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Ray 3.0.0.dev0
Welcome to Ray!
Ray
Overview
Getting Started
Installation
Use Cases
Example Gallery
Ecosystem
Ray Core
Ray Data
Ray Train
Ray Tune
Getting Started
Key Concepts
User Guides
Ray Tune Examples
Ray Tune FAQ
Ray Tune API
Tune Execution (tune.Tuner)
Tune Experiment Results (tune.ResultGrid)
Training in Tune (tune.Trainable, session.report)
Tune Search Space API
Tune Search Algorithms (tune.search)
Tune Trial Schedulers (tune.schedulers)
Tune Stopping Mechanisms (tune.stopper)
Tune Console Output (Reporters)
Syncing in Tune (tune.SyncConfig, tune.Syncer)
Tune Loggers (tune.logger)
Tune Callbacks (tune.Callback)
Environment variables used by Ray Tune
Tune Scikit-Learn API (tune.sklearn)
External library integrations for Ray Tune
ray.tune.integration.pytorch_lightning.TuneReportCallback
ray.tune.integration.pytorch_lightning.TuneReportCheckpointCallback
ray.tune.integration.xgboost.TuneReportCallback
ray.tune.integration.xgboost.TuneReportCheckpointCallback
ray.tune.integration.lightgbm.TuneReportCallback
ray.tune.integration.lightgbm.TuneReportCheckpointCallback
Tune Internals
Tune Client API
Tune CLI (Experimental)
Ray Serve
Ray RLlib
More Libraries
Ray Clusters
Monitoring and Debugging
References
Developer Guides
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ray.tune.integration.xgboost.TuneReportCallback.after_training
ray.tune.integration.xgboost.TuneReportCallback.after_training
#
TuneReportCallback.
after_training
(
model
:
Any
)
→
Any
#
Run after training is finished.