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Ray 3.0.0.dev0
Welcome to Ray!
Ray
Overview
Getting Started
Installation
Use Cases
Ecosystem
Ray Core
Ray AI Runtime (AIR)
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)
ray.tune.Trainable
ray.tune.Trainable.setup
ray.tune.Trainable.save_checkpoint
ray.tune.Trainable.load_checkpoint
ray.tune.Trainable.step
ray.tune.Trainable.reset_config
ray.tune.Trainable.cleanup
ray.tune.Trainable.default_resource_request
ray.tune.with_parameters
ray.tune.with_resources
ray.tune.execution.placement_groups.PlacementGroupFactory
ray.tune.utils.wait_for_gpu
ray.tune.utils.diagnose_serialization
ray.tune.utils.validate_save_restore
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
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.Trainable.uses_cloud_checkpointing
ray.tune.Trainable.uses_cloud_checkpointing
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Trainable.
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