Start working toward Python3 compatibility. (#117)

README.md

@@ -34,7 +34,7 @@ for _ in range(10):
 
 # Fetch the results of the tasks and print their average.
 estimate = np.mean(ray.get(result_ids))
-print "Pi is approximately {}.".format(estimate)
+print("Pi is approximately {}.".format(estimate))
 ```
 
 Within the for loop, each call to `estimate_pi.remote(100)` sends a message to

doc/using-ray-with-tensorflow.md

@@ -190,5 +190,5 @@ for iteration in range(NUM_ITERS):
   # Print the current weights. They should converge to roughly to the values 0.1
   # and 0.3 used in generate_fake_x_y_data.
   if iteration % 20 == 0:
-    print "Iteration {}: weights are {}".format(iteration, weights)
+    print("Iteration {}: weights are {}".format(iteration, weights))
 ```

examples/alexnet/alexnet.py

@@ -1,6 +1,10 @@
 # The code for AlexNet is copied and adapted from the TensorFlow repository
 # https://github.com/tensorflow/tensorflow/blob/master/tensorflow/models/image/alexnet/alexnet_benchmark.py.
 
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
 import ray
 import numpy as np
 import tarfile, io
@@ -390,7 +394,7 @@ def shuffle(batches):
   # Randomly permute the order of the batches.
   permuted_batches = np.random.permutation(batches)
   new_batches = []
-  for i in range(len(batches) / 2):
+  for i in range(len(batches) // 2):
     # Swap data between consecutive batches.
     shuffled_batch1, shuffled_batch2 = shuffle_pair(permuted_batches[2 * i], permuted_batches[2 * i + 1])
     new_batches += [shuffled_batch1, shuffled_batch2]

examples/alexnet/driver.py

@@ -1,3 +1,7 @@
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
 import numpy as np
 import ray
 import os
@@ -28,7 +32,7 @@ if __name__ == "__main__":
   imagenet_bucket = s3_resource.Bucket(args.s3_bucket)
   objects = imagenet_bucket.objects.filter(Prefix=args.key_prefix)
   image_tar_files = [str(obj.key) for obj in objects.all()]
-  print "Images will be downloaded from {} files.".format(len(image_tar_files))
+  print("Images will be downloaded from {} files.".format(len(image_tar_files)))
 
   # Downloading the label file, and create a dictionary mapping the filenames of
   # the images to their labels.
@@ -38,7 +42,7 @@ if __name__ == "__main__":
   filename_label_pairs = [line.split(" ") for line in filename_label_str]
   filename_label_dict = dict([(os.path.basename(name), label) for name, label in filename_label_pairs])
   filename_label_dict_id = ray.put(filename_label_dict)
-  print "Labels extracted."
+  print("Labels extracted.")
 
   # Download the imagenet dataset.
   imagenet_data = alexnet.load_tarfiles_from_s3(args.s3_bucket, image_tar_files, [256, 256])
@@ -60,7 +64,7 @@ if __name__ == "__main__":
   # Initialize the network and optimizer weights. This is only run once on the
   # driver. We initialize the weights manually on the workers.
   sess.run(init_all_variables)
-  print "Initialized network weights."
+  print("Initialized network weights.")
 
   iteration = 0
   while True:
@@ -82,7 +86,7 @@ if __name__ == "__main__":
       gradient_ids.append(alexnet.compute_grad.remote(x_id, y_id, mean_id, weights_id))
 
     # Print the accuracy on a random training batch.
-    print "Iteration {}: accuracy = {:.3}%".format(iteration, 100 * ray.get(accuracy))
+    print("Iteration {}: accuracy = {:.3}%".format(iteration, 100 * ray.get(accuracy)))
 
     # Fetch the gradients. This blocks until the gradients have been computed.
     gradient_sets = ray.get(gradient_ids)

examples/hyperopt/README.md

@@ -148,7 +148,7 @@ while len(remaining_ids) > 0:
   ready_ids, remaining_ids = ray.wait(remaining_ids, num_returns=1)
   # Get the accuracy corresponding to the ready object ID.
   accuracy = ray.get(ready_ids[0])
-  print "Accuracy {}".format(accuracy)
+  print("Accuracy {}".format(accuracy))
 ```
 
 Note that the above example does not associate the accuracy with the parameters

examples/hyperopt/driver.py

@@ -1,5 +1,10 @@
 # Most of the tensorflow code is adapted from Tensorflow's tutorial on using CNNs to train MNIST
 # https://www.tensorflow.org/versions/r0.9/tutorials/mnist/pros/index.html#build-a-multilayer-convolutional-network
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
 import numpy as np
 import ray
 import argparse
@@ -24,7 +29,7 @@ if __name__ == "__main__":
   steps = args.steps
 
   # Load the mnist data and turn the data into remote objects.
-  print "Downloading the MNIST dataset. This may take a minute."
+  print("Downloading the MNIST dataset. This may take a minute.")
   mnist = input_data.read_data_sets("MNIST_data", one_hot=True)
   train_images = ray.put(mnist.train.images)
   train_labels = ray.put(mnist.train.labels)
@@ -65,20 +70,20 @@ if __name__ == "__main__":
     result_id = ready_ids[0]
     params = params_mapping[result_id]
     accuracy = ray.get(result_id)
-    print """We achieve accuracy {:.3}% with
+    print("""We achieve accuracy {:.3}% with
         learning_rate: {:.2}
         batch_size: {}
         dropout: {:.2}
         stddev: {:.2}
-      """.format(100 * accuracy, params["learning_rate"], params["batch_size"], params["dropout"], params["stddev"])
+      """.format(100 * accuracy, params["learning_rate"], params["batch_size"], params["dropout"], params["stddev"]))
     if accuracy > best_accuracy:
       best_params = params
       best_accuracy = accuracy
 
   # Record the best performing set of hyperparameters.
-  print """Best accuracy over {} trials was {:.3} with
+  print("""Best accuracy over {} trials was {:.3} with
         learning_rate: {:.2}
         batch_size: {}
         dropout: {:.2}
         stddev: {:.2}
-    """.format(trials, 100 * best_accuracy, best_params["learning_rate"], best_params["batch_size"], best_params["dropout"], best_params["stddev"])
+    """.format(trials, 100 * best_accuracy, best_params["learning_rate"], best_params["batch_size"], best_params["dropout"], best_params["stddev"]))

examples/hyperopt/hyperopt.py

@@ -1,3 +1,7 @@
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
 import ray
 import numpy as np
 import tensorflow as tf
@@ -6,7 +10,7 @@ def get_batch(data, batch_index, batch_size):
   # This method currently drops data when num_data is not divisible by
   # batch_size.
   num_data = data.shape[0]
-  num_batches = num_data / batch_size
+  num_batches = num_data // batch_size
   batch_index %= num_batches
   return data[(batch_index * batch_size):((batch_index + 1) * batch_size)]
 

examples/lbfgs/README.md

@@ -32,7 +32,7 @@ built in methods for loading the data.
 from tensorflow.examples.tutorials.mnist import input_data
 mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
 batch_size = 100
-num_batches = mnist.train.num_examples / batch_size
+num_batches = mnist.train.num_examples // batch_size
 batches = [mnist.train.next_batch(batch_size) for _ in range(num_batches)]
 ```
 

examples/lbfgs/driver.py

@@ -1,3 +1,7 @@
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
 import ray
 
 import numpy as np
@@ -115,16 +119,16 @@ if __name__ == "__main__":
   # algorithm.
 
   # Load the mnist data and turn the data into remote objects.
-  print "Downloading the MNIST dataset. This may take a minute."
+  print("Downloading the MNIST dataset. This may take a minute.")
   mnist = input_data.read_data_sets("MNIST_data", one_hot=True)
   batch_size = 100
-  num_batches = mnist.train.num_examples / batch_size
+  num_batches = mnist.train.num_examples // batch_size
   batches = [mnist.train.next_batch(batch_size) for _ in range(num_batches)]
-  print "Putting MNIST in the object store."
+  print("Putting MNIST in the object store.")
   batch_ids = [(ray.put(xs), ray.put(ys)) for (xs, ys) in batches]
 
   # Initialize the weights for the network to the vector of all zeros.
   theta_init = 1e-2 * np.random.normal(size=dim)
   # Use L-BFGS to minimize the loss function.
-  print "Running L-BFGS."
+  print("Running L-BFGS.")
   result = scipy.optimize.fmin_l_bfgs_b(full_loss, theta_init, maxiter=10, fprime=full_grad, disp=True)

examples/rl_pong/driver.py

@@ -1,6 +1,10 @@
 # This code is copied and adapted from Andrej Karpathy's code for learning to
 # play Pong https://gist.github.com/karpathy/a4166c7fe253700972fcbc77e4ea32c5.
 
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
 import numpy as np
 import cPickle as pickle
 import ray
@@ -135,7 +139,7 @@ if __name__ == "__main__":
       reward_sum = ray.get(reward_sums[i])
       for k in model: grad_buffer[k] += grad[k] # accumulate grad over batch
       running_reward = reward_sum if running_reward is None else running_reward * 0.99 + reward_sum * 0.01
-      print "Batch {}. episode reward total was {}. running mean: {}".format(batch_num, reward_sum, running_reward)
+      print("Batch {}. episode reward total was {}. running mean: {}".format(batch_num, reward_sum, running_reward))
     for k, v in model.iteritems():
       g = grad_buffer[k] # gradient
       rmsprop_cache[k] = decay_rate * rmsprop_cache[k] + (1 - decay_rate) * g ** 2

lib/python/ray/__init__.py

@@ -1,3 +1,7 @@
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
 # Ray version string
 __version__ = "0.01"
 
@@ -9,8 +13,7 @@ if hasattr(ctypes, "windll"):
   # This is done by associating all child processes with a "job" object that imposes this behavior.
   (lambda kernel32: (lambda job: (lambda n: kernel32.SetInformationJobObject(job, 9, "\0" * 17 + chr(0x8 | 0x4 | 0x20) + "\0" * (n - 18), n))(0x90 if ctypes.sizeof(ctypes.c_void_p) > ctypes.sizeof(ctypes.c_int) else 0x70) and kernel32.AssignProcessToJobObject(job, ctypes.c_void_p(kernel32.GetCurrentProcess())))(ctypes.c_void_p(kernel32.CreateJobObjectW(None, None))) if kernel32 is not None else None)(ctypes.windll.kernel32)
 
-import config
-import serialization
-from worker import register_class, error_info, init, connect, disconnect, get, put, wait, remote
-from worker import Reusable, reusables
-from worker import SCRIPT_MODE, WORKER_MODE, PYTHON_MODE, SILENT_MODE
+import ray.serialization
+from ray.worker import register_class, error_info, init, connect, disconnect, get, put, wait, remote
+from ray.worker import Reusable, reusables
+from ray.worker import SCRIPT_MODE, WORKER_MODE, PYTHON_MODE, SILENT_MODE

lib/python/ray/array/distributed/core.py

@@ -1,3 +1,7 @@
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
 import numpy as np
 import ray.array.remote as ra
 import ray
@@ -141,10 +145,10 @@ def blockwise_dot(*matrices):
   n = len(matrices)
   if n % 2 != 0:
     raise Exception("blockwise_dot expects an even number of arguments, but len(matrices) is {}.".format(n))
-  shape = (matrices[0].shape[0], matrices[n / 2].shape[1])
+  shape = (matrices[0].shape[0], matrices[n // 2].shape[1])
   result = np.zeros(shape)
-  for i in range(n / 2):
-    result += np.dot(matrices[i], matrices[n / 2 + i])
+  for i in range(n // 2):
+    result += np.dot(matrices[i], matrices[n // 2 + i])
   return result
 
 @ray.remote

lib/python/ray/array/distributed/linalg.py

@@ -1,8 +1,12 @@
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
 import numpy as np
 import ray.array.remote as ra
 import ray
 
-from core import *
+from .core import *
 
 __all__ = ["tsqr", "modified_lu", "tsqr_hr", "qr"]
 
@@ -68,7 +72,7 @@ def tsqr(a):
       else:
         lower = [a.shape[1], 0]
         upper = [2 * a.shape[1], BLOCK_SIZE]
-      ith_index /= 2
+      ith_index //= 2
       q_block_current = ra.dot.remote(q_block_current, ra.subarray.remote(q_tree[ith_index, j], lower, upper))
     q_result.objectids[i] = q_block_current
   r = current_rs[0]

lib/python/ray/array/distributed/random.py

@@ -1,8 +1,12 @@
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
 import numpy as np
 import ray.array.remote as ra
 import ray
 
-from core import *
+from .core import *
 
 @ray.remote
 def normal(shape):

lib/python/ray/array/remote/core.py

@@ -1,3 +1,7 @@
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
 import numpy as np
 import ray
 

lib/python/ray/array/remote/linalg.py

@@ -1,3 +1,7 @@
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
 import numpy as np
 import ray
 

lib/python/ray/array/remote/random.py

@@ -1,3 +1,7 @@
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
 import numpy as np
 import ray
 

lib/python/ray/config.py

@@ -1,9 +0,0 @@
-import datetime
-import os.path
-import sys
-import tempfile
-
-def get_log_file_path(name):
-  return os.path.join(
-    os.path.join("/tmp" if sys.platform.startswith("darwin") else tempfile.gettempdir(), "raylogs"),
-    ("{:%Y-%m-%d-%H-%M-%S}-{}").format(datetime.datetime.now(), name.replace(":", "-")))

lib/python/ray/pickling.py

@@ -1,5 +1,9 @@
 # Note that a little bit of code here is taken and slightly modified from the pickler because it was not possible to change its behavior otherwise.
 
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
 import sys
 from ctypes import c_void_p
 from cloudpickle import pickle, cloudpickle, CloudPickler, load, loads

lib/python/ray/serialization.py

@@ -1,9 +1,12 @@
+from __future__ import absolute_import
+from __future__ import division
 from __future__ import print_function
 
 import numpy as np
-import pickling
 import numbuf
 
+import ray.pickling as pickling
+
 def check_serializable(cls):
   """Throws an exception if Ray cannot serialize this class efficiently.
 

lib/python/ray/services.py

@@ -1,3 +1,5 @@
+from __future__ import absolute_import
+from __future__ import division
 from __future__ import print_function
 
 import psutil
@@ -10,7 +12,6 @@ import sys
 import time
 
 # Ray modules
-import config
 import photon
 import plasma
 import global_scheduler

lib/python/ray/test/test_functions.py

@@ -1,3 +1,7 @@
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
 import ray
 
 import numpy as np

lib/python/ray/worker.py

@@ -1,3 +1,5 @@
+from __future__ import absolute_import
+from __future__ import division
 from __future__ import print_function
 
 import hashlib
@@ -16,10 +18,9 @@ import threading
 import string
 
 # Ray modules
-import config
-import pickling
-import serialization
-import services
+import ray.pickling as pickling
+import ray.serialization as serialization
+import ray.services as services
 import numbuf
 import photon
 import plasma

lib/python/ray/workers/default_worker.py

@@ -1,3 +1,5 @@
+from __future__ import absolute_import
+from __future__ import division
 from __future__ import print_function
 
 import sys

lib/python/setup.py

@@ -1,3 +1,5 @@
+from __future__ import absolute_import
+from __future__ import division
 from __future__ import print_function
 
 import os

numbuf/CMakeLists.txt

@@ -12,17 +12,17 @@ message(STATUS "Trying custom approach for finding Python.")
 # Start off by figuring out which Python executable to use.
 find_program(CUSTOM_PYTHON_EXECUTABLE python)
 message(STATUS "Found Python program: ${CUSTOM_PYTHON_EXECUTABLE}")
-execute_process(COMMAND ${CUSTOM_PYTHON_EXECUTABLE} -c "import sys; print 'python' + sys.version[0:3]"
+execute_process(COMMAND ${CUSTOM_PYTHON_EXECUTABLE} -c "import sys; print('python' + sys.version[0:3])"
                 OUTPUT_VARIABLE PYTHON_LIBRARY_NAME OUTPUT_STRIP_TRAILING_WHITESPACE)
 message(STATUS "PYTHON_LIBRARY_NAME: " ${PYTHON_LIBRARY_NAME})
 # Now find the Python include directories.
-execute_process(COMMAND ${CUSTOM_PYTHON_EXECUTABLE} -c "from distutils.sysconfig import *; print get_python_inc()"
+execute_process(COMMAND ${CUSTOM_PYTHON_EXECUTABLE} -c "from distutils.sysconfig import *; print(get_python_inc())"
                 OUTPUT_VARIABLE PYTHON_INCLUDE_DIRS OUTPUT_STRIP_TRAILING_WHITESPACE)
 message(STATUS "PYTHON_INCLUDE_DIRS: " ${PYTHON_INCLUDE_DIRS})
 # Now find the Python libraries. We'll start by looking near the Python
 # executable. If that fails, then we'll look near the Python include
 # directories.
-execute_process(COMMAND ${CUSTOM_PYTHON_EXECUTABLE} -c "import sys; print sys.exec_prefix"
+execute_process(COMMAND ${CUSTOM_PYTHON_EXECUTABLE} -c "import sys; print(sys.exec_prefix)"
                 OUTPUT_VARIABLE PYTHON_PREFIX OUTPUT_STRIP_TRAILING_WHITESPACE)
 message(STATUS "PYTHON_PREFIX: " ${PYTHON_PREFIX})
 FIND_LIBRARY(PYTHON_LIBRARIES

numbuf/numbuf/__init__.py

@@ -1 +1,5 @@
-from libnumbuf import *
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from numbuf.libnumbuf import *

numbuf/python/test/runtest.py

@@ -1,3 +1,7 @@
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
 import unittest
 import numbuf
 import numpy as np

numbuf/setup.py

@@ -1,3 +1,7 @@
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
 import subprocess
 from setuptools import setup, find_packages
 import setuptools.command.install as _install

scripts/cluster.py

@@ -1,3 +1,7 @@
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
 import os
 import subprocess
 import socket
@@ -63,7 +67,7 @@ class RayCluster(object):
       raise Exception("Commands run over ssh must not contain the single quote character. This command does: {}".format(command))
     full_command = "ssh -o StrictHostKeyChecking=no -i {} {}@{} '{}'".format(self.key_file, self.username, node_ip_address, command)
     subprocess.call([full_command], shell=True)
-    print "Finished running command '{}' on {}@{}.".format(command, self.username, node_ip_address)
+    print("Finished running command '{}' on {}@{}.".format(command, self.username, node_ip_address))
 
   def _run_parallel_functions(self, functions, inputs):
     """Run functions in parallel.
@@ -117,7 +121,7 @@ class RayCluster(object):
       self._run_command_over_ssh(node_ip_address, command)
     inputs = zip(node_ip_addresses, commands)
     self._run_parallel_functions(len(self.node_ip_addresses) * [function], inputs)
-    print "Finished running commands {} on all nodes.".format(inputs)
+    print("Finished running commands {} on all nodes.".format(inputs))
 
   def install_ray(self):
     """Install Ray on every node in the cluster.
@@ -172,7 +176,7 @@ class RayCluster(object):
     self.run_command_over_ssh_on_all_nodes_in_parallel(start_workers_commands)
 
     setup_env_path = os.path.join(self.installation_directory, "ray/setup-env.sh")
-    print """
+    print("""
       The cluster has been started. You can attach to the cluster by sshing to the head node with the following command.
 
           ssh -i {} {}@{}
@@ -185,7 +189,7 @@ class RayCluster(object):
 
           import ray
           ray.init(node_ip_address="{}", scheduler_address="{}:10001")
-    """.format(self.key_file, self.username, self.node_ip_addresses[0], setup_env_path, self.node_private_ip_addresses[0], self.node_private_ip_addresses[0])
+    """.format(self.key_file, self.username, self.node_ip_addresses[0], setup_env_path, self.node_private_ip_addresses[0], self.node_private_ip_addresses[0]))
 
   def stop_ray(self):
     """Kill all of the processes in the Ray cluster.
@@ -317,7 +321,7 @@ if __name__ == "__main__":
   # will be expanded locally instead of remotely.
   echo_home_command = "ssh -o StrictHostKeyChecking=no -i {} {}@{} 'echo $HOME'".format(key_file, username, node_ip_addresses[0])
   installation_directory = subprocess.check_output(echo_home_command, shell=True).strip()
-  print "Using '{}' as the home directory on the cluster.".format(installation_directory)
+  print("Using '{}' as the home directory on the cluster.".format(installation_directory))
   # Create the Raycluster object.
   cluster = RayCluster(node_ip_addresses, node_private_ip_addresses, username, key_file, installation_directory)
   # Drop into an IPython shell.

src/common/test/test.py

@@ -1,3 +1,5 @@
+from __future__ import absolute_import
+from __future__ import division
 from __future__ import print_function
 
 import numpy as np

src/global_scheduler/lib/python/global_scheduler_services.py

@@ -1,3 +1,5 @@
+from __future__ import absolute_import
+from __future__ import division
 from __future__ import print_function
 
 import os

src/global_scheduler/test/test.py

@@ -1,3 +1,5 @@
+from __future__ import absolute_import
+from __future__ import division
 from __future__ import print_function
 
 import numpy as np

src/photon/CMakeLists.txt

@@ -6,17 +6,17 @@ message(STATUS "Trying custom approach for finding Python.")
 # Start off by figuring out which Python executable to use.
 find_program(CUSTOM_PYTHON_EXECUTABLE python)
 message(STATUS "Found Python program: ${CUSTOM_PYTHON_EXECUTABLE}")
-execute_process(COMMAND ${CUSTOM_PYTHON_EXECUTABLE} -c "import sys; print 'python' + sys.version[0:3]"
+execute_process(COMMAND ${CUSTOM_PYTHON_EXECUTABLE} -c "import sys; print('python' + sys.version[0:3])"
                 OUTPUT_VARIABLE PYTHON_LIBRARY_NAME OUTPUT_STRIP_TRAILING_WHITESPACE)
 message(STATUS "PYTHON_LIBRARY_NAME: " ${PYTHON_LIBRARY_NAME})
 # Now find the Python include directories.
-execute_process(COMMAND ${CUSTOM_PYTHON_EXECUTABLE} -c "from distutils.sysconfig import *; print get_python_inc()"
+execute_process(COMMAND ${CUSTOM_PYTHON_EXECUTABLE} -c "from distutils.sysconfig import *; print(get_python_inc())"
                 OUTPUT_VARIABLE PYTHON_INCLUDE_DIRS OUTPUT_STRIP_TRAILING_WHITESPACE)
 message(STATUS "PYTHON_INCLUDE_DIRS: " ${PYTHON_INCLUDE_DIRS})
 # Now find the Python libraries. We'll start by looking near the Python
 # executable. If that fails, then we'll look near the Python include
 # directories.
-execute_process(COMMAND ${CUSTOM_PYTHON_EXECUTABLE} -c "import sys; print sys.exec_prefix"
+execute_process(COMMAND ${CUSTOM_PYTHON_EXECUTABLE} -c "import sys; print(sys.exec_prefix)"
                 OUTPUT_VARIABLE PYTHON_PREFIX OUTPUT_STRIP_TRAILING_WHITESPACE)
 message(STATUS "PYTHON_PREFIX: " ${PYTHON_PREFIX})
 FIND_LIBRARY(PYTHON_LIBRARIES

src/photon/photon/__init__.py

@@ -1,2 +1,6 @@
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
 from libphoton import *
 from photon_services import *

src/photon/photon/photon_services.py

@@ -1,3 +1,5 @@
+from __future__ import absolute_import
+from __future__ import division
 from __future__ import print_function
 
 import os

src/photon/test/test.py

@@ -1,3 +1,5 @@
+from __future__ import absolute_import
+from __future__ import division
 from __future__ import print_function
 
 import numpy as np

src/plasma/CMakeLists.txt

@@ -6,17 +6,17 @@ message(STATUS "Trying custom approach for finding Python.")
 # Start off by figuring out which Python executable to use.
 find_program(CUSTOM_PYTHON_EXECUTABLE python)
 message(STATUS "Found Python program: ${CUSTOM_PYTHON_EXECUTABLE}")
-execute_process(COMMAND ${CUSTOM_PYTHON_EXECUTABLE} -c "import sys; print 'python' + sys.version[0:3]"
+execute_process(COMMAND ${CUSTOM_PYTHON_EXECUTABLE} -c "import sys; print('python' + sys.version[0:3])"
                 OUTPUT_VARIABLE PYTHON_LIBRARY_NAME OUTPUT_STRIP_TRAILING_WHITESPACE)
 message(STATUS "PYTHON_LIBRARY_NAME: " ${PYTHON_LIBRARY_NAME})
 # Now find the Python include directories.
-execute_process(COMMAND ${CUSTOM_PYTHON_EXECUTABLE} -c "from distutils.sysconfig import *; print get_python_inc()"
+execute_process(COMMAND ${CUSTOM_PYTHON_EXECUTABLE} -c "from distutils.sysconfig import *; print(get_python_inc())"
                 OUTPUT_VARIABLE PYTHON_INCLUDE_DIRS OUTPUT_STRIP_TRAILING_WHITESPACE)
 message(STATUS "PYTHON_INCLUDE_DIRS: " ${PYTHON_INCLUDE_DIRS})
 # Now find the Python libraries. We'll start by looking near the Python
 # executable. If that fails, then we'll look near the Python include
 # directories.
-execute_process(COMMAND ${CUSTOM_PYTHON_EXECUTABLE} -c "import sys; print sys.exec_prefix"
+execute_process(COMMAND ${CUSTOM_PYTHON_EXECUTABLE} -c "import sys; print(sys.exec_prefix)"
                 OUTPUT_VARIABLE PYTHON_PREFIX OUTPUT_STRIP_TRAILING_WHITESPACE)
 message(STATUS "PYTHON_PREFIX: " ${PYTHON_PREFIX})
 FIND_LIBRARY(PYTHON_LIBRARIES

src/plasma/lib/python/plasma.py

@@ -1,8 +1,13 @@
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
 import os
 import random
 import subprocess
 import time
-import libplasma
+
+from . import libplasma
 
 PLASMA_ID_SIZE = 20
 PLASMA_WAIT_TIMEOUT = 2 ** 30

src/plasma/lib/python/setup.py

@@ -1,3 +1,7 @@
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
 from setuptools import setup, find_packages
 import setuptools.command.install as _install
 

src/plasma/test/test.py

@@ -1,3 +1,5 @@
+from __future__ import absolute_import
+from __future__ import division
 from __future__ import print_function
 
 import numpy as np
@@ -534,7 +536,7 @@ class TestPlasmaManager(unittest.TestCase):
     # loop slow down the manager so much that some of the asynchronous Redis
     # commands timeout triggering fatal failure callbacks.
     n = 40
-    for i in range(n * (n + 1) / 2):
+    for i in range(n * (n + 1) // 2):
       if i % 2 == 0:
         object_id, _, _ = create_object(self.client1, 200, 200)
       else:

test/array_test.py

@@ -1,3 +1,7 @@
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
 import unittest
 import ray
 import numpy as np
@@ -130,7 +134,7 @@ class DistributedArrayTest(unittest.TestCase):
 
     # test da.linalg.modified_lu
     def test_modified_lu(d1, d2):
-      print "testing dist_modified_lu with d1 = " + str(d1) + ", d2 = " + str(d2)
+      print("testing dist_modified_lu with d1 = " + str(d1) + ", d2 = " + str(d2))
       assert d1 >= d2
       k = min(d1, d2)
       m = ra.random.normal.remote([d1, d2])
@@ -153,7 +157,7 @@ class DistributedArrayTest(unittest.TestCase):
 
     # test dist_tsqr_hr
     def test_dist_tsqr_hr(d1, d2):
-      print "testing dist_tsqr_hr with d1 = " + str(d1) + ", d2 = " + str(d2)
+      print("testing dist_tsqr_hr with d1 = " + str(d1) + ", d2 = " + str(d2))
       a = da.random.normal.remote([d1, d2])
       y, t, y_top, r = da.linalg.tsqr_hr.remote(a)
       a_val = ray.get(da.assemble.remote(a))
@@ -171,7 +175,7 @@ class DistributedArrayTest(unittest.TestCase):
       test_dist_tsqr_hr(d1, d2)
 
     def test_dist_qr(d1, d2):
-      print "testing qr with d1 = {}, and d2 = {}.".format(d1, d2)
+      print("testing qr with d1 = {}, and d2 = {}.".format(d1, d2))
       a = da.random.normal.remote([d1, d2])
       K = min(d1, d2)
       q, r = da.linalg.qr.remote(a)

test/failure_test.py

@@ -1,8 +1,12 @@
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
 import unittest
 import ray
 import time
 
-import test_functions
+import ray.test.test_functions as test_functions
 
 def wait_for_errors(error_type, num_errors, timeout=10):
   start_time = time.time()

test/memory_leak_deserialize.py

@@ -1,14 +0,0 @@
-# This code reproduces a memory leak we had in the past
-
-import os
-import numpy as np
-import ray
-
-ray.init(start_ray_local=True, num_workers=1)
-
-d = {"w": np.zeros(1000000)}
-
-obj_capsule, contained_objectids = ray.libraylib.serialize_object(ray.worker.global_worker.handle, d)
-
-while True:
-  ray.libraylib.deserialize_object(ray.worker.global_worker.handle, obj_capsule)

test/microbenchmarks.py

@@ -1,9 +1,13 @@
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
 import unittest
 import ray
 import time
 import numpy as np
 
-import test_functions
+import ray.test.test_functions as test_functions
 
 class MicroBenchmarkTest(unittest.TestCase):
 
@@ -20,11 +24,11 @@ class MicroBenchmarkTest(unittest.TestCase):
       elapsed_times.append(end_time - start_time)
     elapsed_times = np.sort(elapsed_times)
     average_elapsed_time = sum(elapsed_times) / 1000
-    print "Time required to submit an empty function call:"
-    print "    Average: {}".format(average_elapsed_time)
-    print "    90th percentile: {}".format(elapsed_times[900])
-    print "    99th percentile: {}".format(elapsed_times[990])
-    print "    worst:           {}".format(elapsed_times[999])
+    print("Time required to submit an empty function call:")
+    print("    Average: {}".format(average_elapsed_time))
+    print("    90th percentile: {}".format(elapsed_times[900]))
+    print("    99th percentile: {}".format(elapsed_times[990]))
+    print("    worst:           {}".format(elapsed_times[999]))
     # average_elapsed_time should be about 0.00038
 
     # measure the time required to submit a remote task to the scheduler (where the remote task returns one value)
@@ -36,11 +40,11 @@ class MicroBenchmarkTest(unittest.TestCase):
       elapsed_times.append(end_time - start_time)
     elapsed_times = np.sort(elapsed_times)
     average_elapsed_time = sum(elapsed_times) / 1000
-    print "Time required to submit a trivial function call:"
-    print "    Average: {}".format(average_elapsed_time)
-    print "    90th percentile: {}".format(elapsed_times[900])
-    print "    99th percentile: {}".format(elapsed_times[990])
-    print "    worst:           {}".format(elapsed_times[999])
+    print("Time required to submit a trivial function call:")
+    print("    Average: {}".format(average_elapsed_time))
+    print("    90th percentile: {}".format(elapsed_times[900]))
+    print("    99th percentile: {}".format(elapsed_times[990]))
+    print("    worst:           {}".format(elapsed_times[999]))
     # average_elapsed_time should be about 0.001
 
     # measure the time required to submit a remote task to the scheduler and get the result
@@ -53,11 +57,11 @@ class MicroBenchmarkTest(unittest.TestCase):
       elapsed_times.append(end_time - start_time)
     elapsed_times = np.sort(elapsed_times)
     average_elapsed_time = sum(elapsed_times) / 1000
-    print "Time required to submit a trivial function call and get the result:"
-    print "    Average: {}".format(average_elapsed_time)
-    print "    90th percentile: {}".format(elapsed_times[900])
-    print "    99th percentile: {}".format(elapsed_times[990])
-    print "    worst:           {}".format(elapsed_times[999])
+    print("Time required to submit a trivial function call and get the result:")
+    print("    Average: {}".format(average_elapsed_time))
+    print("    90th percentile: {}".format(elapsed_times[900]))
+    print("    99th percentile: {}".format(elapsed_times[990]))
+    print("    worst:           {}".format(elapsed_times[999]))
     # average_elapsed_time should be about 0.0013
 
     # measure the time required to do do a put
@@ -69,11 +73,11 @@ class MicroBenchmarkTest(unittest.TestCase):
       elapsed_times.append(end_time - start_time)
     elapsed_times = np.sort(elapsed_times)
     average_elapsed_time = sum(elapsed_times) / 1000
-    print "Time required to put an int:"
-    print "    Average: {}".format(average_elapsed_time)
-    print "    90th percentile: {}".format(elapsed_times[900])
-    print "    99th percentile: {}".format(elapsed_times[990])
-    print "    worst:           {}".format(elapsed_times[999])
+    print("Time required to put an int:")
+    print("    Average: {}".format(average_elapsed_time))
+    print("    90th percentile: {}".format(elapsed_times[900]))
+    print("    99th percentile: {}".format(elapsed_times[990]))
+    print("    worst:           {}".format(elapsed_times[999]))
     # average_elapsed_time should be about 0.00087
 
     ray.worker.cleanup()

test/runtest.py

@@ -1,3 +1,5 @@
+from __future__ import absolute_import
+from __future__ import division
 from __future__ import print_function
 
 import unittest
@@ -8,7 +10,7 @@ import string
 import sys
 from collections import namedtuple
 
-import test_functions
+import ray.test.test_functions as test_functions
 import ray.array.remote as ra
 import ray.array.distributed as da
 

test/stress_tests.py

@@ -1,3 +1,5 @@
+from __future__ import absolute_import
+from __future__ import division
 from __future__ import print_function
 
 import unittest