Operations
Keeping GPU clusters running at high availability requires a combination of manual maintenance procedures, automated health checks, and increasingly autonomous agent-driven remediation. From basic operations like GPU resets and driver reloads that recover individual devices, to rolling restart strategies that update firmware across hundreds of nodes without disrupting training jobs, operational excellence determines whether a cluster achieves 95% or 99.5% effective uptime. This section covers the operational concepts and procedures essential for GPU infrastructure management — including MTTR (mean time to recovery), runbook automation, AIOps-driven anomaly detection, and the progression from reactive incident response to proactive, self-healing infrastructure. Each term includes practical context on how Factryze automates these operational workflows to reduce manual toil and minimize the blast radius of GPU failures.
AIOps (AI for IT Operations)
AI-driven GPU infrastructure operations moving beyond traditional alerting to autonomous remediation.
Driver Reload
Reloading nvidia.ko via rmmod/modprobe to clear driver state without a full reboot.
Firmware Update
Updating GPU InfoROM, VBIOS, and NVSwitch firmware during scheduled maintenance windows.
GPU Reset
Hardware GPU reset via nvidia-smi -r with escalation to ipmitool or cold reboot.
Health Check
DCGM diagnostic tests (Level 1/2/3) validating GPU hardware integrity between jobs.
MTTR (Mean Time to Resolution)
Average 47-minute GPU issue resolution time covering detection, diagnosis, and repair.
Rolling Restart
Sequential node restarts for kernel updates and driver upgrades while maintaining cluster capacity.
Runbook
Executable remediation procedures with conditional logic and approval gates for GPU issues.