[tune] Add Nevergrad to Tune (#3985)

python/ray/tune/suggest/nevergrad.py

@@ -0,0 +1,89 @@
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+try:
+    import nevergrad
+except Exception:
+    nevergrad = None
+
+from ray.tune.suggest.suggestion import SuggestionAlgorithm
+
+
+class NevergradSearch(SuggestionAlgorithm):
+    """A wrapper around Nevergrad to provide trial suggestions.
+
+    Requires Nevergrad to be installed.
+    Nevergrad is an open source tool from Facebook for derivative free
+    optimization of parameters and/or hyperparameters. It features a wide
+    range of optimizers in a standard ask and tell interface. More information
+    can be found at https://github.com/facebookresearch/nevergrad.
+
+    Parameters:
+        optimizer (nevergrad.optimization.Optimizer): Optimizer provided
+            from Nevergrad.
+        parameter_names (list): List of parameter names. Should match
+            the dimension of the optimizer output.
+        max_concurrent (int): Number of maximum concurrent trials. Defaults
+            to 10.
+        reward_attr (str): The training result objective value attribute.
+            This refers to an increasing value.
+
+    Example:
+        >>> from nevergrad.optimization import optimizerlib
+        >>> optimizer = optimizerlib.OnePlusOne(dimension=1, budget=100)
+        >>> config = {
+        >>>     "my_exp": {
+        >>>         "run": "exp",
+        >>>         "num_samples": 10,
+        >>>         "stop": {
+        >>>             "training_iteration": 100
+        >>>         },
+        >>>     }
+        >>> }
+        >>> algo = NevergradSearch(
+        >>>     optimizer, max_concurrent=4, reward_attr="neg_mean_loss")
+    """
+
+    def __init__(self,
+                 optimizer,
+                 parameter_names,
+                 max_concurrent=10,
+                 reward_attr="episode_reward_mean",
+                 **kwargs):
+        assert nevergrad is not None, "Nevergrad must be installed!"
+        assert type(max_concurrent) is int and max_concurrent > 0
+        self._max_concurrent = max_concurrent
+        self._parameters = parameter_names
+        self._reward_attr = reward_attr
+        self._nevergrad_opt = optimizer
+        self._live_trial_mapping = {}
+        super(NevergradSearch, self).__init__(**kwargs)
+
+    def _suggest(self, trial_id):
+        if self._num_live_trials() >= self._max_concurrent:
+            return None
+        suggested_config = self._nevergrad_opt.ask()
+        self._live_trial_mapping[trial_id] = suggested_config
+        return dict(zip(self._parameters, suggested_config))
+
+    def on_trial_result(self, trial_id, result):
+        pass
+
+    def on_trial_complete(self,
+                          trial_id,
+                          result=None,
+                          error=False,
+                          early_terminated=False):
+        """Passes the result to Nevergrad unless early terminated or errored.
+
+        The result is internally negated when interacting with Nevergrad
+        so that Nevergrad Optimizers can "maximize" this value,
+        as it minimizes on default.
+        """
+        ng_trial_info = self._live_trial_mapping.pop(trial_id)
+        if result:
+            self._nevergrad_opt.tell(ng_trial_info, -result[self._reward_attr])
+
+    def _num_live_trials(self):
+        return len(self._live_trial_mapping)