8d89fe80ff
- Archive: 85 agent .txt files → docs/archive/agents/legacy_txt/ - Scripts: Move 110 shell scripts → scripts/ (keep deploy.sh in root) - Models: Move 18 .safetensors → ml/models/checkpoints/training_artifacts/ - Delete: 34 directories (~33GB freed) - target/, coverage_*, test artifacts - Build: Clean 14 build artifacts (.rlib, .o, .pid, binaries) - Tests: Move 14 .rs files → tests/standalone/ - SQL: Move 5 files → sql/ (keep init-db*.sql for Docker) - Wave 153: Archive to docs/archive/historical/wave153/ - Docs: Archive 9 markdown files to wave_d/reports/ and historical/ Total impact: ~34GB freed (both waves), root directory cleaned from 583 to ~40 essential files Directory count reduced from 65 to 31 (52% reduction) All historical data preserved in organized archive structure
42 lines
1.2 KiB
Bash
Executable File
42 lines
1.2 KiB
Bash
Executable File
#!/bin/bash
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# Quick DQN Deployment Script
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# Date: 2025-10-24
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# Purpose: Deploy DQN training to RunPod with correct binary selection
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set -euo pipefail
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echo "=========================================="
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echo "RunPod DQN Training Deployment"
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echo "=========================================="
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echo ""
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echo "Using: deploy_runpod_graphql.py (CORRECT script)"
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echo "Binary: train_dqn"
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echo "Dataset: ES_FUT_small.parquet (~13K bars)"
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echo "GPU: RTX A4000 (16GB, $0.25/hr)"
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echo "Expected time: ~1 minute"
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echo "Expected cost: ~$0.004"
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echo ""
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# Deploy DQN training
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python3 scripts/deploy_runpod_graphql.py \
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--binary train_dqn \
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--parquet-file /runpod-volume/test_data/ES_FUT_small.parquet \
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--epochs 100 \
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--gpu-type "NVIDIA RTX A4000" \
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--pod-name foxhunt-dqn-training
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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 "Next steps:"
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echo "1. Copy the Pod ID from output above"
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echo "2. Monitor training:"
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echo " ssh root@POD_ID.ssh.runpod.io"
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echo " nvidia-smi -l 1"
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echo "3. Check models after training:"
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echo " ls -lh /runpod-volume/models/"
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echo "4. Terminate pod to stop billing"
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echo ""
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