Infrastructure Monitor
Real-time GPU utilization, memory usage, temperature, and system metrics
Overview
FlexAI’s Observability platform provides comprehensive monitoring and analytics capabilities for your AI workloads, enabling you to track infrastructure metrics, monitor training performance, and optimize resource utilization across your machine learning operations.
The Observability platform enables you to:
- Monitor infrastructure metrics in real-time during training and inference
- Track training performance with detailed visualizations and analytics
- Optimize resource utilization to maximize compute efficiency
- Debug performance issues with comprehensive system insights
- Analyze cost patterns and resource consumption trends
Key Features
Section titled “Key Features”TensorBoard Integration
Native TensorBoard support for training metrics visualization and comparison
Performance Analytics
Detailed analysis of compute efficiency, bottlenecks, and optimization opportunities
Historical Data
30-day data retention for trend analysis and performance comparison
Infrastructure Monitoring
Section titled “Infrastructure Monitoring”FlexAI Infrastructure Monitor
Section titled “FlexAI Infrastructure Monitor”Access real-time infrastructure metrics at dashboards.flex.ai 🔗:
- GPU Metrics - Utilization percentage, memory consumption, temperature monitoring
- System Resources - CPU usage, RAM consumption, disk I/O patterns
- Network Activity - Data transfer rates, network latency, bandwidth utilization
- Power Consumption - Energy usage tracking and efficiency metrics
Real-Time Dashboards
Section titled “Real-Time Dashboards”Monitor your workloads with comprehensive dashboard views:
- Live updates - Real-time metric updates during active training jobs
- Historical trends - View performance patterns over time
- Comparative analysis - Compare metrics across different training runs
- Custom filtering - Focus on specific time ranges, jobs, or resource types
Performance Optimization
Section titled “Performance Optimization”Use infrastructure metrics to optimize your workloads:
- Batch size tuning - Identify optimal batch sizes for maximum GPU utilization
- Memory optimization - Prevent OOM errors and maximize memory efficiency
- Bottleneck identification - Locate performance bottlenecks in your training pipeline
- Resource scaling - Determine when to scale up or down compute resources
Training Analytics
Section titled “Training Analytics”TensorBoard Integration
Section titled “TensorBoard Integration”Native TensorBoard support at tensorboard.flex.ai 🔗:
- Training metrics - Loss curves, accuracy trends, validation scores
- Model visualization - Network architecture and computational graphs
- Hyperparameter tracking - Compare different hyperparameter configurations
- Blueprint comparison - Side-by-side analysis of multiple training runs
Training Performance Metrics
Section titled “Training Performance Metrics”Comprehensive tracking of training progression:
- Loss functions - Training and validation loss over time
- Learning curves - Model performance improvement tracking
- Convergence analysis - Identify when models reach optimal performance
- Overfitting detection - Early warning signs of model overfitting
Model Quality Assessment
Section titled “Model Quality Assessment”Evaluate model quality with detailed analytics:
- Accuracy metrics - Precision, recall, F1-score, and custom metrics
- Validation performance - Generalization capability assessment
- Statistical analysis - Distribution analysis and statistical significance
- Quality degradation - Monitor for model performance degradation over time
Resource Utilization Analytics
Section titled “Resource Utilization Analytics”Compute Efficiency
Section titled “Compute Efficiency”Maximize the value of your compute resources:
- GPU utilization rates - Track how effectively GPUs are being used
- Memory efficiency - Monitor memory allocation and usage patterns
- Idle time analysis - Identify periods of resource underutilization
- Cost per training step - Calculate efficiency metrics for cost optimization
Capacity Planning
Section titled “Capacity Planning”Plan future resource needs with historical data:
- Usage patterns - Understand peak and average resource consumption
- Growth projections - Forecast future compute requirements
- Resource allocation - Optimize resource distribution across workloads
- Budget planning - Predict and plan for compute costs
Multi-GPU Analysis
Section titled “Multi-GPU Analysis”Specialized monitoring for distributed training:
- GPU synchronization - Monitor communication between GPUs
- Load balancing - Ensure even distribution across available GPUs
- Scaling efficiency - Measure performance improvements with additional GPUs
- Communication overhead - Track inter-GPU communication costs
Debugging and Troubleshooting
Section titled “Debugging and Troubleshooting”Performance Debugging
Section titled “Performance Debugging”Identify and resolve performance issues:
- Slow training detection - Automatic alerts for unusually slow training
- Memory leak identification - Detect gradual memory consumption increases
- I/O bottlenecks - Identify data loading and storage performance issues
- Network latency - Monitor distributed training communication delays
System Health Monitoring
Section titled “System Health Monitoring”Comprehensive system health tracking:
- Hardware status - Monitor GPU health, temperature warnings, and errors
- Service availability - Track uptime and service reliability
- Error logging - Centralized collection and analysis of system errors
- Alert systems - Proactive notifications for system issues
Root Cause Analysis
Section titled “Root Cause Analysis”Tools for deep performance investigation:
- Timeline analysis - Detailed execution timeline for performance events
- Resource correlation - Correlate performance issues with resource usage
- Comparative debugging - Compare problematic runs with successful ones
- Historical context - Understand how current issues relate to past performance
Performance Optimizations
Section titled “Performance Optimizations”- Underutilized resources - Track opportunities to reduce resource allocation
- Optimal instance types - Identify the most cost-effective hardware
- Batch optimization - Find optimal batch sizes for enhanced cost-performance ratio
Data Retention and Access
Section titled “Data Retention and Access”Data Storage
Section titled “Data Storage”Comprehensive data retention policies:
- 30-day retention - All metrics and logs retained for 30 days
- Export capabilities - Download metrics data for external analysis
- API access - Programmatic access to observability data
- Integration support - Connect with external monitoring systems
Data Privacy and Security
Section titled “Data Privacy and Security”Secure handling of observability data:
- Encrypted storage - All metrics data encrypted at rest
- Access controls - Role-based access to observability dashboards
- Audit trails - Track access to sensitive performance data
- Compliance support - Meet regulatory requirements for data handling
Integration and APIs
Section titled “Integration and APIs”Third-Party Integrations
Section titled “Third-Party Integrations”Connect with external monitoring tools:
- Prometheus - Export metrics to Prometheus for custom dashboards
- Grafana - Visualize FlexAI metrics in Grafana dashboards
- DataDog - Stream metrics to DataDog for unified monitoring
- Custom integrations - Build custom integrations using API calls or webhooks in conjunction with FlexAI Secrets
Getting Started
Section titled “Getting Started”Ready to start monitoring your AI workloads? Explore these resources:
Infrastructure Dashboard
Access real-time infrastructure metrics and performance data
TensorBoard Dashboard
Visualize training metrics and compare blueprints
Interactive Training
Debug performance issues with SSH access to training environments
FAQ: Monitoring
Common questions about monitoring and observability