[tune] Reduce sampling API clutter (#4739)

Adds some sugar for tune sampling API (for commonplace sampling idioms).

python/ray/tune/__init__.py

@@ -7,10 +7,12 @@ from ray.tune.tune import run_experiments, run
 from ray.tune.experiment import Experiment
 from ray.tune.registry import register_env, register_trainable
 from ray.tune.trainable import Trainable
-from ray.tune.suggest import grid_search, function, sample_from
+from ray.tune.suggest import grid_search
+from ray.tune.sample import (function, sample_from, uniform, choice, randint,
+                             randn)
 
 __all__ = [
     "Trainable", "TuneError", "grid_search", "register_env",
     "register_trainable", "run", "run_experiments", "Experiment", "function",
-    "sample_from"
+    "sample_from", "uniform", "choice", "randint", "randn"
 ]

python/ray/tune/examples/mnist_pytorch.py

@@ -154,7 +154,6 @@ if __name__ == "__main__":
     datasets.MNIST("~/data", train=True, download=True)
     args = parser.parse_args()
 
-    import numpy as np
     import ray
     from ray import tune
     from ray.tune.schedulers import AsyncHyperBandScheduler
@@ -183,9 +182,7 @@ if __name__ == "__main__":
             },
             "num_samples": 1 if args.smoke_test else 10,
             "config": {
-                "lr": tune.sample_from(
-                    lambda spec: np.random.uniform(0.001, 0.1)),
-                "momentum": tune.sample_from(
-                    lambda spec: np.random.uniform(0.1, 0.9)),
+                "lr": tune.uniform(0.001, 0.1),
+                "momentum": tune.uniform(0.1, 0.9),
             }
         })

python/ray/tune/examples/mnist_pytorch_trainable.py

@@ -169,7 +169,6 @@ if __name__ == "__main__":
     datasets.MNIST("~/data", train=True, download=True)
     args = parser.parse_args()
 
-    import numpy as np
     import ray
     from ray import tune
     from ray.tune.schedulers import HyperBandScheduler
@@ -193,9 +192,7 @@ if __name__ == "__main__":
             "checkpoint_at_end": True,
             "config": {
                 "args": args,
-                "lr": tune.sample_from(
-                    lambda spec: np.random.uniform(0.001, 0.1)),
-                "momentum": tune.sample_from(
-                    lambda spec: np.random.uniform(0.1, 0.9)),
+                "lr": tune.uniform(0.001, 0.1),
+                "momentum": tune.uniform(0.1, 0.9),
             }
         })

python/ray/tune/log_sync.py

@@ -19,7 +19,7 @@ import ray
 from ray.tune.cluster_info import get_ssh_key, get_ssh_user
 from ray.tune.error import TuneError
 from ray.tune.result import DEFAULT_RESULTS_DIR
-from ray.tune.suggest.variant_generator import function as tune_function
+from ray.tune.sample import function as tune_function
 
 logger = logging.getLogger(__name__)
 _log_sync_warned = False

python/ray/tune/sample.py

@@ -0,0 +1,71 @@
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import logging
+import numpy as np
+
+logger = logging.getLogger(__name__)
+
+
+class sample_from(object):
+    """Specify that tune should sample configuration values from this function.
+
+    The use of function arguments in tune configs must be disambiguated by
+    either wrapped the function in tune.sample_from() or tune.function().
+
+    Arguments:
+        func: An callable function to draw a sample from.
+    """
+
+    def __init__(self, func):
+        self.func = func
+
+    def __str__(self):
+        return "tune.sample_from({})".format(str(self.func))
+
+    def __repr__(self):
+        return "tune.sample_from({})".format(repr(self.func))
+
+
+class function(object):
+    """Wraps `func` to make sure it is not expanded during resolution.
+
+    The use of function arguments in tune configs must be disambiguated by
+    either wrapped the function in tune.sample_from() or tune.function().
+
+    Arguments:
+        func: A function literal.
+    """
+
+    def __init__(self, func):
+        self.func = func
+
+    def __call__(self, *args, **kwargs):
+        return self.func(*args, **kwargs)
+
+    def __str__(self):
+        return "tune.function({})".format(str(self.func))
+
+    def __repr__(self):
+        return "tune.function({})".format(repr(self.func))
+
+
+def uniform(*args, **kwargs):
+    """A wrapper around np.random.uniform."""
+    return sample_from(lambda _: np.random.uniform(*args, **kwargs))
+
+
+def choice(*args, **kwargs):
+    """A wrapper around np.random.choice."""
+    return sample_from(lambda _: np.random.choice(*args, **kwargs))
+
+
+def randint(*args, **kwargs):
+    """A wrapper around np.random.randint."""
+    return sample_from(lambda _: np.random.randint(*args, **kwargs))
+
+
+def randn(*args, **kwargs):
+    """A wrapper around np.random.randn."""
+    return sample_from(lambda _: np.random.randn(*args, **kwargs))

python/ray/tune/suggest/__init__.py

@@ -1,16 +1,11 @@
 from ray.tune.suggest.search import SearchAlgorithm
 from ray.tune.suggest.basic_variant import BasicVariantGenerator
 from ray.tune.suggest.suggestion import SuggestionAlgorithm
-from ray.tune.suggest.variant_generator import grid_search, function, \
-    sample_from
+from ray.tune.suggest.variant_generator import grid_search
 
 __all__ = [
-    "SearchAlgorithm",
-    "BasicVariantGenerator",
-    "SuggestionAlgorithm",
-    "grid_search",
-    "function",
-    "sample_from",
+    "SearchAlgorithm", "BasicVariantGenerator", "SuggestionAlgorithm",
+    "grid_search"
 ]
 
 

python/ray/tune/suggest/variant_generator.py

@@ -9,6 +9,7 @@ import random
 import types
 
 from ray.tune import TuneError
+from ray.tune.sample import sample_from
 
 logger = logging.getLogger(__name__)
 
@@ -54,49 +55,6 @@ def grid_search(values):
     return {"grid_search": values}
 
 
-class sample_from(object):
-    """Specify that tune should sample configuration values from this function.
-
-    The use of function arguments in tune configs must be disambiguated by
-    either wrapped the function in tune.sample_from() or tune.function().
-
-    Arguments:
-        func: An callable function to draw a sample from.
-    """
-
-    def __init__(self, func):
-        self.func = func
-
-    def __str__(self):
-        return "tune.sample_from({})".format(str(self.func))
-
-    def __repr__(self):
-        return "tune.sample_from({})".format(repr(self.func))
-
-
-class function(object):
-    """Wraps `func` to make sure it is not expanded during resolution.
-
-    The use of function arguments in tune configs must be disambiguated by
-    either wrapped the function in tune.sample_from() or tune.function().
-
-    Arguments:
-        func: A function literal.
-    """
-
-    def __init__(self, func):
-        self.func = func
-
-    def __call__(self, *args, **kwargs):
-        return self.func(*args, **kwargs)
-
-    def __str__(self):
-        return "tune.function({})".format(str(self.func))
-
-    def __repr__(self):
-        return "tune.function({})".format(repr(self.func))
-
-
 _STANDARD_IMPORTS = {
     "random": random,
     "np": numpy,

python/ray/tune/trial_runner.py

@@ -16,7 +16,7 @@ from ray.tune import TuneError
 from ray.tune.ray_trial_executor import RayTrialExecutor
 from ray.tune.result import TIME_THIS_ITER_S, RESULT_DUPLICATE
 from ray.tune.trial import Trial, Checkpoint
-from ray.tune.suggest import function
+from ray.tune.sample import function
 from ray.tune.schedulers import FIFOScheduler, TrialScheduler
 from ray.tune.util import warn_if_slow
 from ray.utils import binary_to_hex, hex_to_binary