Monitoring & Operations AI Infrastructure
AI clusters are:
- GPU-dense
- Power-hungry
- Network-intensive
- Storage-dependent
Goal of monitoring:
- Maximize GPU utilization
- Detect failures early
- Prevent downtime
- Optimize performance
- Ensure thermal and power stability
Monitoring Layers
AI data centers monitor multiple layers:
1️⃣ Hardware Layer
- GPU temperature
- GPU utilization
- Power draw
- CPU usage
- Memory usage
- Disk I/O
- NIC throughput
2️⃣ Network Layer
- Latency
- Packet loss
- RDMA errors
- Congestion
- Throughput
3️⃣ Storage Layer
- IOPS
- Throughput
- Latency
- File system saturation
4️⃣ Application Layer
- Training job status
- Job queue depth
- Container health
- Pod failures (Kubernetes)
1. GPU Monitoring Tools
1.1. nvidia-smi
Checking GPU status on single system
- CLI tool
- Quick GPU diagnostics
- Per-GPU statistics
1.2 DCGM (Data Center GPU Manager)
-
Monitoring 10+ GPU nodes
- Kubernetes Cluster level GPU Management
- Historical data collection for Health checks
- Allow adding Alerting
- Cluster-wide GPU insights
Keep Eyes on:
- Temperature
- Power consumption
- ECC errors
- Utilization metrics
1.3 Base Command Manager (BCM)
- Mange entire cluster of GPU nodes in AI Data Center
- Job Scheduling and Monitoring
- Multi Team/ User/ environment management
- Ensure Scale optimal resource allocation
2. Infrastructure Monitoring Tools
2.1 Prometheus
- Time-series metrics collection
- Scrapes metrics from nodes
- Stores and queries metrics
2.2 Grafana
- Visualization dashboards
- Real-time monitoring
- Alerting integration
2.3 Node Exporter
- Collects system-level metrics
- CPU, memory, disk, network
3 Cluster Orchestration Monitoring
3.1 Kubernetes
Monitor:
- Pod status
- Node health
- Resource usage
- Scheduling issues
3.2 Slurm (Simple Linux Utility for Resource Management) (HPC environments)
open-source cluster resource management and job scheduling system that strives to be simple, scalable, portable, fault-tolerant, and interconnect agnostic
Monitors:
- Job queue
- Resource allocation
- Failed jobs
- Node states
Logging Systems
Logs provide:
- Error tracing
- Job debugging
- Security auditing
- System failure analysis
Centralized logging:
- Aggregated logs
- Searchable
- Long-term retention
Alerting Strategy
Monitoring without alerting = useless.
Effective alerts:
- Temperature threshold exceeded
- GPU ECC errors
- Node unreachable
- Disk nearly full
- Network congestion
Alerts should be:
- Actionable
- Prioritized
- Not noisy
Power & Cooling Monitoring
AI clusters consume massive power.
Monitor:
- Rack power draw
- PSU health
- Cooling system efficiency
- Data center temperature
- Airflow
Failure to monitor → thermal shutdown.
Cooling Options
1. Air Colling
- Max at 30 kW per rack
- Less efficient at high densities
- Lower infrastructure cost
2. Liquid Cooling
- Better for high density racks (30–80 kW+)
- More efficient heat removal
- Expensive infrastructure
High Availability (HA)
AI infrastructure should support:
- Redundant power supplies
- Redundant networking paths
- Failover nodes
- Backup storage
Single point of failure = unacceptable.
Failure Scenarios to Understand
Common failures:
- GPU overheating
- Node crash
- Network congestion
- Storage saturation
- Job scheduler deadlock
Monitoring enables:
- Rapid detection
- Root cause analysis
- Faster recovery
Security Monitoring
Includes:
- Unauthorized access attempts
- Configuration changes
- Network anomalies
- DPU isolation policies
- Role-based access control
Capacity Planning
Operations teams must:
- Track GPU utilization trends
- Forecast storage growth
- Monitor network saturation
- Plan rack power expansion
Goal: Avoid capacity shortages.
Lifecycle Management
Operations includes:
- Firmware updates
- Driver updates
- CUDA updates
- Security patches
- Hardware replacement
Change management must:
- Minimize downtime
- Be documented
- Be tested
Observability vs Monitoring
Monitoring:
- What is happening?
Observability:
- Why is it happening?
Observability includes:
- Metrics
- Logs
- Traces
GPU Utilization Optimization
Low GPU utilization may indicate:
- Storage bottleneck
- Network congestion
- Poor job scheduling
- Insufficient batch size
- CPU bottleneck
Operations teams investigate before scaling hardware.
Exam Scenarios to Recognize
If question mentions:
- GPU temperature spikes → Thermal monitoring
- ECC memory errors → DCGM
- Dashboard visualization → Grafana
- Metric scraping → Prometheus
- HPC job queue management → Slurm
- Container orchestration → Kubernetes
- Rack power issue → Data center monitoring
- Underutilized GPUs → Operational inefficiency
Quick Memory Anchors
- DCGM = GPU health monitoring
- Prometheus = Metrics collection
- Grafana = Visualization
- Slurm = HPC job scheduler
- Kubernetes = Container orchestration
- Monitoring prevents GPU idle time
- Alerting must be actionable