Files
foxhunt/PERFORMANCE_REGRESSION_QUICKSTART.md
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

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Markdown

# Performance Regression Testing - Quick Start Guide
**Status**: ✅ Production Ready
**Time to Setup**: 5 minutes
**Time to Run**: 10 minutes
---
## 1. Quick Start (5 Minutes)
### 1.1 Run Benchmarks
```bash
# Run performance regression suite
cargo bench --bench performance_regression
# Expected output:
# - 8 benchmark groups
# - ~40 individual benchmarks
# - 10 minutes runtime
# - HTML report at target/criterion/report/index.html
```
### 1.2 View Results
```bash
# Open HTML report
open target/criterion/report/index.html
# Check metrics:
# ✅ ML Prediction: P50 < 20μs, P99 < 50μs
# ✅ Hot-Swap: P50 < 1μs
# ✅ DB Writes: >1000/sec
# ✅ Backtest: >1100 bars/sec
# ✅ Order Processing: P99 < 100μs
# ✅ Risk Validation: P99 < 50μs
```
---
## 2. Record Baseline (1 Command)
```bash
# Record baseline for main branch
./scripts/record_baseline_metrics.sh main
# Output files:
# - performance_metrics/metrics_main_*.json
# - performance_metrics/baseline_summary_main.md
# - target/criterion/baselines/main/
```
---
## 3. Compare Performance (1 Command)
```bash
# After making changes, compare against baseline
cargo bench --bench performance_regression -- --baseline main
# Criterion will show:
# - Green: Performance improved
# - White: No significant change
# - Red: Performance regressed >5%
```
---
## 4. Generate Flame Graphs (Optional)
```bash
# Generate flame graphs for profiling (Linux only)
./scripts/generate_flame_graphs.sh
# View interactive flame graphs
open flame_graphs/index.html
# Or profile specific benchmark
./scripts/generate_flame_graphs.sh ml_prediction 60
```
---
## 5. CI Integration (Automatic)
Performance regression checks run automatically on:
- ✅ Pull requests (compare vs main)
- ✅ Main branch commits (save new baseline)
**CI Fails If**:
- Performance degrades >10%
- Critical metrics miss targets
---
## 6. Common Workflows
### 6.1 Before Committing
```bash
# 1. Run benchmarks
cargo bench --bench performance_regression
# 2. Check for regressions
cargo bench --bench performance_regression -- --baseline main
# 3. If regression found, investigate
./scripts/generate_flame_graphs.sh <benchmark-name> 60
```
### 6.2 After Optimization
```bash
# 1. Record pre-optimization baseline
cargo bench --bench performance_regression -- --save-baseline pre_opt
# 2. Make optimization changes
# 3. Compare improvement
cargo bench --bench performance_regression -- --baseline pre_opt
# 4. Save new baseline if satisfied
cargo bench --bench performance_regression -- --save-baseline main
```
### 6.3 Investigating Regression
```bash
# 1. Identify problematic benchmark
cargo bench --bench performance_regression -- --baseline main
# 2. Generate flame graph
./scripts/generate_flame_graphs.sh <benchmark-name> 60
# 3. Analyze flame graph
open flame_graphs/flamegraph_<benchmark-name>_*.svg
# 4. Look for wide frames (high CPU time)
# 5. Fix identified bottlenecks
# 6. Re-run benchmarks to verify
```
---
## 7. Benchmark Descriptions
| Benchmark | Target | Purpose |
|-----------|--------|---------|
| `ml_prediction_latency` | P50 20μs, P99 50μs | ML model inference speed |
| `hot_swap_latency` | P50 1μs | Model switching overhead |
| `database_writes` | 1000/sec | DB write throughput |
| `backtest_performance` | 1100 bars/sec | Strategy backtesting speed |
| `order_processing` | P99 100μs | Order operations latency |
| `risk_validation` | P99 50μs | Risk check overhead |
| `memory_allocation` | <1μs | Allocation overhead |
| `concurrent_access` | <10μs | Lock contention |
---
## 8. Troubleshooting
### 8.1 Benchmarks Taking Too Long
```bash
# Use quick mode (fewer samples)
cargo bench --bench performance_regression -- --sample-size 10
```
### 8.2 High Variance in Results
```bash
# Close other applications
# Disable CPU frequency scaling
# Use longer measurement time
cargo bench --bench performance_regression -- --measurement-time 60
```
### 8.3 Flame Graphs Not Working
```bash
# Flame graphs require Linux
# Install perf tools
sudo apt-get install linux-tools-$(uname -r)
# Set perf permissions
sudo sysctl kernel.perf_event_paranoid=-1
```
---
## 9. Key Files
| File | Purpose |
|------|---------|
| `benches/performance_regression.rs` | Main benchmark suite |
| `scripts/record_baseline_metrics.sh` | Baseline recording |
| `scripts/generate_flame_graphs.sh` | Flame graph generation |
| `target/criterion/baselines/` | Saved baselines |
| `target/criterion/report/` | HTML reports |
| `performance_metrics/` | Metrics JSON/markdown |
| `flame_graphs/` | Generated flame graphs |
---
## 10. Next Steps
**Immediate**: Run benchmarks to establish baseline
```bash
cargo bench --bench performance_regression
./scripts/record_baseline_metrics.sh main
```
**Before PR**: Compare against baseline
```bash
cargo bench --bench performance_regression -- --baseline main
```
**Optimization**: Generate flame graphs
```bash
./scripts/generate_flame_graphs.sh
```
---
## 11. Help
**Full Documentation**: See `PERFORMANCE_REGRESSION_TEST_REPORT.md`
**Issues**:
- Compilation errors: `cargo clean && cargo build --bench performance_regression`
- CI failures: Review PR comments and HTML reports
- Performance questions: Check flame graphs
**Links**:
- [Criterion.rs Docs](https://bheisler.github.io/criterion.rs/)
- [Flame Graphs Guide](https://www.brendangregg.com/flamegraphs.html)
---
**Last Updated**: 2025-10-14
**Status**: ✅ Production Ready