o $i@sfddlmZmZmZmZddlmZddlmZddl m Z e GdddZ e Gddde Z d S) )AnyDictListOptional) Checkpoint)TrainRunContext) DeveloperAPIc@seZdZdZdS)RayTrainCallbackz"Base Ray Train callback interface.N)__name__ __module__ __qualname____doc__rrV/home/ubuntu/veenaModal/venv/lib/python3.10/site-packages/ray/train/v2/api/callback.pyr sr c@sPeZdZdZdedeeeefde e fddZ dedee e ffdd Zd S) UserCallbackzCallback interface for custom user-defined callbacks to handling events during training. This callback is called on the Ray Train controller process, not on the worker processes. run_contextmetrics checkpointcCdS)a*Called after all workers have reported a metric + checkpoint via `ray.train.report`. Args: run_context: The `TrainRunContext` for the current training run. metrics: A list of metric dictionaries reported by workers, where metrics[i] is the metrics dict reported by worker i. checkpoint: A Checkpoint object that has been persisted to storage. This is None if no workers reported a checkpoint (e.g. `ray.train.report(metrics, checkpoint=None)`). Nr)selfrrrrrr after_reportszUserCallback.after_reportworker_exceptionscCr)aCalled after one or more workers have raised an exception. Args: run_context: The `TrainRunContext` for the current training run. worker_exceptions: A dict from worker world rank to the exception raised by that worker. Nr)rrrrrrafter_exception+s zUserCallback.after_exceptionN)r r r r rrrstrrrrrint Exceptionrrrrrrs  rN) typingrrrr ray.trainr(ray.train.v2._internal.execution.contextrray.util.annotationsrr rrrrrs