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>
113 lines
4.3 KiB
Bash
Executable File
113 lines
4.3 KiB
Bash
Executable File
#!/bin/bash
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# DQN Retrain Deployment to Runpod
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# Deploys a Runpod pod to retrain DQN with fixed reward function and monitoring
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set -e
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# Colors for output
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RED='\033[0;31m'
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GREEN='\033[0;32m'
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YELLOW='\033[1;33m'
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NC='\033[0m' # No Color
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# Get script directory
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SCRIPT_DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )"
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cd "$SCRIPT_DIR"
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echo -e "${GREEN}====================================================================${NC}"
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echo -e "${GREEN}DQN Retrain Deployment Script${NC}"
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echo -e "${GREEN}====================================================================${NC}"
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# 1. Check prerequisites
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echo -e "\n${YELLOW}Step 1: Checking prerequisites...${NC}"
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# Check if .venv is activated
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if [[ -z "$VIRTUAL_ENV" ]]; then
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echo -e "${YELLOW}Activating virtual environment...${NC}"
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source .venv/bin/activate
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fi
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# Set PYTHONPATH for runpod module
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export PYTHONPATH="${SCRIPT_DIR}:${PYTHONPATH}"
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# Verify runpod module is available
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if ! python3 -c "import runpod" 2>/dev/null; then
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echo -e "${RED}ERROR: runpod module not found${NC}"
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echo "Install dependencies: pip install -r runpod/requirements.txt"
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exit 1
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fi
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echo -e "${GREEN}✓ Virtual environment and runpod module OK${NC}"
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# Check if code compiles
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echo -e "\n${YELLOW}Step 2: Verifying DQN training code compiles...${NC}"
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echo "(This will take a moment...)"
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if ! cargo build -p ml --example train_dqn --release 2>&1 | tail -5; then
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echo -e "${RED}ERROR: DQN training code failed to compile${NC}"
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exit 1
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fi
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echo -e "${GREEN}✓ DQN training code compiles successfully${NC}"
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# 2. Define training command for Runpod
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# IMPORTANT:
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# - Docker image has train_dqn binary in /usr/local/bin/
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# - train_dqn supports parquet via --parquet-file argument
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# - Path /runpod-volume/ is the volume mount point
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# - Data file: /runpod-volume/test_data/ES_FUT_180d.parquet
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# - We override the Docker CMD to run train_dqn with custom args
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TRAINING_COMMAND="train_dqn --parquet-file /runpod-volume/test_data/ES_FUT_180d.parquet --epochs 100 --min-epochs-before-stopping 50 --learning-rate 0.0001 --batch-size 32 --gamma 0.9626 --epsilon-start 0.3 --epsilon-end 0.05 --epsilon-decay 0.995 --buffer-size 104346 --min-replay-size 500 --checkpoint-frequency 10 --output-dir /runpod-volume/ml_training/dqn_fixed_reward --checkpoint-dir /runpod-volume/ml_training/dqn_fixed_reward/checkpoints --verbose"
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echo -e "\n${YELLOW}Step 3: Deployment Configuration${NC}"
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echo " GPU Type: RTX A4000 (16GB VRAM, \$0.25/hr)"
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echo " Docker Image: jgrusewski/foxhunt:latest"
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echo " Training Command: $TRAINING_COMMAND"
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echo " Expected Duration: ~1-2 hours"
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echo " Expected Cost: ~\$0.25-\$0.50"
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echo ""
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echo "Monitoring features:"
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echo " - Real-time log streaming from S3"
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echo " - Automatic validation of:"
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echo " * Reward variance > 0.1"
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echo " * Action diversity ~30-35% each"
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echo " * Q-value balance across BUY/SELL/HOLD"
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echo ""
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# 3. Ask for confirmation
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read -p "Deploy DQN retrain to Runpod? (y/n): " -n 1 -r
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echo
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if [[ ! $REPLY =~ ^[Yy]$ ]]; then
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echo -e "${YELLOW}Deployment cancelled.${NC}"
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exit 0
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fi
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# 4. Deploy pod using runpod_deploy.py
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echo -e "\n${YELLOW}Step 4: Deploying Runpod pod...${NC}"
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python3 scripts/runpod_deploy.py \
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--gpu-type "RTX A4000" \
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--image "jgrusewski/foxhunt:latest" \
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--command "$TRAINING_COMMAND" \
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--container-disk 50 \
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--monitor \
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--timeout 3h
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# Script will automatically:
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# - Find available RTX A4000 in EUR-IS-1
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# - Deploy pod with volume mounted at /runpod-volume/
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# - Stream training logs in real-time
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# - Show reward/action/Q-value metrics
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echo -e "\n${GREEN}====================================================================${NC}"
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echo -e "${GREEN}Deployment completed!${NC}"
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echo -e "${GREEN}====================================================================${NC}"
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echo -e "\nNext steps:"
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echo " 1. Monitor logs above for:"
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echo " - Reward std > 0.1 (healthy variance)"
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echo " - Action distribution: BUY ~30-35%, SELL ~30-35%, HOLD ~30-35%"
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echo " - Q-value balance (BUY/SELL/HOLD similar magnitudes)"
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echo " 2. Check S3 for saved checkpoints:"
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echo " aws s3 ls s3://se3zdnb5o4/ml_training/dqn_fixed_reward/ --profile runpod --recursive"
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echo " 3. Pod will auto-terminate after 3h timeout or manual termination:"
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echo " curl -X POST -H \"Authorization: Bearer \$RUNPOD_API_KEY\" \\"
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echo " https://rest.runpod.io/v1/pods/<pod_id>/terminate"
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echo ""
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