Files
foxhunt/TUNING_DEPLOYMENT_SUMMARY.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

9.7 KiB

Hyperparameter Tuning Pipeline Deployment Summary

Status: DEPLOYED AND RUNNING Deployment Time: 2025-10-14 18:00 Pipeline Duration: 13.7 hours (ends 2025-10-15 08:13)


Deployment Complete - All Systems Operational

Infrastructure Deployed

1. Auto-Monitor (auto_monitor_and_launch.sh)

  • Status: RUNNING
  • PID: 3991060
  • Function: Monitors DQN completion, auto-launches sequential tuner
  • Updates: Every 5 minutes
  • Log: /tmp/auto_monitor.log

2. Sequential Launcher (sequential_tuning_launcher.sh)

  • Status: READY (will launch when DQN completes)
  • Function: Launches PPO → TFT → MAMBA-2 → Liquid sequentially
  • Trigger: DQN completion (~19:28)

3. Dashboard Monitor (dashboard_monitor.sh)

  • Status: READY
  • Function: Real-time status for all 5 models + GPU
  • Usage: watch -n 30 /home/jgrusewski/Work/foxhunt/scripts/dashboard_monitor.sh

4. Quick Status Checker (quick_status.sh)

  • Status: READY
  • Function: One-line status check
  • Usage: /home/jgrusewski/Work/foxhunt/scripts/quick_status.sh

5. Hyperparameter Extractor (extract_best_hyperparameters.py)

  • Status: READY
  • Function: Extracts best hyperparameters from JSON results
  • Usage: python3 scripts/extract_best_hyperparameters.py

📊 Current Pipeline Status

DQN (In Progress)

  • Status: RUNNING (42% complete)
  • PID: 3911478
  • Runtime: 1h 3m
  • Progress: 21/50 trials
  • ETA: ~19:28 (1.5 hours remaining)
  • GPU: 38% utilization, 135/4096 MiB memory, 66°C
  • Last Trial: Sharpe=2.00, Loss=0.0450, Time=185s

PPO (Pending)

  • Status: WAITING for DQN
  • Start: ~19:28 (auto-launch)
  • Duration: ~3.2 hours
  • End: ~22:42

TFT (Pending)

  • Status: WAITING for PPO
  • Start: ~22:42 (auto-launch)
  • Duration: ~4.2 hours
  • End: ~02:54

MAMBA-2 (Pending)

  • Status: WAITING for TFT
  • Start: ~02:54 (auto-launch)
  • Duration: ~2.1 hours
  • End: ~05:00

Liquid (Pending)

  • Status: WAITING for MAMBA-2
  • Start: ~05:00 (auto-launch)
  • Duration: ~1.7 hours
  • End: ~06:42

🎯 Key Monitoring Commands

Must-Run Commands

# Quick status (run every 30 minutes)
/home/jgrusewski/Work/foxhunt/scripts/quick_status.sh

# Full dashboard (auto-updates every 30 seconds)
watch -n 30 /home/jgrusewski/Work/foxhunt/scripts/dashboard_monitor.sh

# Pipeline status
cat /tmp/tuning_pipeline_status.txt

Optional Monitoring

# Live DQN log
tail -f /tmp/tuning_run.log

# GPU monitoring
nvidia-smi -l 5

# Process check
ps aux | grep tune_hyperparameters

📁 Key Files & Locations

Documentation

  • HYPERPARAMETER_TUNING_EXECUTION_REPORT.md - Main report (will be updated with results)
  • TUNING_PIPELINE_INSTRUCTIONS.md - Detailed monitoring instructions
  • TUNING_DEPLOYMENT_SUMMARY.md - This file

Scripts (All Executable)

  • scripts/auto_monitor_and_launch.sh - Auto-monitor (RUNNING)
  • scripts/sequential_tuning_launcher.sh - Sequential launcher (READY)
  • scripts/dashboard_monitor.sh - Dashboard
  • scripts/quick_status.sh - Quick status
  • scripts/extract_best_hyperparameters.py - Results extractor

Logs (Live)

  • /tmp/tuning_run.log - DQN log (ACTIVE)
  • /tmp/ppo_tuning_run.log - PPO log (future)
  • /tmp/tft_tuning_run.log - TFT log (future)
  • /tmp/mamba2_tuning_run.log - MAMBA-2 log (future)
  • /tmp/liquid_tuning_run.log - Liquid log (future)
  • /tmp/auto_monitor.log - Auto-monitor log (ACTIVE)
  • /tmp/sequential_tuning.log - Sequential launcher log (future)
  • /tmp/tuning_pipeline_status.txt - Pipeline status (UPDATING)

PIDs

  • DQN: 3911478 (RUNNING)
  • Auto-monitor: 3991060 (RUNNING)
  • PPO: Not started
  • TFT: Not started
  • MAMBA-2: Not started
  • Liquid: Not started

Results (Future)

  • results/dqn_tuning_50trials.json - Created when DQN completes
  • results/ppo_tuning_50trials.json - Created when PPO completes
  • results/tft_tuning_50trials.json - Created when TFT completes
  • results/mamba2_tuning_50trials.json - Created when MAMBA-2 completes
  • results/liquid_tuning_50trials.json - Created when Liquid completes

🔔 Monitoring Schedule

/home/jgrusewski/Work/foxhunt/scripts/quick_status.sh

Key Checkpoints

19:30 (DQN Completion Expected)

  • Verify DQN completed 50 trials
  • Verify sequential launcher auto-started
  • Verify PPO is now running

22:45 (PPO Completion Expected)

  • Verify PPO completed 50 trials
  • Verify TFT is now running

03:00 (TFT Completion Expected)

  • Verify TFT completed 50 trials
  • Verify MAMBA-2 is now running

05:05 (MAMBA-2 Completion Expected)

  • Verify MAMBA-2 completed 50 trials
  • Verify Liquid is now running

06:45 (Liquid Completion Expected)

  • Verify Liquid completed 50 trials
  • All 5 models done, 250 trials total

08:00 (Results Extraction)

# Extract best hyperparameters
python3 /home/jgrusewski/Work/foxhunt/scripts/extract_best_hyperparameters.py

# Review updated report
cat HYPERPARAMETER_TUNING_EXECUTION_REPORT.md

🚨 What to Watch For

Normal Operation

  • GPU utilization: 30-60%
  • GPU memory: <2048 MiB (under 50%)
  • GPU temperature: <85°C
  • Trial completion: Every ~3-4 minutes
  • Sharpe ratios: 1.5-3.0
  • No errors in logs

Warning Signs ⚠️

  • GPU utilization: >90% sustained
  • GPU memory: >3500 MiB
  • GPU temperature: >85°C
  • No trial completion: >10 minutes
  • Sharpe ratios: <0.5

Critical Errors

  • CUDA Out of Memory (OOM)
  • Process crashed (PID gone)
  • Auto-monitor stopped
  • GPU temperature: >95°C

🔧 Emergency Procedures

If Any Model Hangs (>15 min no progress)

# 1. Check process
ps -p <PID>

# 2. Check log
tail -50 /tmp/<model>_tuning_run.log

# 3. Kill if necessary
kill -9 <PID>

# 4. Restart manually
nohup /home/jgrusewski/Work/foxhunt/target/release/examples/tune_hyperparameters \
  --model <MODEL> \
  --num-trials 50 \
  --epochs-per-trial 50 \
  --data-dir test_data/real/databento/ml_training \
  --output results/<model>_tuning_50trials.json \
  > /tmp/<model>_tuning_run.log 2>&1 &

If CUDA OOM Occurs

# 1. Kill failed process
kill -9 $(cat /tmp/<model>_tuning.pid)

# 2. Reduce batch size in config
# DQN/PPO/MAMBA-2/Liquid: 256 → 128
# TFT: 128 → 64

# 3. Restart with reduced batch size

If Auto-Monitor Stops

# Restart it
nohup /home/jgrusewski/Work/foxhunt/scripts/auto_monitor_and_launch.sh \
  > /tmp/auto_monitor.log 2>&1 &

Success Criteria

Pipeline succeeds when:

  1. All 5 models complete 50 trials (250 total)
  2. All result JSON files created and valid
  3. Best hyperparameters extracted for each model
  4. Sharpe ratios >1.5 for all models
  5. No OOM or thermal errors
  6. Report updated with final results

📊 Expected Results

Performance Targets

  • DQN: Sharpe 2.0-3.5, Loss 0.01-0.05
  • PPO: Sharpe 2.5-4.0, Loss 0.02-0.08
  • TFT: Sharpe 2.0-3.0, Loss 0.03-0.10
  • MAMBA-2: Sharpe 2.5-4.0, Loss 0.02-0.06
  • Liquid: Sharpe 2.0-3.5, Loss 0.02-0.07

Hyperparameter Ranges (Expected Optimal)

  • Learning Rate: 1e-4 to 5e-4 (most models)
  • Batch Size: 64-128 (most models)
  • Gamma/Discount: 0.95-0.99
  • Hidden Size: 128-192 (TFT, MAMBA-2, Liquid)

📝 Post-Completion Actions

Immediate (When Pipeline Completes)

  1. Run hyperparameter extraction script
  2. Review HYPERPARAMETER_TUNING_EXECUTION_REPORT.md
  3. Verify all 5 result files exist
  4. Check Sharpe ratios meet targets

Within 24 Hours

  1. 📝 Update model configuration files with best hyperparameters
  2. 🚀 Run production training with optimized hyperparameters
  3. 📊 Validate models with comprehensive backtesting
  4. 📈 Compare performance against baseline models

Within 1 Week

  1. 🔬 Analyze hyperparameter distributions
  2. 📉 Study convergence patterns
  3. 🎯 Identify potential hyperparameter correlations
  4. 📄 Document insights for future tuning

📞 Contact & Support

If Issues Arise

  • Check TUNING_PIPELINE_INSTRUCTIONS.md for detailed troubleshooting
  • Review logs in /tmp/*tuning*.log
  • Check GPU status: nvidia-smi
  • Monitor processes: ps aux | grep tune

Critical Issues

  • GPU temperature >95°C → Kill all processes immediately
  • Multiple OOM errors → Reduce batch sizes aggressively
  • System unresponsive → Check system resources (uptime, free -h)

📈 Performance Metrics

Current System Health

  • CPU: Available
  • GPU: RTX 3050 Ti, 4096 MiB VRAM
  • Disk: Sufficient space for results
  • Memory: Sufficient for training
  • Temperature: 66°C (healthy)

Training Data

  • Samples: 665,483 bars
  • Files: 360 DBN files
  • Symbols: ES.FUT, NQ.FUT, ZN.FUT, 6E.FUT
  • Features: 5 OHLCV + 10 technical indicators

🎉 Deployment Success

All systems deployed and operational:

  • Auto-monitor running
  • Sequential launcher ready
  • Dashboard available
  • Quick status available
  • Hyperparameter extractor ready
  • DQN tuning in progress (42%)
  • GPU healthy (66°C, 38% util)
  • No errors detected

Pipeline Status: OPERATIONAL AND MONITORING


Deployment Date: 2025-10-14 18:00 Expected Completion: 2025-10-15 08:13 Total Duration: 13.7 hours Models: DQN, PPO, TFT, MAMBA-2, Liquid Trials: 50 per model (250 total) Objective: Maximize Sharpe ratio (risk-adjusted returns)


Next Review: 30 minutes (18:30) or when DQN completes (~19:28)