8f834a1890
- Added comprehensive plans for training a LightGBM model on M4 Mac Mini and deploying it to an LXC container. - Created scripts for model training, deployment, and a full pipeline execution. - Enhanced model transfer with error handling and logging for better tracking. - Introduced profiling for training time analysis and dataset generation optimization. Made-with: Cursor
35 lines
1.0 KiB
Bash
Executable File
35 lines
1.0 KiB
Bash
Executable File
#!/usr/bin/env bash
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# 맥미니에서 전체 학습 파이프라인을 실행하고 LXC로 배포한다.
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# 사용법: bash scripts/train_and_deploy.sh [LXC_HOST] [LXC_MODELS_PATH]
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#
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# 예시:
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# bash scripts/train_and_deploy.sh
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# bash scripts/train_and_deploy.sh root@10.1.10.24 /root/cointrader/models
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set -euo pipefail
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LXC_HOST="${1:-root@10.1.10.24}"
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LXC_MODELS_PATH="${2:-/root/cointrader/models}"
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SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
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PROJECT_ROOT="$(cd "$SCRIPT_DIR/.." && pwd)"
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cd "$PROJECT_ROOT"
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echo "=== [1/3] 데이터 수집 ==="
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python scripts/fetch_history.py --symbol XRPUSDT --interval 1m --days 90 --output data/xrpusdt_1m.parquet
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echo ""
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echo "=== [2/3] 모델 학습 ==="
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python scripts/train_model.py --data data/xrpusdt_1m.parquet
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echo ""
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echo "=== [3/3] LXC 배포 ==="
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bash scripts/deploy_model.sh "$LXC_HOST" "$LXC_MODELS_PATH"
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echo ""
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echo "=== 전체 파이프라인 완료 ==="
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echo ""
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echo "봇 재시작이 필요하면:"
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echo " ssh ${LXC_HOST} 'cd /root/cointrader && docker compose restart cointrader'"
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