When is an operation profiler useful?
Use an operation profiler to investigate where execution time goes and which operations to examine. PyTorch can collect CPU and CUDA activity. Enable detailed options selectively: collecting stacks and shapes adds overhead, and recording shapes can retain tensor references. See the profiler documentation.
When do you need memory history?
For memory growth or an OOM, inspect allocation history and what is still alive near the failure. A memory snapshot answers different questions from a timing trace. The PyTorch visualizer guide shows how to read these views.
Where does observability fit?
Metrics describe measurements over time, logs record events, and traces connect operations. These signals help locate a failing job or correlate an incident across services; they do not automatically identify the tensor responsible for memory growth. OpenTelemetry describes these signal types.
What does checkpointing contribute?
A checkpoint preserves state for resuming work. PyTorch’s checkpoint tutorial includes model and optimizer state when resuming training. Saving state and explaining the original failure are separate tasks; preserve diagnostic evidence alongside the checkpoint.
How should you evaluate MemSage alongside these tools?
MemSage focuses on memory monitoring, preflight checks, and on-demand diagnosis. Bring the same representative workload and diagnostic question to an evaluation. Compare the evidence obtained, time spent investigating, and measured overhead. Framework support and deployment details must be confirmed for that evaluation.