Files
foxhunt/services/ml_training_service/tests/deployment_tests.rs
jgrusewski 83629f9ca8 feat(deployment): Complete Runpod GPU deployment infrastructure
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>
2025-10-24 01:11:43 +02:00

521 lines
17 KiB
Rust

//! TDD Tests for Automated Model Deployment Pipeline
//!
//! **TDD Approach**: Tests written FIRST, implementation follows to make them GREEN
//!
//! Test Coverage:
//! 1. Deployment trigger on A/B test pass
//! 2. Rolling update with zero downtime
//! 3. Health check validation (model inference working)
//! 4. Rollback on health check failure
//! 5. E2E deployment with real model
use anyhow::Result;
use ml_training_service::deployment_pipeline::{
ABTestResult, DeploymentConfig, DeploymentPipeline, DeploymentResult, DeploymentStatus,
GroupMetrics, HealthCheckConfig, RollbackStrategy, RollingUpdateConfig,
};
use uuid::Uuid;
// ==================== TEST 1: Deployment Trigger on A/B Test Pass ====================
#[tokio::test]
async fn test_deployment_triggers_on_ab_test_pass() {
// Arrange
let config = DeploymentConfig {
enable_auto_deployment: true,
trigger_on_ab_test_pass: true,
min_ab_test_confidence: 0.95,
..Default::default()
};
let pipeline = DeploymentPipeline::new(config).unwrap();
let model_id = Uuid::new_v4();
// Simulate A/B test result that passes thresholds
let ab_test_result = create_passing_ab_test_result(model_id);
// Act
let deployment_result = pipeline.trigger_deployment_on_ab_test(ab_test_result).await;
// Assert
assert!(deployment_result.is_ok());
let result = deployment_result.unwrap();
assert_eq!(result.status, DeploymentStatus::Triggered);
assert_eq!(result.model_id, model_id);
assert!(result.triggered_by_ab_test);
}
#[tokio::test]
async fn test_deployment_skips_on_ab_test_fail() {
// Arrange
let config = DeploymentConfig {
enable_auto_deployment: true,
trigger_on_ab_test_pass: true,
min_ab_test_confidence: 0.95,
..Default::default()
};
let pipeline = DeploymentPipeline::new(config).unwrap();
let model_id = Uuid::new_v4();
// Simulate A/B test result that fails thresholds
let ab_test_result = create_failing_ab_test_result(model_id);
// Act
let deployment_result = pipeline.trigger_deployment_on_ab_test(ab_test_result).await;
// Assert
assert!(deployment_result.is_ok());
let result = deployment_result.unwrap();
assert_eq!(result.status, DeploymentStatus::Skipped);
assert!(!result.triggered_by_ab_test);
}
// ==================== TEST 2: Rolling Update (Zero Downtime) ====================
#[tokio::test]
async fn test_rolling_update_zero_downtime() {
// Arrange
let config = DeploymentConfig {
enable_auto_deployment: true,
rolling_update: RollingUpdateConfig {
batch_size: 1,
batch_delay_seconds: 1,
health_check_retries: 3,
health_check_interval_seconds: 1,
},
min_ab_test_confidence: 0.95,
..Default::default()
};
let pipeline = DeploymentPipeline::new(config).unwrap();
let model_id = Uuid::new_v4();
let model_path = format!("/tmp/models/{}/model.safetensors", model_id);
// Act
let deployment_result = pipeline
.perform_rolling_update(model_id, &model_path, 3) // 3 instances
.await;
// Assert
assert!(deployment_result.is_ok());
let result = deployment_result.unwrap();
assert_eq!(result.status, DeploymentStatus::Completed);
assert_eq!(result.instances_updated, 3);
assert!(result.zero_downtime_achieved);
assert!(result.deployment_duration_seconds < 10); // Should complete quickly
}
#[tokio::test]
async fn test_rolling_update_respects_batch_size() {
// Arrange
let config = DeploymentConfig {
enable_auto_deployment: true,
rolling_update: RollingUpdateConfig {
batch_size: 2, // Update 2 instances at a time
batch_delay_seconds: 1,
health_check_retries: 3,
health_check_interval_seconds: 1,
},
min_ab_test_confidence: 0.95,
..Default::default()
};
let pipeline = DeploymentPipeline::new(config).unwrap();
let model_id = Uuid::new_v4();
let model_path = format!("/tmp/models/{}/model.safetensors", model_id);
// Act
let start = std::time::Instant::now();
let deployment_result = pipeline
.perform_rolling_update(model_id, &model_path, 4) // 4 instances
.await;
let elapsed = start.elapsed();
// Assert
assert!(deployment_result.is_ok());
let result = deployment_result.unwrap();
assert_eq!(result.status, DeploymentStatus::Completed);
assert_eq!(result.instances_updated, 4);
assert_eq!(result.batches_executed, 2); // 4 instances / batch_size=2 = 2 batches
assert!(elapsed.as_secs() >= 1); // Should have delay between batches
}
// ==================== TEST 3: Health Check Validation ====================
#[tokio::test]
async fn test_health_check_validates_model_inference() {
// Arrange
let config = DeploymentConfig {
enable_auto_deployment: true,
health_check: HealthCheckConfig {
enabled: true,
timeout_seconds: 5,
max_latency_ms: 100,
test_predictions: 10,
min_success_rate: 0.95,
},
..Default::default()
};
let pipeline = DeploymentPipeline::new(config).unwrap();
let model_id = Uuid::new_v4();
let instance_id = "trading-service-1".to_string();
// Act
let health_result = pipeline.run_health_check(model_id, &instance_id).await;
// Assert
assert!(health_result.is_ok());
let result = health_result.unwrap();
assert!(result.healthy);
assert!(result.inference_working);
assert!(result.latency_ms < 100.0);
assert!(result.success_rate >= 0.95);
}
#[tokio::test]
async fn test_health_check_fails_on_inference_error() {
// Arrange
let config = DeploymentConfig {
enable_auto_deployment: true,
health_check: HealthCheckConfig {
enabled: true,
timeout_seconds: 5,
max_latency_ms: 100,
test_predictions: 10,
min_success_rate: 0.95,
},
..Default::default()
};
let pipeline = DeploymentPipeline::new(config).unwrap();
let model_id = Uuid::new_v4();
let instance_id = "trading-service-broken".to_string(); // Simulate broken instance
// Act
let health_result = pipeline.run_health_check(model_id, &instance_id).await;
// Assert
assert!(health_result.is_ok()); // Health check runs, but reports unhealthy
let result = health_result.unwrap();
assert!(!result.healthy);
assert!(!result.inference_working);
}
#[tokio::test]
async fn test_health_check_fails_on_high_latency() {
// Arrange
let config = DeploymentConfig {
enable_auto_deployment: true,
health_check: HealthCheckConfig {
enabled: true,
timeout_seconds: 5,
max_latency_ms: 10, // Very strict latency requirement
test_predictions: 10,
min_success_rate: 0.95,
},
..Default::default()
};
let pipeline = DeploymentPipeline::new(config).unwrap();
let model_id = Uuid::new_v4();
let instance_id = "trading-service-slow".to_string(); // Simulate slow instance
// Act
let health_result = pipeline.run_health_check(model_id, &instance_id).await;
// Assert
assert!(health_result.is_ok());
let result = health_result.unwrap();
assert!(!result.healthy);
assert!(result.latency_ms > 10.0);
}
// ==================== TEST 4: Rollback on Failure ====================
#[tokio::test]
async fn test_rollback_on_health_check_failure() {
// Arrange
let config = DeploymentConfig {
enable_auto_deployment: true,
rollback_strategy: RollbackStrategy::Automatic,
rollback_on_health_check_failure: true,
..Default::default()
};
let pipeline = DeploymentPipeline::new(config).unwrap();
let model_id = Uuid::new_v4();
let previous_model_id = Uuid::new_v4();
let model_path = format!("/tmp/models/{}/model_broken.safetensors", model_id);
// Simulate deployment that fails health check
let deployment_result = pipeline
.deploy_with_rollback(model_id, previous_model_id, &model_path, 2)
.await;
// Assert
assert!(deployment_result.is_ok());
let result = deployment_result.unwrap();
assert_eq!(result.status, DeploymentStatus::RolledBack);
assert!(result.rollback_triggered);
assert_eq!(result.active_model_id, previous_model_id); // Should revert to previous
}
#[tokio::test]
async fn test_rollback_restores_previous_model() {
// Arrange
let config = DeploymentConfig {
enable_auto_deployment: true,
rollback_strategy: RollbackStrategy::Automatic,
rollback_on_health_check_failure: true,
..Default::default()
};
let pipeline = DeploymentPipeline::new(config).unwrap();
let new_model_id = Uuid::new_v4();
let previous_model_id = Uuid::new_v4();
// Act - Trigger rollback
let rollback_result = pipeline
.rollback_deployment(new_model_id, previous_model_id)
.await;
// Assert
assert!(rollback_result.is_ok());
let result = rollback_result.unwrap();
assert!(result.rollback_successful);
assert_eq!(result.active_model_id, previous_model_id);
assert!(result.rollback_duration_seconds < 30); // Should be fast
}
#[tokio::test]
async fn test_manual_rollback_strategy() {
// Arrange
let config = DeploymentConfig {
enable_auto_deployment: true,
rollback_strategy: RollbackStrategy::Manual,
rollback_on_health_check_failure: false,
..Default::default()
};
let pipeline = DeploymentPipeline::new(config).unwrap();
let model_id = Uuid::new_v4();
let previous_model_id = Uuid::new_v4();
let model_path = format!("/tmp/models/{}/model_broken.safetensors", model_id);
// Act - Deploy with broken model
let deployment_result = pipeline
.deploy_with_rollback(model_id, previous_model_id, &model_path, 2)
.await;
// Assert - Should NOT automatically rollback with Manual strategy
assert!(deployment_result.is_ok());
let result = deployment_result.unwrap();
assert_eq!(result.status, DeploymentStatus::Failed);
assert!(!result.rollback_triggered); // Manual strategy = no auto-rollback
}
// ==================== TEST 5: E2E Deployment with Real Model ====================
#[tokio::test]
#[ignore = "Run separately: cargo test test_e2e_deployment -- --ignored"]
async fn test_e2e_deployment_with_real_model() {
// Arrange
let config = DeploymentConfig {
enable_auto_deployment: true,
trigger_on_ab_test_pass: true,
rolling_update: RollingUpdateConfig {
batch_size: 1,
batch_delay_seconds: 2,
health_check_retries: 3,
health_check_interval_seconds: 1,
},
health_check: HealthCheckConfig {
enabled: true,
timeout_seconds: 10,
max_latency_ms: 100,
test_predictions: 20,
min_success_rate: 0.95,
},
rollback_strategy: RollbackStrategy::Automatic,
rollback_on_health_check_failure: true,
min_ab_test_confidence: 0.95,
};
let pipeline = DeploymentPipeline::new(config).unwrap();
// Step 1: Create a real trained model (NO MOCKS)
let model_id = Uuid::new_v4();
let model_path = create_real_trained_model(model_id).await.unwrap();
// Step 2: Run A/B test
let ab_test_result = create_passing_ab_test_result(model_id);
// Step 3: Trigger deployment
let trigger_result = pipeline
.trigger_deployment_on_ab_test(ab_test_result)
.await
.unwrap();
assert_eq!(trigger_result.status, DeploymentStatus::Triggered);
// Step 4: Perform rolling update
let deployment_result = pipeline
.perform_rolling_update(model_id, &model_path, 3)
.await
.unwrap();
assert_eq!(deployment_result.status, DeploymentStatus::Completed);
assert_eq!(deployment_result.instances_updated, 3);
// Step 5: Verify all instances are healthy
for instance_id in &deployment_result.updated_instances {
let health = pipeline
.run_health_check(model_id, instance_id)
.await
.unwrap();
assert!(health.healthy);
assert!(health.inference_working);
}
}
// ==================== TEST 6: Deployment Monitoring ====================
#[tokio::test]
async fn test_deployment_status_tracking() {
// Arrange
let config = DeploymentConfig::default();
let pipeline = DeploymentPipeline::new(config).unwrap();
let model_id = Uuid::new_v4();
let deployment_id = Uuid::new_v4();
// Act - Start deployment
pipeline
.start_deployment(deployment_id, model_id)
.await
.unwrap();
// 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())
}