Removes all ML related code from `ray.util`
Removes:
- `ray.util.xgboost`
- `ray.util.lightgbm`
- `ray.util.horovod`
- `ray.util.ray_lightning`
Moves `ray.util.ml_utils` to other locations
Closes#23900
Signed-off-by: Amog Kamsetty <amogkamsetty@yahoo.com>
Signed-off-by: Kai Fricke <kai@anyscale.com>
Co-authored-by: Kai Fricke <kai@anyscale.com>
This PR updates the Ray AIR/Tune ipynb examples to use the Tuner() API instead of tune.run().
Signed-off-by: Kai Fricke <kai@anyscale.com>
Signed-off-by: Richard Liaw <rliaw@berkeley.edu>
Signed-off-by: Xiaowei Jiang <xwjiang2010@gmail.com>
Signed-off-by: Kai Fricke <coding@kaifricke.com>
Co-authored-by: Richard Liaw <rliaw@berkeley.edu>
Co-authored-by: Xiaowei Jiang <xwjiang2010@gmail.com>
Update documentation to use `session.report`.
Next steps:
1. Update our internal caller to use `session.report`. Most importantly, CheckpointManager and DataParallelTrainer.
2. Update `get_trial_resources` to use PGF notions to incorporate the requirement of ResourceChangingScheduler. @Yard1
3. After 2 is done, change all `tune.get_trial_resources` to `session.get_trial_resources`
4. [internal implementation] remove special checkpoint handling logic from huggingface trainer. Optimize the flow for checkpoint conversion with `session.report`.
Co-authored-by: Antoni Baum <antoni.baum@protonmail.com>
**Update**: This PR is now part 3 of a three PR group to consolidate the checkpoints.
1. Part 1 adds the common checkpoint management class #24771
2. Part 2 adds the integration for Ray Train #24772
3. This PR builds on #24772 and includes all changes. It moves the Ray Tune integration to use the new common checkpoint manager class.
Old PR description:
This PR consolidates the Ray Train and Tune checkpoint managers. These concepts previously did something very similar but in different modules. To simplify maintenance in the future, we've consolidated the common core.
- This PR keeps full compatibility with the previous interfaces and implementations. This means that for now, Train and Tune will have separate CheckpointManagers that both extend the common core
- This PR prepares Tune to move to a CheckpointStrategy object
- In follow-up PRs, we can further unify interfacing with the common core, possibly removing any train- or tune-specific adjustments (e.g. moving to setup on init rather on runtime for Ray Train)
Co-authored-by: Antoni Baum <antoni.baum@protonmail.com>
