docs: add info to topic guide for graceful trial termination
@@ -51,6 +51,22 @@ Determined also supports other common hyperparameter search algorithms:
configurations with ones *near* the high-performing points in the
hyperparameter space.
+Handling Trial Errors and Early Stopping Requests
+-------------------------------------------------
+When a trial errors, we try to restart from the latest checkpoint unless we
+have done so ``max_restarts`` times, which is configured in the experiment
+configuration. Once we have reached ``max_restarts``, we set the trial as
+errored. For search methods that adapt to validation metric values
+(:ref:`Adaptive (Simple)<topic-guides_hp-tuning-det_adaptive-simple>`,
+:ref:`Adaptive (Advanced) <topic-guides_hp-tuning-det_adaptive-advanced>`, and
+:ref:`Population-based training (PBT)<topic-guides_hp-tuning-det_pbt>`), we do
+not continue training the model even if the search method would typically call
+for us to continue training.
+
+When a trial requests to stop early via a framework callback or manually via
+``context.set_stop_requested(True)``, we finish the current training or
+validation step, and stop training like above.
+
Next Steps
------------------------
