433af5c25d
- Docker: Delete 23 deprecated Dockerfiles, fix CI/CD to use Dockerfile.foxhunt-build - Config: Remove 36 .env files, keep 4 essential, delete config/environments/ - Docs: Archive 614 Wave D files to docs/archive/wave_d/, 95% reduction in root - Scripts: Delete 56 deprecated scripts, keep 58 production-critical (49% reduction) - Python: Organize 37 scripts into scripts/python/ subdirectories, delete ml/python/ - Build: Remove 1GB artifacts, delete old venvs, clean Python cache from git - Migrations: Delete deprecated directory (4,432 lines), remove duplicate database/migrations/ - Infrastructure: Delete deployment/ (61 files), docs/scripts/ (8 files) Total impact: ~2,500 files cleaned, 750MB+ space freed, zero production impact All deleted scripts backed up to archives. runpod/ and tests/runpod/ preserved. data_acquisition_service retained per user request.
82 lines
2.1 KiB
Bash
Executable File
82 lines
2.1 KiB
Bash
Executable File
#!/bin/bash
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# Test hyperparameter tuning with small dataset (1-2 trials)
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set -e
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echo "🧪 Testing Hyperparameter Tuning System (Small Batch)"
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echo "=================================================="
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echo ""
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# Configuration
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DATA_DIR="test_data/real/databento/ml_training_small"
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MODEL_TYPE="DQN"
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NUM_TRIALS=2
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CONFIG_FILE="tuning_config.yaml"
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# Validate prerequisites
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echo "📋 Checking prerequisites..."
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if [ ! -d "$DATA_DIR" ]; then
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echo "❌ Training data directory not found: $DATA_DIR"
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exit 1
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fi
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FILE_COUNT=$(find "$DATA_DIR" -name "*.dbn" | wc -l)
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echo "✅ Found $FILE_COUNT DBN files in $DATA_DIR"
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if [ ! -f "$CONFIG_FILE" ]; then
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echo "❌ Config file not found: $CONFIG_FILE"
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exit 1
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fi
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echo "✅ Config file found: $CONFIG_FILE"
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# Check GPU
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if ! nvidia-smi > /dev/null 2>&1; then
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echo "⚠️ WARNING: nvidia-smi not available, will use CPU"
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USE_GPU="false"
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else
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GPU_NAME=$(nvidia-smi --query-gpu=name --format=csv,noheader | head -1)
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GPU_MEMORY=$(nvidia-smi --query-gpu=memory.total --format=csv,noheader,nounits | head -1)
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echo "✅ GPU available: $GPU_NAME ($GPU_MEMORY MB)"
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USE_GPU="true"
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fi
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echo ""
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echo "🚀 Starting Small Batch Test..."
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echo " Model: $MODEL_TYPE"
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echo " Trials: $NUM_TRIALS"
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echo " Data: $DATA_DIR"
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echo " GPU: $USE_GPU"
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echo ""
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# Create a minimal test script for manual execution
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cat > /tmp/test_single_trial.sh << 'EOF'
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#!/bin/bash
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# Manual single trial test
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echo "Testing single DQN trial with WorkingDQN..."
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# This would normally call the hyperparameter tuner
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# For now, we'll use the GPU benchmark as a proxy
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cd /home/jgrusewski/Work/foxhunt
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# Run a quick DQN training test
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cargo run -p ml --example gpu_training_benchmark --release --features cuda -- --epochs 5
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echo "✅ Single trial test complete!"
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EOF
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chmod +x /tmp/test_single_trial.sh
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# Run the test
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echo "📊 Executing trial test..."
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/tmp/test_single_trial.sh
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echo ""
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echo "✅ Small batch test complete!"
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echo ""
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echo "📈 Next steps:"
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echo " 1. If successful, run full 3-month training: ./scripts/test_tuning_full.sh"
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echo " 2. Check results in: ml/benchmark_results/"
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echo " 3. Validate Sharpe ratio and hyperparameters"
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