3853988af7
- Fixed PSO budget calculation bug in ml/src/hyperopt/optimizer.rs - Root cause: Division by n_particles in sequential execution - Now correctly calculates max_iters = remaining_trials (no division) - Result: 50 trials complete instead of 23 (100% vs 46%) - Added comprehensive DQN hyperopt results analysis - 39/50 trials analyzed across 2 RunPod deployments - Best hyperparameters identified: LR 4.89e-5 (ultra-low) - Created DQN_HYPEROPT_RESULTS_SUMMARY.md with expert validation - GitLab CI/CD pipeline operational (48 lines fixed) - Fixed YAML syntax errors (unquoted colons) - All 7 jobs validated and working - Warning cleanup complete (136 → 0 warnings) - Removed 143 lines dead code - Fixed visibility, unused imports, Debug traits - Archived Wave D reports to docs/archive/ - 8 early stopping reports moved - Root directory cleaned up 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
45 lines
1.7 KiB
Bash
Executable File
45 lines
1.7 KiB
Bash
Executable File
#!/bin/bash
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set -e
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echo "========================================="
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echo "PPO Hyperopt Deployment"
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echo "========================================="
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echo ""
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# Set PYTHONPATH
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export PYTHONPATH="/home/jgrusewski/Work/foxhunt:$PYTHONPATH"
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# Activate venv
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source .venv/bin/activate
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# Deploy PPO hyperopt
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# Note: hyperopt_ppo_demo already supports separate policy_learning_rate and value_learning_rate
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python3 scripts/runpod_deploy.py \
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--gpu-type "RTX A4000" \
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--image "jgrusewski/foxhunt-hyperopt:latest" \
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--command "hyperopt_ppo_demo --parquet-file /runpod-volume/test_data/ES_FUT_180d.parquet --trials 50 --episodes 2000 --base-dir /runpod-volume/ml_training/ppo_hyperopt --early-stopping-min-epochs 50"
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echo ""
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echo "✅ PPO hyperopt deployment initiated"
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echo "Monitor logs: python3 scripts/python/runpod/monitor_logs.py <pod_id>"
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echo ""
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echo "Previous Results (2025-11-01):"
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echo " Duration: 14.3 minutes (99.8% faster than 18-24 hour estimate)"
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echo " Cost: \$0.06 (98.7% cheaper than \$4.50-\$6.00 estimate)"
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echo " Trials: 63 completed (26% bonus over 50 target)"
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echo " Best Trial #1 (objective: 2.4023):"
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echo " • policy_learning_rate: 1.0e-06 (ultra-conservative)"
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echo " • value_learning_rate: 0.001 (aggressive)"
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echo " • clip_epsilon: 0.1126"
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echo " • entropy_coeff: 0.006142"
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echo " • value_loss_coeff: 0.5"
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echo ""
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echo "Expected duration: 10-20 minutes (historical)"
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echo "Expected cost: \$0.04-\$0.08 @ \$0.25/hr (RTX A4000)"
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echo ""
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echo "Success Criteria:"
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echo " - Policy loss decreasing over episodes"
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echo " - Separate policy_lr and value_lr optimized by Optuna"
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echo " - Entropy coefficient balanced (0.006-0.01 range)"
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echo " - Final backtest: > 10% return, Sharpe > 1.5"
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