Pause and return later

Training can take hours or days. This course does not assume the run finishes while you read.

Let the run reach a saved checkpoint. Record the current step, loss values, elapsed time, and latest sample in your notebook.

A useful resume checkpoint needs more than model weights. Exact continuation commonly needs optimizer state, step and schedule position, and the data-order or random state used by the trainer.

If only weights are restored, training can still continue, but it is a new run segment rather than a bit-for-bit continuation. Record that distinction.

Pause or close the app using its normal controls. Reopen the same project and confirm the checkpoint history is still present before resuming.

After resume, compare these values with the notebook:

Run ID and checkpoint step
Optimizer and learning-rate state
Training and validation loss
Next checkpoint number
Sampling seed and baseline prompt

Loss may move slightly when a new batch arrives. A reset step counter, missing checkpoint, or large unexplained discontinuity needs investigation.

You can stop this course here. When you return, begin by checking that the project name, current step, and latest checkpoint match your notes.

If the run did not save correctly, preserve the error and logs before changing the project. A failed resume is useful diagnostic evidence.

Keep enough free disk for the next checkpoint. A training pause caused by a full disk can leave the newest artifact incomplete.