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add documentation and refactor cluster.py (#238)

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This commit is contained in:
Robert Nishihara
2016-07-12 23:54:18 -07:00
committed by Philipp Moritz
parent 0487b05111
commit 80526f7777
9 changed files with 719 additions and 408 deletions
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lib/python/ray/services.py
+73 -59
View File
@@ -43,16 +43,16 @@ def new_objstore_port():
return 20000 + objstore_port_counter
def cleanup():
"""
"""When running in local mode, shutdown the Ray processes.
This method is used to shutdown processes that were started with
services.start_ray_local(). It kills all scheduler, object store, and worker
processes that were started by this services module. It disconnects driver
processes but does not kill them. Users should not invoke this manually. It
will automatically run at the end when a Python process that imports
services exits. It is ok to run this twice in a row. Note that we manaully
call services.cleanup() in the tests because we need to start and stop many
clusters in the tests, but in the tests, services is only imported and only
exits once.
services.start_ray_local(). It kills all scheduler, object store, and worker
processes that were started by this services module. It disconnects driver
processes but does not kill them. This will automatically run at the end when
a Python process that imports services 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 in the tests, services
is only imported and only exits once.
"""
global drivers
for driver in drivers:
@@ -84,43 +84,47 @@ def cleanup():
atexit.register(cleanup)
def start_scheduler(scheduler_address, local):
"""
This method starts a scheduler process.
"""This method starts a scheduler process.
:param scheduler_address: The ip address and port to use for the scheduler.
:param local: True if using Ray in local mode. If local is true, then this
process will be killed by serices.cleanup() when the Python process that
imported services exits.
Args:
scheduler_address (str): The ip address and port to use for the scheduler.
local (bool): True if using Ray in local mode. If local is true, then this
process will be killed by serices.cleanup() when the Python process that
imported services exits.
"""
p = subprocess.Popen(["scheduler", scheduler_address, "--log-file-name", config.get_log_file_path("scheduler.log")], env=_services_env)
if local:
all_processes.append((p, scheduler_address))
def start_objstore(scheduler_address, objstore_address, local):
"""
This method starts an object store process.
"""This method starts an object store process.
:param scheduler_address: The ip address and port of the scheduler to connect to.
:param objstore_address: The ip address and port to use for the object store.
:param local: True if using Ray in local mode. If local is true, then this
process will be killed by serices.cleanup() when the Python process that
imported services exits.
Args:
scheduler_address (str): The ip address and port of the scheduler to connect
to.
objstore_address (str): The ip address and port to use for the object store.
local (bool): True if using Ray in local mode. If local is true, then this
process will be killed by serices.cleanup() when the Python process that
imported services exits.
"""
p = subprocess.Popen(["objstore", scheduler_address, objstore_address, "--log-file-name", config.get_log_file_path("-".join(["objstore", objstore_address]) + ".log")], env=_services_env)
if local:
all_processes.append((p, objstore_address))
def start_worker(worker_path, scheduler_address, objstore_address, worker_address, local):
"""
This method starts a worker process.
"""This method starts a worker process.
:param worker_path: The path of the source code which the worker process will run.
:param scheduler_address: The ip address and port of the scheduler to connect to.
:param objstore_address: The ip address and port of the object store to connect to.
:param worker_address: The ip address and port to use for the worker.
:param local: True if using Ray in local mode. If local is true, then this
process will be killed by serices.cleanup() when the Python process that
imported services exits.
Args:
worker_path (str): The path of the source code which the worker process will
run.
scheduler_address (str): The ip address and port of the scheduler to connect
to.
objstore_address (str): The ip address and port of the object store to
connect to.
worker_address (str): The ip address and port to use for the worker.
local (bool): True if using Ray in local mode. If local is true, then this
process will be killed by serices.cleanup() when the Python process that
imported services exits.
"""
p = subprocess.Popen(["python",
worker_path,
@@ -131,14 +135,18 @@ def start_worker(worker_path, scheduler_address, objstore_address, worker_addres
all_processes.append((p, worker_address))
def start_node(scheduler_address, node_ip_address, num_workers, worker_path=None):
"""
Start an object store and associated workers that will be part of a larger cluster.
Assumes the scheduler has already been started.
"""Start an object store and associated workers in the cluster setting.
:param scheduler_address: ip address and port of the scheduler (which may run on a different node)
:param node_ip_address: ip address (without port) of the node this function is run on
:param num_workers: the number of workers to be started on this node
:param worker_path: path of the source code that will be run on the worker
This starts an object store and the associated workers when Ray is being used
in the cluster setting. This assumes the scheduler has already been started.
Args:
scheduler_address (str): ip address and port of the scheduler (which may run
on a different node)
node_ip_address (str): ip address (without port) of the node this function
is run on
num_workers (int): the number of workers to be started on this node
worker_path (str): path of the source code that will be run on the worker
"""
objstore_address = address(node_ip_address, new_objstore_port())
start_objstore(scheduler_address, objstore_address, local=False)
@@ -148,35 +156,41 @@ def start_node(scheduler_address, node_ip_address, num_workers, worker_path=None
time.sleep(0.5)
def start_workers(scheduler_address, objstore_address, num_workers, worker_path):
"""
Start a new set of workers on this node. This assumes that the scheduler is
already running and that the object store on this node is already running.
The intended use case is that a developer wants to update the code running
on the worker processes so first kills all of the workers and then runs this
method.
"""Start a new set of workers on this node.
:param scheduler_address: ip address and port of the scheduler (which may run on a different node)
:param objstore_address: ip address and port of the object store (which runs on the same node)
:param num_workers: the number of workers to be started on this node
:param worker_path: path of the source code that will be run on the worker
Start a new set of workers on this node. This assumes that the scheduler is
already running and that the object store on this node is already running. The
intended use case is that a developer wants to update the code running on the
worker processes so first kills all of the workers and then runs this method.
Args:
scheduler_address (str): ip address and port of the scheduler (which may run
on a different node)
objstore_address (str): ip address and port of the object store (which runs
on the same node)
num_workers (int): the number of workers to be started on this node
worker_path (str): path of the source code that will be run on the worker
"""
node_ip_address = objstore_address.split(":")[0]
for _ in range(num_workers):
start_worker(worker_path, scheduler_address, objstore_address, address(node_ip_address, new_worker_port()), local=False)
def start_ray_local(num_workers=0, worker_path=None, driver_mode=ray.SCRIPT_MODE):
"""
This method starts Ray in local mode (as opposed to cluster mode, which is
handled by cluster.py).
"""Start Ray in local mode.
:param num_workers: The number of workers to start.
:param worker_path: The path of the source code that will be run by the worker
:param driver_mode: The mode for the driver, this only affects the printing of
error messages. This should be ray.SCRIPT_MODE if the driver is being run in
a script. It should be ray.SHELL_MODE if it is being used interactively in
the shell. It should be ray.PYTHON_MODE to run things in a manner eqivalent
to serial Python code. It should be ray.WORKER_MODE to surpress the printing
of error messages.
This method starts Ray in local mode (as opposed to cluster mode, which is
handled by cluster.py).
Args:
num_workers (int): The number of workers to start.
worker_path (str): The path of the source code that will be run by the
worker
driver_mode: The mode for the driver, this only affects the printing of
error messages. This should be ray.SCRIPT_MODE if the driver is being run
in a script. It should be ray.SHELL_MODE if it is being used interactively
in the shell. It should be ray.PYTHON_MODE to run things in a manner
equivalent to serial Python code. It should be ray.WORKER_MODE to surpress
the printing of error messages.
"""
start_services_local(num_objstores=1, num_workers_per_objstore=num_workers, worker_path=worker_path, driver_mode=driver_mode)
lib/python/ray/worker.py
+347 -74
View File
@@ -17,38 +17,146 @@ import ray.graph
import services
class RayFailedObject(object):
"""If a task throws an exception during execution, a RayFailedObject is stored in the object store for each of the tasks outputs."""
"""An object used internally to represent a task that threw an exception.
If a task throws an exception during execution, a RayFailedObject is stored in
the object store for each of the tasks outputs. When an object is retrieved
from the object store, the Python method that retrieved it should check to see
if the object is a RayFailedObject and if it is then an exception should be
thrown containing the error message.
Attributes
error_message (str): The error message raised by the task that failed.
"""
def __init__(self, error_message=None):
"""Initialize a RayFailedObject.
Args:
error_message (str): The error message raised by the task for which a
RayFailedObject is being created.
"""
self.error_message = error_message
def deserialize(self, primitives):
"""Create a RayFailedObject from a primitive object.
This initializes a RayFailedObject from a primitive object created by the
serialize method. This method is required in order for Ray to serialize
custom Python classes.
Note:
This method should not be called by users.
Args:
primitives (str): The object's error message.
"""
self.error_message = primitives
def serialize(self):
"""Turn a RayFailedObject into a primitive object.
This method is required in order for Ray to serialize
custom Python classes.
Note:
The output of this method should only be used by the deserialize method.
This method should not be called by users.
Args:
primitives (str): The object's error message.
Returns:
A primitive representation of a RayFailedObject.
"""
return self.error_message
class RayDealloc(object):
"""An object used internally to properly implement reference counting.
When we call get_object with a particular object reference, 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."""
ray.lib.unmap_object(self.handle, self.segmentid)
class Worker(object):
"""The methods in this class are considered unexposed to the user. The functions outside of this class are considered exposed."""
"""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.
"""
def __init__(self):
"""Initialize a Worker object."""
self.functions = {}
self.handle = None
def set_mode(self, mode):
"""Set the mode of the worker.
The mode ray.SCRIPT_MODE should be used if this Worker is a driver that is
being run as a Python script. It will print information about task failures.
The mode ray.SHELL_MODE should be used if this Worker is a driver that is
being run interactively in a Python shell. It will print information about
task failures and successes.
The mode ray.WORKER_MODE should be used if this Worker is not a driver. It
will not print information about tasks.
The mode ray.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.
args:
mode: One of ray.SCRIPT_MODE, ray.WORKER_MODE, ray.SHELL_MODE, and
ray.PYTHON_MODE.
"""
self.mode = mode
colorama.init()
def put_object(self, objref, value):
"""Put `value` in the local object store with objref `objref`. This assumes that the value for `objref` has not yet been placed in the local object store."""
"""Put value in the local object store with object reference objref.
This assumes that the value for objref has not yet been placed in the
local object store.
Args:
objref (ray.ObjRef): The object reference of the value to be put.
value (serializable object): The value to put in the object store.
"""
if serialization.is_arrow_serializable(value):
ray.lib.put_arrow(self.handle, objref, value)
else:
@@ -56,11 +164,13 @@ class Worker(object):
ray.lib.put_object(self.handle, objref, object_capsule, contained_objrefs)
def get_object(self, objref):
"""
Return the value from the local object store for objref `objref`. This will
block until the value for `objref` has been written to the local object store.
"""Get the value in the local object store associated with objref.
WARNING: get_object can only be called on a canonical objref.
Return the value from the local object store for objref. This will block
until the value for objref has been written to the local object store.
Args:
objref (ray.ObjRef): The object reference of the value to retrieve.
"""
if ray.lib.is_arrow(self.handle, objref):
result, segmentid = ray.lib.get_arrow(self.handle, objref)
@@ -97,27 +207,55 @@ class Worker(object):
return result
def alias_objrefs(self, alias_objref, target_objref):
"""Make `alias_objref` refer to the same object that `target_objref` refers to."""
"""Make two object references refer to the same object."""
ray.lib.alias_objrefs(self.handle, alias_objref, target_objref)
def register_function(self, function):
"""Notify the scheduler that this worker can execute the function with name `func_name`. Store the function `function` locally."""
"""Register a function with the scheduler.
Notify the scheduler that this worker can execute the function with name
func_name. After this call, the scheduler can send tasks for executing
the function to this worker.
Args:
function (Callable): The remote function that this worker can execute.
"""
ray.lib.register_function(self.handle, function.func_name, len(function.return_types))
self.functions[function.func_name] = function
def submit_task(self, func_name, args):
"""Tell the scheduler to schedule the execution of the function with name `func_name` with arguments `args`. Retrieve object references for the outputs of the function from the scheduler and immediately return them."""
"""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 references 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 references or they can be values. If they are values, they
must be serializable objecs.
"""
task_capsule = serialization.serialize_task(self.handle, func_name, args)
objrefs = ray.lib.submit_task(self.handle, task_capsule)
if self.mode == ray.SHELL_MODE or self.mode == ray.SCRIPT_MODE:
print_task_info(ray.lib.task_info(self.handle), self.mode)
return objrefs
# We make `global_worker` a global variable so that there is one worker per worker process.
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.
"""
# This is a helper method. It should not be called by users.
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: {}
@@ -125,8 +263,14 @@ def print_failed_task(task_status):
Error Message: \n{}
""".format(task_status["function_name"], task_status["operationid"], task_status["error_message"])
# This is a helper method. It should not be called by users.
def print_task_info(task_data, mode):
"""Print information about tasks.
Args:
task_data (Dict): A dictionary containing information about tasks that have
failed, succeeded, or are still running.
mode: The mode of the Worker object.
"""
num_tasks_succeeded = task_data["num_succeeded"]
num_tasks_in_progress = len(task_data["running_tasks"])
num_tasks_failed = len(task_data["failed_tasks"])
@@ -146,26 +290,26 @@ def print_task_info(task_data, mode):
print ", ".join(info_strings)
def scheduler_info(worker=global_worker):
"""Return information about the state of the scheduler."""
return ray.lib.scheduler_info(worker.handle)
def visualize_computation_graph(file_path=None, view=False, worker=global_worker):
"""
Write the computation graph to a pdf file.
"""Write the computation graph to a pdf file.
Args:
file_path: A .pdf file that the rendered computation graph will be written to
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.
view: If true, the result the python graphviz package will try to open the
result in a viewer
Examples:
In ray/scripts, call "python shell.py" and try the following code.
Example:
In ray/scripts, call "python shell.py" and paste in the following code.
x = da.zeros([20, 20])
y = da.zeros([20, 20])
z = da.dot(x, y)
ray.visualize_computation_graph("computation_graph.pdf")
>>> x = da.zeros([20, 20])
>>> y = da.zeros([20, 20])
>>> z = da.dot(x, y)
>>> ray.visualize_computation_graph(view=True)
"""
if file_path is None:
@@ -186,15 +330,17 @@ def visualize_computation_graph(file_path=None, view=False, worker=global_worker
print "Wrote computation graph to file {}.pdf".format(base_path)
def task_info(worker=global_worker):
"""Tell the scheduler to return task information. Currently includes a list of all failed tasks since the start of the cluster."""
"""Return information about failed tasks."""
return ray.lib.task_info(worker.handle)
def register_module(module, worker=global_worker):
"""
This registers each remote function in the module with the scheduler, so tasks
with those functions can be scheduled on this worker.
"""Register each remote function in a module with the scheduler.
:param module: The module of functions to register.
This registers each remote function in the module with the scheduler, so tasks
with those functions can be scheduled on this worker.
args:
module (module): The module of functions to register.
"""
logging.info("registering functions in module {}.".format(module.__name__))
for name in dir(module):
@@ -204,6 +350,17 @@ def register_module(module, worker=global_worker):
worker.register_function(val)
def connect(scheduler_address, objstore_address, worker_address, is_driver=False, worker=global_worker, mode=ray.WORKER_MODE):
"""Connect this worker to the scheduler and an object store.
Args:
scheduler_address (str): The ip address and port of the scheduler.
objstore_address (str): The ip address and port of the local object store.
worker_address (str): The ip address and port of this worker. The port can
be chosen arbitrarily.
is_driver (bool): True if this worker is a driver and false otherwise.
mode: The mode of the worker. One of ray.SCRIPT_MODE, ray.WORKER_MODE,
ray.SHELL_MODE, and ray.PYTHON_MODE.
"""
if hasattr(worker, "handle"):
del worker.handle
worker.scheduler_address = scheduler_address
@@ -216,15 +373,23 @@ def connect(scheduler_address, objstore_address, worker_address, is_driver=False
ray.lib.set_log_config(config.get_log_file_path("-".join(["worker", worker_address, "c++"]) + ".log"))
def disconnect(worker=global_worker):
"""Disconnect this worker from the scheduler and object store."""
if worker.handle is not None:
ray.lib.disconnect(worker.handle)
def get(objref, worker=global_worker):
"""
Get a remote object from an object store.
"""Get a remote object from an object store.
:param objref: Object reference to the object you want to get
:rtype: A Python value
This method blocks until the object corresponding to objref 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:
objref (ray.ObjRef): Object reference to the object to get.
Returns:
A Python object
"""
if worker.mode == ray.PYTHON_MODE:
return objref # In ray.PYTHON_MODE, ray.get is the identity operation (the input will actually be a value not an objref)
@@ -237,11 +402,13 @@ def get(objref, worker=global_worker):
return value
def put(value, worker=global_worker):
"""
Store an object in the object store.
"""Store an object in the object store.
:param value: The Python object to be stored
:rtype: Object reference
Args:
value (serializable object): The Python object to be stored.
Returns:
The object reference assigned to this value.
"""
if worker.mode == ray.PYTHON_MODE:
return value # In ray.PYTHON_MODE, ray.put is the identity operation
@@ -252,35 +419,52 @@ def put(value, worker=global_worker):
return objref
def kill_workers(worker=global_worker):
"""
This method kills all of the workers in the cluster. It does not kill drivers.
"""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 = ray.lib.kill_workers(worker.handle)
if not success:
print "Could not kill all workers. Check that there are no tasks currently running."
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):
"""
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.
"""Restart workers locally.
:param num_workers: the number of workers to be started
:param worker_path: path of the source code that will be run on the worker
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):
"""
This method takes an backtrace from an exception and makes it nicer by
removing a few uninformative lines and adding some space to indent the
remaining lines nicely.
"""Improve the formatting of an exception thrown by a remote function.
:param exception_message: a string generated by traceback.format_exc()
:rtype: a string
This method takes an backtrace 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
@@ -290,6 +474,21 @@ def format_error_message(exception_message):
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 tasks from the scheduler.
When it receives a task, it first deserializes the task. Then it retrieves the
values for any arguments that were passed in as object references. Then it
passes the arguments to the actual function. Then it stores the outputs of the
function in the local object store. Then it notifies the scheduler that it
completed the task.
If the process of getting the arguments for execution (which does some type
checking) or the process of executing the task fail, then the main loop will
catch the exception and store RayFailedObject objects containing the relevant
error messages in the object store in place of the actual outputs. These
objects are used to propagate the error messages.
"""
if not ray.lib.connected(worker.handle):
raise Exception("Worker is attempting to enter main_loop but has not been connected yet.")
ray.lib.start_worker_service(worker.handle)
@@ -321,15 +520,15 @@ def main_loop(worker=global_worker):
process_task(task)
def remote(arg_types, return_types, worker=global_worker):
"""
This is a decorator to indicate that a Python function is to be executed remotely.
"""This decorator is used to create remote functions.
:param arg_types: List of Python types of the function arguments
:param return_types: List of Python types of the return values
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_executor(arguments):
"""This is what gets executed remotely on a worker after a remote function is scheduled by the scheduler."""
"""This gets run when the remote function is executed."""
logging.info("Calling function {}".format(func.__name__))
start_time = time.time()
result = func(*arguments)
@@ -338,7 +537,7 @@ def remote(arg_types, return_types, worker=global_worker):
logging.info("Finished executing function {}, it took {} seconds".format(func.__name__, end_time - start_time))
return result
def func_call(*args, **kwargs):
"""This is what gets run immediately when a worker calls a remote function."""
"""This gets run immediately when a worker calls a remote function."""
args = list(args)
args.extend([kwargs[keyword] if kwargs.has_key(keyword) else default for keyword, default in func_call.keyword_defaults[len(args):]]) # fill in the remaining arguments
if worker.mode == ray.PYTHON_MODE:
@@ -365,10 +564,18 @@ def remote(arg_types, return_types, worker=global_worker):
return func_call
return remote_decorator
# helper method, this should not be called by the user
# we currently do not support the functionality that we test for in this method,
# but in the future we could
def check_signature_supported(function):
"""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:
function (Callable): The function to check.
Raises:
Exception: An exception is raised if the signature is not supported.
"""
# check if the user specified kwargs
if function.has_kwargs_param:
raise "Function {} has a **kwargs argument, which is currently not supported.".format(function.__name__)
@@ -377,8 +584,18 @@ def check_signature_supported(function):
raise "Function {} has a *args argument as well as a keyword argument, which is currently not supported.".format(function.__name__)
# helper method, this should not be called by the user
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:
@@ -403,6 +620,17 @@ def check_return_values(function, result):
raise Exception("The {}th return value for function {} has type {}, but the @remote decorator expected a return value of type {} or an ObjRef.".format(i, function.__name__, type(result[i]), function.return_types[i]))
def typecheck_arg(arg, expected_type, i, function):
"""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.
function (Callable): The remote function whose argument is being checked.
Raises:
Exception: 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
@@ -413,8 +641,19 @@ def typecheck_arg(arg, expected_type, i, function):
else:
raise Exception("Argument {} for function {} has type {} but an argument of type {} was expected.".format(i, function.__name__, type(arg), expected_type))
# helper method, this should not be called by the user
def check_arguments(function, 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:
function (Callable): The remote function whose arguments are being checked.
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(function.arg_types) and not function.has_vararg_param:
raise Exception("Function {} expects {} arguments, but received {}.".format(function.__name__, len(function.arg_types), len(args)))
@@ -435,17 +674,33 @@ def check_arguments(function, args):
else:
typecheck_arg(arg, expected_type, i, function)
# helper method, this should not be called by the user
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 references. 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:
Exception: An exception is raised the args do not all have the right types.
"""
# 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)))
"""
# # 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:
@@ -468,8 +723,26 @@ def get_arguments_for_execution(function, args, worker=global_worker):
arguments.append(argument)
return arguments
# helper method, this should not be called by the user
def store_outputs_in_objstore(objrefs, 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 references, then these
object references are aliased with the object references that the scheduler
assigned for the return values. This is called by the worker that executes the
remote function.
Note:
The arguments objrefs and outputs should have the same length.
Args:
objrefs (List[ray.ObjRef]): The object references 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(objrefs)):
if isinstance(outputs[i], ray.lib.ObjRef):
# An ObjRef is being returned, so we must alias objrefs[i] so that it refers to the same object that outputs[i] refers to