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## 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>
132 lines
4.7 KiB
Markdown
132 lines
4.7 KiB
Markdown
# DQN Hyperparameter Tuning Summary Report
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**Agent**: Agent 119 - DQN Tuning Monitor
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**Date**: 2025-10-14
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**Status**: INCOMPLETE (Process terminated prematurely)
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## Execution Overview
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- **Planned Trials**: 50
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- **Completed Trials**: 36/50 (72%)
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- **Failed Trials**: 1 (trial_36 - incomplete)
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- **Start Time**: 17:00 (trial_0)
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- **End Time**: 18:45 (trial_35 completed, trial_36 started but not finished)
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- **Total Duration**: 1 hour 45 minutes (105 minutes)
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- **Average Time per Trial**: ~2.9 minutes (105 min / 36 trials)
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## Trial Progress Analysis
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### Completed Checkpoints
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All successful trials produced checkpoint files at epoch 50:
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- Size: 75,628 bytes (~74 KB) per checkpoint
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- Format: SafeTensors
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- Location: `/home/jgrusewski/Work/foxhunt/ml/tuning_checkpoints/trial_*/`
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### Timeline
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- Trials 0-9: 17:00 - 17:27 (27 minutes, ~2.7 min/trial)
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- Trials 10-19: 17:30 - 17:57 (27 minutes, ~2.7 min/trial)
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- Trials 20-29: 18:00 - 18:29 (29 minutes, ~2.9 min/trial)
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- Trials 30-35: 18:31 - 18:45 (14 minutes, ~2.3 min/trial)
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- Trial 36: Started 18:45, did not complete
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## Status Assessment
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### What Happened
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The tuning process (PID 3907078) is no longer running. The process completed 36 trials successfully but terminated before finishing the planned 50 trials.
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### Possible Causes
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1. **User interruption** (Ctrl+C or kill signal)
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2. **System resource constraints** (though no OOM evidence found)
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3. **Time-based termination** (if a timeout was configured)
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4. **Error in trial 36** (directory exists but no checkpoint)
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### Available Results
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- **Pilot Results**: `/home/jgrusewski/Work/foxhunt/results/tuning_pilot_dqn.json`
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- Only contains 3 trials from an earlier pilot run
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- Best trial: Trial 2 with Sharpe ratio 1.5
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- Config: learning_rate=0.001, batch_size=230, gamma=0.99, epsilon_decay=0.995
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- **Full Results**: NOT AVAILABLE (no final JSON output or Optuna database found)
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## Data Recovery Options
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Since the process terminated without generating final results, we have several options:
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### Option 1: Extract Metrics from Checkpoints
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Analyze the 36 checkpoint files to extract:
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- Training loss curves
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- Model weights for validation
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- Manual Sharpe ratio calculation through backtesting
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### Option 2: Resume Tuning
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Continue from trial 37 to complete the remaining 14 trials:
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```bash
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# Restart tuning with trials 37-50
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# Would require modifying the tuning script to skip completed trials
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```
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### Option 3: Use Pilot Results
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The pilot run shows Trial 2 performed best with:
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- Learning rate: 0.001
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- Batch size: 230
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- Gamma: 0.99
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- Epsilon decay: 0.995
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- Sharpe ratio: 1.5
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However, this is based on only 3 trials, not a comprehensive search.
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## Recommendations
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### Immediate Actions
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1. **Investigate termination cause**: Check system logs, user history
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2. **Validate checkpoint integrity**: Ensure all 36 checkpoints are loadable
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3. **Extract available metrics**: Parse checkpoint metadata if available
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### Short-term Actions
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1. **Backtest checkpoint models**: Run backtests on a sample of the 36 checkpoints to estimate their Sharpe ratios
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2. **Identify best performer**: Compare results to find the optimal hyperparameter combination
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3. **Document findings**: Update tuning results based on manual analysis
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### Long-term Actions
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1. **Implement robust tuning infrastructure**:
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- Add checkpoint resumption capability
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- Store trial results incrementally (not just at completion)
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- Use Optuna's JournalStorage for fault tolerance
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2. **Complete remaining trials**: If the current 36 trials show promising variance, complete the full 50-trial search
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3. **Consider alternative approaches**: If trials are too homogeneous, expand the search space
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## Performance Estimation
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Based on the 36 completed trials:
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- **Estimated total time for 50 trials**: 145 minutes (2.4 hours)
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- **Time spent**: 105 minutes (72% of estimated total)
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- **Time remaining**: 40 minutes for 14 trials
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## Next Steps
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**Priority 1 (IMMEDIATE)**:
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- Determine if results can be extracted from the 36 checkpoints
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- Check if any intermediate metrics were logged
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**Priority 2 (SHORT-TERM)**:
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- Backtest a sample of checkpoints to estimate performance
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- Compare with pilot results to validate improvements
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**Priority 3 (MEDIUM-TERM)**:
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- Decide whether to resume tuning or work with available results
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- Update tuning infrastructure to prevent data loss
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## Conclusion
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While the tuning run was interrupted prematurely, we have:
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- **36 checkpoint files** from successful trials
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- **Pilot results** showing baseline performance (Sharpe 1.5)
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- **Clear performance metrics** (2.9 min/trial average)
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The next agent should focus on extracting value from these 36 checkpoints through systematic backtesting and comparison.
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---
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**Report Generated**: 2025-10-14 19:03
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**Agent**: Agent 119
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**Status**: MONITORING COMPLETE - AWAITING RESULTS EXTRACTION
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