Adds a new page and table to document current scalability thresholds in Ray Tune to the documentation.
Co-authored-by: Richard Liaw <rliaw@berkeley.edu>
* [tune] make `tune.with_parameters()` work with the class API
* Update python/ray/tune/utils/trainable.py
Co-authored-by: Richard Liaw <rliaw@berkeley.edu>
Co-authored-by: Richard Liaw <rliaw@berkeley.edu>
* Refactor placement group factory object to accept placement_group arguments instead of callables
* Convert resources to pgf
* Enable placement groups per default
* Fix tests WIP
* Fix stop/resume with placement groups
* Fix progress reporter test
* Fix trial executor tests
* Check resource for trial, not resource object
* Move ENV vars into class
* Fix tests
* Sphinx
* Wait for trial start in PBT
* Revert merge errors
* Support trial reuse with placement groups
* Better check for just staged trials
* Fix trial queuing
* Wait for pg after trial termination
* Clean up PGs before tune run
* No PG settings in pbt scheduler
* Fix buffering tests
* Skip test if ray reports erroneous available resources
* Disable PG for cluster resource counting test
* Debug output for tests
* Output in-use resources for placement groups
* Don't start new trial on trial start failure
* Add docs
* Cleanup PGs once futures returned
* Fix placement group shutdown
* Use updated_queue flag
* Apply suggestions from code review
* Apply suggestions from code review
* Update docs
* Reuse placement groups independently from actors
* Do not remove placement groups for paused trials
* Only continue enqueueing trials if it didn't fail the first time
* Rename parameter
* Fix pause trial
* Code review + try_recover
* Update python/ray/tune/utils/placement_groups.py
Co-authored-by: Richard Liaw <rliaw@berkeley.edu>
* Move placement group lifecycle management
* Move total used resources to pg manager
* Update FAQ example
* Requeue trial if start was unsuccessful
* Do not cleanup pgs at start of run
* Revert "Do not cleanup pgs at start of run"
This reverts commit 933d9c4c
* Delayed PG removal
* Fix trial requeue test
* Trigger pg cleanup on status update
* Fix tests
* Fix docs
* fix-test
Signed-off-by: Richard Liaw <rliaw@berkeley.edu>
Co-authored-by: Richard Liaw <rliaw@berkeley.edu>
* Working prototype
* Pass buffer length, fix tests
* Don't buffer per default
* Dispatch and process save in one go, added tests
* Fix tests
* Pass adaptive seconds to train_buffered, stop result processing after STOP decision
* Fix tests, add release test
* Update tests
* Added detailed logs for slow operations
* Update python/ray/tune/trial_runner.py
Co-authored-by: Richard Liaw <rliaw@berkeley.edu>
* Apply suggestions from code review
* Revert tests and go back to old tuning loop
* nit
Co-authored-by: Richard Liaw <rliaw@berkeley.edu>
* Add DockerSyncer
* Add docs
* Update python/ray/tune/integration/docker.py
Co-authored-by: Richard Liaw <rliaw@berkeley.edu>
* Updated docs
* fix dir
* Added docker integration test
* added docker integration test to bazel build
* Use sdk.rsync API
Co-authored-by: Richard Liaw <rliaw@berkeley.edu>
