[tune] TensorBoard HParams for TF2.0 (#5678)

python/ray/tune/suggest/basic_variant.py

@@ -8,7 +8,8 @@ import random
 from ray.tune.error import TuneError
 from ray.tune.experiment import convert_to_experiment_list
 from ray.tune.config_parser import make_parser, create_trial_from_spec
-from ray.tune.suggest.variant_generator import generate_variants
+from ray.tune.suggest.variant_generator import (generate_variants, format_vars,
+                                                flatten_resolved_vars)
 from ray.tune.suggest.search import SearchAlgorithm
 
 
@@ -78,13 +79,13 @@ class BasicVariantGenerator(SearchAlgorithm):
             for resolved_vars, spec in generate_variants(unresolved_spec):
                 experiment_tag = str(self._counter)
                 if resolved_vars:
-                    experiment_tag += "_{}".format(resolved_vars)
+                    experiment_tag += "_{}".format(format_vars(resolved_vars))
                 self._counter += 1
                 yield create_trial_from_spec(
                     spec,
                     output_path,
                     self._parser,
-                    evaluated_params=resolved_vars,
+                    evaluated_params=flatten_resolved_vars(resolved_vars),
                     experiment_tag=experiment_tag)
 
     def is_finished(self):

python/ray/tune/suggest/suggestion.py

@@ -7,7 +7,7 @@ import copy
 
 from ray.tune.error import TuneError
 from ray.tune.trial import Trial
-from ray.tune.util import merge_dicts
+from ray.tune.util import merge_dicts, flatten_dict
 from ray.tune.experiment import convert_to_experiment_list
 from ray.tune.config_parser import make_parser, create_trial_from_spec
 from ray.tune.suggest.search import SearchAlgorithm
@@ -89,7 +89,8 @@ class SuggestionAlgorithm(SearchAlgorithm):
                 else:
                     break
             spec = copy.deepcopy(experiment_spec)
-            spec["config"] = merge_dicts(spec["config"], suggested_config)
+            spec["config"] = merge_dicts(spec["config"],
+                                         copy.deepcopy(suggested_config))
             flattened_config = resolve_nested_dict(spec["config"])
             self._counter += 1
             tag = "{0}_{1}".format(
@@ -98,7 +99,7 @@ class SuggestionAlgorithm(SearchAlgorithm):
                 spec,
                 output_path,
                 self._parser,
-                evaluated_params=list(suggested_config),
+                evaluated_params=flatten_dict(suggested_config),
                 experiment_tag=tag,
                 trial_id=trial_id)
 

python/ray/tune/suggest/variant_generator.py

@@ -39,10 +39,15 @@ def generate_variants(unresolved_spec):
 
         "activation": {"grid_search": ["relu", "tanh"]}
         "cpu": {"eval": "spec.config.num_workers"}
+
+    Use `format_vars` to format the returned dict of hyperparameters.
+
+    Yields:
+        (Dict of resolved variables, Spec object)
     """
     for resolved_vars, spec in _generate_variants(unresolved_spec):
         assert not _unresolved_values(spec)
-        yield format_vars(resolved_vars), spec
+        yield resolved_vars, spec
 
 
 def grid_search(values):
@@ -79,6 +84,7 @@ def resolve_nested_dict(nested_dict):
 
 
 def format_vars(resolved_vars):
+    """Formats the resolved variable dict into a single string."""
     out = []
     for path, value in sorted(resolved_vars.items()):
         if path[0] in ["run", "env", "resources_per_trial"]:
@@ -96,6 +102,17 @@ def format_vars(resolved_vars):
     return ",".join(out)
 
 
+def flatten_resolved_vars(resolved_vars):
+    """Formats the resolved variable dict into a mapping of (str -> value)."""
+    flattened_resolved_vars_dict = {}
+    for pieces, value in resolved_vars.items():
+        if pieces[0] == "config":
+            pieces = pieces[1:]
+        pieces = [str(piece) for piece in pieces]
+        flattened_resolved_vars_dict["/".join(pieces)] = value
+    return flattened_resolved_vars_dict
+
+
 def _clean_value(value):
     if isinstance(value, float):
         return "{:.5}".format(value)