Files
foxhunt/benchmark_ensemble_db_quick.sh
jgrusewski 35feadf55e 🚀 Wave 160 Phase 6: CUDA Mandatory + TDD Testing + TFT Complete (21 Agents)
## 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>
2025-10-14 23:13:34 +02:00

174 lines
6.4 KiB
Bash
Executable File

#!/bin/bash
# ================================================================================================
# Quick Ensemble Database Performance Benchmark
# Fast version for immediate feedback
# ================================================================================================
set -euo pipefail
RED='\033[0;31m'
GREEN='\033[0;32m'
YELLOW='\033[1;33m'
BLUE='\033[0;34m'
NC='\033[0m'
DB_URL="postgresql://foxhunt:foxhunt_dev_password@localhost:5432/foxhunt"
echo -e "${BLUE}Quick Ensemble Database Benchmark${NC}"
echo ""
# ================================================================================================
# TEST 1: SIMPLE WRITE THROUGHPUT (1000 rows)
# ================================================================================================
echo -e "${YELLOW}[1/4] Write Throughput Test${NC}"
# Generate simple insert test
START=$(date +%s%N)
for i in {1..10}; do
psql "$DB_URL" -c "
INSERT INTO ensemble_predictions (
timestamp, symbol, ensemble_action, ensemble_signal,
ensemble_confidence, disagreement_rate
)
SELECT
NOW() - (random() * INTERVAL '1 hour'),
'TEST_SYM',
CASE WHEN random() < 0.33 THEN 'BUY' WHEN random() < 0.66 THEN 'SELL' ELSE 'HOLD' END,
(random() * 2 - 1)::DOUBLE PRECISION,
random()::DOUBLE PRECISION,
random()::DOUBLE PRECISION
FROM generate_series(1, 100);
" > /dev/null 2>&1
done
END=$(date +%s%N)
DURATION_MS=$(( (END - START) / 1000000 ))
WRITES_PER_SEC=$(( 1000 * 1000 / DURATION_MS ))
echo -e "Inserted 1000 rows in ${BLUE}${DURATION_MS}ms${NC}"
echo -e "Write throughput: ${BLUE}${WRITES_PER_SEC} inserts/sec${NC}"
if [ $WRITES_PER_SEC -ge 1000 ]; then
echo -e "${GREEN}✅ PASS: Write throughput >= 1000/sec${NC}"
else
echo -e "${YELLOW}⚠️ WARNING: Write throughput < 1000/sec${NC}"
fi
echo ""
# ================================================================================================
# TEST 2: QUERY LATENCY (10 key queries)
# ================================================================================================
echo -e "${YELLOW}[2/4] Query Latency Test${NC}"
declare -a QUERIES=(
"Recent predictions|SELECT * FROM ensemble_predictions ORDER BY timestamp DESC LIMIT 100"
"High disagreement|SELECT * FROM ensemble_predictions WHERE disagreement_rate > 0.5 LIMIT 100"
"P&L by symbol|SELECT symbol, SUM(pnl) FROM ensemble_predictions WHERE pnl IS NOT NULL GROUP BY symbol"
"Action distribution|SELECT ensemble_action, COUNT(*) FROM ensemble_predictions GROUP BY ensemble_action"
"Avg confidence|SELECT ensemble_action, AVG(ensemble_confidence) FROM ensemble_predictions GROUP BY ensemble_action"
"Latency P99|SELECT PERCENTILE_CONT(0.99) WITHIN GROUP (ORDER BY inference_latency_us) FROM ensemble_predictions WHERE inference_latency_us IS NOT NULL"
"Win rate by symbol|SELECT symbol, COUNT(CASE WHEN pnl > 0 THEN 1 END)::FLOAT / NULLIF(COUNT(*), 0) FROM ensemble_predictions WHERE pnl IS NOT NULL GROUP BY symbol"
"Recent high confidence|SELECT * FROM ensemble_predictions WHERE ensemble_confidence > 0.8 ORDER BY timestamp DESC LIMIT 100"
"Model performance|SELECT model_id, AVG(accuracy) FROM model_performance_attribution WHERE window_hours = 24 GROUP BY model_id"
"Hourly metrics|SELECT * FROM ensemble_performance_hourly ORDER BY bucket DESC LIMIT 24"
)
declare -a LATENCIES=()
for query_spec in "${QUERIES[@]}"; do
IFS='|' read -r NAME SQL <<< "$query_spec"
START=$(date +%s%N)
psql "$DB_URL" -c "$SQL" > /dev/null 2>&1
END=$(date +%s%N)
LATENCY_MS=$(( (END - START) / 1000000 ))
LATENCIES+=($LATENCY_MS)
echo -e "${NAME}: ${BLUE}${LATENCY_MS}ms${NC}"
done
# Calculate P99
IFS=$'\n' SORTED=($(sort -n <<<"${LATENCIES[*]}"))
P99_INDEX=$(( (${#LATENCIES[@]} * 99) / 100 ))
P99_LATENCY=${SORTED[$P99_INDEX]}
echo ""
echo -e "P99 Query Latency: ${BLUE}${P99_LATENCY}ms${NC}"
if [ $P99_LATENCY -le 100 ]; then
echo -e "${GREEN}✅ PASS: P99 latency <= 100ms${NC}"
else
echo -e "${YELLOW}⚠️ WARNING: P99 latency > 100ms${NC}"
fi
echo ""
# ================================================================================================
# TEST 3: INDEX USAGE
# ================================================================================================
echo -e "${YELLOW}[3/4] Index Usage Statistics${NC}"
psql "$DB_URL" -c "
SELECT
LEFT(indexname, 40) as index_name,
idx_scan as scans,
pg_size_pretty(pg_relation_size(indexrelid)) as size
FROM pg_stat_user_indexes
WHERE tablename IN ('ensemble_predictions', 'model_performance_attribution')
ORDER BY idx_scan DESC
LIMIT 10;
"
echo ""
# ================================================================================================
# TEST 4: TABLE STATISTICS
# ================================================================================================
echo -e "${YELLOW}[4/4] Table Statistics${NC}"
psql "$DB_URL" -c "
SELECT
'ensemble_predictions' as table_name,
COUNT(*) as row_count,
pg_size_pretty(pg_total_relation_size('ensemble_predictions')) as total_size,
pg_size_pretty(pg_relation_size('ensemble_predictions')) as table_size,
pg_size_pretty(pg_indexes_size('ensemble_predictions')) as indexes_size
FROM ensemble_predictions
UNION ALL
SELECT
'model_performance_attribution',
COUNT(*),
pg_size_pretty(pg_total_relation_size('model_performance_attribution')),
pg_size_pretty(pg_relation_size('model_performance_attribution')),
pg_size_pretty(pg_indexes_size('model_performance_attribution'))
FROM model_performance_attribution;
"
echo ""
# ================================================================================================
# SUMMARY
# ================================================================================================
echo -e "${BLUE}================================================================================================${NC}"
echo -e "${BLUE}BENCHMARK SUMMARY${NC}"
echo -e "${BLUE}================================================================================================${NC}"
echo ""
echo -e "Write Throughput: ${BLUE}${WRITES_PER_SEC}/sec${NC} (target: 1000/sec)"
echo -e "P99 Query Latency: ${BLUE}${P99_LATENCY}ms${NC} (target: <100ms)"
echo ""
if [ $WRITES_PER_SEC -ge 1000 ] && [ $P99_LATENCY -le 100 ]; then
echo -e "${GREEN}✅ ALL TESTS PASSED${NC}"
exit 0
else
echo -e "${YELLOW}⚠️ Some metrics below target - database under optimization${NC}"
exit 0
fi