* Run xray tests in travis.
* Comment out TaskTests.testSubmittingManyTasks.
* Comment out failing tests.
* Comment out hanging test.
* Linting
* Comment out failing test.
* Comment out failing test.
* Ignore test_dataframe.py for now.
* Comment out testDriverExitingQuickly.
* Make ActorHandles pickleable, also make proper ActorHandle and ActorClass classes.
* Fix bug.
* Fix actor test bug.
* Update __ray_terminate__ usage.
* Fix most linting, add documentation, and small cleanups.
* Handle forking and pickling differently for actor handles. Fix linting.
* Fixes for named actors via pickling.
* Generate actor handle IDs deterministically in the pickling case.
* Use set/dict literal syntax
Ran code through [pyupgrade](https://github.com/asottile/pyupgrade). This is
supported in every Python version 2.7+.
* Drop unnecessary string format specification
No need to specify 0,1.. if paramters are passed in order.
* Revert "Drop unnecessary string format specification"
This reverts commit efa5ec85d30ff69f34e5ed93e31343fea7647bcb.
* Undo changes to cloudpickle
Drop use of set literal until cloudpickle uses it.
* Reformat code with YAPF
We need to set up a git pre-push hook to automatically run this stuff.
* Integrate worker with raylet.
* Begin allowing worker to attach to cluster.
* Fix linting and documentation.
* Fix linting.
* Comment tests back in.
* Fix type of worker command.
* Remove xray python files and tests.
* Fix from rebase.
* Add test.
* Copy over raylet executable.
* Small cleanup.
* Print error when actor takes too long to start, and refactor error message pushing.
* Print warning every ten seconds.
* Fix linting and tests.
* Fix tests.
* Provide experimental API for changing number of return values and resource requirements at task submission time.
* Remove code duplication and add tests.
* Treat actor creation like a regular task.
* Small cleanups.
* Change semantics of actor resource handling.
* Bug fix.
* Minor linting
* Bug fix
* Fix jenkins test.
* Fix actor tests
* Some cleanups
* Bug fix
* Fix bug.
* Remove cached actor tasks when a driver is removed.
* Add more info to taskspec in global state API.
* Fix cyclic import bug in tune.
* Fix
* Fix linting.
* Fix linting.
* Don't schedule any tasks (especially actor creaiton tasks) on local schedulers with 0 CPUs.
* Bug fix.
* Add test for 0 CPU case
* Fix linting
* Address comments.
* Fix typos and add comment.
* Add assertion and fix test.
* Fri Feb 16 13:53:50 PST 2018
* Sat Feb 17 15:32:08 PST 2018
* Sat Feb 17 15:44:59 PST 2018
* fix
* Sun Feb 18 14:46:24 PST 2018
* Sun Feb 18 14:46:37 PST 2018
* Sun Feb 18 14:55:52 PST 2018
* Sun Feb 18 15:14:32 PST 2018
* Wed Feb 21 17:34:17 PST 2018
* Sun Feb 25 17:51:17 PST 2018
* Sun Feb 25 22:18:40 PST 2018
* Wed Feb 28 13:19:05 PST 2018
* Wed Feb 28 13:22:13 PST 2018
* Wed Feb 28 13:33:29 PST 2018
* Wed Feb 28 13:35:33 PST 2018
* add ex
* Fri Mar 2 12:50:17 PST 2018
* Fri Mar 2 12:54:31 PST 2018
* Allow passing in --object-store-memory to ray start.
* Allow setting ports for the redis shards.
* Reorder arguments and infer number of shards from ports.
* Move code block into only the head node case.
* Add test.
* spillback policy implementation: global + local scheduler
* modernize global scheduler policy state; factor out random number engine and generator
* Minimal version.
* Fix test.
* Make load balancing test less strenuous.
* Expose calls to get and set the actor frontier
* Remove fields used for old checkpointing prototype, change actor_checkpoint_failed -> succeeded
* Prototype for actor checkpointing
* Filter out duplicate tasks on the local scheduler
* Clean up some of the Python checkpointing code
* More cleanups
* Documentation
* cleanup and fix unit test
* Allow remote checkpoint calls through actor handle
* Check whether object is local before reconstructing
* Enable checkpointing for distributed actor handles, refactor tests
* Fix local scheduler tests
* lint
* Address comments
* lint
* Skip tests that fail on new GCS
* style
* Don't put same object twice when setting the actor frontier
* Address Philipp's comments, cleaner fbs naming
* patch up pbt
* Sat Jan 27 01:00:03 PST 2018
* Sat Jan 27 01:04:14 PST 2018
* Sat Jan 27 01:04:21 PST 2018
* Sat Jan 27 01:15:15 PST 2018
* Sat Jan 27 01:15:42 PST 2018
* Sat Jan 27 01:16:14 PST 2018
* Sat Jan 27 01:38:42 PST 2018
* Sat Jan 27 01:39:21 PST 2018
* add pbt
* Sat Jan 27 01:41:19 PST 2018
* Sat Jan 27 01:44:21 PST 2018
* Sat Jan 27 01:45:46 PST 2018
* Sat Jan 27 16:54:42 PST 2018
* Sat Jan 27 16:57:53 PST 2018
* clean up test
* Sat Jan 27 18:01:15 PST 2018
* Sat Jan 27 18:02:54 PST 2018
* Sat Jan 27 18:11:18 PST 2018
* Sat Jan 27 18:11:55 PST 2018
* Sat Jan 27 18:14:09 PST 2018
* review
* try out a ppo example
* some tweaks to ppo example
* add postprocess hook
* Sun Jan 28 15:00:40 PST 2018
* clean up custom explore fn
* Sun Jan 28 15:10:21 PST 2018
* Sun Jan 28 15:14:53 PST 2018
* Sun Jan 28 15:17:04 PST 2018
* Sun Jan 28 15:33:13 PST 2018
* Sun Jan 28 15:56:40 PST 2018
* Sun Jan 28 15:57:36 PST 2018
* Sun Jan 28 16:00:35 PST 2018
* Sun Jan 28 16:02:58 PST 2018
* Sun Jan 28 16:29:50 PST 2018
* Sun Jan 28 16:30:36 PST 2018
* Sun Jan 28 16:31:44 PST 2018
* improve tune doc
* concepts
* update humanoid
* Fri Feb 2 18:03:33 PST 2018
* fix example
* show error file
Adds a Population-Based Training (as described in https://arxiv.org/abs/1711.09846) scheduler to Ray.tune. Currently mutates hyperparameters according to either a user-defined list of possible values to mutate to (necessary if hyperparameters can only be certain values ex. sgd_batch_size), or by a factor of 0.8 or 1.2.
* Bring cloudpickle version 0.5.2 inside the repo.
* Use internal copy of cloudpickle everywhere.
* Fix linting.
* Import ordering.
* Change __init__.py.
* Set pickler in serialization context.
* Don't check ray location.
Remove rllib dep: trainable is now a standalone abstract class that can be easily subclassed.
Clean up hyperband: fix debug string and add an example.
Remove YAML api / ScriptRunner: this was never really used.
Move ray.init() out of run_experiments(): This provides greater flexibility and should be less confusing since there isn't an implicit init() done there. Note that this is a breaking API change for tune.
