[tune] Move util to utils package (#6682)

* Move util.py to utils

* Fix import

python/ray/tune/examples/pbt_convnet_example.py

@@ -17,7 +17,7 @@ from ray.tune.examples.mnist_pytorch import train, test, ConvNet,\
 import ray
 from ray import tune
 from ray.tune.schedulers import PopulationBasedTraining
-from ray.tune.util import validate_save_restore
+from ray.tune.utils import validate_save_restore
 from ray.tune.trial import ExportFormat
 
 # __tutorial_imports_end__

python/ray/tune/logger.py

@@ -13,12 +13,12 @@ import numbers
 import numpy as np
 
 import ray.cloudpickle as cloudpickle
-from ray.tune.util import flatten_dict
-from ray.tune.syncer import get_node_syncer
 from ray.tune.result import (NODE_IP, TRAINING_ITERATION, TIME_TOTAL_S,
                              TIMESTEPS_TOTAL, EXPR_PARAM_FILE,
                              EXPR_PARAM_PICKLE_FILE, EXPR_PROGRESS_FILE,
                              EXPR_RESULT_FILE)
+from ray.tune.syncer import get_node_syncer
+from ray.tune.utils import flatten_dict
 
 logger = logging.getLogger(__name__)
 

python/ray/tune/progress_reporter.py

@@ -5,7 +5,7 @@ import collections
 from ray.tune.result import (DEFAULT_RESULT_KEYS, CONFIG_PREFIX,
                              EPISODE_REWARD_MEAN, MEAN_ACCURACY, MEAN_LOSS,
                              TRAINING_ITERATION, TIME_TOTAL_S, TIMESTEPS_TOTAL)
-from ray.tune.util import flatten_dict
+from ray.tune.utils import flatten_dict
 
 try:
     from tabulate import tabulate

python/ray/tune/ray_trial_executor.py

@@ -14,14 +14,13 @@ from ray.exceptions import RayTimeoutError
 from ray import ray_constants
 from ray.resource_spec import ResourceSpec
 from ray.tune.durable_trainable import DurableTrainable
-from ray.tune.error import AbortTrialExecution
+from ray.tune.error import AbortTrialExecution, TuneError
 from ray.tune.logger import NoopLogger
 from ray.tune.resources import Resources
 from ray.tune.trainable import TrainableUtil
 from ray.tune.trial import Trial, Checkpoint, Location
 from ray.tune.trial_executor import TrialExecutor
-from ray.tune.util import warn_if_slow
-from ray.tune.error import TuneError
+from ray.tune.utils import warn_if_slow
 
 logger = logging.getLogger(__name__)
 

python/ray/tune/suggest/suggestion.py

@@ -6,12 +6,12 @@ import itertools
 import copy
 
 from ray.tune.error import TuneError
-from ray.tune.trial import Trial
-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
 from ray.tune.suggest.variant_generator import format_vars, resolve_nested_dict
+from ray.tune.trial import Trial
+from ray.tune.utils import merge_dicts, flatten_dict
 
 
 class SuggestionAlgorithm(SearchAlgorithm):

python/ray/tune/tests/test_api.py

@@ -23,11 +23,12 @@ from ray.tune.result import (TIMESTEPS_TOTAL, DONE, HOSTNAME, NODE_IP, PID,
                              TIMESTEPS_THIS_ITER, TIME_THIS_ITER_S,
                              TIME_TOTAL_S, TRIAL_ID, EXPERIMENT_TAG)
 from ray.tune.logger import Logger
-from ray.tune.util import pin_in_object_store, get_pinned_object, flatten_dict
 from ray.tune.experiment import Experiment
 from ray.tune.resources import Resources
 from ray.tune.suggest import grid_search
 from ray.tune.suggest.suggestion import _MockSuggestionAlgorithm
+from ray.tune.utils import (flatten_dict, get_pinned_object,
+                            pin_in_object_store)
 from ray.tune.utils.mock import mock_storage_client, MOCK_REMOTE_DIR
 
 

python/ray/tune/tests/test_tune_restore.py

@@ -3,25 +3,25 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-from hyperopt import hp
 import os
 import shutil
 import tempfile
 import unittest
 import skopt
 import numpy as np
+from hyperopt import hp
+from nevergrad.optimization import optimizerlib
 
 import ray
 from ray import tune
 from ray.test_utils import recursive_fnmatch
-from ray.tune.util import validate_save_restore
 from ray.rllib import _register_all
 from ray.tune.suggest.hyperopt import HyperOptSearch
 from ray.tune.suggest.bayesopt import BayesOptSearch
 from ray.tune.suggest.skopt import SkOptSearch
 from ray.tune.suggest.nevergrad import NevergradSearch
-from nevergrad.optimization import optimizerlib
 from ray.tune.suggest.sigopt import SigOptSearch
+from ray.tune.utils import validate_save_restore
 
 
 class TuneRestoreTest(unittest.TestCase):

python/ray/tune/trainable.py

@@ -21,8 +21,7 @@ from ray.tune.result import (DEFAULT_RESULTS_DIR, TIME_THIS_ITER_S,
                              TIMESTEPS_THIS_ITER, DONE, TIMESTEPS_TOTAL,
                              EPISODES_THIS_ITER, EPISODES_TOTAL,
                              TRAINING_ITERATION, RESULT_DUPLICATE)
-
-from ray.tune.util import UtilMonitor
+from ray.tune.utils import UtilMonitor
 
 logger = logging.getLogger(__name__)
 
@@ -524,7 +523,7 @@ class Trainable:
         Use ``validate_save_restore`` to catch ``_save``/``_restore`` errors
         before execution.
 
-        >>> from ray.tune.util import validate_save_restore
+        >>> from ray.tune.utils import validate_save_restore
         >>> validate_save_restore(MyTrainableClass)
         >>> validate_save_restore(MyTrainableClass, use_object_store=True)
 

python/ray/tune/trial.py

@@ -16,14 +16,14 @@ from ray.tune import TuneError
 from ray.tune.checkpoint_manager import Checkpoint, CheckpointManager
 from ray.tune.durable_trainable import DurableTrainable
 from ray.tune.logger import pretty_print, UnifiedLogger
-from ray.tune.util import flatten_dict
 # NOTE(rkn): We import ray.tune.registry here instead of importing the names we
 # need because there are cyclic imports that may cause specific names to not
 # have been defined yet. See https://github.com/ray-project/ray/issues/1716.
 from ray.tune.registry import get_trainable_cls, validate_trainable
 from ray.tune.result import DEFAULT_RESULTS_DIR, DONE, TRAINING_ITERATION
-from ray.utils import binary_to_hex, hex_to_binary
 from ray.tune.resources import Resources, json_to_resources, resources_to_json
+from ray.tune.utils import flatten_dict
+from ray.utils import binary_to_hex, hex_to_binary
 
 DEBUG_PRINT_INTERVAL = 5
 MAX_LEN_IDENTIFIER = int(os.environ.get("MAX_LEN_IDENTIFIER", 130))

python/ray/tune/trial_runner.py

@@ -21,9 +21,9 @@ from ray.tune.syncer import get_cloud_syncer
 from ray.tune.trial import Checkpoint, Trial
 from ray.tune.schedulers import FIFOScheduler, TrialScheduler
 from ray.tune.suggest import BasicVariantGenerator
-from ray.tune.util import warn_if_slow, flatten_dict
-from ray.utils import binary_to_hex, hex_to_binary
+from ray.tune.utils import warn_if_slow, flatten_dict
 from ray.tune.web_server import TuneServer
+from ray.utils import binary_to_hex, hex_to_binary
 
 MAX_DEBUG_TRIALS = 20
 

python/ray/tune/utils/__init__.py

@@ -0,0 +1,9 @@
+from ray.tune.utils.util import (deep_update, flatten_dict, get_pinned_object,
+                                 merge_dicts, pin_in_object_store, UtilMonitor,
+                                 validate_save_restore, warn_if_slow)
+
+__all__ = [
+    "deep_update", "flatten_dict", "get_pinned_object", "merge_dicts",
+    "pin_in_object_store", "UtilMonitor", "validate_save_restore",
+    "warn_if_slow"
+]

python/ray/tune/utils/visual_utils.py

@@ -10,7 +10,7 @@ import os.path as osp
 import numpy as np
 import json
 
-from ray.tune.util import flatten_dict
+from ray.tune.utils import flatten_dict
 
 logger = logging.getLogger(__name__)