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>
347 lines
11 KiB
Rust
347 lines
11 KiB
Rust
//! TLI Tune Command Integration Tests
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//!
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//! Smoke tests to verify the TLI tuning commands can connect to the API Gateway
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//! and validate infrastructure before full E2E testing.
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//!
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//! # Test Coverage
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//! 1. API Gateway connectivity check (port 50051)
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//! 2. gRPC connection establishment
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//! 3. JWT token format validation
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//! 4. Basic tuning job start (if services are running)
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//!
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//! # Test Modes
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//! - **Mock Mode**: Tests JWT validation and command parsing (always runs)
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//! - **Live Mode**: Tests real API Gateway connection (requires services running)
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// Suppress false-positive unused_crate_dependencies warnings
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// dev-dependencies are shared across ALL test targets in the crate
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// This test may not use all deps, but they are required by other integration tests
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#![allow(unused_crate_dependencies)]
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use anyhow::{Context, Result};
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use std::time::Duration;
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use tokio::time::timeout;
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use uuid::Uuid;
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// JWT token structures for validation
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use serde::{Deserialize, Serialize};
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/// JWT token claims structure
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#[derive(Debug, Clone, Serialize, Deserialize)]
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struct JwtClaims {
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sub: String,
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exp: u64,
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iat: u64,
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jti: String,
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roles: Vec<String>,
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permissions: Vec<String>,
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}
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/// Check if API Gateway is reachable at localhost:50051
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async fn check_api_gateway_availability() -> Result<bool> {
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use tokio::net::TcpStream;
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// Try to connect to port 50051 with 2-second timeout
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let connect_result = timeout(
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Duration::from_secs(2),
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TcpStream::connect("localhost:50051"),
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)
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.await;
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match connect_result {
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Ok(Ok(_stream)) => {
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println!("✅ API Gateway is reachable at localhost:50051");
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Ok(true)
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},
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Ok(Err(e)) => {
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println!("❌ API Gateway not reachable: {}", e);
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Ok(false)
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},
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Err(_) => {
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println!("⚠️ API Gateway connection timeout (not running)");
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Ok(false)
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},
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}
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}
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/// Validate JWT token format (basic structure check)
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fn validate_jwt_format(token: &str) -> Result<()> {
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// JWT should have 3 parts separated by dots
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let parts: Vec<&str> = token.split('.').collect();
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if parts.len() != 3 {
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anyhow::bail!(
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"Invalid JWT format: expected 3 parts (header.payload.signature), got {}",
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parts.len()
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);
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}
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// All parts should be base64url encoded (non-empty)
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for (i, part) in parts.iter().enumerate() {
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if part.is_empty() {
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anyhow::bail!("Invalid JWT format: part {} is empty", i);
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}
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}
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println!("✅ JWT token format is valid (3 parts, base64url encoded)");
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Ok(())
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}
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/// Create a mock JWT token for testing (NOT for production use)
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fn create_mock_jwt_token() -> String {
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use std::time::{SystemTime, UNIX_EPOCH};
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let now = SystemTime::now()
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.duration_since(UNIX_EPOCH)
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.unwrap()
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.as_secs();
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let claims = JwtClaims {
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sub: "test-user".to_string(),
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exp: now + 3600, // 1 hour expiry
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iat: now,
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jti: Uuid::new_v4().to_string(),
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roles: vec!["trader".to_string()],
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permissions: vec![
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"ml_training:start".to_string(),
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"ml_training:status".to_string(),
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],
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};
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// Base64url encode header (simplified for testing)
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let header = base64_helper::encode_no_pad(r#"{"alg":"HS256","typ":"JWT"}"#);
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// Base64url encode payload
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let payload = base64_helper::encode_no_pad(serde_json::to_string(&claims).unwrap());
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// Mock signature (not cryptographically secure - testing only)
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let signature = base64_helper::encode_no_pad("mock_signature");
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format!("{}.{}.{}", header, payload, signature)
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}
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/// Test: JWT token format validation
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#[test]
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fn test_jwt_token_format_validation() {
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println!("\n🧪 Testing JWT token format validation...");
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// Valid JWT format (3 parts)
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let valid_token = create_mock_jwt_token();
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let result = validate_jwt_format(&valid_token);
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assert!(result.is_ok(), "Valid JWT token should pass validation");
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// Invalid JWT formats
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let invalid_tokens = vec![
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"invalid", // Single part
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"header.payload", // Two parts
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"header.payload.sig.extra", // Four parts
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"header..signature", // Empty payload
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];
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for (i, token) in invalid_tokens.iter().enumerate() {
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let result = validate_jwt_format(token);
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assert!(
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result.is_err(),
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"Invalid token {} should fail validation",
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i
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);
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}
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println!("✅ JWT token format validation tests passed");
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}
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/// Test: API Gateway connectivity check
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#[tokio::test]
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async fn test_api_gateway_connectivity() {
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println!("\n🧪 Testing API Gateway connectivity...");
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let is_available = check_api_gateway_availability()
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.await
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.expect("Connectivity check should not fail");
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if is_available {
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println!("✅ API Gateway is running and accepting connections");
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} else {
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println!("⚠️ API Gateway not available (this is OK for CI/CD)");
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println!(" To test live connectivity:");
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println!(" 1. cargo run -p api_gateway &");
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println!(" 2. cargo test -p tli --test tune_integration_test");
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}
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}
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/// Test: gRPC connection establishment (mock mode)
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#[tokio::test]
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async fn test_grpc_connection_mock() {
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println!("\n🧪 Testing gRPC connection establishment (mock mode)...");
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// Import ML training proto client
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// Note: This is a smoke test - we don't require services to be running
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use tonic::transport::Channel;
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// Try to parse API Gateway URL (validates URL format)
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let api_gateway_url = "http://localhost:50051";
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let endpoint = Channel::from_shared(api_gateway_url.to_string());
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assert!(endpoint.is_ok(), "API Gateway URL should be valid");
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println!("✅ gRPC endpoint URL parsing successful");
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// Create mock JWT token
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let mock_token = create_mock_jwt_token();
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let validation = validate_jwt_format(&mock_token);
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assert!(validation.is_ok(), "Mock JWT token should be valid");
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println!("✅ gRPC connection mock test passed");
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}
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/// Test: Tuning job start with mock data (no actual service call)
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#[tokio::test]
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async fn test_tuning_job_start_mock() {
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println!("\n🧪 Testing tuning job start with mock data...");
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// Validate model type
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let valid_models = ["DQN", "PPO", "MAMBA_2", "TLOB", "TFT", "LIQUID"];
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for model in &valid_models {
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// This should not panic
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println!(" ✓ Model type '{}' is valid", model);
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}
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// Validate UUID generation
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let job_id = Uuid::new_v4();
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assert!(!job_id.to_string().is_empty(), "Job ID should not be empty");
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println!(" ✓ Generated job ID: {}", job_id);
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// Validate config file path (mock)
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let config_path = "/tmp/mock_tuning_config.yaml";
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println!(" ✓ Config path: {}", config_path);
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// Create mock JWT token
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let mock_token = create_mock_jwt_token();
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validate_jwt_format(&mock_token).expect("Mock JWT token should be valid");
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println!(" ✓ JWT token generated and validated");
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println!("✅ Tuning job start mock test passed");
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}
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/// Test: Live API Gateway connection (requires services running)
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/// This test is ignored by default - run with `cargo test -- --ignored`
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#[tokio::test]
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#[ignore = "Requires API Gateway to be running (use --ignored to run)"]
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async fn test_api_gateway_connection_live() {
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println!("\n🧪 Testing live API Gateway connection...");
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// Check if API Gateway is available
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let is_available = check_api_gateway_availability()
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.await
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.expect("Connectivity check should not fail");
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if !is_available {
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println!("❌ SKIPPED: API Gateway not running");
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println!(" Start with: cargo run -p api_gateway");
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return;
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}
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// Try to establish gRPC connection
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use tonic::transport::Channel;
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let api_gateway_url = "http://localhost:50051";
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let channel_result = timeout(
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Duration::from_secs(5),
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Channel::from_shared(api_gateway_url.to_string())
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.unwrap()
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.connect(),
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)
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.await;
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match channel_result {
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Ok(Ok(_channel)) => {
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println!("✅ Successfully established gRPC connection to API Gateway");
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},
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Ok(Err(e)) => {
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println!("❌ Failed to connect to API Gateway: {}", e);
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panic!("API Gateway connection failed (is TLS configured correctly?)");
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},
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Err(_) => {
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println!("❌ Connection timeout (API Gateway not responding)");
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panic!("API Gateway not responding within 5 seconds");
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},
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}
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}
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/// Test: Mock tuning job status query
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#[tokio::test]
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async fn test_tuning_status_query_mock() {
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println!("\n🧪 Testing tuning status query (mock mode)...");
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// Generate mock job ID
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let job_id = Uuid::new_v4();
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println!(" Job ID: {}", job_id);
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// Validate UUID parsing
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let parsed_id = Uuid::parse_str(&job_id.to_string());
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assert!(parsed_id.is_ok(), "Job ID should be parseable");
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// Create mock status response
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let mock_status = MockTuningStatus {
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job_id: job_id.to_string(),
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status: "TUNING_RUNNING".to_string(),
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current_trial: 15,
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total_trials: 50,
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progress_percent: 30.0,
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best_sharpe_ratio: 1.85,
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};
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// Validate mock status fields
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assert_eq!(mock_status.status, "TUNING_RUNNING");
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assert!(mock_status.progress_percent >= 0.0 && mock_status.progress_percent <= 100.0);
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assert!(mock_status.current_trial <= mock_status.total_trials);
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println!(" Status: {}", mock_status.status);
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println!(
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" Progress: {}/{} trials ({:.1}%)",
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mock_status.current_trial, mock_status.total_trials, mock_status.progress_percent
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);
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println!(" Best Sharpe: {:.2}", mock_status.best_sharpe_ratio);
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println!("✅ Tuning status query mock test passed");
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}
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/// Mock tuning status structure
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#[derive(Debug, Clone)]
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struct MockTuningStatus {
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job_id: String,
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status: String,
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current_trial: u32,
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total_trials: u32,
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progress_percent: f32,
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best_sharpe_ratio: f32,
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}
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/// Integration test summary
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#[test]
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fn test_integration_summary() {
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println!("\n📋 TLI Tune Integration Test Summary");
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println!("=====================================");
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println!("✅ JWT token format validation");
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println!("✅ API Gateway connectivity check");
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println!("✅ gRPC connection mock tests");
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println!("✅ Tuning job start mock tests");
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println!("✅ Tuning status query mock tests");
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println!();
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println!("💡 Live Tests (requires running services):");
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println!(" cargo test -p tli --test tune_integration_test -- --ignored");
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println!();
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println!("🚀 To start services:");
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println!(" docker-compose up -d");
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println!(" cargo run -p api_gateway &");
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println!(" cargo run -p ml_training_service &");
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}
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// Helper module for base64 encoding
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mod base64_helper {
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use base64::{engine::general_purpose, Engine};
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pub fn encode_no_pad(data: impl AsRef<[u8]>) -> String {
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general_purpose::URL_SAFE_NO_PAD.encode(data)
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}
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}
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