Revert "[tune] PB2 (ray-project#11466)"

This reverts commit e7aafd7.

doc/source/tune/api_docs/schedulers.rst

@@ -16,7 +16,7 @@ All Trial Schedulers take in a ``metric``, which is a value returned in the resu
 Summary
 -------
 
-Tune includes distributed implementations of early stopping algorithms such as `Median Stopping Rule <https://research.google.com/pubs/pub46180.html>`__, `HyperBand <https://arxiv.org/abs/1603.06560>`__, and `ASHA <https://openreview.net/forum?id=S1Y7OOlRZ>`__. Tune also includes a distributed implementation of `Population Based Training (PBT) <https://deepmind.com/blog/population-based-training-neural-networks>`__ and `Population Based Bandits (PB2) <https://arxiv.org/abs/2002.02518>`__.
+Tune includes distributed implementations of early stopping algorithms such as `Median Stopping Rule <https://research.google.com/pubs/pub46180.html>`__, `HyperBand <https://arxiv.org/abs/1603.06560>`__, and `ASHA <https://openreview.net/forum?id=S1Y7OOlRZ>`__. Tune also includes a distributed implementation of `Population Based Training (PBT) <https://deepmind.com/blog/population-based-training-neural-networks>`__.
 
 .. tip:: The easiest scheduler to start with is the ``ASHAScheduler`` which will aggressively terminate low-performing trials.
 
@@ -48,11 +48,7 @@ When using schedulers, you may face compatibility issues, as shown in the below
    * - :ref:`Population Based Training <tune-scheduler-pbt>`
      - Yes
      - Not Compatible
-     - :doc:`Link </tune/examples/pbt_function>`
-   * - :ref:`Population Based Bandits <tune-scheduler-pb2>`
-     - Yes
-     - Not Compatible
-     - :doc:`Basic Example </tune/examples/pb2_example>`, :doc:`PPO example </tune/examples/pb2_ppo_example>`
+     - :doc:`Link </tune/examples/pbt_example>`
 
 .. _tune-scheduler-hyperband:
 
@@ -176,38 +172,6 @@ replay utility in practice.
 
 .. autoclass:: ray.tune.schedulers.PopulationBasedTrainingReplay
 
-
-.. _tune-scheduler-pb2:
-
-Population Based Bandits (PB2) (tune.schedulers.PB2)
--------------------------------------------------------------------
-
-Tune includes a distributed implementation of `Population Based Bandits (PB2) <https://arxiv.org/abs/2002.02518>`__. This can be enabled by setting the ``scheduler`` parameter of ``tune.run``, e.g.
-
-.. code-block:: python
-
-    pb2_scheduler = PB2(
-            time_attr='time_total_s',
-            metric='mean_accuracy',
-            mode='max',
-            perturbation_interval=600.0,
-            hyperparam_bounds={
-                "lr": [1e-3, 1e-5],
-                "alpha": [0.0, 1.0],
-            ...
-            })
-    tune.run( ... , scheduler=pb2_scheduler)
-
-This code builds upon PBT, with the main difference being that instead of using random perturbations, PB2 selects new hyperparameter configurations using a Gaussian Process model. 
-
-When the PB2 scheduler is enabled, each trial variant is treated as a member of the population. Periodically, top-performing trials are checkpointed (this requires your Trainable to support :ref:`save and restore <tune-checkpoint>`). Low-performing trials clone the checkpoints of top performers and perturb the configurations in the hope of discovering an even better variation.
-
-The primary motivation for PB2 is the ability to find promising hyperparamters with only a small population size. With that in mind, you can run this :doc:`PB2 PPO example </tune/examples/pb2_ppo_example>` to compare PB2 vs. PBT, with a population size of ``4`` (as in the paper). The example uses the ``BipedalWalker`` environment so does not require any additional licenses.
-
-
-.. autoclass:: ray.tune.schedulers.PB2
-
-
 .. _tune-scheduler-bohb:
 
 BOHB (tune.schedulers.HyperBandForBOHB)

doc/source/tune/examples/index.rst

@@ -18,7 +18,6 @@ General Examples
 - :doc:`/tune/examples/pbt_example`: Example of using a Trainable class with PopulationBasedTraining scheduler.
 - :doc:`/tune/examples/pbt_function`: Example of using the function API with a PopulationBasedTraining scheduler.
 - :doc:`/tune/examples/pbt_ppo_example`: Example of optimizing a distributed RLlib algorithm (PPO) with the PopulationBasedTraining scheduler.
-- :doc:`/tune/examples/pb2_ppo_example`: Example of optimizing a distributed RLlib algorithm (PPO) with the PB2 scheduler. Uses a small population size of 4, so can train on a laptop.
 - :doc:`/tune/examples/logging_example`: Example of custom loggers and custom trial directory naming.
 
 Search Algorithm Examples

python/ray/tune/BUILD

@@ -80,7 +80,7 @@ py_test(
 
 py_test(
     name = "test_experiment_analysis_mem",
-    size = "medium",
+    size = "small",
     srcs = ["tests/test_experiment_analysis_mem.py"],
     deps = [":tune_lib"],
 )
@@ -520,15 +520,6 @@ py_test(
     args = ["--smoke-test"]
 )
 
-py_test(
-    name = "pb2_example",
-    size = "medium",
-    srcs = ["examples/pb2_example.py"],
-    deps = [":tune_lib"],
-    tags = ["exclusive", "example"],
-    args = ["--smoke-test"]
-)
-
 py_test(
     name = "pbt_convnet_example",
     size = "small",

python/ray/tune/schedulers/__init__.py

@@ -6,7 +6,6 @@
 from ray.tune.schedulers.median_stopping_rule import MedianStoppingRule
 from ray.tune.schedulers.pbt import (PopulationBasedTraining,
                                      PopulationBasedTrainingReplay)
-from ray.tune.schedulers.pb2 import PB2
 
 
 def create_scheduler(
@@ -38,7 +37,6 @@ def create_scheduler(
         "hb_bohb": HyperBandForBOHB,
         "pbt": PopulationBasedTraining,
         "pbt_replay": PopulationBasedTrainingReplay,
-        "pb2": PB2,
     }
     scheduler = scheduler.lower()
     if scheduler not in SCHEDULER_IMPORT:
@@ -54,5 +52,5 @@ def create_scheduler(
     "TrialScheduler", "HyperBandScheduler", "AsyncHyperBandScheduler",
     "ASHAScheduler", "MedianStoppingRule", "FIFOScheduler",
     "PopulationBasedTraining", "PopulationBasedTrainingReplay",
-    "HyperBandForBOHB", "PB2"
+    "HyperBandForBOHB"
 ]