Kernel termination during Kaggle Notebook timeout prevents state persistence

Kaggle Notebooks enforce hard execution limits—typically 12 hours for CPU and 9 hours for GPU instances. When these limits are reached, or a session is manually cancelled, the kernel process terminates immediately, resulting in the loss of all volatile memory state not explicitly committed to the /kaggle/working directory.

Because the environment may not reliably trigger standard SIGTERM handlers, traditional Python try-finally blocks or signal interceptors often fail to execute final cleanup or model-saving logic. This presents a challenge for long-running machine learning tasks where model weights must be persisted before the environment is preempted.

What is the most reliable method to monitor the remaining session duration within a training loop to trigger a graceful checkpoint? Additionally, how can one ensure that a file write operation completes fully if the kernel is killed mid-write during a hard timeout?