35feadf55e
## Major Achievements ### 1. CUDA Made Default & Mandatory (Agent 143) - CUDA now default feature in ml/Cargo.toml - All training requires GPU (no silent CPU fallback) - Added get_training_device() helper with fail-fast errors - Removed --use-gpu flags (GPU mandatory) - **Impact**: No more wasting time on accidental CPU training ### 2. TFT Training COMPLETE (Agent 144) - ✅ Training completed successfully in 7.6 minutes - ✅ Early stopping at epoch 100/200 (best val loss: 0.097318) - ✅ 11 checkpoints saved to ml/trained_models/production/tft/ - ✅ GPU Performance: 99% utilization, 367MB VRAM, 4.4s/epoch - ✅ 10x speedup vs CPU (4.4s vs 43-55s per epoch) - **Status**: PRODUCTION READY ### 3. TFT CUDA Tensor Contiguity Fix (Agent 142) - Fixed "matmul not supported for non-contiguous tensors" error - Added .contiguous() call after narrow() operation in QuantileLayer - Enabled CUDA-accelerated TFT training - **Files**: ml/src/tft/quantile_outputs.rs ### 4. MAMBA-2 CUDA Layer Normalization (Agent 145) - Created CudaLayerNorm wrapper for missing CUDA kernel - Implemented manual layer norm: γ * (x - μ) / sqrt(σ² + ε) + β - MAMBA-2 now runs on CUDA (no more "no cuda implementation" error) - **Files**: ml/src/mamba/mod.rs ### 5. TDD E2E Test Suite (Agent 146) ⭐ - Created comprehensive MAMBA-2 test suite (297 lines) - 7 tests: shapes, batches, CUDA, gradients, configs - **16x faster debugging**: 5s per iteration vs 80s - Already caught dtype mismatch bug (F32 vs F64) - **Files**: ml/tests/e2e_mamba2_training.rs ## Agent Summary (Agents 126-146) ### Code Fixes (Parallel - Agents 137-141) - **Agent 137**: MAMBA-2 batch dimension fix (streaming + batch loaders) - **Agent 138**: Liquid NN API fix (mutable loader, iterator fix) - **Agent 139**: PPO CheckpointMetadata fix (signature fields) - **Agent 140**: Paper trading executor (498 lines, 100ms polling) - **Agent 141**: Real model loading (RealDQNModel, RealPPOModel) ### Infrastructure (Agents 143-146) - **Agent 143**: CUDA mandatory (Cargo.toml, device helpers) - **Agent 144**: TFT verification (completion monitoring) - **Agent 145**: MAMBA-2 CUDA layer norm wrapper - **Agent 146**: TDD E2E test suite (16x faster debugging) ## Files Modified ### Core ML Infrastructure - ml/Cargo.toml: Added default = ["minimal-inference", "cuda"] - ml/src/lib.rs: Added get_training_device() helper (+109 lines) - ml/src/tft/quantile_outputs.rs: Fixed tensor contiguity - ml/src/mamba/mod.rs: Added CudaLayerNorm wrapper (+41 lines) ### Training Scripts - ml/examples/train_tft_dbn.rs: Removed --use-gpu flag - ml/examples/train_ppo.rs: Removed --use-gpu flag - ml/examples/train_mamba2_dbn.rs: Forced CUDA-only mode - ml/examples/train_liquid_dbn.rs: Fixed API usage ### Data Loaders - ml/src/data_loaders/dbn_sequence_loader.rs: Fixed batch dimensions - ml/src/data_loaders/streaming_dbn_loader.rs: Fixed batch dimensions ### Trading Service - services/trading_service/src/paper_trading_executor.rs: New executor (+498 lines) - services/trading_service/src/services/enhanced_ml.rs: Real model loading - services/trading_service/src/ensemble_coordinator.rs: Integration ### Tests - ml/tests/e2e_mamba2_training.rs: New TDD test suite (+297 lines) ### Trainers - ml/src/trainers/tft.rs: Fixed CheckpointMetadata signature fields ## Performance Metrics ### TFT Training - Duration: 7.6 minutes (100 epochs with early stopping) - GPU Utilization: 99% - GPU Memory: 367MB / 4GB (9%) - Epoch Time: 4.4 seconds (vs 43-55s on CPU) - Speedup: 10x vs CPU - Status: ✅ PRODUCTION READY ### TDD Testing - Test Execution: 5-10 seconds per test - Debugging Iteration: 5 seconds (vs 80 seconds before) - Speedup: 16x faster debugging - First Bug Found: <1 minute (dtype mismatch) ## Documentation - 21 comprehensive agent reports - TDD quick start guide - CUDA troubleshooting guide - Training verification procedures ## Next Steps 1. Fix MAMBA-2 dtype mismatch (F32→F64) - 2 minutes 2. Run MAMBA-2 tests until passing - 5-10 minutes 3. Launch full MAMBA-2 training - 200 epochs 4. Launch Liquid NN training ## System Status - TFT: ✅ COMPLETE (production ready) - MAMBA-2: 🧪 IN TESTING (TDD suite ready) - CUDA: ✅ DEFAULT (mandatory for training) - Tests: ✅ 16x faster debugging 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
73 lines
3.2 KiB
Bash
Executable File
73 lines
3.2 KiB
Bash
Executable File
#!/bin/bash
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# Database Query Optimization Verification Script
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# Agent 135 - 2025-10-14
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DB_URL="postgresql://foxhunt:foxhunt_dev_password@localhost:5432/foxhunt"
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echo "========================================"
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echo "DATABASE OPTIMIZATION VERIFICATION"
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echo "========================================"
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echo ""
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# Test 1: Check migration applied
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echo "Test 1: Migration 025 Objects Created"
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echo "--------------------------------------"
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echo -n "Function check_model_activity_health: "
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psql "$DB_URL" -tAc "SELECT COUNT(*) FROM pg_proc WHERE proname = 'check_model_activity_health';"
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echo -n "Function get_ensemble_performance_summary: "
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psql "$DB_URL" -tAc "SELECT COUNT(*) FROM pg_proc WHERE proname = 'get_ensemble_performance_summary';"
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echo -n "Materialized view model_activity_realtime: "
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psql "$DB_URL" -tAc "SELECT COUNT(*) FROM pg_matviews WHERE matviewname = 'model_activity_realtime';"
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echo -n "Materialized view paper_trading_execution_summary: "
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psql "$DB_URL" -tAc "SELECT COUNT(*) FROM pg_matviews WHERE matviewname = 'paper_trading_execution_summary';"
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echo -n "Indexes with 'active' pattern: "
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psql "$DB_URL" -tAc "SELECT COUNT(*) FROM pg_indexes WHERE indexname LIKE '%active%';"
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echo ""
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# Test 2: Query performance comparison
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echo "Test 2: Query Performance Benchmark"
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echo "------------------------------------"
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echo -n "Raw table aggregation (ms): "
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psql "$DB_URL" -tAc "EXPLAIN ANALYZE SELECT AVG(ensemble_confidence), AVG(disagreement_rate) FROM ensemble_predictions WHERE timestamp > NOW() - INTERVAL '1 day';" 2>&1 | grep "Execution Time" | awk '{print $3}'
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echo -n "Continuous aggregate (ms): "
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psql "$DB_URL" -tAc "EXPLAIN ANALYZE SELECT AVG(avg_confidence), AVG(avg_disagreement) FROM ensemble_performance_5min WHERE bucket > NOW() - INTERVAL '1 day';" 2>&1 | grep "Execution Time" | awk '{print $3}'
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echo ""
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# Test 3: Model activity health check
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echo "Test 3: Model Activity Diagnostics"
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echo "-----------------------------------"
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psql "$DB_URL" -c "SELECT model_name, is_active, predictions_in_window, activity_rate_pct FROM check_model_activity_health(60);"
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echo ""
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# Test 4: Paper trading execution summary
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echo "Test 4: Paper Trading Execution Rate"
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echo "-------------------------------------"
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psql "$DB_URL" -c "SELECT COUNT(*) as bucket_count, SUM(total_predictions) as total_preds, SUM(executed_orders) as total_orders, ROUND(100.0 * SUM(executed_orders) / NULLIF(SUM(total_predictions), 0), 2) as execution_rate_pct FROM paper_trading_execution_summary WHERE bucket > NOW() - INTERVAL '1 hour';"
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echo ""
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# Test 5: Index usage
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echo "Test 5: New Index Verification"
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echo "-------------------------------"
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psql "$DB_URL" -c "SELECT indexname FROM pg_indexes WHERE tablename = 'ensemble_predictions' AND indexname LIKE 'idx_ensemble_predictions_%active%' ORDER BY indexname;"
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echo ""
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echo "========================================"
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echo "VERIFICATION COMPLETE"
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echo "========================================"
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echo ""
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echo "Expected Results:"
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echo "- All object counts should be > 0"
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echo "- Continuous aggregate should be <0.2ms (10x+ faster)"
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echo "- All models should show 'false' for is_active (confirms NULL votes issue)"
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echo "- Execution rate should be 0% (confirms Agent 123's finding)"
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echo "- 4 model-specific indexes should be created"
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