Stable GPU workload operations: drivers, CUDA, memory, and logs

GPU job failures often come from environment mismatch, resource pressure, data paths, leftover processes, or missing logs rather than from the card itself. A fixed troubleshooting order reduces recovery time.

Why it matters

Long-running workloads are expensive to debug without observability. If the team does not know which model version, CUDA version, batch size, input path, and error stack were used, the same failure can repeat and waste both GPU time and engineer time.

How to apply it

Check driver, CUDA, framework, and image versions first. Then check GPU memory, CPU, RAM, disk, network, and process state. Make scripts print key parameters and write structured logs. For long tasks, add checkpoints and clear output directories.

Next steps

Create a runbook for common failures: dependency mismatch, out-of-memory, disk full, process residue, and missing model files. Stable operations come from repeatable evidence, not from a single successful start.