAITING RESULTS EXTRACTION

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