2df1ea92e1
BREAKING CHANGES: - Removed orphaned dqn.rs monolithic trainer (4,975 lines) - Removed orphaned dqn_ensemble.rs module (816 lines) - Removed orphaned tft.rs and tft_complete_int8_integration_test.rs - TFT trainer split into modular directory structure DQN Module Refactoring: - Split trainers/dqn.rs into modular structure (config.rs, statistics.rs, trainer.rs) - Fixed hyperopt 39D search space (continuous params only) - Boolean flags (use_dueling, use_double_dqn, use_per, use_noisy_nets) are now FIXED architectural decisions - use_distributional defaults to false (Candle BUG #36 - scatter_add gradient issues) Clean Module Structure: - ml/src/trainers/dqn/ directory with proper mod.rs exports - ml/src/trainers/tft/ directory with config.rs, types.rs, model.rs, trainer.rs, tests.rs - All P0 features validated: TD-error clamping, batch diversity, LR scheduler, priority staleness Documentation: - Added comprehensive docs in docs/codebase-cleanup/ - ADR-001 for DQN refactoring decisions - Rainbow DQN component matrix and quick reference guides Build Status: Compiles with zero errors 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
99 lines
3.2 KiB
Bash
Executable File
99 lines
3.2 KiB
Bash
Executable File
#!/usr/bin/env bash
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# DQN Hyperparameter Optimization Deployment (Optimized Parameters)
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# Generated: 2025-11-02
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# Based on: DQN_HYPEROPT_RESULTS_SUMMARY.md (Trial #8, Run 1)
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#
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# BEST HYPERPARAMETERS:
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# - Learning Rate: 4.89e-5 (ultra-low, critical for DQN stability)
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# - Batch Size: 151
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# - Gamma: 0.9838
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# - Epsilon Decay: 0.9917
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# - Buffer Size: 185066
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# - Trials: 50 (complete the hyperopt properly)
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#
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# CRITICAL NOTE: DQN requires ultra-low learning rates (4.89e-5 to 1.40e-4)
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# This is 10-100x lower than PPO's optimal range due to off-policy replay buffer dynamics.
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set -euo pipefail
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# Configuration
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GPU_TYPE="${1:-RTX A4000}" # Default: RTX A4000 ($0.25/hr), alternative: RTX 4090 ($0.59/hr)
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DOCKER_IMAGE="jgrusewski/foxhunt:dqn-checkpoint-fix"
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PARQUET_FILE="/runpod-volume/test_data/ES_FUT_180d.parquet"
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OUTPUT_BASE="/runpod-volume/ml_training"
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TIMESTAMP=$(date +%Y%m%d_%H%M%S)
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OUTPUT_DIR="${OUTPUT_BASE}/dqn_hyperopt_optimized_${TIMESTAMP}"
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# Best hyperparameters from Trial #8 (Run 1)
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TRIALS=50
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EPOCHS=20 # Per trial
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N_INITIAL=2 # Initial random samples
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SEED=42 # Reproducibility
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# Early stopping configuration
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EARLY_STOPPING_PLATEAU_WINDOW=5
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EARLY_STOPPING_MIN_EPOCHS=10
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# Display configuration
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echo "=========================================="
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echo "DQN Hyperopt Deployment (Optimized)"
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echo "=========================================="
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echo "GPU: ${GPU_TYPE}"
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echo "Docker Image: ${DOCKER_IMAGE}"
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echo "Parquet File: ${PARQUET_FILE}"
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echo "Output Directory: ${OUTPUT_DIR}"
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echo ""
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echo "Hyperopt Configuration:"
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echo " Trials: ${TRIALS}"
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echo " Epochs per trial: ${EPOCHS}"
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echo " Initial random samples: ${N_INITIAL}"
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echo " Random seed: ${SEED}"
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echo ""
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echo "Expected Duration: ~40 min (RTX A4000) or ~25 min (RTX 4090)"
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echo "Expected Cost: ~\$0.17 (RTX A4000) or ~\$0.25 (RTX 4090)"
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echo "=========================================="
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echo ""
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# Build hyperopt command
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COMMAND="hyperopt_dqn_demo \
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--parquet-file ${PARQUET_FILE} \
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--trials ${TRIALS} \
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--epochs ${EPOCHS} \
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--n-initial ${N_INITIAL} \
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--seed ${SEED} \
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--base-dir ${OUTPUT_DIR} \
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--run-type hyperopt \
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--early-stopping-plateau-window ${EARLY_STOPPING_PLATEAU_WINDOW} \
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--early-stopping-min-epochs ${EARLY_STOPPING_MIN_EPOCHS}"
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echo "Command: ${COMMAND}"
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echo ""
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# Deploy using foxhunt-deploy CLI
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if [ ! -f "/home/jgrusewski/Work/foxhunt/target/release/foxhunt-deploy" ]; then
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echo "ERROR: foxhunt-deploy CLI not found at /home/jgrusewski/Work/foxhunt/target/release/foxhunt-deploy"
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echo "Please build it first: cargo build --release -p foxhunt-deploy"
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exit 1
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fi
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# Deploy pod
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echo "Deploying RunPod pod..."
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/home/jgrusewski/Work/foxhunt/target/release/foxhunt-deploy deploy \
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--gpu-type "${GPU_TYPE}" \
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--tag dqn-checkpoint-fix \
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--command "${COMMAND}" \
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--name "dqn-hyperopt-optimized-$(date +%Y%m%d-%H%M%S)" \
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--yes
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echo ""
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echo "=========================================="
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echo "Deployment Complete!"
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echo "=========================================="
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echo ""
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echo "Monitor progress:"
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echo " python3 scripts/python/runpod/monitor_logs.py <pod_id>"
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echo ""
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echo "Verify results (after completion):"
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echo " aws s3 ls s3://se3zdnb5o4/ml_training/dqn_hyperopt_optimized_${TIMESTAMP}/ --profile runpod --endpoint-url https://s3api-eur-is-1.runpod.io --recursive"
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echo ""
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