* Support `yield` and `return` statements in Tune trainable functions
* Support anonymous metric with ``tune.report(value)``
* Raise on invalid return/yield value
* Fix end to end reporter test
* Remove all __future__ imports from RLlib.
* Remove (object) again from tf_run_builder.py::TFRunBuilder.
* Fix 2xLINT warnings.
* Fix broken appo_policy import (must be appo_tf_policy)
* Remove future imports from all other ray files (not just RLlib).
* Remove future imports from all other ray files (not just RLlib).
* Remove future import blocks that contain `unicode_literals` as well.
Revert appo_tf_policy.py to appo_policy.py (belongs to another PR).
* Add two empty lines before Schedule class.
* Put back __future__ imports into determine_tests_to_run.py. Fails otherwise on a py2/print related error.
Introduces a minimally invasive utility for logging experiment results. A broad requirement for this tool is that it should integrate seamlessly with Tune execution.
## What do these changes do?
This is a re-implementation of the `FunctionRunner` which enforces some synchronicity between the thread running the training function and the thread running the Trainable which logs results. The main purpose is to make logging consistent across APIs in anticipation of a new function API which will be generator based (through `yield` statements). Without these changes, it will be impossible for the (possibly soon to be) deprecated reporter based API to behave the same as the generator based API.
This new implementation provides additional guarantees to prevent results from being dropped. This makes the logging behavior more intuitive and consistent with how results are handled in custom subclasses of Trainable.
New guarantees for the tune function API:
- Every reported result, i.e., `reporter(**kwargs)` calls, is forwarded to the appropriate loggers instead of being dropped if not enough time has elapsed since the last results.
- The wrapped function only runs if the `FunctionRunner` expects a result, i.e., when `FunctionRunner._train()` has been called. This removes the possibility that a result will be generated by the function but never logged.
- The wrapped function is not called until the first `_train()` call. Currently, the wrapped function is started during the setup phase which could result in dropped results if the trial is cancelled between `_setup()` and the first `_train()` call.
- Exceptions raised by the wrapped function won't be propagated until all results are logged to prevent dropped results.
- The thread running the wrapped function is explicitly stopped when the `FunctionRunner` is stopped with `_stop()`.
- If the wrapped function terminates without reporting `done=True`, a duplicate result with `{"done": True}`, is reported to explicitly terminate the trial, and components will be notified with a duplicate of the last reported result, but this duplicate will not be logged.
## Related issue number
Closes#3956.
#3949#3834
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
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.
Amog Kamsetty