Process Computing Jobs → Monitor GPU Usage → Alert on Issues
Automatically monitor high-performance computing jobs, track GPU utilization metrics, and receive instant alerts when jobs fail or resources are underutilized.
Workflow Steps
Python Script
Create GPU monitoring script
Write a Python script using nvidia-smi and psutil to collect GPU usage, memory utilization, temperature, and running process data. Save metrics to a JSON log file every 5 minutes.
GitHub Actions
Schedule automated monitoring
Set up a GitHub Actions workflow that runs your monitoring script on a schedule. Configure it to run every 5 minutes and commit the results to a monitoring branch in your repository.
Python Script
Analyze usage patterns
Create a second script that analyzes the collected data for anomalies: GPU usage below 50% for >30 minutes, temperature above 85°C, or memory usage above 95%. Generate alert flags when thresholds are exceeded.
PagerDuty
Send critical alerts
Configure PagerDuty integration to trigger high-priority alerts when critical thresholds are met. Set up escalation rules to notify team leads if issues aren't acknowledged within 15 minutes.
Grafana
Visualize performance metrics
Connect Grafana to your JSON logs to create real-time dashboards showing GPU utilization trends, job completion rates, and system health over time. Set up automatic screenshots for daily reports.
Workflow Flow
Step 1
Python Script
Create GPU monitoring script
Step 2
GitHub Actions
Schedule automated monitoring
Step 3
Python Script
Analyze usage patterns
Step 4
PagerDuty
Send critical alerts
Step 5
Grafana
Visualize performance metrics
Why This Works
Python scripts provide precise hardware monitoring, GitHub Actions ensures reliable scheduling, PagerDuty prevents costly downtime, and Grafana offers intuitive visualization for capacity planning.
Best For
DevOps teams, ML engineers, and research labs managing GPU-intensive computing workloads
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