Implement comprehensive Runpod deployment with S3 volume mount architecture for FP32 ML model training on Tesla V100 GPUs. ## Infrastructure Components ### Deployment Scripts (scripts/) - runpod_deploy.sh: Master deployment orchestrator (8-step workflow) - runpod_upload.sh: S3 upload for binaries and test data - upload_env_to_runpod.sh: Secure .env credentials upload - runpod_deploy_test.sh: Prerequisites validation ### Docker Configuration - Dockerfile.runpod: Multi-stage CUDA 12.1 runtime (~2GB, no binaries) - entrypoint.sh: Volume verification and training execution - Architecture: Volume mount (NO S3 downloads in pods) ### S3 Configuration - Bucket: se3zdnb5o4 (Iceland region: eur-is-1) - Endpoint: https://s3api-eur-is-1.runpod.io - Structure: binaries/, test_data/, models/, .env ### OpenTofu Infrastructure (terraform/runpod/) - main.tf: Pod and volume resources - variables.tf: Configuration variables - outputs.tf: Pod connection info - Security: NO credentials in state (uses volume .env) ## Deployment Assets Uploaded ### Training Binaries (77MB) - train_tft_parquet (23M) - TFT-225 features - train_mamba2_parquet (22M) - MAMBA-2 state space - train_dqn (22M) - Deep Q-Network - train_ppo (13M) - Proximal Policy Optimization ### Test Data (13.8 MB) - 9 Parquet files: ES.FUT, NQ.FUT, 6E.FUT, ZN.FUT (180-day datasets) ### Credentials - .env file (1.5 KB, private access, chmod 600) ## Documentation ### Deployment Guides - RUNPOD_DEPLOYMENT_READY_SUMMARY.md: Complete deployment status - RUNPOD_VOLUME_DEPLOYMENT_GUIDE.md: Step-by-step guide (42KB) - RUNPOD_DEPLOYMENT_QUICK_START.md: Quick reference - RUNPOD_UPLOAD_GUIDE.md: S3 upload instructions - RUNPOD_VOLUME_CONFIGURATION_COMPLETE.md: S3 setup report - RUNPOD_S3_PARQUET_UPLOAD_REPORT.md: Data upload verification ### Architecture Documentation - RUNPOD_VOLUME_MOUNT_ARCHITECTURE.md: Volume mount design - RUNPOD_S3_ARCHITECTURE_DIAGRAM.txt: S3 API vs filesystem access - DOCKERFILE_RUNPOD_FINAL_SUMMARY.md: Docker image specification ### Decision Documentation - RUNPOD_DEPLOYMENT_CHECKLIST.md: Go/no-go decision matrix (27KB) - RUNPOD_DEPLOYMENT_DECISION_TREE.md: Decision workflow - FP32_RUNPOD_DEPLOYMENT_READY.md: FP32 deployment readiness ## QAT Enhancements ### Core QAT Infrastructure - ml/src/memory_optimization/qat.rs: Enhanced QAT observer (+226 lines) - ml/src/memory_optimization/auto_batch_size.rs: OOM recovery (+84 lines) - ml/src/tft/qat_tft.rs: QAT TFT wrapper (+154 lines) - ml/src/trainers/tft.rs: QAT training integration (+433 lines) - ml/src/qat_metrics_exporter.rs: NEW - QAT metrics export ### QAT Testing - ml/tests/qat_integration_tests.rs: NEW - Integration test suite - ml/tests/qat_gradient_clipping_test.rs: NEW - Gradient clipping tests - ml/tests/qat_device_consistency_test.rs: Device mismatch tests (+205 lines) - ml/tests/qat_accuracy_validation_test.rs: Accuracy validation - ml/tests/qat_tft_integration_test.rs: TFT QAT integration ### QAT Documentation - ml/docs/QAT_GUIDE.md: Comprehensive QAT guide (+616 lines) - ml/docs/QAT_GRADIENT_CHECKPOINTING_WORKAROUND.md: NEW - Workaround guide - QAT_BLOCKERS_ROOT_CAUSE_ANALYSIS.md: P0 blocker analysis (44KB) - QAT_ACCURACY_VALIDATION_REPORT.md: Accuracy comparison - QAT_GRADIENT_CLIPPING_VALIDATION_REPORT.md: Clipping validation ### QAT Monitoring - config/grafana/dashboards/qat-training-metrics.json: NEW - Grafana dashboard ## AWS CLI Configuration ### Credentials Setup - ~/.aws/credentials: Runpod profile configured - Access Key: user_2xxA3XcIFj16yfL3aBon9niiSpr - Secret Key: (from RUNPOD_S3_SECRET) - ~/.aws/config: Iceland region (eur-is-1) ## Production Readiness ### FP32 Models: ✅ READY FOR DEPLOYMENT - DQN: 15-20s training, ~6MB GPU memory - PPO: 7-10s training, ~145MB GPU memory - MAMBA-2: 2-3 min training, ~164MB GPU memory - TFT-225: 3-5 min training, ~500MB GPU memory - Total GPU Budget: 815MB (fits on 4GB+ Tesla V100) ### QAT Models: 🔴 BLOCKED - 24 tests implemented but DO NOT COMPILE (11 errors) - 3 P0 blockers: device mismatch, gradient checkpointing, OOM recovery - Timeline: 1-2 weeks to fix (13h P0 fixes + validation) ### Wave D Features: ✅ OPERATIONAL - 225 features fully integrated - Feature extraction: 5.10μs/bar (196x faster than target) - Wave D backtest: Sharpe 2.00, Win Rate 60%, Drawdown 15% - Database migration 045: Applied cleanly, zero conflicts ## Cost Analysis ### One-Time Setup - Network Volume: $4/month (50GB SSD) - Upload costs: FREE (S3 API included) ### Per Training Run (TFT-225) - GPU: Tesla V100-PCIE-16GB @ $0.29/hr - Training Time: ~4 hours - Cost per run: $1.16 ### Monthly (20 Training Runs) - Storage: $4.00/month - Training: $23.20/month (20 runs × $1.16) - Total: $27.20/month ## Security ### Credentials Management - ✅ NO credentials in Docker image - ✅ NO credentials in Terraform state - ✅ .env gitignored and not committed - ✅ .env file private on S3 (HTTP 401 on public access) - ✅ Docker Hub repository PRIVATE (jgrusewski/foxhunt) ### Access Control - S3 API: Local client uploads only - Volume mount: Pod filesystem access only - Authentication: AWS CLI with Runpod profile required ## Next Steps 1. ✅ COMPLETE: Build Docker image 2. ⏳ PENDING: Push to Docker Hub 3. ⏳ PENDING: Deploy pod via Runpod console 4. ⏳ PENDING: Validate training on Tesla V100 ## Performance Targets - Build time: 5-10 min - Upload time: ~20 sec (90MB total) - Pod startup: ~30 sec - Training time: 3-5 min (TFT-225) - Total deployment: ~40 min from start to first training run ## Test Status - FP32 tests: 597/608 passing (98.2%) - QAT tests: 0/24 passing (compilation errors) - Overall: 2,062/2,086 passing (98.8% excluding QAT) 🤖 Generated with Claude Code (https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
Foxhunt E2E Testing Framework
A comprehensive End-to-End testing framework for the Foxhunt High-Frequency Trading system. This framework tests the complete integration between TLI client, all three services (Trading, Backtesting, ML Training), database interactions, ML model inference, and complete trading workflows.
🎯 Overview
The E2E testing framework provides:
- Service Orchestration: Automated startup/shutdown of all services
- gRPC Client Testing: Authentication, streaming, and error handling
- Database Integration: Transaction management and configuration hot-reload
- ML Pipeline Testing: Model inference, training, and ensemble predictions
- Complete Workflow Testing: End-to-end trading scenarios
- Performance Benchmarking: Load testing and performance metrics
- Corrode-MCP Integration: Advanced test execution and reporting
🏗️ Architecture
tests/e2e/
├── Cargo.toml # Project configuration
├── build.rs # gRPC proto compilation
├── src/
│ ├── lib.rs # Main library and test macros
│ ├── framework.rs # Core E2E testing framework
│ ├── services.rs # Service management and orchestration
│ ├── clients.rs # gRPC test clients
│ ├── database.rs # Database testing harness
│ ├── ml_pipeline.rs # ML model testing framework
│ ├── workflows.rs # Complete trading workflow tests
│ ├── utils.rs # Test utilities and data generation
│ ├── corrode.rs # Corrode-MCP integration
│ └── bin/
│ ├── test_runner.rs # Test execution runner
│ └── service_orchestrator.rs # Service management tool
├── tests/
│ └── integration_test.rs # Example integration tests
└── README.md # This file
🚀 Quick Start
Prerequisites
- Rust Toolchain: Ensure you have Rust 1.75+ installed
- PostgreSQL: Running instance for database tests
- Corrode-MCP: Install corrode for advanced test execution
# Install corrode-mcp (if not already installed)
cargo install corrode-mcp
# Set up environment
export DATABASE_URL="postgresql://localhost/foxhunt_test"
export RUST_LOG="info"
Running Tests
Option 1: Using Test Runner (Recommended)
# Build the test runner
cargo build --bin test_runner --release
# Run all E2E tests
./target/release/test_runner run --test all
# Run specific test categories
./target/release/test_runner run --test trading --parallel 2
./target/release/test_runner run --test ml --verbose
./target/release/test_runner run --test smoke --fail-fast
# List available tests
./target/release/test_runner list
# Generate test report
./target/release/test_runner report --results-dir ./test-results --format html
Option 2: Using Service Orchestrator
# Build the service orchestrator
cargo build --bin service_orchestrator --release
# Start all services for testing
./target/release/service_orchestrator start --services all --wait
# Check service status
./target/release/service_orchestrator status
# Run specific tests against running services
cargo test --package foxhunt-e2e
# Stop services when done
./target/release/service_orchestrator stop --services all
Option 3: Direct Cargo Testing
# Run all integration tests
cargo test --package foxhunt-e2e
# Run specific test
cargo test --package foxhunt-e2e test_complete_trading_workflow
# Run with output
cargo test --package foxhunt-e2e -- --nocapture
📋 Test Categories
🔧 Service Tests
- service_startup: Verify all services start and respond to health checks
- service_shutdown: Test graceful service shutdown
- service_recovery: Test service recovery after failures
🗄️ Database Tests
- database_integration: Test PostgreSQL integration and queries
- database_migrations: Test database schema migrations
- database_performance: Test database query performance
📡 gRPC Tests
- grpc_clients: Test all gRPC client connections and authentication
- grpc_streaming: Test streaming gRPC calls (market data, order updates)
- grpc_error_handling: Test gRPC error scenarios and recovery
🤖 ML Pipeline Tests
- ml_inference: Test ML model inference pipelines
- ml_training: Test ML model training workflows
- ml_ensemble: Test ensemble prediction workflows
💼 Trading Tests
- trading_workflows: Complete trading workflow tests
- order_lifecycle: Order submission to execution lifecycle
- risk_management: Risk management and safety mechanisms
- emergency_stop: Emergency stop and kill switch tests
🎯 Full Suite
- all: Run complete E2E test suite
- smoke: Run smoke tests for quick validation
- performance: Run performance and load tests
🛠️ Framework Components
E2ETestFramework
The core framework that orchestrates all components:
use foxhunt_e2e::{e2e_test, framework::E2ETestFramework};
e2e_test!(my_test, |framework: E2ETestFramework| async {
// Your test logic here
let tli_client = framework.get_tli_client().await?;
let health = framework.check_services_health().await?;
assert!(health.all_healthy);
Ok(())
});
Service Management
Automated service lifecycle management:
use foxhunt_e2e::services::ServiceManager;
let mut manager = ServiceManager::new();
manager.start_all_services().await?;
// Tests run here
manager.stop_all_services().await?;
gRPC Clients
Type-safe gRPC client implementations:
use foxhunt_e2e::clients::{TradingServiceClient, MLTrainingServiceClient};
let mut trading = TradingServiceClient::new("http://localhost:50051").await?;
let portfolio = trading.get_portfolio().await?;
let mut ml = MLTrainingServiceClient::new("http://localhost:50053").await?;
let prediction = ml.predict(features).await?;
Database Testing
Transaction-isolated database testing:
use foxhunt_e2e::database::DatabaseTestHarness;
let db = DatabaseTestHarness::new().await?;
let mut tx = db.begin_test_transaction().await?;
// Database operations here - will auto-rollback
ML Pipeline Testing
Mock ML models for testing:
use foxhunt_e2e::ml_pipeline::MLPipelineTestHarness;
let ml = MLPipelineTestHarness::new().await?;
let result = ml.test_model_inference("mamba", features).await?;
let ensemble = ml.test_ensemble_prediction(features).await?;
🎛️ Configuration
Environment Variables
DATABASE_URL: PostgreSQL connection string for test databaseRUST_LOG: Log level (debug, info, warn, error)FOXHUNT_TEST_MODE: Set to "true" for test modeCUDA_VISIBLE_DEVICES: GPU configuration for ML testsTORCH_DEVICE: PyTorch device (cpu/cuda) for ML tests
Test Configuration
# tests/e2e/Cargo.toml
[package.metadata.e2e]
default_timeout = 600
max_parallel_sessions = 4
service_startup_timeout = 120
database_url = "postgresql://localhost/foxhunt_test"
📊 Performance Benchmarks
The framework includes comprehensive performance testing:
Order Submission Performance
- Target: >10 orders/second
- Success rate: >90%
- Latency: <100ms average
ML Inference Performance
- Target: >20 inferences/second
- Latency: <50ms average
- GPU utilization monitoring
Database Performance
- Query execution time monitoring
- Connection pool performance
- Transaction throughput
🔍 Debugging and Troubleshooting
Enable Debug Logging
export RUST_LOG=debug
cargo test --package foxhunt-e2e -- --nocapture
Service Logs
# View service logs
./target/release/service_orchestrator logs trading --follow
# Check service status
./target/release/service_orchestrator status
Database Issues
# Check database connection
psql $DATABASE_URL -c "SELECT 1;"
# Reset test database
dropdb foxhunt_test && createdb foxhunt_test
Common Issues
- Service startup timeouts: Increase
startup_timeoutin service configs - gRPC connection errors: Verify services are running and ports are correct
- Database connection failures: Check PostgreSQL is running and credentials
- ML model loading errors: Ensure model files exist or use mock models
🧪 Writing Custom Tests
Basic Test Structure
use foxhunt_e2e::{e2e_test, framework::E2ETestFramework};
use anyhow::Result;
e2e_test!(test_my_feature, |framework: E2ETestFramework| async {
// Test setup
let client = framework.get_tli_client().await?;
// Test execution
let result = client.my_operation().await?;
// Assertions
assert!(result.success, "Operation failed");
// Cleanup (automatic)
Ok(())
});
Advanced Test Features
e2e_test!(test_complex_workflow, |framework: E2ETestFramework| async {
// Use test data generator
let mut generator = TestDataGenerator::new();
let market_data = generator.generate_market_data()?;
// Measure performance
let (result, duration) = TestUtils::measure_execution_time(|| async {
// Your operation here
Ok(42)
}).await?;
// Database testing
let db = &framework.database_harness;
let mut tx = db.begin_test_transaction().await?;
// Database operations...
// ML testing
let ml = &framework.ml_pipeline;
let prediction = ml.test_ensemble_prediction(features).await?;
Ok(())
});
📈 Continuous Integration
GitHub Actions Example
name: E2E Tests
on: [push, pull_request]
jobs:
e2e-tests:
runs-on: ubuntu-latest
services:
postgres:
image: postgres:15
env:
POSTGRES_PASSWORD: postgres
POSTGRES_DB: foxhunt_test
options: >-
--health-cmd pg_isready
--health-interval 10s
--health-timeout 5s
--health-retries 5
steps:
- uses: actions/checkout@v3
- uses: actions-rs/toolchain@v1
with:
toolchain: stable
- name: Install corrode-mcp
run: cargo install corrode-mcp
- name: Run E2E tests
env:
DATABASE_URL: postgresql://postgres:postgres@localhost/foxhunt_test
RUST_LOG: info
run: |
cargo build --bin service_orchestrator --release
./target/release/service_orchestrator start --services all --wait --background &
sleep 10
cargo test --package foxhunt-e2e
🤝 Contributing
- Add new tests: Create new test functions using the
e2e_test!macro - Extend framework: Add new components to the framework modules
- Improve performance: Optimize test execution and resource usage
- Documentation: Update this README and code documentation
Test Naming Convention
test_[component]_[scenario]: e.g.,test_trading_order_lifecycle- Use descriptive names that explain what is being tested
- Group related tests in the same file
Code Style
- Follow Rust standard formatting (
cargo fmt) - Add comprehensive error handling
- Include informative log messages
- Write clear assertions with descriptive failure messages
📝 License
This E2E testing framework is part of the Foxhunt HFT Trading System and follows the same license terms as the main project.