Hotfixes

Author: dewitt4

README.md

@@ -1,9 +1,10 @@
 # AgentaFlow SRO Community Edition
 
-**AI Infrastructure Tooling & Optimization Platform**
+## AI Infrastructure Tooling & Optimization Platform
 
-### Author: DeWitt Gibson (@dewitt4)
-**Repository**: https://github.com/Finoptimize/agentaflow-sro-community
+**Author**: DeWitt Gibson (@dewitt4)
+
+**Repository**: <https://github.com/Finoptimize/agentaflow-sro-community>
 
 
 Deploy and manage AI infrastructure more efficiently with tools for GPU orchestration, model serving optimization, and comprehensive observability.
@@ -14,22 +15,28 @@ Deploy and manage AI infrastructure more efficiently with tools for GPU orchestr
 ## 🚀 Features
 
 ### GPU Orchestration & Scheduling
+
 Tools that optimize GPU utilization across workloads, reducing waste:
+
 - **Smart Scheduling**: Multiple strategies (least-utilized, best-fit, priority, round-robin)
 - **Kubernetes Integration**: Native Kubernetes GPU scheduling with Custom Resource Definitions
 - **Resource Optimization**: Reduce GPU idle time by up to 40%
 - **Workload Management**: Efficient queuing and distribution across GPU clusters
 - **Real-time Monitoring**: Track utilization, memory, temperature, and power
 
 ### AI Model Serving Optimization
+
 Software that reduces inference costs through better batching, caching, and routing:
+
 - **Request Batching**: Improve throughput by 3-5x with intelligent batching
 - **Smart Caching**: Reduce latency by up to 50% with TTL-based caching
 - **Load Balancing**: Multiple routing strategies for optimal distribution
 - **Cost Reduction**: Minimize inference costs through efficient resource use
 
 ### Observability Tools for AI Systems
+
 Enterprise-grade monitoring, debugging, and cost tracking for LLM applications and training runs:
+
 - **Prometheus Integration**: Production-ready metrics export with 20+ GPU and cost metrics
 - **Grafana Dashboards**: Pre-built visual analytics for GPU clusters and cost optimization
 - **Real-time Alerting**: Automatic threshold monitoring and notification system
@@ -313,7 +320,8 @@ go run main.go
 ```
 
 This starts:
-- **Prometheus metrics server** on http://localhost:8080/metrics
+
+- **Prometheus metrics server** on <http://localhost:8080/metrics>
 - **Real-time GPU monitoring** with automatic export
 - **Cost tracking** with live calculations
 - **Performance analytics** and efficiency scoring
@@ -337,25 +345,29 @@ kubectl port-forward svc/prometheus-service 9090:9090 -n agentaflow-monitoring
 ### 🎯 Available Metrics & Dashboards
 
 **GPU Performance Metrics:**
+
 - `agentaflow_gpu_utilization_percent` - Real-time GPU utilization
 - `agentaflow_gpu_memory_used_bytes` - Memory consumption tracking
 - `agentaflow_gpu_temperature_celsius` - Thermal monitoring
 - `agentaflow_gpu_power_draw_watts` - Power consumption tracking
 - `agentaflow_gpu_fan_speed_percent` - Cooling system status
 
 **Cost & Efficiency Analytics:**
+
 - `agentaflow_cost_total_dollars` - Real-time cost tracking
 - `agentaflow_gpu_efficiency_score` - Efficiency scoring (0-100)
 - `agentaflow_gpu_idle_time_percent` - Resource waste tracking
 - `agentaflow_cost_per_hour` - Live hourly cost calculation
 
 **Workload & Scheduling Metrics:**
+
 - `agentaflow_workloads_pending` - Job queue depth
 - `agentaflow_workloads_completed_total` - Completion tracking
 - `agentaflow_scheduler_decisions_total` - Scheduling decisions
 - `agentaflow_gpu_assignments_total` - Resource assignments
 
 **System Health & Alerts:**
+
 - Component status monitoring
 - Automatic threshold alerts
 - Performance trend analysis
@@ -364,6 +376,7 @@ kubectl port-forward svc/prometheus-service 9090:9090 -n agentaflow-monitoring
 ### 📈 Pre-built Grafana Dashboards
 
 The integration includes production-ready dashboards:
+
 - **GPU Cluster Overview** - Multi-node GPU monitoring
 - **Cost Analysis Dashboard** - Real-time cost tracking and forecasting
 - **Performance Analytics** - Efficiency scoring and optimization insights
@@ -382,7 +395,7 @@ cd examples/demo/web-dashboard
 go run main.go
 ```
 
-**Access the dashboard**: http://localhost:8090
+**Access the dashboard**: <http://localhost:8090>
 
 ### ✨ Dashboard Features
 
@@ -407,6 +420,7 @@ For detailed dashboard documentation, see [examples/demo/web-dashboard/README.md
 For detailed documentation, see [DOCUMENTATION.md](DOCUMENTATION.md)
 
 Topics covered:
+
 - Detailed API reference
 - Scheduling strategies
 - Performance optimization
@@ -451,7 +465,7 @@ Contributions are welcome! This is a community edition focused on providing acce
 Looking for advanced features for production environments? Our **Enterprise Edition** will include:
 
 - **Multi-cluster Orchestration**: Manage GPU resources across multiple Kubernetes clusters
-- **Multi-cloud GPU resource support**: Support for running in Azure, Google Cloud, Vercel, or DigitalOcean 
+- **Multi-cloud GPU resource support**: Support for running in Azure, Google Cloud, Vercel, or DigitalOcean
 - **Advanced Scheduling Algorithms**: Cost optimization algorithms and priority queues for enterprise workloads  
 - **RBAC and Audit Logs**: Role-based access control and comprehensive audit logging
 - **Enterprise Integrations**: Slack alerts, DataDog monitoring, and other enterprise tools
@@ -467,4 +481,4 @@ For questions, issues, or contributions, please open an issue on GitHub.
 
 ---
 
-**Built with ❤️ by FinOptimize for AgentaFlow**
+Built with ❤️ by FinOptimize for AgentaFlow

examples/demo/web-dashboard/main.go

@@ -59,8 +59,7 @@ func main() {
 
 	// Create web dashboard
 	fmt.Println("🌐 Setting up web dashboard...")
-	dashboard := observability.NewWebDashboard(dashboardConfig, monitoringService,
-		metricsCollector, prometheusExporter)
+	dashboard := observability.NewWebDashboard(monitoringService, metricsCollector, prometheusExporter, dashboardConfig)
 
 	// Start metrics collection
 	fmt.Println("📡 Starting GPU metrics collection...")
@@ -73,14 +72,8 @@ func main() {
 
 		// Generate alerts for demonstration
 		if metrics.Temperature > 75 {
-			alert := observability.Alert{
-				ID:        fmt.Sprintf("temp-%s-%d", metrics.GPUID, time.Now().Unix()),
-				Level:     "warning",
-				Message:   fmt.Sprintf("High temperature detected on GPU %s: %.1f°C", metrics.GPUID, metrics.Temperature),
-				Source:    metrics.GPUID,
-				Timestamp: time.Now(),
-			}
-			dashboard.BroadcastAlert(alert)
+			fmt.Printf("⚠️  High temperature detected on GPU %s: %.1f°C\n", metrics.GPUID, metrics.Temperature)
+			// Note: Alert broadcasting would be implemented here
 		}
 	})
 

pkg/gpu/metrics_collector.go

@@ -496,6 +496,23 @@ func (mc *MetricsCollector) ExportMetricsJSON(gpuID string, since time.Time) ([]
 	return json.MarshalIndent(history, "", "  ")
 }
 
+// CollectMetrics collects and returns current metrics for the first available GPU
+// This method provides backwards compatibility
+func (mc *MetricsCollector) CollectMetrics() (*GPUMetrics, error) {
+	latest := mc.GetLatestMetrics()
+
+	if len(latest) == 0 {
+		return nil, fmt.Errorf("no GPU metrics available")
+	}
+
+	// Return the first available metric
+	for _, metrics := range latest {
+		return &metrics, nil
+	}
+
+	return nil, fmt.Errorf("no GPU metrics available")
+}
+
 // GetSystemOverview provides a system-wide GPU overview
 func (mc *MetricsCollector) GetSystemOverview() map[string]interface{} {
 	mc.mu.RLock()