ray/lib/python/ray/worker.py

import os
import time
import traceback
import copy
import logging
from types import ModuleType
import typing
import funcsigs
import numpy as np
import colorama
import atexit

# Ray modules
import config
import pickling
import serialization
import internal.graph_pb2
import graph
import services
import libnumbuf
import libraylib as raylib

class RayTaskError(Exception):
  """An object used internally to represent a task that threw an exception.

  If a task throws an exception during execution, a RayTaskError is stored in
  the object store for each of the task's outputs. When an object is retrieved
  from the object store, the Python method that retrieved it checks to see if
  the object is a RayTaskError and if it is then an exceptionis thrown
  propagating the error message.

  Currently, we either use the exception attribute or the traceback attribute
  but not both.

  Attributes:
    function_name (str): The name of the function that failed and produced the
      RayTaskError.
    exception (Exception): The exception object thrown by the failed task.
    traceback_str (str): The traceback from the exception.
  """

  def __init__(self, function_name, exception, traceback_str):
    """Initialize a RayTaskError."""
    self.function_name = function_name
    if isinstance(exception, RayGetError) or isinstance(exception, RayGetArgumentError) or isinstance(exception, RayGetArgumentTypeError):
      self.exception = exception
    else:
      self.exception = None
    self.traceback_str = traceback_str

  @staticmethod
  def deserialize(primitives):
    """Create a RayTaskError from a primitive object."""
    function_name, exception, traceback_str = primitives
    if exception[0] == "RayGetError":
      exception = RayGetError.deserialize(exception[1])
    elif exception[0] == "RayGetArgumentError":
      exception = RayGetArgumentError.deserialize(exception[1])
    elif exception[0] == "RayGetArgumentTypeError":
      exception = RayGetArgumentTypeError.deserialize(exception[1])
    elif exception[0] == "None":
      exception = None
    else:
      assert False, "This code should be unreachable."
    return RayTaskError(function_name, exception, traceback_str)

  def serialize(self):
    """Turn a RayTaskError into a primitive object."""
    if isinstance(self.exception, RayGetError):
      serialized_exception = ("RayGetError", self.exception.serialize())
    elif isinstance(self.exception, RayGetArgumentError):
      serialized_exception = ("RayGetArgumentError", self.exception.serialize())
    elif isinstance(self.exception, RayGetArgumentTypeError):
      serialized_exception = ("RayGetArgumentTypeError", self.exception.serialize())
    elif self.exception is None:
      serialized_exception = ("None",)
    else:
      assert False, "This code should be unreachable."
    return (self.function_name, serialized_exception, self.traceback_str)

  def __str__(self):
    """Format a RayTaskError as a string."""
    if self.traceback_str is None:
      # This path is taken if getting the task arguments failed.
      return "Remote function {}{}{} failed with:\n\n{}".format(colorama.Fore.RED, self.function_name, colorama.Fore.RESET, self.exception)
    else:
      # This path is taken if the task execution failed.
      return "Remote function {}{}{} failed with:\n\n{}".format(colorama.Fore.RED, self.function_name, colorama.Fore.RESET, self.traceback_str)

class RayGetError(Exception):
  """An exception used when get is called on an output of a failed task.

  Attributes:
    objectid (lib.ObjectID): The ObjectID that get was called on.
    task_error (RayTaskError): The RayTaskError object created by the failed
      task.
  """

  def __init__(self, objectid, task_error):
    """Initialize a RayGetError object."""
    self.objectid = objectid
    self.task_error = task_error

  @staticmethod
  def deserialize(primitives):
    """Create a RayGetError from a primitive object."""
    objectid, task_error = primitives
    return RayGetError(objectid, RayTaskError.deserialize(task_error))

  def serialize(self):
    """Turn a RayGetError into a primitive object."""
    return (self.objectid, self.task_error.serialize())

  def __str__(self):
    """Format a RayGetError as a string."""
    return "Could not get objectid {}. It was created by remote function {}{}{} which failed with:\n\n{}".format(self.objectid, colorama.Fore.RED, self.task_error.function_name, colorama.Fore.RESET, self.task_error)

class RayGetArgumentError(Exception):
  """An exception used when a task's argument was produced by a failed task.

  Attributes:
    argument_index (int): The index (zero indexed) of the failed argument in
      present task's remote function call.
    function_name (str): The name of the function for the current task.
    objectid (lib.ObjectID): The ObjectID that was passed in as the argument.
    task_error (RayTaskError): The RayTaskError object created by the failed
      task.
  """

  def __init__(self, function_name, argument_index, objectid, task_error):
    """Initialize a RayGetArgumentError object."""
    self.argument_index = argument_index
    self.function_name = function_name
    self.objectid = objectid
    self.task_error = task_error

  @staticmethod
  def deserialize(primitives):
    """Create a RayGetArgumentError from a primitive object."""
    function_name, argument_index, objectid, task_error = primitives
    return RayGetArgumentError(function_name, argument_index, objectid, RayTaskError.deserialize(task_error))

  def serialize(self):
    """Turn a RayGetArgumentError into a primitive object."""
    return (self.function_name, self.argument_index, self.objectid, self.task_error.serialize())

  def __str__(self):
    """Format a RayGetArgumentError as a string."""
    return "Failed to get objectid {} as argument {} for remote function {}{}{}. It was created by remote function {}{}{} which failed with:\n{}".format(self.objectid, self.argument_index, colorama.Fore.RED, self.function_name, colorama.Fore.RESET, colorama.Fore.RED, self.task_error.function_name, colorama.Fore.RESET, self.task_error)

class RayGetArgumentTypeError(Exception):
  """An exception used when a task's argument doesn't type check.

  Attributes:
    function_name (str): The name of the function for the current task.
    argument_index (int): The index (zero indexed) of the argument in the
      present task's remote function call.
    received_type: The type of the argument that was passed in.
    expected_type: The type that was expected. This is determined by the remote
      decorator.
  """

  def __init__(self, function_name, argument_index, received_type, expected_type):
    """Initialize a RayGetArgumentTypeError object."""
    self.function_name = function_name
    self.argument_index = argument_index
    # TODO(rkn): when we support the serialization of types, then we should
    # remove the string conversions below.
    self.received_type = str(received_type)
    self.expected_type = str(expected_type)

  @staticmethod
  def deserialize(primitives):
    """Create a RayGetArgumentTypeError from a primitive object."""
    function_name, argument_index, received_type, expected_type = primitives
    return RayGetArgumentTypeError(function_name, argument_index, received_type, expected_type)

  def serialize(self):
    """Turn a RayGetArgumentTypeError into a primitive object."""
    return (self.function_name, self.argument_index, self.received_type, self.expected_type)

  def __str__(self):
    """Format a RayGetArgumentTypeError as a string."""
    return "Argument {} for remote function {}{}{} has type {} but an argument of type {} was expected.".format(self.argument_index, colorama.Fore.RED, self.function_name, colorama.Fore.RESET, self.received_type, self.expected_type)

class RayDealloc(object):
  """An object used internally to properly implement reference counting.

  When we call get_object with a particular object ID, we create a RayDealloc
  object with the information necessary to properly handle closing the relevant
  memory segment when the object is no longer needed by the worker. The
  RayDealloc object is stored as a field in the object returned by get_object so
  that its destructor is only called when the worker no longer has any
  references to the object.

  Attributes
    handle (worker capsule): A Python object wrapping a C++ Worker object.
    segmentid (int): The id of the segment that contains the object that holds
      this RayDealloc object.
  """

  def __init__(self, handle, segmentid):
    """Initialize a RayDealloc object.

    Args:
      handle (worker capsule): A Python object wrapping a C++ Worker object.
      segmentid (int): The id of the segment that contains the object that holds
        this RayDealloc object.
    """
    self.handle = handle
    self.segmentid = segmentid

  def __del__(self):
    """Deallocate the relevant segment to avoid a memory leak."""
    raylib.unmap_object(self.handle, self.segmentid)

class Reusable(object):
  """An Python object that can be shared between tasks.

  Attributes:
    initializer (Callable[[], object]): A function used to create and initialize
      the reusable variable.
    reinitializer (Optional[Callable[[object], object]]): An optional function
      used to reinitialize the reusable variable after it has been used. This
      argument can be used as an optimization if there is a fast way to
      reinitialize the state of the variable other than rerunning the
      initializer.
  """

  def __init__(self, initializer, reinitializer=None):
    """Initialize a Reusable object."""
    if not isinstance(initializer, typing.Callable):
      raise Exception("When creating a RayReusable, initializer must be a function.")
    self.initializer = initializer
    if reinitializer is None:
      # If no reinitializer is passed in, use a wrapped version of the initializer.
      reinitializer = lambda value: initializer()
    if not isinstance(reinitializer, typing.Callable):
      raise Exception("When creating a RayReusable, reinitializer must be a function.")
    self.reinitializer = reinitializer

class RayReusables(object):
  """An object used to store Python variables that are shared between tasks.

  Each worker process will have a single RayReusables object. This class serves
  two purposes. First, some objects are not serializable, and so the code that
  creates those objects must be run on the worker that uses them. This class is
  responsible for running the code that creates those objects. Second, some of
  these objects are expensive to create, and so they should be shared between
  tasks. However, if a task mutates a variable that is shared between tasks,
  then the behavior of the overall program may be nondeterministic (it could
  depend on scheduling decisions). To fix this, if a task uses a one of these
  shared objects, then that shared object will be reinitialized after the task
  finishes. Since the initialization may be expensive, the user can pass in
  custom reinitialization code that resets the state of the shared variable to
  the way it was after initialization. If the reinitialization code does not do
  this, then the behavior of the overall program is undefined.

  Attributes:
    _names (List[str]): A list of the names of all the reusable variables.
    _reusables (Dict[str, Reusable]): A dictionary mapping the name of the
      reusable variables to the corresponding Reusable object.
    _cached_reusables (List[Tuple[str, Reusable]]): A list of pairs. The first
      element of each pair is the name of a reusable variable, and the second
      element is the Reusable object. This list is used to store reusable
      variables that are defined before the driver is connected. Once the driver
      is connected, these variables will be exported.
    _used (List[str]): A list of the names of all the reusable variables that
      have been accessed within the scope of the current task. This is reset to
      the empty list after each task.
  """

  def __init__(self):
    """Initialize a RayReusables object."""
    self._names = set()
    self._reusables = {}
    self._cached_reusables = []
    self._used = set()
    self._slots = ("_names", "_reusables", "_cached_reusables", "_used", "_slots", "_reinitialize", "__getattribute__", "__setattr__", "__delattr__")
    # CHECKPOINT: Attributes must not be added after _slots. The above attributes are protected from deletion.

  def _reinitialize(self):
    """Reinitialize the reusable variables that the current task used."""
    for name in self._used:
      current_value = getattr(self, name)
      new_value = self._reusables[name].reinitializer(current_value)
      object.__setattr__(self, name, new_value)
    self._used.clear() # Reset the _used list.

  def __getattribute__(self, name):
    """Get an attribute. This handles reusable variables as a special case.

    When __getattribute__ is called with the name of a reusable variable, that
    name is added to the list of variables that were used in the current task.

    Args:
      name (str): The name of the attribute to get.
    """
    if name == "_slots":
      return object.__getattribute__(self, name)
    if name in self._slots:
      return object.__getattribute__(self, name)
    if name in self._names and name not in self._used:
      self._used.add(name)
    return object.__getattribute__(self, name)

  def __setattr__(self, name, value):
    """Set an attribute. This handles reusable variables as a special case.

    This is used to create reusable variables. When it is called, it runs the
    function for initializing the variable to create the variable. If this is
    called on the driver, then the functions for initializing and reinitializing
    the variable are shipped to the workers.

    Args:
      name (str): The name of the attribute to set. This is either a whitelisted
        name or it is treated as the name of a reusable variable.
      value: If name is a whitelisted name, then value can be any value. If name
        is the name of a reusable variable, then this is either the serialized
        initializer code or it is a tuple of the serialized initializer and
        reinitializer code.
    """
    try:
      slots = self._slots
    except AttributeError:
      slots = ()
    if slots == ():
      return object.__setattr__(self, name, value)
    if name in slots:
      return object.__setattr__(self, name, value)
    reusable = value
    if not issubclass(type(reusable), Reusable):
      raise Exception("To set a reusable variable, you must pass in a Reusable object")
    self._names.add(name)
    self._reusables[name] = reusable
    if _mode() in [raylib.SCRIPT_MODE, raylib.SILENT_MODE]:
      _export_reusable_variable(name, reusable)
    elif _mode() is None:
      self._cached_reusables.append((name, reusable))
    object.__setattr__(self, name, reusable.initializer())

  def __delattr__(self, name):
    """We do not allow attributes of RayReusables to be deleted.

    Args:
      name (str): The name of the attribute to delete.
    """
    raise Exception("Attempted deletion of attribute {}. Attributes of a RayReusable object may not be deleted.".format(name))

class Worker(object):
  """A class used to define the control flow of a worker process.

  Note:
    The methods in this class are considered unexposed to the user. The
    functions outside of this class are considered exposed.

  Attributes:
    functions (Dict[str, Callable]): A dictionary mapping the name of a remote
      function to the remote function itself. This is the set of remote
      functions that can be executed by this worker.
    handle (worker capsule): A Python object wrapping a C++ Worker object.
    mode: The mode of the worker. One of SCRIPT_MODE, PYTHON_MODE, SILENT_MODE,
      and WORKER_MODE.
    cached_remote_functions (List[Tuple[str, str]]): A list of pairs
      representing the remote functions that were defined before he worker
      called connect. The first element is the name of the remote function, and
      the second element is the serialized remote function. When the worker
      eventually does call connect, if it is a driver, it will export these
      functions to the scheduler. If cached_remote_functions is None, that means
      that connect has been called already.
    num_failed_tasks (int): The number of tasks that have failed and whose error
      messages have been displayed to the user. We use this value to know when
      a failed task hasn't been seen by the user and should be displayed.
  """

  def __init__(self):
    """Initialize a Worker object."""
    self.functions = {}
    self.handle = None
    self.mode = None
    self.cached_remote_functions = []
    self.num_failed_tasks = 0

  def set_mode(self, mode):
    """Set the mode of the worker.

    The mode SCRIPT_MODE should be used if this Worker is a driver that is being
    run as a Python script or interactively in a shell. It will print
    information about task failures.

    The mode WORKER_MODE should be used if this Worker is not a driver. It will
    not print information about tasks.

    The mode PYTHON_MODE should be used if this Worker is a driver and if you
    want to run the driver in a manner equivalent to serial Python for debugging
    purposes. It will not send remote function calls to the scheduler and will
    insead execute them in a blocking fashion.

    The mode SILENT_MODE should be used only during testing. It does not print
    any information about errors because some of the tests intentionally fail.

    args:
      mode: One of SCRIPT_MODE, WORKER_MODE, PYTHON_MODE, and SILENT_MODE.
    """
    self.mode = mode
    colorama.init()

  def put_object(self, objectid, value):
    """Put value in the local object store with object id objectid.

    This assumes that the value for objectid has not yet been placed in the
    local object store.

    Args:
      objectid (raylib.ObjectID): The object ID of the value to be put.
      value (serializable object): The value to put in the object store.
    """
    try:
      # We put the value into a list here because in arrow the concept of
      # "serializing a single object" does not exits.
      schema, size, serialized = libnumbuf.serialize_list([value])
      # TODO(pcm): Right now, metadata is serialized twice, change that in the future
      # in the following line, the "8" is for storing the metadata size,
      # the len(schema) is for storing the metadata and the 4096 is for storing
      # the metadata in the batch (see INITIAL_METADATA_SIZE in arrow)
      size = size + 8 + len(schema) + 4096
      buff, segmentid = raylib.allocate_buffer(self.handle, objectid, size)
      # write the metadata length
      np.frombuffer(buff, dtype="int64", count=1)[0] = len(schema)
      # metadata buffer
      metadata = np.frombuffer(buff, dtype="byte", offset=8, count=len(schema))
      # write the metadata
      metadata[:] = schema
      data = np.frombuffer(buff, dtype="byte")[8 + len(schema):]
      metadata_offset = libnumbuf.write_to_buffer(serialized, memoryview(data))
      raylib.finish_buffer(self.handle, objectid, segmentid, metadata_offset)
    except:
      # At the moment, custom object and objects that contain object IDs take this path
      # TODO(pcm): Make sure that these are the only objects getting serialized to protobuf
      object_capsule, contained_objectids = serialization.serialize(self.handle, value) # contained_objectids is a list of the objectids contained in object_capsule
      raylib.put_object(self.handle, objectid, object_capsule, contained_objectids)

  def get_object(self, objectid):
    """Get the value in the local object store associated with objectid.

    Return the value from the local object store for objectid. This will block
    until the value for objectid has been written to the local object store.

    Args:
      objectid (raylib.ObjectID): The object ID of the value to retrieve.
    """
    if raylib.is_arrow(self.handle, objectid):
      ## this is the new codepath
      buff, segmentid, metadata_offset = raylib.get_buffer(self.handle, objectid)
      metadata_size = np.frombuffer(buff, dtype="int64", count=1)[0]
      metadata = np.frombuffer(buff, dtype="byte", offset=8, count=metadata_size)
      data = np.frombuffer(buff, dtype="byte")[8 + metadata_size:]
      serialized = libnumbuf.read_from_buffer(memoryview(data), bytearray(metadata), metadata_offset)
      deserialized = libnumbuf.deserialize_list(serialized)
      # Unwrap the object from the list (it was wrapped put_object)
      assert len(deserialized) == 1
      result = deserialized[0]
      ## this is the old codepath
      # result, segmentid = raylib.get_arrow(self.handle, objectid)
    else:
      object_capsule, segmentid = raylib.get_object(self.handle, objectid)
      result = serialization.deserialize(self.handle, object_capsule)

    if isinstance(result, int):
      result = serialization.Int(result)
    elif isinstance(result, long):
      result = serialization.Long(result)
    elif isinstance(result, float):
      result = serialization.Float(result)
    elif isinstance(result, bool):
      raylib.unmap_object(self.handle, segmentid) # need to unmap here because result is passed back "by value" and we have no reference to unmap later
      return result # can't subclass bool, and don't need to because there is a global True/False
    elif isinstance(result, list):
      result = serialization.List(result)
    elif isinstance(result, dict):
      result = serialization.Dict(result)
    elif isinstance(result, tuple):
      result = serialization.Tuple(result)
    elif isinstance(result, str):
      result = serialization.Str(result)
    elif isinstance(result, unicode):
      result = serialization.Unicode(result)
    elif isinstance(result, np.ndarray):
      result = result.view(serialization.NDArray)
    elif isinstance(result, np.generic):
      return result
      # TODO(pcm): close the associated memory segment; if we don't, this leaks memory (but very little, so it is ok for now)
    elif result is None:
      raylib.unmap_object(self.handle, segmentid) # need to unmap here because result is passed back "by value" and we have no reference to unmap later
      return None # can't subclass None and don't need to because there is a global None
    result.ray_objectid = objectid # TODO(pcm): This could be done only for the "get" case in the future if we want to increase performance
    result.ray_deallocator = RayDealloc(self.handle, segmentid)
    return result

  def alias_objectids(self, alias_objectid, target_objectid):
    """Make two object IDs refer to the same object."""
    raylib.alias_objectids(self.handle, alias_objectid, target_objectid)

  def submit_task(self, func_name, args):
    """Submit a remote task to the scheduler.

    Tell the scheduler to schedule the execution of the function with name
    func_name with arguments args. Retrieve object IDs for the outputs of
    the function from the scheduler and immediately return them.

    Args:
      func_name (str): The name of the function to be executed.
      args (List[Any]): The arguments to pass into the function. Arguments can
        be object IDs or they can be values. If they are values, they
        must be serializable objecs.
    """
    task_capsule = serialization.serialize_task(self.handle, func_name, args)
    objectids = raylib.submit_task(self.handle, task_capsule)
    return objectids

global_worker = Worker()
"""Worker: The global Worker object for this worker process.

We use a global Worker object to ensure that there is a single worker object
per worker process.
"""

reusables = RayReusables()
"""RayReusables: The reusable variables that are shared between tasks.

Each worker process has its own RayReusables object, and these objects should be
the same in all workers. This is used for storing variables that are not
serializable but must be used by remote tasks. In addition, it is used to
reinitialize these variables after they are used so that changes to their state
made by one task do not affect other tasks.
"""

logger = logging.getLogger("ray")
"""Logger: The logging object for the Python worker code."""

def check_connected(worker=global_worker):
  """Check if the worker is connected.

  Raises:
    Exception: An exception is raised if the worker is not connected.
  """
  if worker.handle is None:
    raise Exception("This command cannot be called before a Ray cluster has been started. You can start one with 'ray.init(start_ray_local=True, num_workers=1)'.")

def print_failed_task(task_status):
  """Print information about failed tasks.

  Args:
    task_status (Dict): A dictionary containing the name, operationid, and
      error message for a failed task.
  """
  print """
    Error: Task failed
      Function Name: {}
      Task ID: {}
      Error Message: \n{}
  """.format(task_status["function_name"], task_status["operationid"], task_status["error_message"])

def scheduler_info(worker=global_worker):
  """Return information about the state of the scheduler."""
  check_connected(worker)
  return raylib.scheduler_info(worker.handle)

def visualize_computation_graph(file_path=None, view=False, worker=global_worker):
  """Write the computation graph to a pdf file.

  Args:
    file_path (str): The name of a pdf file that the rendered computation graph
      will be written to. If this argument is None, a temporary path will be
      used.
    view (bool): If true, the result the python graphviz package will try to
      open the result in a viewer.

  Examples:
    Try the following code.

    >>> import ray.array.distributed as da
    >>> x = da.zeros([20, 20])
    >>> y = da.zeros([20, 20])
    >>> z = da.dot(x, y)
    >>> ray.visualize_computation_graph(view=True)
  """
  check_connected(worker)
  if file_path is None:
    file_path = config.get_log_file_path("computation-graph.pdf")

  base_path, extension = os.path.splitext(file_path)
  if extension != ".pdf":
    raise Exception("File path must be a .pdf file")
  proto_path = base_path + ".binaryproto"

  raylib.dump_computation_graph(worker.handle, proto_path)
  g = internal.graph_pb2.CompGraph()
  g.ParseFromString(open(proto_path).read())
  graph.graph_to_graphviz(g).render(base_path, view=view)

  print "Wrote graph dot description to file {}".format(base_path)
  print "Wrote graph protocol buffer description to file {}".format(proto_path)
  print "Wrote computation graph to file {}.pdf".format(base_path)

def task_info(worker=global_worker):
  """Return information about failed tasks."""
  check_connected(worker)
  return raylib.task_info(worker.handle)

def init(start_ray_local=False, num_workers=None, num_objstores=None, scheduler_address=None, node_ip_address=None, driver_mode=raylib.SCRIPT_MODE):
  """Either connect to an existing Ray cluster or start one and connect to it.

  This method handles two cases. Either a Ray cluster already exists and we
  just attach this driver to it, or we start all of the processes associated
  with a Ray cluster and attach to the newly started cluster.

  Args:
    start_ray_local (Optional[bool]): If True then this will start a scheduler
      an object store, and some workers. If False, this will attach to an
      existing Ray cluster.
    num_workers (Optional[int]): The number of workers to start if
      start_ray_local is True.
    num_objstores (Optional[int]): The number of object stores to start if
      start_ray_local is True.
    scheduler_address (Optional[str]): The address of the scheduler to connect
      to if start_ray_local is False.
    node_ip_address (Optional[str]): The address of the node the worker is
      running on. It is required if start_ray_local is False and it cannot be
      provided otherwise.
    driver_mode (Optional[bool]): The mode in which to start the driver. This
      should be one of SCRIPT_MODE, PYTHON_MODE, and SILENT_MODE.

    raises:
      Exception: An exception is raised if an inappropriate combination of
        arguments is passed in.
  """
  if start_ray_local:
    # In this case, we launch a scheduler, a new object store, and some workers,
    # and we connect to them.
    if (scheduler_address is not None) or (node_ip_address is not None):
      raise Exception("If start_ray_local=True, then you cannot pass in a scheduler_address or a node_ip_address.")
    if driver_mode not in [raylib.SCRIPT_MODE, raylib.PYTHON_MODE, raylib.SILENT_MODE]:
      raise Exception("If start_ray_local=True, then driver_mode must be in [ray.SCRIPT_MODE, ray.PYTHON_MODE, ray.SILENT_MODE].")
    # Use the address 127.0.0.1 in local mode.
    node_ip_address = "127.0.0.1"
    num_workers = 1 if num_workers is None else num_workers
    num_objstores = 1 if num_objstores is None else num_objstores
    # Start the scheduler, object store, and some workers. These will be killed
    # by the call to cleanup(), which happens when the Python script exits.
    scheduler_address, _ = services.start_ray_local(num_objstores=num_objstores, num_workers=num_workers, worker_path=None)
  else:
    # In this case, there is an existing scheduler and object store, and we do
    # not need to start any processes.
    if (num_workers is not None) or (num_objstores is not None):
      raise Exception("The arguments num_workers and num_objstores must not be provided unless start_ray_local=True.")
    if node_ip_address is None:
      raise Exception("When start_ray_local=False, the node_ip_address of the current node must be provided.")
  # Connect this driver to the scheduler and object store. The corresponing call
  # to disconnect will happen in the call to cleanup() when the Python script
  # exits.
  connect(node_ip_address, scheduler_address, is_driver=True, worker=global_worker, mode=driver_mode)

def cleanup(worker=global_worker):
  """Disconnect the driver, and terminate any processes started in init.

  This will automatically run at the end when a Python process that uses Ray
  exits. It is ok to run this twice in a row. Note that we manually call
  services.cleanup() in the tests because we need to start and stop many
  clusters in the tests, but the import and exit only happen once.
  """
  disconnect()
  worker.set_mode(None)
  services.cleanup()

atexit.register(cleanup)

def connect(node_ip_address, scheduler_address, objstore_address=None, is_driver=False, worker=global_worker, mode=raylib.WORKER_MODE):
  """Connect this worker to the scheduler and an object store.

  Args:
    node_ip_address (str): The ip address of the node the worker runs on.
    scheduler_address (str): The ip address and port of the scheduler.
    objstore_address (Optional[str]): The ip address and port of the local
      object store. Normally, this argument should be omitted and the scheduler
      will tell the worker what object store to connect to.
    is_driver (bool): True if this worker is a driver and false otherwise.
    mode: The mode of the worker. One of SCRIPT_MODE, WORKER_MODE, PYTHON_MODE,
      and SILENT_MODE.
  """
  if hasattr(worker, "handle"):
    del worker.handle
  worker.scheduler_address = scheduler_address
  worker.handle, worker.worker_address = raylib.create_worker(node_ip_address, scheduler_address, objstore_address if objstore_address is not None else "", is_driver)
  worker.set_mode(mode)
  FORMAT = "%(asctime)-15s %(message)s"
  # Configure the Python logging module. Note that if we do not provide our own
  # logger, then our logging will interfere with other Python modules that also
  # use the logging module.
  log_handler = logging.FileHandler(config.get_log_file_path("-".join(["worker", worker.worker_address]) + ".log"))
  log_handler.setLevel(logging.DEBUG)
  log_handler.setFormatter(logging.Formatter(FORMAT))
  _logger().addHandler(log_handler)
  _logger().setLevel(logging.DEBUG)
  _logger().propagate = False
  # Configure the logging from the worker C++ code.
  raylib.set_log_config(config.get_log_file_path("-".join(["worker", worker.worker_address, "c++"]) + ".log"))
  if mode in [raylib.SCRIPT_MODE, raylib.SILENT_MODE]:
    for function_name, function_to_export in worker.cached_remote_functions:
      raylib.export_remote_function(worker.handle, function_name, function_to_export)
    for name, reusable_variable in reusables._cached_reusables:
      _export_reusable_variable(name, reusable_variable)
  worker.cached_remote_functions = None
  reusables._cached_reusables = None
  # Start the driver's WorkerService (if this is a driver). This will receive
  # GRPC commands from the scheduler to print error messages. We pass in the
  # mode below. This tells the WorkerService whether it is operating for a
  # driver or a worker and whether it should surpress errors or not.
  if is_driver:
    raylib.start_worker_service(worker.handle, mode)

def disconnect(worker=global_worker):
  """Disconnect this worker from the scheduler and object store."""
  if worker.handle is not None:
    raylib.disconnect(worker.handle)
  # Reset the list of cached remote functions so that if more remote functions
  # are defined and then connect is called again, the remote functions will be
  # exported. This is mostly relevant for the tests.
  worker.handle = None
  worker.cached_remote_functions = []
  reusables._cached_reusables = []

def get(objectid, worker=global_worker):
  """Get a remote object from an object store.

  This method blocks until the object corresponding to objectid is available in
  the local object store. If this object is not in the local object store, it
  will be shipped from an object store that has it (once the object has been
  created).

  Args:
    objectid (raylib.ObjectID): Object ID to the object to get.

  Returns:
    A Python object
  """
  check_connected(worker)
  if worker.mode == raylib.PYTHON_MODE:
    return objectid # In raylib.PYTHON_MODE, ray.get is the identity operation (the input will actually be a value not an objectid)
  raylib.request_object(worker.handle, objectid)
  value = worker.get_object(objectid)
  if isinstance(value, RayTaskError):
    # If the result is a RayTaskError, then the task that created this object
    # failed, and we should propagate the error message here.
    raise RayGetError(objectid, value)
  return value

def put(value, worker=global_worker):
  """Store an object in the object store.

  Args:
    value (serializable object): The Python object to be stored.

  Returns:
    The object ID assigned to this value.
  """
  check_connected(worker)
  if worker.mode == raylib.PYTHON_MODE:
    return value # In raylib.PYTHON_MODE, ray.put is the identity operation
  objectid = raylib.get_objectid(worker.handle)
  worker.put_object(objectid, value)
  return objectid

def kill_workers(worker=global_worker):
  """Kill all of the workers in the cluster. This does not kill drivers.

  Note:
    Currently, we only support killing workers if all submitted tasks have been
    run. If some workers are still running tasks or if the scheduler still has
    tasks in its queue, then this method will not do anything.

  Returns:
    True if workers were successfully killed. False otherwise.
  """
  success = raylib.kill_workers(worker.handle)
  if not success:
    print "Could not kill all workers. We currently do not support killing workers when tasks are running."
  return success

def restart_workers_local(num_workers, worker_path, worker=global_worker):
  """Restart workers locally.

  This method kills all of the workers and starts new workers locally on the
  same node as the driver. This is intended for use in the case where Ray is
  being used on a single node.

  Args:
    num_workers (int): The number of workers to be started.
    worker_path (str): The path of the source code that workers will run.

  Returns:
    True if workers were successfully restarted. False otherwise.
  """
  if not kill_workers(worker):
    return False
  services.start_workers(worker.scheduler_address, worker.objstore_address, num_workers, worker_path)
  return True

def format_error_message(exception_message):
  """Improve the formatting of an exception thrown by a remote function.

  This method takes a traceback from an exception and makes it nicer by
  removing a few uninformative lines and adding some space to indent the
  remaining lines nicely.

  Args:
    exception_message (str): A message generated by traceback.format_exc().

  Returns:
    A string of the formatted exception message.
  """
  lines = exception_message.split("\n")
  # Remove lines 1, 2, 3, and 4, which are always the same, they just contain
  # information about the main loop.
  lines = lines[0:1] + lines[5:]
  return "\n".join(lines)

def main_loop(worker=global_worker):
  """The main loop a worker runs to receive and execute tasks.

  This method is an infinite loop. It waits to receive commands from the
  scheduler. A command may consist of a task to execute, a remote function to
  import, a reusable variable to import, or an order to terminate the worker
  process. The worker executes the command, notifies the scheduler of any errors
  that occurred while executing the command, and waits for the next command.
  """
  if not raylib.connected(worker.handle):
    raise Exception("Worker is attempting to enter main_loop but has not been connected yet.")
  # We pass in raylib.WORKER_MODE below to indicate that the WorkerService is
  # operating for a worker and not a driver.
  raylib.start_worker_service(worker.handle, raylib.WORKER_MODE)

  def process_task(task): # wrapping these lines in a function should cause the local variables to go out of scope more quickly, which is useful for inspecting reference counts
    """Execute a task assigned to this worker.

    This method deserializes a task from the scheduler, and attempts to execute
    the task. If the task succeeds, the outputs are stored in the local object
    store. If the task throws an exception, RayTaskError objects are stored in
    the object store to represent the failed task (these will be retrieved by
    calls to get or by subsequent tasks that use the outputs of this task).
    After the task executes, the worker resets any reusable variables that were
    accessed by the task.
    """
    function_name, args, return_objectids = serialization.deserialize_task(worker.handle, task)
    try:
      arguments = get_arguments_for_execution(worker.functions[function_name], args, worker) # get args from objstore
      outputs = worker.functions[function_name].executor(arguments) # execute the function
      if len(return_objectids) == 1:
        outputs = (outputs,)
    except Exception as e:
      # If the task threw an exception, then record the traceback. We determine
      # whether the exception was thrown in the task execution by whether the
      # variable "arguments" is defined.
      traceback_str = format_error_message(traceback.format_exc()) if "arguments" in locals() else None
      failure_object = RayTaskError(function_name, e, traceback_str)
      failure_objects = [failure_object for _ in range(len(return_objectids))]
      store_outputs_in_objstore(return_objectids, failure_objects, worker)
      # Notify the scheduler that the task failed.
      raylib.notify_failure(worker.handle, function_name, str(failure_object), raylib.FailedTask)
      _logger().info("While running function {}, worker threw exception with message: \n\n{}\n".format(function_name, str(failure_object)))
    else:
      store_outputs_in_objstore(return_objectids, outputs, worker) # store output in local object store
    # Notify the scheduler that the task is done. This happens regardless of
    # whether the task succeeded or failed.
    raylib.notify_task_completed(worker.handle)
    try:
      # Reinitialize the values of reusable variables that were used in the task
      # above so that changes made to their state do not affect other tasks.
      reusables._reinitialize()
    except Exception as e:
      # The attempt to reinitialize the reusable variables threw an exception.
      # We record the traceback and notify the scheduler.
      traceback_str = format_error_message(traceback.format_exc())
      raylib.notify_failure(worker.handle, function_name, traceback_str, raylib.FailedReinitializeReusableVariable)
      _logger().info("While attempting to reinitialize the reusable variables after running function {}, the worker threw exception with message: \n\n{}\n".format(function_name, traceback_str))

  def process_remote_function(function_name, serialized_function):
    """Import a remote function."""
    try:
      (function, arg_types, return_types, module) = pickling.loads(serialized_function)
    except:
      # If an exception was thrown when the remote function was imported, we
      # record the traceback and notify the scheduler of the failure.
      traceback_str = format_error_message(traceback.format_exc())
      _logger().info("Failed to import remote function {}. Failed with message: \n\n{}\n".format(function_name, traceback_str))
      # Notify the scheduler that the remote function failed to import.
      raylib.notify_failure(worker.handle, function_name, traceback_str, raylib.FailedRemoteFunctionImport)
    else:
      # TODO(rkn): Why is the below line necessary?
      function.__module__ = module
      assert function_name == "{}.{}".format(function.__module__, function.__name__), "The remote function name does not match the name that was passed in."
      worker.functions[function_name] = remote(arg_types, return_types, worker)(function)
      _logger().info("Successfully imported remote function {}.".format(function_name))
      # Noify the scheduler that the remote function imported successfully.
      # We pass an empty error message string because the import succeeded.
      raylib.register_remote_function(worker.handle, function_name, len(return_types))

  def process_reusable_variable(reusable_variable_name, initializer_str, reinitializer_str):
    """Import a reusable variable."""
    try:
      initializer = pickling.loads(initializer_str)
      reinitializer = pickling.loads(reinitializer_str)
      reusables.__setattr__(reusable_variable_name, Reusable(initializer, reinitializer))
    except:
      # If an exception was thrown when the reusable variable was imported, we
      # record the traceback and notify the scheduler of the failure.
      traceback_str = format_error_message(traceback.format_exc())
      _logger().info("Failed to import reusable variable {}. Failed with message: \n\n{}\n".format(reusable_variable_name, traceback_str))
      # Notify the scheduler that the reusable variable failed to import.
      raylib.notify_failure(worker.handle, reusable_variable_name, traceback_str, raylib.FailedReusableVariableImport)
    else:
      _logger().info("Successfully imported reusable variable {}.".format(reusable_variable_name))

  while True:
    command, command_args = raylib.wait_for_next_message(worker.handle)
    try:
      if command == "die":
        # We use this as a mechanism to allow the scheduler to kill workers.
        _logger().info("Received a 'die' command, and will exit now.")
        break
      elif command == "task":
        process_task(command_args)
      elif command == "function":
        function_name, serialized_function = command_args
        process_remote_function(function_name, serialized_function)
      elif command == "reusable_variable":
        name, initializer_str, reinitializer_str = command_args
        process_reusable_variable(name, initializer_str, reinitializer_str)
      else:
        _logger().info("Reached the end of the if-else loop in the main loop. This should be unreachable.")
        assert False, "This code should be unreachable."
    finally:
      # Allow releasing the variables BEFORE we wait for the next message or exit the block
      del command_args

def _submit_task(func_name, args, worker=global_worker):
  """This is a wrapper around worker.submit_task.

  We use this wrapper so that in the remote decorator, we can call _submit_task
  instead of worker.submit_task. The difference is that when we attempt to
  serialize remote functions, we don't attempt to serialize the worker object,
  which cannot be serialized.
  """
  return worker.submit_task(func_name, args)

def _mode(worker=global_worker):
  """This is a wrapper around worker.mode.

  We use this wrapper so that in the remote decorator, we can call _mode()
  instead of worker.mode. The difference is that when we attempt to serialize
  remote functions, we don't attempt to serialize the worker object, which
  cannot be serialized.
  """
  return worker.mode

def _logger():
  """Return the logger object.

  We use this wrapper because so that functions which do logging can be pickled.
  Normally a logger object is specific to a machine (it opens a local file), and
  so cannot be pickled.
  """
  return logger

def _export_reusable_variable(name, reusable, worker=global_worker):
  """Export a reusable variable to the workers. This is only called by a driver.

  Args:
    name (str): The name of the variable to export.
    reusable (Reusable): The reusable object containing code for initializing
      and reinitializing the variable.
  """
  if _mode(worker) not in [raylib.SCRIPT_MODE, raylib.SILENT_MODE]:
    raise Exception("_export_reusable_variable can only be called on a driver.")
  raylib.export_reusable_variable(worker.handle, name, pickling.dumps(reusable.initializer), pickling.dumps(reusable.reinitializer))

def remote(arg_types, return_types, worker=global_worker):
  """This decorator is used to create remote functions.

  Args:
    arg_types (List[type]): List of Python types of the function arguments.
    return_types (List[type]): List of Python types of the return values.
  """
  def remote_decorator(func):
    def func_call(*args, **kwargs):
      """This gets run immediately when a worker calls a remote function."""
      check_connected()
      args = list(args)
      args.extend([kwargs[keyword] if kwargs.has_key(keyword) else default for keyword, default in keyword_defaults[len(args):]]) # fill in the remaining arguments
      if _mode() == raylib.PYTHON_MODE:
        # In raylib.PYTHON_MODE, remote calls simply execute the function. We copy the
        # arguments to prevent the function call from mutating them and to match
        # the usual behavior of immutable remote objects.
        return func(*copy.deepcopy(args))
      check_arguments(arg_types, has_vararg_param, func_name, args) # throws an exception if args are invalid
      objectids = _submit_task(func_name, args)
      if len(objectids) == 1:
        return objectids[0]
      elif len(objectids) > 1:
        return objectids
    def func_executor(arguments):
      """This gets run when the remote function is executed."""
      _logger().info("Calling function {}".format(func.__name__))
      start_time = time.time()
      result = func(*arguments)
      end_time = time.time()
      check_return_values(func_invoker, result) # throws an exception if result is invalid
      _logger().info("Finished executing function {}, it took {} seconds".format(func.__name__, end_time - start_time))
      return result
    def func_invoker(*args, **kwargs):
      """This is returned by the decorator and used to invoke the function."""
      raise Exception("Remote functions cannot be called directly. Instead of running '{}()', try '{}.remote()'.".format(func_name, func_name))
    func_invoker.remote = func_call
    func_invoker.executor = func_executor
    func_invoker.arg_types = arg_types
    func_invoker.return_types = return_types
    func_invoker.is_remote = True
    func_name = "{}.{}".format(func.__module__, func.__name__)
    func_invoker.func_name = func_name
    func_invoker.func_doc = func.func_doc
    sig_params = [(k, v) for k, v in funcsigs.signature(func).parameters.iteritems()]
    keyword_defaults = [(k, v.default) for k, v in sig_params]
    has_vararg_param = any([v.kind == v.VAR_POSITIONAL for k, v in sig_params])
    func_invoker.has_vararg_param = has_vararg_param
    has_kwargs_param = any([v.kind == v.VAR_KEYWORD for k, v in sig_params])
    check_signature_supported(has_kwargs_param, has_vararg_param, keyword_defaults, func_name)

    # Everything ready - export the function
    if worker.mode in [None, raylib.SCRIPT_MODE, raylib.SILENT_MODE]:
      func_name_global_valid = func.__name__ in func.__globals__
      func_name_global_value = func.__globals__.get(func.__name__)
      # Set the function globally to make it refer to itself
      func.__globals__[func.__name__] = func_invoker  # Allow the function to reference itself as a global variable
      try:
        to_export = pickling.dumps((func, arg_types, return_types, func.__module__))
      finally:
        # Undo our changes
        if func_name_global_valid: func.__globals__[func.__name__] = func_name_global_value
        else: del func.__globals__[func.__name__]
    if worker.mode in [raylib.SCRIPT_MODE, raylib.SILENT_MODE]:
      raylib.export_remote_function(worker.handle, func_name, to_export)
    elif worker.mode is None:
      worker.cached_remote_functions.append((func_name, to_export))
    return func_invoker
  return remote_decorator

def check_signature_supported(has_kwargs_param, has_vararg_param, keyword_defaults, name):
  """Check if we support the signature of this function.

  We currently do not allow remote functions to have **kwargs. We also do not
  support keyword argumens in conjunction with a *args argument.

  Args:
    has_kwards_param (bool): True if the function being checked has a **kwargs
      argument.
    has_vararg_param (bool): True if the function being checked has a *args
      argument.
    keyword_defaults (List): A list of the default values for the arguments to
      the function being checked.
    name (str): The name of the function to check.

  Raises:
    Exception: An exception is raised if the signature is not supported.
  """
  # check if the user specified kwargs
  if has_kwargs_param:
    raise "Function {} has a **kwargs argument, which is currently not supported.".format(name)
  # check if the user specified a variable number of arguments and any keyword arguments
  if has_vararg_param and any([d != funcsigs._empty for _, d in keyword_defaults]):
    raise "Function {} has a *args argument as well as a keyword argument, which is currently not supported.".format(name)


def check_return_values(function, result):
  """Check the types and number of return values.

  Args:
    function (Callable): The remote function whose outputs are being checked.
    result: The value returned by an invocation of the remote function. The
    expected types and number are defined in the remote decorator.

  Raises:
    Exception: An exception is raised if the return values have incorrect types
      or the function returned the wrong number of return values.
  """
  # If the @remote decorator declares that the function has no return values,
  # then all we do is check that there were in fact no return values.
  if len(function.return_types) == 0:
    if result is not None:
      raise Exception("The @remote decorator for function {} has 0 return values, but {} returned more than 0 values.".format(function.__name__, function.__name__))
    return
  # If a function has multiple return values, Python returns a tuple of the
  # values. If there is a single return value, then Python does not return a
  # tuple, it simply returns the value. That is why we place result with
  # (result,) when there is only one return value, so we can treat these two
  # cases similarly.
  if len(function.return_types) == 1:
    result = (result,)
  # Below we check that the number of values returned by the function match the
  # number of return values declared in the @remote decorator.
  if len(result) != len(function.return_types):
    raise Exception("The @remote decorator for function {} has {} return values with types {}, but {} returned {} values.".format(function.__name__, len(function.return_types), function.return_types, function.__name__, len(result)))
  # Here we do some limited type checking to make sure the return values have
  # the right types.
  for i in range(len(result)):
    if (not issubclass(type(result[i]), function.return_types[i])) and (not isinstance(result[i], raylib.ObjectID)):
      raise Exception("The {}th return value for function {} has type {}, but the @remote decorator expected a return value of type {} or an ObjectID.".format(i, function.__name__, type(result[i]), function.return_types[i]))

def typecheck_arg(arg, expected_type, i, name):
  """Check that an argument has the expected type.

  Args:
    arg: An argument to function.
    expected_type (type): The expected type of arg.
    i (int): The position of the argument to the function.
    name (str): The name of the function.

  Raises:
    RayGetArgumentTypeError: An exception is raised if arg does not have the
      expected type.
  """
  if issubclass(type(arg), expected_type):
    # Passed the type-checck
    # TODO(rkn): This check doesn't really work, e.g., issubclass(type([1, 2, 3]), typing.List[str]) == True
    pass
  elif isinstance(arg, long) and issubclass(int, expected_type):
    # TODO(mehrdadn): Should long really be convertible to int?
    pass
  else:
    raise RayGetArgumentTypeError(name, i, type(arg), expected_type)

def check_arguments(arg_types, has_vararg_param, name, args):
  """Check that the arguments to the remote function have the right types.

  This is called by the worker that calls the remote function (not the worker
  that executes the remote function).

  Args:
    arg_types (List[type]): A list of the types of the arguments to the function
      being checked.
    has_vararg_param (bool): True if the function being checked has a *args
      argument.
    name (str): The name of the function.
    args (List): The arguments to the function.

  Raises:
    Exception: An exception is raised the args do not all have the right types.
  """
  # check the number of args
  if len(args) != len(arg_types) and not has_vararg_param:
    raise Exception("Function {} expects {} arguments, but received {}.".format(name, len(arg_types), len(args)))
  elif len(args) < len(arg_types) - 1 and has_vararg_param:
    raise Exception("Function {} expects at least {} arguments, but received {}.".format(name, len(arg_types) - 1, len(args)))

  for (i, arg) in enumerate(args):
    if i <= len(arg_types) - 1:
      expected_type = arg_types[i]
    elif has_vararg_param:
      expected_type = arg_types[-1]
    else:
      assert False, "This code should be unreachable."

    if isinstance(arg, raylib.ObjectID):
      # TODO(rkn): When we have type information in the ObjectID, do type checking here.
      pass
    else:
      typecheck_arg(arg, expected_type, i, name)

def get_arguments_for_execution(function, args, worker=global_worker):
  """Retrieve the arguments for the remote function.

  This retrieves the values for the arguments to the remote function that were
  passed in as object IDs. Argumens that were passed by value are not changed.
  This also does some type checking. This is called by the worker that is
  executing the remote function.

  Args:
    function (Callable): The remote function whose arguments are being
      retrieved.
    args (List): The arguments to the function.

  Returns:
    The retrieved arguments in addition to the arguments that were passed by
    value.

  Raises:
    RayGetArgumentError: This exception is raised if a task that created one of
      the arguments failed.
    RayGetArgumentTypeError: This exception is raised (via typecheck_arg) if one
      of the arguments does not have the expected type.
  """
  # TODO(rkn): Eventually, all of the type checking can be put in `check_arguments` above so that the error will happen immediately when calling a remote function.
  arguments = []
  # # check the number of args
  # if len(args) != len(function.arg_types) and function.arg_types[-1] is not None:
  #   raise Exception("Function {} expects {} arguments, but received {}.".format(function.__name__, len(function.arg_types), len(args)))
  # elif len(args) < len(function.arg_types) - 1 and function.arg_types[-1] is None:
  #   raise Exception("Function {} expects at least {} arguments, but received {}.".format(function.__name__, len(function.arg_types) - 1, len(args)))

  for (i, arg) in enumerate(args):
    if i <= len(function.arg_types) - 1:
      expected_type = function.arg_types[i]
    elif function.has_vararg_param and len(function.arg_types) >= 1:
      expected_type = function.arg_types[-1]
    else:
      assert False, "This code should be unreachable."

    if isinstance(arg, raylib.ObjectID):
      # get the object from the local object store
      _logger().info("Getting argument {} for function {}.".format(i, function.__name__))
      argument = worker.get_object(arg)
      if isinstance(argument, RayTaskError):
        # If the result is a RayTaskError, then the task that created this
        # object failed, and we should propagate the error message here.
        raise RayGetArgumentError(function.__name__, i, arg, argument)
      _logger().info("Successfully retrieved argument {} for function {}.".format(i, function.__name__))
    else:
      # pass the argument by value
      argument = arg

    typecheck_arg(argument, expected_type, i, function.__name__)
    arguments.append(argument)
  return arguments

def store_outputs_in_objstore(objectids, outputs, worker=global_worker):
  """Store the outputs of a remote function in the local object store.

  This stores the values that were returned by a remote function in the local
  object store. If any of the return values are object IDs, then these object
  IDs are aliased with the object IDs that the scheduler assigned for the return
  values. This is called by the worker that executes the remote function.

  Note:
    The arguments objectids and outputs should have the same length.

  Args:
    objectids (List[raylib.ObjectID]): The object IDs that were assigned to the
      outputs of the remote function call.
    outputs (Tuple): The value returned by the remote function. If the remote
      function was supposed to only return one value, then its output was
      wrapped in a tuple with one element prior to being passed into this
      function.
  """
  for i in range(len(objectids)):
    if isinstance(outputs[i], raylib.ObjectID):
      # An ObjectID is being returned, so we must alias objectids[i] so that it refers to the same object that outputs[i] refers to
      _logger().info("Aliasing objectids {} and {}".format(objectids[i].id, outputs[i].id))
      worker.alias_objectids(objectids[i], outputs[i])
      pass
    else:
      worker.put_object(objectids[i], outputs[i])