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>
521 lines
17 KiB
Rust
521 lines
17 KiB
Rust
//! TDD Tests for Automated Model Deployment Pipeline
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//!
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//! **TDD Approach**: Tests written FIRST, implementation follows to make them GREEN
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//!
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//! Test Coverage:
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//! 1. Deployment trigger on A/B test pass
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//! 2. Rolling update with zero downtime
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//! 3. Health check validation (model inference working)
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//! 4. Rollback on health check failure
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//! 5. E2E deployment with real model
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use anyhow::Result;
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use ml_training_service::deployment_pipeline::{
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ABTestResult, DeploymentConfig, DeploymentPipeline, DeploymentResult, DeploymentStatus,
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GroupMetrics, HealthCheckConfig, RollbackStrategy, RollingUpdateConfig,
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};
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use uuid::Uuid;
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// ==================== TEST 1: Deployment Trigger on A/B Test Pass ====================
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#[tokio::test]
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async fn test_deployment_triggers_on_ab_test_pass() {
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// Arrange
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let config = DeploymentConfig {
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enable_auto_deployment: true,
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trigger_on_ab_test_pass: true,
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min_ab_test_confidence: 0.95,
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..Default::default()
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};
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let pipeline = DeploymentPipeline::new(config).unwrap();
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let model_id = Uuid::new_v4();
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// Simulate A/B test result that passes thresholds
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let ab_test_result = create_passing_ab_test_result(model_id);
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// Act
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let deployment_result = pipeline.trigger_deployment_on_ab_test(ab_test_result).await;
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// Assert
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assert!(deployment_result.is_ok());
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let result = deployment_result.unwrap();
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assert_eq!(result.status, DeploymentStatus::Triggered);
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assert_eq!(result.model_id, model_id);
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assert!(result.triggered_by_ab_test);
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}
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#[tokio::test]
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async fn test_deployment_skips_on_ab_test_fail() {
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// Arrange
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let config = DeploymentConfig {
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enable_auto_deployment: true,
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trigger_on_ab_test_pass: true,
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min_ab_test_confidence: 0.95,
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..Default::default()
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};
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let pipeline = DeploymentPipeline::new(config).unwrap();
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let model_id = Uuid::new_v4();
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// Simulate A/B test result that fails thresholds
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let ab_test_result = create_failing_ab_test_result(model_id);
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// Act
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let deployment_result = pipeline.trigger_deployment_on_ab_test(ab_test_result).await;
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// Assert
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assert!(deployment_result.is_ok());
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let result = deployment_result.unwrap();
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assert_eq!(result.status, DeploymentStatus::Skipped);
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assert!(!result.triggered_by_ab_test);
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}
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// ==================== TEST 2: Rolling Update (Zero Downtime) ====================
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#[tokio::test]
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async fn test_rolling_update_zero_downtime() {
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// Arrange
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let config = DeploymentConfig {
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enable_auto_deployment: true,
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rolling_update: RollingUpdateConfig {
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batch_size: 1,
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batch_delay_seconds: 1,
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health_check_retries: 3,
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health_check_interval_seconds: 1,
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},
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min_ab_test_confidence: 0.95,
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..Default::default()
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};
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let pipeline = DeploymentPipeline::new(config).unwrap();
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let model_id = Uuid::new_v4();
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let model_path = format!("/tmp/models/{}/model.safetensors", model_id);
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// Act
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let deployment_result = pipeline
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.perform_rolling_update(model_id, &model_path, 3) // 3 instances
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.await;
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// Assert
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assert!(deployment_result.is_ok());
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let result = deployment_result.unwrap();
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assert_eq!(result.status, DeploymentStatus::Completed);
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assert_eq!(result.instances_updated, 3);
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assert!(result.zero_downtime_achieved);
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assert!(result.deployment_duration_seconds < 10); // Should complete quickly
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}
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#[tokio::test]
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async fn test_rolling_update_respects_batch_size() {
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// Arrange
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let config = DeploymentConfig {
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enable_auto_deployment: true,
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rolling_update: RollingUpdateConfig {
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batch_size: 2, // Update 2 instances at a time
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batch_delay_seconds: 1,
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health_check_retries: 3,
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health_check_interval_seconds: 1,
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},
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min_ab_test_confidence: 0.95,
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..Default::default()
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};
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let pipeline = DeploymentPipeline::new(config).unwrap();
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let model_id = Uuid::new_v4();
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let model_path = format!("/tmp/models/{}/model.safetensors", model_id);
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// Act
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let start = std::time::Instant::now();
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let deployment_result = pipeline
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.perform_rolling_update(model_id, &model_path, 4) // 4 instances
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.await;
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let elapsed = start.elapsed();
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// Assert
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assert!(deployment_result.is_ok());
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let result = deployment_result.unwrap();
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assert_eq!(result.status, DeploymentStatus::Completed);
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assert_eq!(result.instances_updated, 4);
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assert_eq!(result.batches_executed, 2); // 4 instances / batch_size=2 = 2 batches
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assert!(elapsed.as_secs() >= 1); // Should have delay between batches
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}
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// ==================== TEST 3: Health Check Validation ====================
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#[tokio::test]
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async fn test_health_check_validates_model_inference() {
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// Arrange
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let config = DeploymentConfig {
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enable_auto_deployment: true,
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health_check: HealthCheckConfig {
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enabled: true,
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timeout_seconds: 5,
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max_latency_ms: 100,
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test_predictions: 10,
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min_success_rate: 0.95,
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},
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..Default::default()
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};
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let pipeline = DeploymentPipeline::new(config).unwrap();
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let model_id = Uuid::new_v4();
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let instance_id = "trading-service-1".to_string();
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// Act
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let health_result = pipeline.run_health_check(model_id, &instance_id).await;
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// Assert
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assert!(health_result.is_ok());
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let result = health_result.unwrap();
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assert!(result.healthy);
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assert!(result.inference_working);
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assert!(result.latency_ms < 100.0);
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assert!(result.success_rate >= 0.95);
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}
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#[tokio::test]
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async fn test_health_check_fails_on_inference_error() {
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// Arrange
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let config = DeploymentConfig {
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enable_auto_deployment: true,
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health_check: HealthCheckConfig {
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enabled: true,
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timeout_seconds: 5,
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max_latency_ms: 100,
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test_predictions: 10,
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min_success_rate: 0.95,
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},
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..Default::default()
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};
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let pipeline = DeploymentPipeline::new(config).unwrap();
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let model_id = Uuid::new_v4();
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let instance_id = "trading-service-broken".to_string(); // Simulate broken instance
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// Act
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let health_result = pipeline.run_health_check(model_id, &instance_id).await;
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// Assert
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assert!(health_result.is_ok()); // Health check runs, but reports unhealthy
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let result = health_result.unwrap();
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assert!(!result.healthy);
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assert!(!result.inference_working);
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}
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#[tokio::test]
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async fn test_health_check_fails_on_high_latency() {
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// Arrange
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let config = DeploymentConfig {
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enable_auto_deployment: true,
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health_check: HealthCheckConfig {
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enabled: true,
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timeout_seconds: 5,
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max_latency_ms: 10, // Very strict latency requirement
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test_predictions: 10,
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min_success_rate: 0.95,
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},
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..Default::default()
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};
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let pipeline = DeploymentPipeline::new(config).unwrap();
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let model_id = Uuid::new_v4();
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let instance_id = "trading-service-slow".to_string(); // Simulate slow instance
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// Act
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let health_result = pipeline.run_health_check(model_id, &instance_id).await;
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// Assert
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assert!(health_result.is_ok());
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let result = health_result.unwrap();
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assert!(!result.healthy);
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assert!(result.latency_ms > 10.0);
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}
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// ==================== TEST 4: Rollback on Failure ====================
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#[tokio::test]
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async fn test_rollback_on_health_check_failure() {
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// Arrange
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let config = DeploymentConfig {
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enable_auto_deployment: true,
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rollback_strategy: RollbackStrategy::Automatic,
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rollback_on_health_check_failure: true,
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..Default::default()
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};
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let pipeline = DeploymentPipeline::new(config).unwrap();
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let model_id = Uuid::new_v4();
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let previous_model_id = Uuid::new_v4();
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let model_path = format!("/tmp/models/{}/model_broken.safetensors", model_id);
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// Simulate deployment that fails health check
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let deployment_result = pipeline
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.deploy_with_rollback(model_id, previous_model_id, &model_path, 2)
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.await;
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// Assert
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assert!(deployment_result.is_ok());
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let result = deployment_result.unwrap();
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assert_eq!(result.status, DeploymentStatus::RolledBack);
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assert!(result.rollback_triggered);
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assert_eq!(result.active_model_id, previous_model_id); // Should revert to previous
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}
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#[tokio::test]
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async fn test_rollback_restores_previous_model() {
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// Arrange
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let config = DeploymentConfig {
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enable_auto_deployment: true,
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rollback_strategy: RollbackStrategy::Automatic,
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rollback_on_health_check_failure: true,
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..Default::default()
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};
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let pipeline = DeploymentPipeline::new(config).unwrap();
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let new_model_id = Uuid::new_v4();
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let previous_model_id = Uuid::new_v4();
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// Act - Trigger rollback
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let rollback_result = pipeline
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.rollback_deployment(new_model_id, previous_model_id)
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.await;
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// Assert
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assert!(rollback_result.is_ok());
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let result = rollback_result.unwrap();
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assert!(result.rollback_successful);
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assert_eq!(result.active_model_id, previous_model_id);
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assert!(result.rollback_duration_seconds < 30); // Should be fast
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}
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#[tokio::test]
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async fn test_manual_rollback_strategy() {
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// Arrange
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let config = DeploymentConfig {
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enable_auto_deployment: true,
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rollback_strategy: RollbackStrategy::Manual,
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rollback_on_health_check_failure: false,
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..Default::default()
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};
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let pipeline = DeploymentPipeline::new(config).unwrap();
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let model_id = Uuid::new_v4();
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let previous_model_id = Uuid::new_v4();
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let model_path = format!("/tmp/models/{}/model_broken.safetensors", model_id);
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// Act - Deploy with broken model
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let deployment_result = pipeline
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.deploy_with_rollback(model_id, previous_model_id, &model_path, 2)
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.await;
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// Assert - Should NOT automatically rollback with Manual strategy
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assert!(deployment_result.is_ok());
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let result = deployment_result.unwrap();
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assert_eq!(result.status, DeploymentStatus::Failed);
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assert!(!result.rollback_triggered); // Manual strategy = no auto-rollback
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}
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// ==================== TEST 5: E2E Deployment with Real Model ====================
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#[tokio::test]
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#[ignore = "Run separately: cargo test test_e2e_deployment -- --ignored"]
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async fn test_e2e_deployment_with_real_model() {
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// Arrange
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let config = DeploymentConfig {
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enable_auto_deployment: true,
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trigger_on_ab_test_pass: true,
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rolling_update: RollingUpdateConfig {
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batch_size: 1,
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batch_delay_seconds: 2,
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health_check_retries: 3,
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health_check_interval_seconds: 1,
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},
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health_check: HealthCheckConfig {
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enabled: true,
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timeout_seconds: 10,
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max_latency_ms: 100,
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test_predictions: 20,
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min_success_rate: 0.95,
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},
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rollback_strategy: RollbackStrategy::Automatic,
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rollback_on_health_check_failure: true,
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min_ab_test_confidence: 0.95,
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};
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let pipeline = DeploymentPipeline::new(config).unwrap();
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// Step 1: Create a real trained model (NO MOCKS)
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let model_id = Uuid::new_v4();
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let model_path = create_real_trained_model(model_id).await.unwrap();
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// Step 2: Run A/B test
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let ab_test_result = create_passing_ab_test_result(model_id);
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// Step 3: Trigger deployment
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let trigger_result = pipeline
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.trigger_deployment_on_ab_test(ab_test_result)
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.await
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.unwrap();
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assert_eq!(trigger_result.status, DeploymentStatus::Triggered);
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// Step 4: Perform rolling update
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let deployment_result = pipeline
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.perform_rolling_update(model_id, &model_path, 3)
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.await
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.unwrap();
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assert_eq!(deployment_result.status, DeploymentStatus::Completed);
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assert_eq!(deployment_result.instances_updated, 3);
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// Step 5: Verify all instances are healthy
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for instance_id in &deployment_result.updated_instances {
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let health = pipeline
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.run_health_check(model_id, instance_id)
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.await
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.unwrap();
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assert!(health.healthy);
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assert!(health.inference_working);
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}
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}
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// ==================== TEST 6: Deployment Monitoring ====================
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#[tokio::test]
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async fn test_deployment_status_tracking() {
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// Arrange
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let config = DeploymentConfig::default();
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let pipeline = DeploymentPipeline::new(config).unwrap();
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let model_id = Uuid::new_v4();
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let deployment_id = Uuid::new_v4();
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// Act - Start deployment
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pipeline
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.start_deployment(deployment_id, model_id)
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.await
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.unwrap();
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|
|
// Query status
|
|
let status = pipeline.get_deployment_status(deployment_id).await.unwrap();
|
|
|
|
// Assert
|
|
assert_eq!(status.deployment_id, deployment_id);
|
|
assert_eq!(status.model_id, model_id);
|
|
assert!(matches!(
|
|
status.status,
|
|
DeploymentStatus::InProgress | DeploymentStatus::Triggered
|
|
));
|
|
}
|
|
|
|
#[tokio::test]
|
|
async fn test_deployment_history_tracking() {
|
|
// Arrange
|
|
let config = DeploymentConfig::default();
|
|
let pipeline = DeploymentPipeline::new(config).unwrap();
|
|
|
|
// Act - Perform multiple deployments
|
|
for _ in 0..3 {
|
|
let model_id = Uuid::new_v4();
|
|
let model_path = format!("/tmp/models/{}/model.safetensors", model_id);
|
|
let _ = pipeline
|
|
.perform_rolling_update(model_id, &model_path, 1)
|
|
.await;
|
|
}
|
|
|
|
// Query history
|
|
let history = pipeline.get_deployment_history(10).await.unwrap();
|
|
|
|
// Assert
|
|
assert!(history.len() >= 3);
|
|
for deployment in &history {
|
|
assert!(matches!(
|
|
deployment.status,
|
|
DeploymentStatus::Completed | DeploymentStatus::Failed | DeploymentStatus::RolledBack
|
|
));
|
|
}
|
|
}
|
|
|
|
// ==================== TEST 7: Concurrent Deployment Prevention ====================
|
|
|
|
#[tokio::test]
|
|
async fn test_prevents_concurrent_deployments() {
|
|
// Arrange
|
|
let config = DeploymentConfig::default();
|
|
let pipeline = DeploymentPipeline::new(config).unwrap();
|
|
|
|
// Act - Start first deployment
|
|
let model_id_1 = Uuid::new_v4();
|
|
let model_path_1 = format!("/tmp/models/{}/model.safetensors", model_id_1);
|
|
let deployment_1 = pipeline.perform_rolling_update(model_id_1, &model_path_1, 2);
|
|
|
|
// Try to start second deployment concurrently
|
|
let model_id_2 = Uuid::new_v4();
|
|
let model_path_2 = format!("/tmp/models/{}/model.safetensors", model_id_2);
|
|
let deployment_2 = pipeline.perform_rolling_update(model_id_2, &model_path_2, 2);
|
|
|
|
// Wait for both
|
|
let (result_1, result_2) = tokio::join!(deployment_1, deployment_2);
|
|
|
|
// Assert - One should succeed, other should be rejected
|
|
assert!(result_1.is_ok() || result_2.is_ok()); // At least one succeeds
|
|
assert!(result_1.is_err() || result_2.is_err()); // At least one fails (concurrent rejection)
|
|
}
|
|
|
|
// ==================== HELPER FUNCTIONS ====================
|
|
|
|
/// Create A/B test result that passes thresholds
|
|
fn create_passing_ab_test_result(model_id: Uuid) -> ABTestResult {
|
|
ABTestResult {
|
|
experiment_id: Uuid::new_v4(),
|
|
model_id,
|
|
control_metrics: GroupMetrics {
|
|
avg_latency_ms: 50.0,
|
|
error_rate: 0.01,
|
|
sharpe_ratio: 1.5,
|
|
},
|
|
treatment_metrics: GroupMetrics {
|
|
avg_latency_ms: 45.0, // Better latency
|
|
error_rate: 0.005, // Lower error rate
|
|
sharpe_ratio: 1.8, // Higher Sharpe ratio
|
|
},
|
|
statistical_significance: 0.99, // High confidence
|
|
p_value: 0.001,
|
|
passed: true,
|
|
}
|
|
}
|
|
|
|
/// Create A/B test result that fails thresholds
|
|
fn create_failing_ab_test_result(model_id: Uuid) -> ABTestResult {
|
|
ABTestResult {
|
|
experiment_id: Uuid::new_v4(),
|
|
model_id,
|
|
control_metrics: GroupMetrics {
|
|
avg_latency_ms: 50.0,
|
|
error_rate: 0.01,
|
|
sharpe_ratio: 1.5,
|
|
},
|
|
treatment_metrics: GroupMetrics {
|
|
avg_latency_ms: 80.0, // Worse latency
|
|
error_rate: 0.05, // Higher error rate
|
|
sharpe_ratio: 1.2, // Lower Sharpe ratio
|
|
},
|
|
statistical_significance: 0.85, // Low confidence
|
|
p_value: 0.15,
|
|
passed: false,
|
|
}
|
|
}
|
|
|
|
/// Create real trained model for E2E test (NO MOCKS)
|
|
async fn create_real_trained_model(model_id: Uuid) -> Result<String> {
|
|
use std::path::PathBuf;
|
|
|
|
mod test_helpers;
|
|
|
|
let model_dir = PathBuf::from(format!("/tmp/models/{}", model_id));
|
|
tokio::fs::create_dir_all(&model_dir).await?;
|
|
|
|
// Create real DQN checkpoint using test_helpers
|
|
let checkpoint_path = test_helpers::create_real_dqn_checkpoint(&model_dir, model_id).await?;
|
|
|
|
Ok(checkpoint_path.to_string_lossy().to_string())
|
|
}
|