* Change the log syncing behavior
* fix up abstractions for syncer
* Finished checkpoint syncing
* Code
* Set of changes to get things running
* Fixes for log syncing
* Fix parts
* Lint and other fixes
* fix some test
* Remove extra parsing functionality
* some test fixes
* Fix up cloud syncing
* Another thing to do
* Fix up tests and local sync
Changes LogSync into a mixin, and adds tests for different
functionalities.
* Fix up tests, start on local migration
* fix distributed migrations
* comments
* formatting
* Better checkpoint directory handling
* fix tests
* fix tests
* fix click
* comments
* formatting comments
* formatting and comments
* sync function deprecations
* syncfunction
* Add documentation for Syncing and Uploading
* nit
* BaseSyncer as base for Mixin in edge case
* more docs
* clean up assertions
* validate
* nit
* Update test_cluster.py
* betterdoc
* Update tune-usage.rst
* cleanup
* nit
* Instructions for running Tensorboard without sudo
When we run Tensorboard to visualize the results of Ray outputs on multi-user clusters where we don't have sudo access, such as RISE clusters, a few commands need to first be run to make sure tensorboard can edit the tmp directory. This is a pretty common usecase so I figured we may as well put it in the documentation for Tune.
* Update tune-usage.rst
## What do these changes do?
Add documentation for the `--output` flag for ls / lsx in the Tune CLI.
## Related issue number
Closes#4511
## Linter
- [x] I've run `scripts/format.sh` to lint the changes in this PR.
## What do these changes do?
Adds filter flag (--filter) to ls / lsx commands for Tune CLI.
Usage: `tune ls [path] --filter [column] [operator] [value]`
e.g. `tune lsx ~/ray_results/my_project --filter total_trials == 1`
Uses `tune.run` to execute experiments as preferred API.
@noahgolmant
This does not break backwards compat, but will slowly internalize `Experiment`.
In a separate PR, Tune schedulers should only support 1 running experiment at a time.
This PR introduces cluster-level fault tolerance for Tune by checkpointing global state. This occurs with relatively high frequency and allows users to easily resume experiments when the cluster crashes.
Note that this PR may affect automated workflows due to auto-prompting, but this is resolvable.
* Added checkpoint_at_end option. To fix#2740
* Added ability to checkpoint at the end of trials if the option is set to True
* checkpoint_at_end option added; Consistent with Experience and Trial runner
* checkpoint_at_end option mentioned in the tune usage guide
* Moved the redundant checkpoint criteria check out of the if-elif
* Added note that checkpoint_at_end is enabled only when checkpoint_freq is not 0
* Added test case for checkpoint_at_end
* Made checkpoint_at_end have an effect regardless of checkpoint_freq
* Removed comment from the test case
* Fixed the indentation
* Fixed pep8 E231
* Handled cases when trainable does not have _save implemented
* Constrained test case to a particular exp using the MockAgent
* Revert "Constrained test case to a particular exp using the MockAgent"
This reverts commit e965a9358ec7859b99a3aabb681286d6ba3c3906.
* Revert "Handled cases when trainable does not have _save implemented"
This reverts commit 0f5382f996ff0cbf3d054742db866c33494d173a.
* Simpler test case for checkpoint_at_end
* Preserved bools from loosing their actual value
* Revert "Moved the redundant checkpoint criteria check out of the if-elif"
This reverts commit 783005122902240b0ee177e9e206e397356af9c5.
* Fix linting error.
This PR introduces the following changes:
* Ray Tune -> Tune
* [breaking] Creation of `schedulers/`, moving PBT, HyperBand into a submodule
* [breaking] Search Algorithms now must take in experiment configurations via `add_configurations` rather through initialization
* Support `"run": (function | class | str)` with automatic registering of trainable
* Documentation Changes
