Files
foxhunt/services/stress_tests/src/metrics.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

247 lines
7.5 KiB
Rust

//! Resilience and Recovery Metrics Collection
//!
//! Tracks recovery times, error rates, and system behavior under stress.
use hdrhistogram::Histogram;
use serde::{Deserialize, Serialize};
use std::sync::Arc;
use std::time::{Duration, Instant};
use tokio::sync::RwLock;
/// Recovery metrics for measuring system resilience
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct RecoveryMetrics {
/// Time taken to detect the failure
pub detection_time: Duration,
/// Time taken to recover from the failure
pub recovery_time: Duration,
/// Total downtime experienced
pub total_downtime: Duration,
/// Number of retry attempts before recovery
pub retry_attempts: u32,
/// Whether circuit breaker activated
pub circuit_breaker_activated: bool,
/// Whether graceful degradation occurred
pub graceful_degradation: bool,
/// Data consistency maintained during failure
pub data_consistency_maintained: bool,
}
impl RecoveryMetrics {
/// Create new recovery metrics
pub fn new() -> Self {
Self {
detection_time: Duration::ZERO,
recovery_time: Duration::ZERO,
total_downtime: Duration::ZERO,
retry_attempts: 0,
circuit_breaker_activated: false,
graceful_degradation: false,
data_consistency_maintained: true,
}
}
/// Calculate uptime percentage
pub fn uptime_percentage(&self) -> f64 {
let total_time = self.total_downtime + Duration::from_secs(1); // Avoid div by zero
let uptime = total_time.saturating_sub(self.total_downtime);
(uptime.as_secs_f64() / total_time.as_secs_f64()) * 100.0
}
/// Check if meets 99.9% uptime SLA
pub fn meets_sla(&self, target_uptime: f64) -> bool {
self.uptime_percentage() >= target_uptime
}
}
impl Default for RecoveryMetrics {
fn default() -> Self {
Self::new()
}
}
/// Comprehensive resilience metrics
pub struct ResilienceMetrics {
/// Recovery time histogram (microseconds)
recovery_times: Arc<RwLock<Histogram<u64>>>,
/// Detection time histogram (microseconds)
detection_times: Arc<RwLock<Histogram<u64>>>,
/// Error count by category
error_counts: Arc<RwLock<std::collections::HashMap<String, u64>>>,
/// Circuit breaker activations
circuit_breaker_count: Arc<RwLock<u64>>,
/// Total test scenarios run
total_scenarios: Arc<RwLock<u64>>,
/// Successful recoveries
successful_recoveries: Arc<RwLock<u64>>,
}
impl ResilienceMetrics {
/// Create new resilience metrics collector
pub fn new() -> Self {
Self {
recovery_times: Arc::new(RwLock::new(
Histogram::<u64>::new(5).expect("Failed to create recovery histogram"),
)),
detection_times: Arc::new(RwLock::new(
Histogram::<u64>::new(5).expect("Failed to create detection histogram"),
)),
error_counts: Arc::new(RwLock::new(std::collections::HashMap::new())),
circuit_breaker_count: Arc::new(RwLock::new(0)),
total_scenarios: Arc::new(RwLock::new(0)),
successful_recoveries: Arc::new(RwLock::new(0)),
}
}
/// Record recovery metrics from a test scenario
pub async fn record_recovery(&self, metrics: &RecoveryMetrics) {
// Record recovery time
if self
.recovery_times
.write()
.await
.record(u64::try_from(metrics.recovery_time.as_micros()).unwrap_or(u64::MAX))
.is_ok()
{
// Recorded successfully
}
// Record detection time
if self
.detection_times
.write()
.await
.record(u64::try_from(metrics.detection_time.as_micros()).unwrap_or(u64::MAX))
.is_ok()
{
// Recorded successfully
}
// Increment circuit breaker count if activated
if metrics.circuit_breaker_activated {
*self.circuit_breaker_count.write().await += 1;
}
// Increment successful recoveries if recovered
if metrics.recovery_time > Duration::ZERO {
*self.successful_recoveries.write().await += 1;
}
*self.total_scenarios.write().await += 1;
}
/// Record an error occurrence
pub async fn record_error(&self, category: &str) {
let mut counts = self.error_counts.write().await;
*counts.entry(category.to_string()).or_insert(0) += 1;
}
/// Get mean recovery time
pub async fn mean_recovery_time(&self) -> Duration {
let hist = self.recovery_times.read().await;
// hist.mean() returns f64, convert safely
let mean_micros = hist.mean();
let mean_u64 = mean_micros as u64;
Duration::from_micros(mean_u64)
}
/// Get p99 recovery time
pub async fn p99_recovery_time(&self) -> Duration {
let hist = self.recovery_times.read().await;
Duration::from_micros(hist.value_at_quantile(0.99))
}
/// Get success rate
pub async fn success_rate(&self) -> f64 {
let total = *self.total_scenarios.read().await as f64;
if total == 0.0 {
return 0.0;
}
let successful = *self.successful_recoveries.read().await as f64;
(successful / total) * 100.0
}
/// Get circuit breaker activation rate
pub async fn circuit_breaker_rate(&self) -> f64 {
let total = *self.total_scenarios.read().await as f64;
if total == 0.0 {
return 0.0;
}
let activations = *self.circuit_breaker_count.read().await as f64;
(activations / total) * 100.0
}
/// Generate summary report
pub async fn generate_report(&self) -> String {
let mean_recovery = self.mean_recovery_time().await;
let p99_recovery = self.p99_recovery_time().await;
let success_rate = self.success_rate().await;
let cb_rate = self.circuit_breaker_rate().await;
let total = *self.total_scenarios.read().await;
format!(
r#"
Resilience Metrics Summary
==========================
Total Scenarios: {}
Success Rate: {:.2}%
Circuit Breaker Activation Rate: {:.2}%
Recovery Times:
Mean: {:?}
P99: {:?}
Error Counts:
"#,
total, success_rate, cb_rate, mean_recovery, p99_recovery,
)
}
}
impl Default for ResilienceMetrics {
fn default() -> Self {
Self::new()
}
}
/// Recovery timer for measuring recovery phases
pub struct RecoveryTimer {
start: Instant,
detection_time: Option<Duration>,
recovery_time: Option<Duration>,
}
impl RecoveryTimer {
/// Start a new recovery timer
pub fn start() -> Self {
Self {
start: Instant::now(),
detection_time: None,
recovery_time: None,
}
}
/// Mark failure detection
pub fn mark_detection(&mut self) {
self.detection_time = Some(self.start.elapsed());
}
/// Mark recovery completion
pub fn mark_recovery(&mut self) {
self.recovery_time = Some(self.start.elapsed());
}
/// Build recovery metrics
pub fn build_metrics(self) -> RecoveryMetrics {
RecoveryMetrics {
detection_time: self.detection_time.unwrap_or(Duration::ZERO),
recovery_time: self.recovery_time.unwrap_or(Duration::ZERO),
total_downtime: self.recovery_time.unwrap_or(Duration::ZERO),
retry_attempts: 0,
circuit_breaker_activated: false,
graceful_degradation: false,
data_consistency_maintained: true,
}
}
}