diff --git a/python/ray/tune/automlboard/run.py b/python/ray/tune/automlboard/run.py
index 45a393447..b48ca9af9 100644
--- a/python/ray/tune/automlboard/run.py
+++ b/python/ray/tune/automlboard/run.py
@@ -2,6 +2,7 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+import logging
 import os
 import re
 import django
@@ -11,6 +12,7 @@ from django.core.management import execute_from_command_line
 from common.exception import DatabaseError
 
 root_path = os.path.dirname(os.path.abspath(__file__))
+logger = logging.getLogger(__name__)
 
 
 def run_board(args):
@@ -33,7 +35,7 @@ def run_board(args):
     service.run()
 
     # frontend service
-    print("Try to start automlboard on port %s\n" % args.port)
+    logger.info("Try to start automlboard on port %s\n" % args.port)
     command = [
         os.path.join(root_path, 'manage.py'), 'runserver',
         '0.0.0.0:%s' % args.port, '--noreload'
@@ -64,12 +66,12 @@ def init_config(args):
             os.environ["AUTOMLBOARD_DB_HOST"] = match.group(4)
             os.environ["AUTOMLBOARD_DB_PORT"] = match.group(5)
             os.environ["AUTOMLBOARD_DB_NAME"] = match.group(6)
-            print("Using %s as the database backend." % match.group(1))
+            logger.info("Using %s as the database backend." % match.group(1))
         except BaseException as e:
             raise DatabaseError(e)
     else:
-        print("Using sqlite3 as the database backend, "
-              "information will be stored in automlboard.db")
+        logger.info("Using sqlite3 as the database backend, "
+                    "information will be stored in automlboard.db")
 
     os.environ.setdefault("DJANGO_SETTINGS_MODULE",
                           "ray.tune.automlboard.settings")
diff --git a/python/ray/tune/experiment.py b/python/ray/tune/experiment.py
index ed5e742b1..390a66193 100644
--- a/python/ray/tune/experiment.py
+++ b/python/ray/tune/experiment.py
@@ -3,6 +3,7 @@ from __future__ import division
 from __future__ import print_function
 
 import copy
+import logging
 import six
 import types
 
@@ -10,6 +11,8 @@ from ray.tune.result import DEFAULT_RESULTS_DIR
 from ray.tune.error import TuneError
 from ray.tune.registry import register_trainable
 
+logger = logging.getLogger(__name__)
+
 
 class Experiment(object):
     """Tracks experiment specifications.
@@ -158,7 +161,8 @@ class Experiment(object):
             return run_object
         elif isinstance(run_object, types.FunctionType):
             if run_object.__name__ == "<lambda>":
-                print("Not auto-registering lambdas - resolving as variant.")
+                logger.warning(
+                    "Not auto-registering lambdas - resolving as variant.")
                 return run_object
             else:
                 name = run_object.__name__
@@ -202,8 +206,8 @@ def convert_to_experiment_list(experiments):
     if (type(exp_list) is list
             and all(isinstance(exp, Experiment) for exp in exp_list)):
         if len(exp_list) > 1:
-            print("Warning: All experiments will be"
-                  " using the same Search Algorithm.")
+            logger.warning("All experiments will be "
+                           "using the same SearchAlgorithm.")
     else:
         raise TuneError("Invalid argument: {}".format(experiments))
 
diff --git a/python/ray/tune/function_runner.py b/python/ray/tune/function_runner.py
index 477ebcec0..d1704b6aa 100644
--- a/python/ray/tune/function_runner.py
+++ b/python/ray/tune/function_runner.py
@@ -2,14 +2,16 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+import logging
 import time
 import threading
-import traceback
 
 from ray.tune import TuneError
 from ray.tune.trainable import Trainable
 from ray.tune.result import TIMESTEPS_TOTAL
 
+logger = logging.getLogger(__name__)
+
 
 class StatusReporter(object):
     """Object passed into your main() that you can report status through.
@@ -74,7 +76,7 @@ class _RunnerThread(threading.Thread):
             self._entrypoint(*self._entrypoint_args)
         except Exception as e:
             self._status_reporter._error = e
-            print("Runner thread raised: {}".format(traceback.format_exc()))
+            logger.exception("Runner Thread raised error.")
             raise e
         finally:
             self._status_reporter._done = True
diff --git a/python/ray/tune/log_sync.py b/python/ray/tune/log_sync.py
index 6b71b4303..2e18a8658 100644
--- a/python/ray/tune/log_sync.py
+++ b/python/ray/tune/log_sync.py
@@ -3,6 +3,7 @@ from __future__ import division
 from __future__ import print_function
 
 import distutils.spawn
+import logging
 import os
 import subprocess
 import time
@@ -17,6 +18,8 @@ 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
 
+logger = logging.getLogger(__name__)
+
 # Map from (logdir, remote_dir) -> syncer
 _syncers = {}
 
@@ -73,7 +76,8 @@ class _LogSyncer(object):
         self.sync_process = None
         self.local_ip = ray.services.get_node_ip_address()
         self.worker_ip = None
-        print("Created LogSyncer for {} -> {}".format(local_dir, remote_dir))
+        logger.info("Created LogSyncer for {} -> {}".format(
+            local_dir, remote_dir))
 
     def set_worker_ip(self, worker_ip):
         """Set the worker ip to sync logs from."""
@@ -87,7 +91,7 @@ class _LogSyncer(object):
     def sync_now(self, force=False):
         self.last_sync_time = time.time()
         if not self.worker_ip:
-            print("Worker ip unknown, skipping log sync for {}".format(
+            logger.info("Worker ip unknown, skipping log sync for {}".format(
                 self.local_dir))
             return
 
@@ -97,11 +101,11 @@ class _LogSyncer(object):
             ssh_key = get_ssh_key()
             ssh_user = get_ssh_user()
             if ssh_key is None or ssh_user is None:
-                print("Error: log sync requires cluster to be setup with "
-                      "`ray create_or_update`.")
+                logger.error("Log sync requires cluster to be setup with "
+                             "`ray create_or_update`.")
                 return
             if not distutils.spawn.find_executable("rsync"):
-                print("Error: log sync requires rsync to be installed.")
+                logger.error("Log sync requires rsync to be installed.")
                 return
             worker_to_local_sync_cmd = ((
                 """rsync -avz -e "ssh -i {} -o ConnectTimeout=120s """
@@ -125,7 +129,7 @@ class _LogSyncer(object):
                 if force:
                     self.sync_process.kill()
                 else:
-                    print("Warning: last sync is still in progress, skipping")
+                    logger.warning("Last sync is still in progress, skipping.")
                     return
 
         if worker_to_local_sync_cmd or local_to_remote_sync_cmd:
@@ -136,7 +140,7 @@ class _LogSyncer(object):
                 if final_cmd:
                     final_cmd += " && "
                 final_cmd += local_to_remote_sync_cmd
-            print("Running log sync: {}".format(final_cmd))
+            logger.info("Running log sync: {}".format(final_cmd))
             self.sync_process = subprocess.Popen(final_cmd, shell=True)
 
     def wait(self):
diff --git a/python/ray/tune/logger.py b/python/ray/tune/logger.py
index f9b76240c..f73aa4a1e 100644
--- a/python/ray/tune/logger.py
+++ b/python/ray/tune/logger.py
@@ -4,6 +4,7 @@ from __future__ import print_function
 
 import csv
 import json
+import logging
 import numpy as np
 import os
 import yaml
@@ -12,11 +13,14 @@ from ray.tune.log_sync import get_syncer
 from ray.tune.result import NODE_IP, TRAINING_ITERATION, TIME_TOTAL_S, \
     TIMESTEPS_TOTAL
 
+logger = logging.getLogger(__name__)
+
 try:
     import tensorflow as tf
 except ImportError:
     tf = None
-    print("Couldn't import TensorFlow - this disables TensorBoard logging.")
+    logger.warning("Couldn't import TensorFlow - "
+                   "disabling TensorBoard logging.")
 
 
 class Logger(object):
@@ -60,7 +64,8 @@ class UnifiedLogger(Logger):
         self._loggers = []
         for cls in [_JsonLogger, _TFLogger, _VisKitLogger]:
             if cls is _TFLogger and tf is None:
-                print("TF not installed - cannot log with {}...".format(cls))
+                logger.info("TF not installed - "
+                            "cannot log with {}...".format(cls))
                 continue
             self._loggers.append(cls(self.config, self.logdir, self.uri))
         self._log_syncer = get_syncer(self.logdir, self.uri)
diff --git a/python/ray/tune/ray_trial_executor.py b/python/ray/tune/ray_trial_executor.py
index 196240ae2..86f09cda3 100644
--- a/python/ray/tune/ray_trial_executor.py
+++ b/python/ray/tune/ray_trial_executor.py
@@ -3,6 +3,7 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+import logging
 import os
 import time
 import traceback
@@ -12,6 +13,8 @@ from ray.tune.logger import NoopLogger
 from ray.tune.trial import Trial, Resources, Checkpoint
 from ray.tune.trial_executor import TrialExecutor
 
+logger = logging.getLogger(__name__)
+
 
 class RayTrialExecutor(TrialExecutor):
     """An implemention of TrialExecutor based on Ray."""
@@ -97,7 +100,7 @@ class RayTrialExecutor(TrialExecutor):
                 _, unfinished = ray.wait(
                     stop_tasks, num_returns=2, timeout=250)
         except Exception:
-            print("Error stopping runner:", traceback.format_exc())
+            logger.exception("Error stopping runner.")
             trial.status = Trial.ERROR
         finally:
             trial.runner = None
@@ -112,15 +115,15 @@ class RayTrialExecutor(TrialExecutor):
         try:
             self._start_trial(trial, checkpoint_obj)
         except Exception:
+            logger.exception("Error stopping runner - retrying...")
             error_msg = traceback.format_exc()
-            print("Error starting runner, retrying:", error_msg)
             time.sleep(2)
             self._stop_trial(trial, error=True, error_msg=error_msg)
             try:
                 self._start_trial(trial)
             except Exception:
+                logger.exception("Error starting runner, aborting!")
                 error_msg = traceback.format_exc()
-                print("Error starting runner, abort:", error_msg)
                 self._stop_trial(trial, error=True, error_msg=error_msg)
                 # note that we don't return the resources, since they may
                 # have been lost
@@ -245,12 +248,13 @@ class RayTrialExecutor(TrialExecutor):
             can_overcommit = False  # requested resource is already saturated
 
         if can_overcommit:
-            print("WARNING:tune:allowing trial to start even though the "
-                  "cluster does not have enough free resources. Trial actors "
-                  "may appear to hang until enough resources are added to the "
-                  "cluster (e.g., via autoscaling). You can disable this "
-                  "behavior by specifying `queue_trials=False` in "
-                  "ray.tune.run_experiments().")
+            logger.warning(
+                "Allowing trial to start even though the "
+                "cluster does not have enough free resources. Trial actors "
+                "may appear to hang until enough resources are added to the "
+                "cluster (e.g., via autoscaling). You can disable this "
+                "behavior by specifying `queue_trials=False` in "
+                "ray.tune.run_experiments().")
             return True
 
         return False
@@ -286,7 +290,7 @@ class RayTrialExecutor(TrialExecutor):
         if checkpoint is None or checkpoint.value is None:
             return True
         if trial.runner is None:
-            print("Unable to restore - no runner")
+            logger.error("Unable to restore - no runner.")
             trial.status = Trial.ERROR
             return False
         try:
@@ -298,6 +302,6 @@ class RayTrialExecutor(TrialExecutor):
                 ray.get(trial.runner.restore.remote(value))
             return True
         except Exception:
-            print("Error restoring runner:", traceback.format_exc())
+            logger.exception("Error restoring runner.")
             trial.status = Trial.ERROR
             return False
diff --git a/python/ray/tune/schedulers/async_hyperband.py b/python/ray/tune/schedulers/async_hyperband.py
index fc19273ad..61e7b8153 100644
--- a/python/ray/tune/schedulers/async_hyperband.py
+++ b/python/ray/tune/schedulers/async_hyperband.py
@@ -2,10 +2,13 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+import logging
 import numpy as np
 
 from ray.tune.schedulers.trial_scheduler import FIFOScheduler, TrialScheduler
 
+logger = logging.getLogger(__name__)
+
 
 class AsyncHyperBandScheduler(FIFOScheduler):
     """Implements the Async Successive Halving.
@@ -133,8 +136,8 @@ class _Bracket():
                 if cutoff is not None and cur_rew < cutoff:
                     action = TrialScheduler.STOP
                 if cur_rew is None:
-                    print("Reward attribute is None! Consider"
-                          " reporting using a different field.")
+                    logger.warning("Reward attribute is None! Consider"
+                                   " reporting using a different field.")
                 else:
                     recorded[trial.trial_id] = cur_rew
                 break
diff --git a/python/ray/tune/schedulers/hyperband.py b/python/ray/tune/schedulers/hyperband.py
index 9d0ffd777..8d451e70d 100644
--- a/python/ray/tune/schedulers/hyperband.py
+++ b/python/ray/tune/schedulers/hyperband.py
@@ -4,10 +4,13 @@ from __future__ import print_function
 
 import collections
 import numpy as np
+import logging
 
 from ray.tune.schedulers.trial_scheduler import FIFOScheduler, TrialScheduler
 from ray.tune.trial import Trial
 
+logger = logging.getLogger(__name__)
+
 
 # Implementation notes:
 #    This implementation contains 3 logical levels.
@@ -112,7 +115,7 @@ class HyperBandScheduler(FIFOScheduler):
                 s = len(cur_band)
                 assert s < self._s_max_1, "Current band is filled!"
                 if self._get_r0(s) == 0:
-                    print("Bracket too small - Retrying...")
+                    logger.info("Bracket too small - Retrying...")
                     cur_bracket = None
                 else:
                     retry = False
diff --git a/python/ray/tune/schedulers/median_stopping_rule.py b/python/ray/tune/schedulers/median_stopping_rule.py
index 378b42e48..33ff0d513 100644
--- a/python/ray/tune/schedulers/median_stopping_rule.py
+++ b/python/ray/tune/schedulers/median_stopping_rule.py
@@ -3,11 +3,14 @@ from __future__ import division
 from __future__ import print_function
 
 import collections
+import logging
 import numpy as np
 
 from ray.tune.trial import Trial
 from ray.tune.schedulers.trial_scheduler import FIFOScheduler, TrialScheduler
 
+logger = logging.getLogger(__name__)
+
 
 class MedianStoppingRule(FIFOScheduler):
     """Implements the median stopping rule as described in the Vizier paper:
@@ -67,11 +70,12 @@ class MedianStoppingRule(FIFOScheduler):
         median_result = self._get_median_result(time)
         best_result = self._best_result(trial)
         if self._verbose:
-            print("Trial {} best res={} vs median res={} at t={}".format(
+            logger.info("Trial {} best res={} vs median res={} at t={}".format(
                 trial, best_result, median_result, time))
         if best_result < median_result and time > self._grace_period:
             if self._verbose:
-                print("MedianStoppingRule: early stopping {}".format(trial))
+                logger.info("MedianStoppingRule: "
+                            "early stopping {}".format(trial))
             self._stopped_trials.add(trial)
             if self._hard_stop:
                 return TrialScheduler.STOP
diff --git a/python/ray/tune/schedulers/pbt.py b/python/ray/tune/schedulers/pbt.py
index 4fcce0885..64dad97ca 100644
--- a/python/ray/tune/schedulers/pbt.py
+++ b/python/ray/tune/schedulers/pbt.py
@@ -5,12 +5,15 @@ from __future__ import print_function
 import random
 import math
 import copy
+import logging
 
 from ray.tune.error import TuneError
 from ray.tune.trial import Trial, Checkpoint
 from ray.tune.schedulers import FIFOScheduler, TrialScheduler
 from ray.tune.suggest.variant_generator import format_vars
 
+logger = logging.getLogger(__name__)
+
 # Parameters are transferred from the top PBT_QUANTILE fraction of trials to
 # the bottom PBT_QUANTILE fraction.
 PBT_QUANTILE = 0.25
@@ -69,7 +72,7 @@ def explore(config, mutations, resample_probability, custom_explore_fn):
         new_config = custom_explore_fn(new_config)
         assert new_config is not None, \
             "Custom explore fn failed to return new config"
-    print("[explore] perturbed config from {} -> {}".format(
+    logger.info("[explore] perturbed config from {} -> {}".format(
         config, new_config))
     return new_config
 
@@ -210,15 +213,16 @@ class PopulationBasedTraining(FIFOScheduler):
         trial_state = self._trial_state[trial]
         new_state = self._trial_state[trial_to_clone]
         if not new_state.last_checkpoint:
-            print("[pbt] warn: no checkpoint for trial, skip exploit", trial)
+            logger.warning("[pbt]: no checkpoint for trial"
+                           "skip exploit for Trial {}".format(trial))
             return
         new_config = explore(trial_to_clone.config, self._hyperparam_mutations,
                              self._resample_probability,
                              self._custom_explore_fn)
-        print("[exploit] transferring weights from trial "
-              "{} (score {}) -> {} (score {})".format(
-                  trial_to_clone, new_state.last_score, trial,
-                  trial_state.last_score))
+        logger.warning("[exploit] transferring weights from trial "
+                       "{} (score {}) -> {} (score {})".format(
+                           trial_to_clone, new_state.last_score, trial,
+                           trial_state.last_score))
         # TODO(ekl) restarting the trial is expensive. We should implement a
         # lighter way reset() method that can alter the trial config.
         new_tag = make_experiment_tag(trial_state.orig_tag, new_config,
diff --git a/python/ray/tune/trainable.py b/python/ray/tune/trainable.py
index 815888295..63ffec603 100644
--- a/python/ray/tune/trainable.py
+++ b/python/ray/tune/trainable.py
@@ -6,6 +6,7 @@ from datetime import datetime
 
 import gzip
 import io
+import logging
 import os
 import pickle
 import shutil
@@ -19,6 +20,8 @@ from ray.tune.result import (DEFAULT_RESULTS_DIR, TIME_THIS_ITER_S,
                              TIMESTEPS_THIS_ITER, DONE, TIMESTEPS_TOTAL)
 from ray.tune.trial import Resources
 
+logger = logging.getLogger(__name__)
+
 
 class Trainable(object):
     """Abstract class for trainable models, functions, etc.
@@ -231,7 +234,8 @@ class Trainable(object):
                 "data": data,
             })
             if len(compressed) > 10e6:  # getting pretty large
-                print("Checkpoint size is {} bytes".format(len(compressed)))
+                logger.info("Checkpoint size is {} bytes".format(
+                    len(compressed)))
             f.write(compressed)
 
         shutil.rmtree(tmpdir)
diff --git a/python/ray/tune/trial.py b/python/ray/tune/trial.py
index 0008f2a2d..98fcbc6d5 100644
--- a/python/ray/tune/trial.py
+++ b/python/ray/tune/trial.py
@@ -2,13 +2,14 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-import tempfile
 from collections import namedtuple
 from datetime import datetime
+import logging
 import time
-import ray
+import tempfile
 import os
 
+import ray
 from ray.tune import TuneError
 from ray.tune.logger import pretty_print, UnifiedLogger
 # NOTE(rkn): We import ray.tune.registry here instead of importing the names we
@@ -21,6 +22,7 @@ from ray.utils import random_string, binary_to_hex
 
 DEBUG_PRINT_INTERVAL = 5
 MAX_LEN_IDENTIFIER = 130
+logger = logging.getLogger(__name__)
 
 
 def date_str():
@@ -274,8 +276,9 @@ class Trial(object):
             result.update(done=True)
         if self.verbose and (terminate or time.time() - self.last_debug >
                              DEBUG_PRINT_INTERVAL):
-            print("Result for {}:".format(self))
-            print("  {}".format(pretty_print(result).replace("\n", "\n  ")))
+            logger.info("Result for {}:".format(self))
+            logger.info("  {}".format(
+                pretty_print(result).replace("\n", "\n  ")))
             self.last_debug = time.time()
         self.last_result = result
         self.result_logger.on_result(self.last_result)
diff --git a/python/ray/tune/trial_executor.py b/python/ray/tune/trial_executor.py
index 89a56d0f7..b961d12b8 100644
--- a/python/ray/tune/trial_executor.py
+++ b/python/ray/tune/trial_executor.py
@@ -3,10 +3,13 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+import logging
 import traceback
 
 from ray.tune.trial import Trial, Checkpoint
 
+logger = logging.getLogger(__name__)
+
 
 class TrialExecutor(object):
     """Manages platform-specific details such as resource handling
@@ -66,14 +69,15 @@ class TrialExecutor(object):
             error_msg (str): Optional error message.
         """
         try:
-            print("Attempting to recover trial state from last checkpoint")
+            logger.info(
+                "Attempting to recover trial state from last checkpoint")
             self.stop_trial(
                 trial, error=True, error_msg=error_msg, stop_logger=False)
             trial.result_logger.flush()
             self.start_trial(trial)
         except Exception:
             error_msg = traceback.format_exc()
-            print("Error recovering trial from checkpoint, abort:", error_msg)
+            logger.exception("Error recovering trial from checkpoint, abort.")
             self.stop_trial(trial, error=True, error_msg=error_msg)
 
     def continue_training(self, trial):
@@ -92,7 +96,7 @@ class TrialExecutor(object):
             self.stop_trial(trial, stop_logger=False)
             trial.status = Trial.PAUSED
         except Exception:
-            print("Error pausing runner:", traceback.format_exc())
+            logger.exception("Error pausing runner.")
             trial.status = Trial.ERROR
 
     def unpause_trial(self, trial):
diff --git a/python/ray/tune/trial_runner.py b/python/ray/tune/trial_runner.py
index 6fd2eaef7..6423d6a95 100644
--- a/python/ray/tune/trial_runner.py
+++ b/python/ray/tune/trial_runner.py
@@ -3,6 +3,7 @@ from __future__ import division
 from __future__ import print_function
 
 import collections
+import logging
 import os
 import re
 import time
@@ -17,6 +18,8 @@ from ray.tune.web_server import TuneServer
 
 MAX_DEBUG_TRIALS = 20
 
+logger = logging.getLogger(__name__)
+
 
 def _naturalize(string):
     """Provides a natural representation for string for nice sorting."""
@@ -92,7 +95,7 @@ class TrialRunner(object):
         """Returns whether all trials have finished running."""
 
         if self._total_time > self._global_time_limit:
-            print("Exceeded global time limit {} / {}".format(
+            logger.warning("Exceeded global time limit {} / {}".format(
                 self._total_time, self._global_time_limit))
             return True
 
@@ -270,8 +273,8 @@ class TrialRunner(object):
                 assert False, "Invalid scheduling decision: {}".format(
                     decision)
         except Exception:
+            logger.exception("Error processing event.")
             error_msg = traceback.format_exc()
-            print("Error processing event:", error_msg)
             if trial.status == Trial.RUNNING:
                 if trial.has_checkpoint() and \
                         trial.num_failures < trial.max_failures:
@@ -284,11 +287,12 @@ class TrialRunner(object):
 
     def _try_recover(self, trial, error_msg):
         try:
-            print("Attempting to recover trial state from last checkpoint")
+            logger.info("Attempting to recover"
+                        " trial state from last checkpoint.")
             self.trial_executor.restart_trial(trial, error_msg)
         except Exception:
             error_msg = traceback.format_exc()
-            print("Error recovering trial from checkpoint, abort:", error_msg)
+            logger.warning("Error recovering trial from checkpoint, abort.")
             self.trial_executor.stop_trial(trial, True, error_msg=error_msg)
 
     def _update_trial_queue(self, blocking=False, timeout=600):
@@ -307,7 +311,7 @@ class TrialRunner(object):
             start = time.time()
             while (not trials and not self.is_finished()
                    and time.time() - start < timeout):
-                print("Blocking for next trial...")
+                logger.info("Blocking for next trial...")
                 trials = self._search_alg.next_trials()
                 time.sleep(1)
 
@@ -349,7 +353,7 @@ class TrialRunner(object):
                     trial.trial_id, result=result)
             except Exception:
                 error_msg = traceback.format_exc()
-                print("Error processing event:", error_msg)
+                logger.exception("Error processing event.")
                 self._scheduler_alg.on_trial_error(self, trial)
                 self._search_alg.on_trial_complete(trial.trial_id, error=True)
                 error = True
diff --git a/python/ray/tune/tune.py b/python/ray/tune/tune.py
index f783f98a2..e84304624 100644
--- a/python/ray/tune/tune.py
+++ b/python/ray/tune/tune.py
@@ -2,6 +2,7 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+import logging
 import time
 
 from ray.tune.error import TuneError
@@ -13,6 +14,8 @@ from ray.tune.schedulers import (HyperBandScheduler, AsyncHyperBandScheduler,
                                  FIFOScheduler, MedianStoppingRule)
 from ray.tune.web_server import TuneServer
 
+logger = logging.getLogger(__name__)
+
 _SCHEDULERS = {
     "FIFO": FIFOScheduler,
     "MedianStopping": MedianStoppingRule,
@@ -95,16 +98,16 @@ def run_experiments(experiments=None,
         queue_trials=queue_trials,
         trial_executor=trial_executor)
 
-    print(runner.debug_string(max_debug=99999))
+    logger.info(runner.debug_string(max_debug=99999))
 
     last_debug = 0
     while not runner.is_finished():
         runner.step()
         if time.time() - last_debug > DEBUG_PRINT_INTERVAL:
-            print(runner.debug_string())
+            logger.info(runner.debug_string())
             last_debug = time.time()
 
-    print(runner.debug_string(max_debug=99999))
+    logger.info(runner.debug_string(max_debug=99999))
 
     errored_trials = []
     for trial in runner.get_trials():
diff --git a/python/ray/tune/visual_utils.py b/python/ray/tune/visual_utils.py
index 36d7d7476..e872526ff 100644
--- a/python/ray/tune/visual_utils.py
+++ b/python/ray/tune/visual_utils.py
@@ -4,12 +4,14 @@ from __future__ import print_function
 
 import pandas as pd
 from pandas.api.types import is_string_dtype, is_numeric_dtype
-
+import logging
 import os
 import os.path as osp
 import numpy as np
 import json
 
+logger = logging.getLogger(__name__)
+
 
 def _flatten_dict(dt):
     while any(type(v) is dict for v in dt.values()):
@@ -35,7 +37,7 @@ def _parse_results(res_path):
                 pass
         res_dict = _flatten_dict(json.loads(line.strip()))
     except Exception as e:
-        print("Importing %s failed...Perhaps empty?" % res_path, e)
+        logger.exception("Importing %s failed...Perhaps empty?" % res_path)
     return res_dict
 
 
@@ -44,7 +46,7 @@ def _parse_configs(cfg_path):
         with open(cfg_path) as f:
             cfg_dict = _flatten_dict(json.load(f))
     except Exception as e:
-        print(e)
+        logger.exception("Config parsing failed.")
     return cfg_dict
 
 
diff --git a/python/ray/tune/web_server.py b/python/ray/tune/web_server.py
index 86e903dd4..06f66887e 100644
--- a/python/ray/tune/web_server.py
+++ b/python/ray/tune/web_server.py
@@ -3,6 +3,7 @@ from __future__ import division
 from __future__ import print_function
 
 import json
+import logging
 import sys
 import threading
 
@@ -15,12 +16,14 @@ if sys.version_info[0] == 2:
 elif sys.version_info[0] == 3:
     from http.server import SimpleHTTPRequestHandler, HTTPServer
 
+logger = logging.getLogger(__name__)
+
 try:
     import requests  # `requests` is not part of stdlib.
 except ImportError:
     requests = None
-    print("Couldn't import `requests` library. Be sure to install it on"
-          " the client side.")
+    logger.exception("Couldn't import `requests` library. "
+                     "Be sure to install it on the client side.")
 
 
 class TuneClient(object):
@@ -149,7 +152,7 @@ class TuneServer(threading.Thread):
         threading.Thread.__init__(self)
         self._port = port if port else self.DEFAULT_PORT
         address = ('localhost', self._port)
-        print("Starting Tune Server...")
+        logger.info("Starting Tune Server...")
         self._server = HTTPServer(address, RunnerHandler(runner))
         self.start()