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e19e2c6284c7bd485a07ca8d53305c4a9f84cb73
ray/python/ray/worker.py
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Wapaul1 e19e2c6284 Print jupyter notebook token when starting web UI. (#887) 
* User now only needs to copy url to get to notebook

* Fixed duplicate code

* Added function to print url

* Added exception for calling function on worker

* Stored webui url in Redis

* Fix linting and simplify code.

* Now uses 24 bytes hex token

* Fixed python 3 compatibility

* Fix linting and python 3 compat

* Added comment explaining generating the token.

* Removed newline

* Small fixes.

* Fixed jenkins failure

* Rebased and changed formatting

* Revert "changed formatting"

This reverts commit 226510cf0cdcaab9cf42ad30bd9588a963683592.
2017-09-05 23:31:44 -07:00

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from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import atexit
import cloudpickle as pickle
import collections
import colorama
import copy
import hashlib
import inspect
import json
import numpy as np
import os
import redis
import signal
import sys
import threading
import time
import traceback
# Ray modules
import pyarrow
import pyarrow.plasma as plasma
import ray.experimental.state as state
import ray.serialization as serialization
import ray.services as services
import ray.signature as signature
import ray.local_scheduler
import ray.plasma
from ray.utils import FunctionProperties, random_string, binary_to_hex
SCRIPT_MODE = 0
WORKER_MODE = 1
PYTHON_MODE = 2
SILENT_MODE = 3
LOG_POINT = 0
LOG_SPAN_START = 1
LOG_SPAN_END = 2
ERROR_KEY_PREFIX = b"Error:"
DRIVER_ID_LENGTH = 20
ERROR_ID_LENGTH = 20
# This must match the definition of NIL_ACTOR_ID in task.h.
NIL_ID = 20 * b"\xff"
NIL_LOCAL_SCHEDULER_ID = NIL_ID
NIL_FUNCTION_ID = NIL_ID
NIL_ACTOR_ID = NIL_ID
# When performing ray.get, wait 1 second before attemping to reconstruct and
# fetch the object again.
GET_TIMEOUT_MILLISECONDS = 1000
# This must be kept in sync with the `error_types` array in
# common/state/error_table.h.
OBJECT_HASH_MISMATCH_ERROR_TYPE = b"object_hash_mismatch"
PUT_RECONSTRUCTION_ERROR_TYPE = b"put_reconstruction"
# This must be kept in sync with the `scheduling_state` enum in common/task.h.
TASK_STATUS_RUNNING = 8
class FunctionID(object):
def __init__(self, function_id):
self.function_id = function_id
def id(self):
return self.function_id
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 exception is
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)):
self.exception = exception
else:
self.exception = None
self.traceback_str = 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
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
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 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.
connected (bool): True if Ray has been started and False otherwise.
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 the
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.
cached_functions_to_run (List): A list of functions to run on all of
the workers that should be exported as soon as connect is called.
"""
def __init__(self):
"""Initialize a Worker object."""
# The functions field is a dictionary that maps a driver ID to a
# dictionary of functions that have been registered for that driver
# (this inner dictionary maps function IDs to a tuple of the function
# name and the function itself). This should only be used on workers
# that execute remote functions.
self.functions = collections.defaultdict(lambda: {})
# The function_properties field is a dictionary that maps a driver ID
# to a dictionary of functions that have been registered for that
# driver (this inner dictionary maps function IDs to a tuple of the
# number of values returned by that function, the number of CPUs
# required by that function, and the number of GPUs required by that
# function). This is used when submitting a function (which can be done
# both on workers and on drivers).
self.function_properties = collections.defaultdict(lambda: {})
# This is a dictionary mapping driver ID to a dictionary that maps
# remote function IDs for that driver to a counter of the number of
# times that remote function has been executed on this worker. The
# counter is incremented every time the function is executed on this
# worker. When the counter reaches the maximum number of executions
# allowed for a particular function, the worker is killed.
self.num_task_executions = collections.defaultdict(lambda: {})
self.connected = False
self.mode = None
self.cached_remote_functions = []
self.cached_functions_to_run = []
self.fetch_and_register_actor = None
self.make_actor = None
self.actors = {}
# Use a defaultdict for the actor counts. If this is accessed with a
# missing key, the default value of 0 is returned, and that key value
# pair is added to the dict.
self.actor_counters = collections.defaultdict(lambda: 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
def store_and_register(self, object_id, value, depth=100):
"""Store an object and attempt to register its class if needed.
Args:
object_id: The ID of the object to store.
value: The value to put in the object store.
depth: The maximum number of classes to recursively register.
Raises:
Exception: An exception is raised if the attempt to store the
object fails. This can happen if there is already an object
with the same ID in the object store or if the object store is
full.
"""
counter = 0
while True:
if counter == depth:
raise Exception("Ray exceeded the maximum number of classes "
"that it will recursively serialize when "
"attempting to serialize an object of "
"type {}.".format(type(value)))
counter += 1
try:
self.plasma_client.put(value, pyarrow.plasma.ObjectID(
object_id.id()), self.serialization_context)
break
except pyarrow.SerializationCallbackError as e:
try:
_register_class(type(e.example_object))
warning_message = ("WARNING: Serializing objects of type "
"{} by expanding them as dictionaries "
"of their fields. This behavior may "
"be incorrect in some cases."
.format(type(e.example_object)))
print(warning_message)
except serialization.RayNotDictionarySerializable:
_register_class(type(e.example_object), pickle=True)
warning_message = ("WARNING: Falling back to serializing "
"objects of type {} by using pickle. "
"This may be inefficient."
.format(type(e.example_object)))
print(warning_message)
def put_object(self, object_id, 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:
object_id (object_id.ObjectID): The object ID of the value to be
put.
value: The value to put in the object store.
Raises:
Exception: An exception is raised if the attempt to store the
object fails. This can happen if there is already an object
with the same ID in the object store or if the object store is
full.
"""
# Make sure that the value is not an object ID.
if isinstance(value, ray.local_scheduler.ObjectID):
raise Exception("Calling 'put' on an ObjectID is not allowed "
"(similarly, returning an ObjectID from a remote "
"function is not allowed). If you really want to "
"do this, you can wrap the ObjectID in a list and "
"call 'put' on it (or return it).")
# Serialize and put the object in the object store.
try:
self.store_and_register(object_id, value)
except pyarrow.PlasmaObjectExists as e:
# The object already exists in the object store, so there is no
# need to add it again. TODO(rkn): We need to compare the hashes
# and make sure that the objects are in fact the same. We also
# should return an error code to the caller instead of printing a
# message.
print("This object already exists in the object store.")
def retrieve_and_deserialize(self, object_ids, timeout, error_timeout=10):
start_time = time.time()
# Only send the warning once.
warning_sent = False
while True:
try:
# We divide very large get requests into smaller get requests
# so that a single get request doesn't block the store for a
# long time, if the store is blocked, it can block the manager
# as well as a consequence.
results = []
get_request_size = 10000
for i in range(0, len(object_ids), get_request_size):
results += self.plasma_client.get(
object_ids[i:(i + get_request_size)],
timeout,
self.serialization_context)
return results
except pyarrow.DeserializationCallbackError as e:
# Wait a little bit for the import thread to import the class.
# If we currently have the worker lock, we need to release it
# so that the import thread can acquire it.
if self.mode == WORKER_MODE:
self.lock.release()
time.sleep(0.01)
if self.mode == WORKER_MODE:
self.lock.acquire()
if time.time() - start_time > error_timeout:
warning_message = ("This worker or driver is waiting to "
"receive a class definition so that it "
"can deserialize an object from the "
"object store. This may be fine, or it "
"may be a bug.")
if not warning_sent:
self.push_error_to_driver(self.task_driver_id.id(),
"wait_for_class",
warning_message)
warning_sent = True
def get_object(self, object_ids):
"""Get the value or values in the object store associated with the IDs.
Return the values from the local object store for object_ids. This will
block until all the values for object_ids have been written to the
local object store.
Args:
object_ids (List[object_id.ObjectID]): A list of the object IDs
whose values should be retrieved.
"""
# Make sure that the values are object IDs.
for object_id in object_ids:
if not isinstance(object_id, ray.local_scheduler.ObjectID):
raise Exception("Attempting to call `get` on the value {}, "
"which is not an ObjectID.".format(object_id))
# Do an initial fetch for remote objects. We divide the fetch into
# smaller fetches so as to not block the manager for a prolonged period
# of time in a single call.
fetch_request_size = 10000
plain_object_ids = [plasma.ObjectID(object_id.id())
for object_id in object_ids]
for i in range(0, len(object_ids), fetch_request_size):
self.plasma_client.fetch(
plain_object_ids[i:(i + fetch_request_size)])
# Get the objects. We initially try to get the objects immediately.
final_results = self.retrieve_and_deserialize(plain_object_ids, 0)
# Construct a dictionary mapping object IDs that we haven't gotten yet
# to their original index in the object_ids argument.
unready_ids = dict((plain_object_ids[i].binary(), i) for (i, val) in
enumerate(final_results)
if val is plasma.ObjectNotAvailable)
was_blocked = (len(unready_ids) > 0)
# Try reconstructing any objects we haven't gotten yet. Try to get them
# until at least GET_TIMEOUT_MILLISECONDS milliseconds passes, then
# repeat.
while len(unready_ids) > 0:
for unready_id in unready_ids:
self.local_scheduler_client.reconstruct_object(unready_id)
# Do another fetch for objects that aren't available locally yet,
# in case they were evicted since the last fetch. We divide the
# fetch into smaller fetches so as to not block the manager for a
# prolonged period of time in a single call.
object_ids_to_fetch = list(map(
plasma.ObjectID, unready_ids.keys()))
for i in range(0, len(object_ids_to_fetch), fetch_request_size):
self.plasma_client.fetch(
object_ids_to_fetch[i:(i + fetch_request_size)])
results = self.retrieve_and_deserialize(
object_ids_to_fetch,
max([GET_TIMEOUT_MILLISECONDS, int(0.01 * len(unready_ids))]))
# Remove any entries for objects we received during this iteration
# so we don't retrieve the same object twice.
for i, val in enumerate(results):
if val is not plasma.ObjectNotAvailable:
object_id = object_ids_to_fetch[i].binary()
index = unready_ids[object_id]
final_results[index] = val
unready_ids.pop(object_id)
# If there were objects that we weren't able to get locally, let the
# local scheduler know that we're now unblocked.
if was_blocked:
self.local_scheduler_client.notify_unblocked()
assert len(final_results) == len(object_ids)
return final_results
def submit_task(self, function_id, args, actor_id=None):
"""Submit a remote task to the scheduler.
Tell the scheduler to schedule the execution of the function with ID
function_id with arguments args. Retrieve object IDs for the outputs of
the function from the scheduler and immediately return them.
Args:
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.
"""
with log_span("ray:submit_task", worker=self):
check_main_thread()
actor_id = (ray.local_scheduler.ObjectID(NIL_ACTOR_ID)
if actor_id is None else actor_id)
# Put large or complex arguments that are passed by value in the
# object store first.
args_for_local_scheduler = []
for arg in args:
if isinstance(arg, ray.local_scheduler.ObjectID):
args_for_local_scheduler.append(arg)
elif ray.local_scheduler.check_simple_value(arg):
args_for_local_scheduler.append(arg)
else:
args_for_local_scheduler.append(put(arg))
# Look up the various function properties.
function_properties = self.function_properties[
self.task_driver_id.id()][function_id.id()]
# Submit the task to local scheduler.
task = ray.local_scheduler.Task(
self.task_driver_id,
ray.local_scheduler.ObjectID(function_id.id()),
args_for_local_scheduler,
function_properties.num_return_vals,
self.current_task_id,
self.task_index,
actor_id,
self.actor_counters[actor_id],
[function_properties.num_cpus, function_properties.num_gpus,
function_properties.num_custom_resource])
# Increment the worker's task index to track how many tasks have
# been submitted by the current task so far.
self.task_index += 1
self.actor_counters[actor_id] += 1
self.local_scheduler_client.submit(task)
return task.returns()
def run_function_on_all_workers(self, function):
"""Run arbitrary code on all of the workers.
This function will first be run on the driver, and then it will be
exported to all of the workers to be run. It will also be run on any
new workers that register later. If ray.init has not been called yet,
then cache the function and export it later.
Args:
function (Callable): The function to run on all of the workers. It
should not take any arguments. If it returns anything, its
return values will not be used.
"""
check_main_thread()
# If ray.init has not been called yet, then cache the function and
# export it when connect is called. Otherwise, run the function on all
# workers.
if self.mode is None:
self.cached_functions_to_run.append(function)
else:
# Attempt to pickle the function before we need it. This could
# fail, and it is more convenient if the failure happens before we
# actually run the function locally.
pickled_function = pickle.dumps(function)
function_to_run_id = random_string()
key = b"FunctionsToRun:" + function_to_run_id
# First run the function on the driver. Pass in the number of
# workers on this node that have already started executing this
# remote function, and increment that value. Subtract 1 so that the
# counter starts at 0.
counter = self.redis_client.hincrby(self.node_ip_address,
key, 1) - 1
function({"counter": counter, "worker": self})
# Run the function on all workers.
self.redis_client.hmset(key,
{"driver_id": self.task_driver_id.id(),
"function_id": function_to_run_id,
"function": pickled_function})
self.redis_client.rpush("Exports", key)
def push_error_to_driver(self, driver_id, error_type, message, data=None):
"""Push an error message to the driver to be printed in the background.
Args:
driver_id: The ID of the driver to push the error message to.
error_type (str): The type of the error.
message (str): The message that will be printed in the background
on the driver.
data: This should be a dictionary mapping strings to strings. It
will be serialized with json and stored in Redis.
"""
error_key = ERROR_KEY_PREFIX + driver_id + b":" + random_string()
data = {} if data is None else data
self.redis_client.hmset(error_key, {"type": error_type,
"message": message,
"data": data})
self.redis_client.rpush("ErrorKeys", error_key)
def _wait_for_actor(self):
"""Wait until the actor has been imported."""
assert self.actor_id != NIL_ACTOR_ID
# Wait until the actor has been imported.
while self.actor_id not in self.actors:
time.sleep(0.001)
def _wait_for_function(self, function_id, driver_id, timeout=10):
"""Wait until the function to be executed is present on this worker.
This method will simply loop until the import thread has imported the
relevant function. If we spend too long in this loop, that may indicate
a problem somewhere and we will push an error message to the user.
If this worker is an actor, then this will wait until the actor has
been defined.
Args:
is_actor (bool): True if this worker is an actor, and false
otherwise.
function_id (str): The ID of the function that we want to execute.
driver_id (str): The ID of the driver to push the error message to
if this times out.
"""
start_time = time.time()
# Only send the warning once.
warning_sent = False
while True:
with self.lock:
if (self.actor_id == NIL_ACTOR_ID and
(function_id.id() in self.functions[driver_id])):
break
elif self.actor_id != NIL_ACTOR_ID and (self.actor_id in
self.actors):
break
if time.time() - start_time > timeout:
warning_message = ("This worker was asked to execute a "
"function that it does not have "
"registered. You may have to restart "
"Ray.")
if not warning_sent:
self.push_error_to_driver(driver_id,
"wait_for_function",
warning_message)
warning_sent = True
time.sleep(0.001)
def _get_arguments_for_execution(self, function_name, serialized_args):
"""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 is called by the worker that is executing the remote
function.
Args:
function_name (str): The name of the remote function whose
arguments are being retrieved.
serialized_args (List): The arguments to the function. These are
either strings representing serialized objects passed by value
or they are ObjectIDs.
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.
"""
arguments = []
for (i, arg) in enumerate(serialized_args):
if isinstance(arg, ray.local_scheduler.ObjectID):
# get the object from the local object store
argument = self.get_object([arg])[0]
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)
else:
# pass the argument by value
argument = arg
arguments.append(argument)
return arguments
def _store_outputs_in_objstore(self, objectids, outputs):
"""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[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)):
self.put_object(objectids[i], outputs[i])
def _process_task(self, task):
"""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).
"""
try:
# The ID of the driver that this task belongs to. This is needed so
# that if the task throws an exception, we propagate the error
# message to the correct driver.
self.task_driver_id = task.driver_id()
self.current_task_id = task.task_id()
self.current_function_id = task.function_id().id()
self.task_index = 0
self.put_index = 0
function_id = task.function_id()
args = task.arguments()
return_object_ids = task.returns()
function_name, function_executor = (self.functions
[self.task_driver_id.id()]
[function_id.id()])
# Get task arguments from the object store.
with log_span("ray:task:get_arguments", worker=self):
arguments = self._get_arguments_for_execution(function_name,
args)
# Execute the task.
with log_span("ray:task:execute", worker=self):
if task.actor_id().id() == NIL_ACTOR_ID:
outputs = function_executor.executor(arguments)
else:
outputs = function_executor(
self.actors[task.actor_id().id()], *arguments)
# Store the outputs in the local object store.
with log_span("ray:task:store_outputs", worker=self):
if len(return_object_ids) == 1:
outputs = (outputs,)
self._store_outputs_in_objstore(return_object_ids, outputs)
except Exception as e:
# We determine whether the exception was caused by the call to
# _get_arguments_for_execution or by the execution of the remote
# function or by the call to _store_outputs_in_objstore. Depending
# on which case occurred, we format the error message differently.
# whether the variables "arguments" and "outputs" are defined.
if "arguments" in locals() and "outputs" not in locals():
if task.actor_id().id() == NIL_ACTOR_ID:
# The error occurred during the task execution.
traceback_str = format_error_message(
traceback.format_exc(), task_exception=True)
else:
# The error occurred during the execution of an actor task.
traceback_str = format_error_message(
traceback.format_exc())
elif "arguments" in locals() and "outputs" in locals():
# The error occurred after the task executed.
traceback_str = format_error_message(traceback.format_exc())
else:
# The error occurred before the task execution.
if (isinstance(e, RayGetError) or
isinstance(e, RayGetArgumentError)):
# In this case, getting the task arguments failed.
traceback_str = None
else:
traceback_str = traceback.format_exc()
failure_object = RayTaskError(function_name, e, traceback_str)
failure_objects = [failure_object for _
in range(len(return_object_ids))]
self._store_outputs_in_objstore(return_object_ids, failure_objects)
# Log the error message.
self.push_error_to_driver(self.task_driver_id.id(), "task",
str(failure_object),
data={"function_id": function_id.id(),
"function_name": function_name})
def _checkpoint_actor_state(self, actor_counter):
"""Checkpoint the actor state.
This currently saves the checkpoint to Redis, but the checkpoint really
needs to go somewhere else.
Args:
actor_counter: The index of the most recent task that ran on this
actor.
"""
print("Saving actor checkpoint. actor_counter = {}."
.format(actor_counter))
actor_key = b"Actor:" + self.actor_id
checkpoint = self.actors[self.actor_id].__ray_save_checkpoint__()
# Save the checkpoint in Redis. TODO(rkn): Checkpoints should not
# be stored in Redis. Fix this.
self.redis_client.hset(
actor_key,
"checkpoint_{}".format(actor_counter),
checkpoint)
# Remove the previous checkpoints if there is one.
checkpoint_indices = [int(key[len(b"checkpoint_"):])
for key in self.redis_client.hkeys(actor_key)
if key.startswith(b"checkpoint_")]
for index in checkpoint_indices:
if index < actor_counter:
self.redis_client.hdel(actor_key,
"checkpoint_{}".format(index))
def _wait_for_and_process_task(self, task):
"""Wait for a task to be ready and process the task.
Args:
task: The task to execute.
"""
function_id = task.function_id()
# Wait until the function to be executed has actually been registered
# on this worker. We will push warnings to the user if we spend too
# long in this loop.
with log_span("ray:wait_for_function", worker=self):
self._wait_for_function(function_id, task.driver_id().id())
# Execute the task.
# TODO(rkn): Consider acquiring this lock with a timeout and pushing a
# warning to the user if we are waiting too long to acquire the lock
# because that may indicate that the system is hanging, and it'd be
# good to know where the system is hanging.
log(event_type="ray:acquire_lock", kind=LOG_SPAN_START, worker=self)
with self.lock:
log(event_type="ray:acquire_lock", kind=LOG_SPAN_END,
worker=self)
function_name, _ = (self.functions[task.driver_id().id()]
[function_id.id()])
contents = {"function_name": function_name,
"task_id": task.task_id().hex(),
"worker_id": binary_to_hex(self.worker_id)}
with log_span("ray:task", contents=contents, worker=self):
self._process_task(task)
# Push all of the log events to the global state store.
flush_log()
# Increase the task execution counter.
(self.num_task_executions[task.driver_id().id()]
[function_id.id()]) += 1
reached_max_executions = (
self.num_task_executions[task.driver_id().id()]
[function_id.id()] ==
self.function_properties[task.driver_id().id()]
[function_id.id()].max_calls)
if reached_max_executions:
ray.worker.global_worker.local_scheduler_client.disconnect()
os._exit(0)
# Checkpoint the actor state if it is the right time to do so.
actor_counter = task.actor_counter()
if (self.actor_id != NIL_ACTOR_ID and
self.actor_checkpoint_interval != -1 and
actor_counter % self.actor_checkpoint_interval == 0):
self._checkpoint_actor_state(actor_counter)
def _get_next_task_from_local_scheduler(self):
"""Get the next task from the local scheduler.
Returns:
A task from the local scheduler.
"""
with log_span("ray:get_task", worker=self):
task = self.local_scheduler_client.get_task()
return task
def main_loop(self):
"""The main loop a worker runs to receive and execute tasks."""
def exit(signum, frame):
cleanup(worker=self)
sys.exit(0)
signal.signal(signal.SIGTERM, exit)
check_main_thread()
while True:
task = self._get_next_task_from_local_scheduler()
self._wait_for_and_process_task(task)
def get_gpu_ids():
"""Get the IDs of the GPU that are available to the worker.
Each ID is an integer in the range [0, NUM_GPUS - 1], where NUM_GPUS is the
number of GPUs that the node has.
"""
return global_worker.local_scheduler_client.gpu_ids()
def _webui_url_helper(client):
"""Parsing for getting the url of the web UI.
Args:
client: A redis client to use to query the primary Redis shard.
Returns:
The URL of the web UI as a string.
"""
result = client.hmget("webui", "url")[0]
return result.decode("ascii") if result is not None else result
def get_webui_url():
"""Get the URL to access the web UI.
Note that the URL does not specify which node the web UI is on.
Returns:
The URL of the web UI as a string.
"""
return _webui_url_helper(global_worker.redis_client)
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.
"""
global_state = state.GlobalState()
class RayConnectionError(Exception):
pass
def check_main_thread():
"""Check that we are currently on the main thread.
Raises:
Exception: An exception is raised if this is called on a thread other
than the main thread.
"""
if threading.current_thread().getName() != "MainThread":
raise Exception("The Ray methods are not thread safe and must be "
"called from the main thread. This method was called "
"from thread {}."
.format(threading.current_thread().getName()))
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 not worker.connected:
raise RayConnectionError("This command cannot be called before Ray "
"has been started. You can start Ray with "
"'ray.init()'.")
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 error_applies_to_driver(error_key, worker=global_worker):
"""Return True if the error is for this driver and false otherwise."""
# TODO(rkn): Should probably check that this is only called on a driver.
# Check that the error key is formatted as in push_error_to_driver.
assert len(error_key) == (len(ERROR_KEY_PREFIX) + DRIVER_ID_LENGTH + 1 +
ERROR_ID_LENGTH), error_key
# If the driver ID in the error message is a sequence of all zeros, then
# the message is intended for all drivers.
generic_driver_id = DRIVER_ID_LENGTH * b"\x00"
driver_id = error_key[len(ERROR_KEY_PREFIX):(len(ERROR_KEY_PREFIX) +
DRIVER_ID_LENGTH)]
return (driver_id == worker.task_driver_id.id() or
driver_id == generic_driver_id)
def error_info(worker=global_worker):
"""Return information about failed tasks."""
check_connected(worker)
check_main_thread()
error_keys = worker.redis_client.lrange("ErrorKeys", 0, -1)
errors = []
for error_key in error_keys:
if error_applies_to_driver(error_key, worker=worker):
error_contents = worker.redis_client.hgetall(error_key)
# If the error is an object hash mismatch, look up the function
# name for the nondeterministic task. TODO(rkn): Change this so
# that we don't have to look up additional information. Ideally all
# relevant information would already be in error_contents.
error_type = error_contents[b"type"]
if error_type in [OBJECT_HASH_MISMATCH_ERROR_TYPE,
PUT_RECONSTRUCTION_ERROR_TYPE]:
function_id = error_contents[b"data"]
if function_id == NIL_FUNCTION_ID:
function_name = b"Driver"
else:
task_driver_id = worker.task_driver_id
function_name = worker.redis_client.hget(
(b"RemoteFunction:" + task_driver_id.id() +
b":" + function_id),
"name")
error_contents[b"data"] = function_name
errors.append(error_contents)
return errors
def _initialize_serialization(worker=global_worker):
"""Initialize the serialization library.
This defines a custom serializer for object IDs and also tells ray to
serialize several exception classes that we define for error handling.
"""
worker.serialization_context = pyarrow.SerializationContext()
# Define a custom serializer and deserializer for handling Object IDs.
def objectid_custom_serializer(obj):
return obj.id()
def objectid_custom_deserializer(serialized_obj):
return ray.local_scheduler.ObjectID(serialized_obj)
worker.serialization_context.register_type(
ray.local_scheduler.ObjectID, 20 * b"\x00", pickle=False,
custom_serializer=objectid_custom_serializer,
custom_deserializer=objectid_custom_deserializer)
# Define a custom serializer and deserializer for handling numpy arrays
# that contain objects.
def array_custom_serializer(obj):
return obj.tolist(), obj.dtype.str
def array_custom_deserializer(serialized_obj):
return np.array(serialized_obj[0], dtype=np.dtype(serialized_obj[1]))
worker.serialization_context.register_type(
np.ndarray, 20 * b"\x01", pickle=False,
custom_serializer=array_custom_serializer,
custom_deserializer=array_custom_deserializer)
if worker.mode in [SCRIPT_MODE, SILENT_MODE]:
# These should only be called on the driver because _register_class
# will export the class to all of the workers.
_register_class(RayTaskError)
_register_class(RayGetError)
_register_class(RayGetArgumentError)
# Tell Ray to serialize lambdas with pickle.
_register_class(type(lambda: 0), pickle=True)
# Tell Ray to serialize sets with pickle.
_register_class(type(set()), pickle=True)
# Tell Ray to serialize types with pickle.
_register_class(type(int), pickle=True)
def get_address_info_from_redis_helper(redis_address, node_ip_address):
redis_ip_address, redis_port = redis_address.split(":")
# For this command to work, some other client (on the same machine as
# Redis) must have run "CONFIG SET protected-mode no".
redis_client = redis.StrictRedis(host=redis_ip_address,
port=int(redis_port))
# The client table prefix must be kept in sync with the file
# "src/common/redis_module/ray_redis_module.cc" where it is defined.
REDIS_CLIENT_TABLE_PREFIX = "CL:"
client_keys = redis_client.keys("{}*".format(REDIS_CLIENT_TABLE_PREFIX))
# Filter to live clients on the same node and do some basic checking.
plasma_managers = []
local_schedulers = []
for key in client_keys:
info = redis_client.hgetall(key)
# Ignore clients that were deleted.
deleted = info[b"deleted"]
deleted = bool(int(deleted))
if deleted:
continue
assert b"ray_client_id" in info
assert b"node_ip_address" in info
assert b"client_type" in info
if info[b"node_ip_address"].decode("ascii") == node_ip_address:
if info[b"client_type"].decode("ascii") == "plasma_manager":
plasma_managers.append(info)
elif info[b"client_type"].decode("ascii") == "local_scheduler":
local_schedulers.append(info)
# Make sure that we got at least one plasma manager and local scheduler.
assert len(plasma_managers) >= 1
assert len(local_schedulers) >= 1
# Build the address information.
object_store_addresses = []
for manager in plasma_managers:
address = manager[b"address"].decode("ascii")
port = services.get_port(address)
object_store_addresses.append(
services.ObjectStoreAddress(
name=manager[b"store_socket_name"].decode("ascii"),
manager_name=manager[b"manager_socket_name"].decode("ascii"),
manager_port=port))
scheduler_names = [
scheduler[b"local_scheduler_socket_name"].decode("ascii")
for scheduler in local_schedulers]
client_info = {"node_ip_address": node_ip_address,
"redis_address": redis_address,
"object_store_addresses": object_store_addresses,
"local_scheduler_socket_names": scheduler_names,
# Web UI should be running.
"webui_url": _webui_url_helper(redis_client)}
return client_info
def get_address_info_from_redis(redis_address, node_ip_address, num_retries=5):
counter = 0
while True:
try:
return get_address_info_from_redis_helper(redis_address,
node_ip_address)
except Exception as e:
if counter == num_retries:
raise
# Some of the information may not be in Redis yet, so wait a little
# bit.
print("Some processes that the driver needs to connect to have "
"not registered with Redis, so retrying. Have you run "
"'ray start' on this node?")
time.sleep(1)
counter += 1
def _init(address_info=None,
start_ray_local=False,
object_id_seed=None,
num_workers=None,
num_local_schedulers=None,
object_store_memory=None,
driver_mode=SCRIPT_MODE,
redirect_output=False,
start_workers_from_local_scheduler=True,
num_cpus=None,
num_gpus=None,
num_custom_resource=None,
num_redis_shards=None):
"""Helper method to connect to an existing Ray cluster or start a new one.
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:
address_info (dict): A dictionary with address information for
processes in a partially-started Ray cluster. If
start_ray_local=True, any processes not in this dictionary will be
started. If provided, an updated address_info dictionary will be
returned to include processes that are newly started.
start_ray_local (bool): If True then this will start any processes not
already in address_info, including Redis, a global scheduler, local
scheduler(s), object store(s), and worker(s). It will also kill
these processes when Python exits. If False, this will attach to an
existing Ray cluster.
object_id_seed (int): Used to seed the deterministic generation of
object IDs. The same value can be used across multiple runs of the
same job in order to generate the object IDs in a consistent
manner. However, the same ID should not be used for different jobs.
num_workers (int): The number of workers to start. This is only
provided if start_ray_local is True.
num_local_schedulers (int): The number of local schedulers to start.
This is only provided if start_ray_local is True.
object_store_memory: The amount of memory (in bytes) to start the
object store with.
driver_mode (bool): The mode in which to start the driver. This should
be one of ray.SCRIPT_MODE, ray.PYTHON_MODE, and ray.SILENT_MODE.
redirect_output (bool): True if stdout and stderr for all the processes
should be redirected to files and false otherwise.
start_workers_from_local_scheduler (bool): If this flag is True, then
start the initial workers from the local scheduler. Else, start
them from Python. The latter case is for debugging purposes only.
num_cpus: A list containing the number of CPUs the local schedulers
should be configured with.
num_gpus: A list containing the number of GPUs the local schedulers
should be configured with.
num_custom_resource: A list containing the quantity of a user-defined
custom resource that the local schedulers should be configured
with.
num_redis_shards: The number of Redis shards to start in addition to
the primary Redis shard.
Returns:
Address information about the started processes.
Raises:
Exception: An exception is raised if an inappropriate combination of
arguments is passed in.
"""
check_main_thread()
if driver_mode not in [SCRIPT_MODE, PYTHON_MODE, SILENT_MODE]:
raise Exception("Driver_mode must be in [ray.SCRIPT_MODE, "
"ray.PYTHON_MODE, ray.SILENT_MODE].")
# Get addresses of existing services.
if address_info is None:
address_info = {}
else:
assert isinstance(address_info, dict)
node_ip_address = address_info.get("node_ip_address")
redis_address = address_info.get("redis_address")
# Start any services that do not yet exist.
if driver_mode == PYTHON_MODE:
# If starting Ray in PYTHON_MODE, don't start any other processes.
pass
elif start_ray_local:
# In this case, we launch a scheduler, a new object store, and some
# workers, and we connect to them. We do not launch any processes that
# are already registered in address_info.
# Use the address 127.0.0.1 in local mode.
node_ip_address = ("127.0.0.1" if node_ip_address is None
else node_ip_address)
# Use 1 local scheduler if num_local_schedulers is not provided. If
# existing local schedulers are provided, use that count as
# num_local_schedulers.
local_schedulers = address_info.get("local_scheduler_socket_names", [])
if num_local_schedulers is None:
if len(local_schedulers) > 0:
num_local_schedulers = len(local_schedulers)
else:
num_local_schedulers = 1
# Use 1 additional redis shard if num_redis_shards is not provided.
num_redis_shards = 1 if num_redis_shards is None else num_redis_shards
# Start the scheduler, object store, and some workers. These will be
# killed by the call to cleanup(), which happens when the Python script
# exits.
address_info = services.start_ray_head(
address_info=address_info,
node_ip_address=node_ip_address,
num_workers=num_workers,
num_local_schedulers=num_local_schedulers,
object_store_memory=object_store_memory,
redirect_output=redirect_output,
start_workers_from_local_scheduler=(
start_workers_from_local_scheduler),
num_cpus=num_cpus,
num_gpus=num_gpus,
num_custom_resource=num_custom_resource,
num_redis_shards=num_redis_shards)
else:
if redis_address is None:
raise Exception("When connecting to an existing cluster, "
"redis_address must be provided.")
if num_workers is not None:
raise Exception("When connecting to an existing cluster, "
"num_workers must not be provided.")
if num_local_schedulers is not None:
raise Exception("When connecting to an existing cluster, "
"num_local_schedulers must not be provided.")
if (num_cpus is not None or num_gpus is not None or
num_custom_resource is not None):
raise Exception("When connecting to an existing cluster, resource "
"labels (e.g., num_gpus, num_cpus, "
"num_custom_resource) must not be provided.")
if num_redis_shards is not None:
raise Exception("When connecting to an existing cluster, "
"num_redis_shards must not be provided.")
if object_store_memory is not None:
raise Exception("When connecting to an existing cluster, "
"object_store_memory must not be provided.")
# Get the node IP address if one is not provided.
if node_ip_address is None:
node_ip_address = services.get_node_ip_address(redis_address)
# Get the address info of the processes to connect to from Redis.
address_info = get_address_info_from_redis(redis_address,
node_ip_address)
# Connect this driver to Redis, the object store, and the local scheduler.
# Choose the first object store and local scheduler if there are multiple.
# The corresponding call to disconnect will happen in the call to cleanup()
# when the Python script exits.
if driver_mode == PYTHON_MODE:
driver_address_info = {}
else:
driver_address_info = {
"node_ip_address": node_ip_address,
"redis_address": address_info["redis_address"],
"store_socket_name": (
address_info["object_store_addresses"][0].name),
"manager_socket_name": (
address_info["object_store_addresses"][0].manager_name),
"local_scheduler_socket_name": (
address_info["local_scheduler_socket_names"][0]),
"webui_url": address_info["webui_url"]}
connect(driver_address_info, object_id_seed=object_id_seed,
mode=driver_mode, worker=global_worker, actor_id=NIL_ACTOR_ID)
return address_info
def init(redis_address=None, node_ip_address=None, object_id_seed=None,
num_workers=None, driver_mode=SCRIPT_MODE, redirect_output=False,
num_cpus=None, num_gpus=None, num_custom_resource=None,
num_redis_shards=None):
"""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:
node_ip_address (str): The IP address of the node that we are on.
redis_address (str): The address of the Redis server to connect to. If
this address is not provided, then this command will start Redis, a
global scheduler, a local scheduler, a plasma store, a plasma
manager, and some workers. It will also kill these processes when
Python exits.
object_id_seed (int): Used to seed the deterministic generation of
object IDs. The same value can be used across multiple runs of the
same job in order to generate the object IDs in a consistent
manner. However, the same ID should not be used for different jobs.
num_workers (int): The number of workers to start. This is only
provided if redis_address is not provided.
driver_mode (bool): The mode in which to start the driver. This should
be one of ray.SCRIPT_MODE, ray.PYTHON_MODE, and ray.SILENT_MODE.
redirect_output (bool): True if stdout and stderr for all the processes
should be redirected to files and false otherwise.
num_cpus (int): Number of cpus the user wishes all local schedulers to
be configured with.
num_gpus (int): Number of gpus the user wishes all local schedulers to
be configured with.
num_custom_resource (int): The quantity of a user-defined custom
resource that the local scheduler should be configured with. This
flag is experimental and is subject to changes in the future.
num_redis_shards: The number of Redis shards to start in addition to
the primary Redis shard.
Returns:
Address information about the started processes.
Raises:
Exception: An exception is raised if an inappropriate combination of
arguments is passed in.
"""
info = {"node_ip_address": node_ip_address,
"redis_address": redis_address}
return _init(address_info=info, start_ray_local=(redis_address is None),
num_workers=num_workers, driver_mode=driver_mode,
redirect_output=redirect_output, num_cpus=num_cpus,
num_gpus=num_gpus, num_custom_resource=num_custom_resource,
num_redis_shards=num_redis_shards)
def cleanup(worker=global_worker):
"""Disconnect the worker, 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)
if hasattr(worker, "local_scheduler_client"):
del worker.local_scheduler_client
if hasattr(worker, "plasma_client"):
worker.plasma_client.disconnect()
if worker.mode in [SCRIPT_MODE, SILENT_MODE]:
# If this is a driver, push the finish time to Redis and clean up any
# other services that were started with the driver.
worker.redis_client.hmset(b"Drivers:" + worker.worker_id,
{"end_time": time.time()})
services.cleanup()
else:
# If this is not a driver, make sure there are no orphan processes,
# besides possibly the worker itself.
for process_type, processes in services.all_processes.items():
if process_type == services.PROCESS_TYPE_WORKER:
assert(len(processes)) <= 1
else:
assert(len(processes) == 0)
worker.set_mode(None)
atexit.register(cleanup)
# Define a custom excepthook so that if the driver exits with an exception, we
# can push that exception to Redis.
normal_excepthook = sys.excepthook
def custom_excepthook(type, value, tb):
# If this is a driver, push the exception to redis.
if global_worker.mode in [SCRIPT_MODE, SILENT_MODE]:
error_message = "".join(traceback.format_tb(tb))
global_worker.redis_client.hmset(b"Drivers:" + global_worker.worker_id,
{"exception": error_message})
# Call the normal excepthook.
normal_excepthook(type, value, tb)
sys.excepthook = custom_excepthook
def print_error_messages(worker):
"""Print error messages in the background on the driver.
This runs in a separate thread on the driver and prints error messages in
the background.
"""
# TODO(rkn): All error messages should have a "component" field indicating
# which process the error came from (e.g., a worker or a plasma store).
# Currently all error messages come from workers.
helpful_message = """
You can inspect errors by running
ray.error_info()
If this driver is hanging, start a new one with
ray.init(redis_address="{}")
""".format(worker.redis_address)
worker.error_message_pubsub_client = worker.redis_client.pubsub()
# Exports that are published after the call to
# error_message_pubsub_client.psubscribe and before the call to
# error_message_pubsub_client.listen will still be processed in the loop.
worker.error_message_pubsub_client.psubscribe("__keyspace@0__:ErrorKeys")
num_errors_received = 0
# Get the exports that occurred before the call to psubscribe.
with worker.lock:
error_keys = worker.redis_client.lrange("ErrorKeys", 0, -1)
for error_key in error_keys:
if error_applies_to_driver(error_key, worker=worker):
error_message = worker.redis_client.hget(
error_key, "message").decode("ascii")
print(error_message)
print(helpful_message)
num_errors_received += 1
try:
for msg in worker.error_message_pubsub_client.listen():
with worker.lock:
for error_key in worker.redis_client.lrange(
"ErrorKeys", num_errors_received, -1):
if error_applies_to_driver(error_key, worker=worker):
error_message = worker.redis_client.hget(
error_key, "message").decode("ascii")
print(error_message)
print(helpful_message)
num_errors_received += 1
except redis.ConnectionError:
# When Redis terminates the listen call will throw a ConnectionError,
# which we catch here.
pass
def fetch_and_register_remote_function(key, worker=global_worker):
"""Import a remote function."""
(driver_id, function_id_str, function_name,
serialized_function, num_return_vals, module, num_cpus,
num_gpus, num_custom_resource, max_calls) = worker.redis_client.hmget(
key, ["driver_id",
"function_id",
"name",
"function",
"num_return_vals",
"module",
"num_cpus",
"num_gpus",
"num_custom_resource",
"max_calls"])
function_id = ray.local_scheduler.ObjectID(function_id_str)
function_name = function_name.decode("ascii")
function_properties = FunctionProperties(
num_return_vals=int(num_return_vals),
num_cpus=int(num_cpus),
num_gpus=int(num_gpus),
num_custom_resource=int(num_custom_resource),
max_calls=int(max_calls))
module = module.decode("ascii")
# This is a placeholder in case the function can't be unpickled. This will
# be overwritten if the function is successfully registered.
def f():
raise Exception("This function was not imported properly.")
remote_f_placeholder = remote(function_id=function_id)(lambda *xs: f())
worker.functions[driver_id][function_id.id()] = (function_name,
remote_f_placeholder)
worker.function_properties[driver_id][function_id.id()] = (
function_properties)
worker.num_task_executions[driver_id][function_id.id()] = 0
try:
function = pickle.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())
# Log the error message.
worker.push_error_to_driver(driver_id, "register_remote_function",
traceback_str,
data={"function_id": function_id.id(),
"function_name": function_name})
else:
# TODO(rkn): Why is the below line necessary?
function.__module__ = module
worker.functions[driver_id][function_id.id()] = (
function_name, remote(function_id=function_id)(function))
# Add the function to the function table.
worker.redis_client.rpush(b"FunctionTable:" + function_id.id(),
worker.worker_id)
def fetch_and_execute_function_to_run(key, worker=global_worker):
"""Run on arbitrary function on the worker."""
driver_id, serialized_function = worker.redis_client.hmget(
key, ["driver_id", "function"])
# Get the number of workers on this node that have already started
# executing this remote function, and increment that value. Subtract 1 so
# the counter starts at 0.
counter = worker.redis_client.hincrby(worker.node_ip_address, key, 1) - 1
try:
# Deserialize the function.
function = pickle.loads(serialized_function)
# Run the function.
function({"counter": counter, "worker": worker})
except:
# If an exception was thrown when the function was run, we record the
# traceback and notify the scheduler of the failure.
traceback_str = traceback.format_exc()
# Log the error message.
name = function.__name__ if ("function" in locals() and
hasattr(function, "__name__")) else ""
worker.push_error_to_driver(driver_id, "function_to_run",
traceback_str, data={"name": name})
def import_thread(worker, mode):
worker.import_pubsub_client = worker.redis_client.pubsub()
# Exports that are published after the call to
# import_pubsub_client.psubscribe and before the call to
# import_pubsub_client.listen will still be processed in the loop.
worker.import_pubsub_client.psubscribe("__keyspace@0__:Exports")
# Keep track of the number of imports that we've imported.
num_imported = 0
# Get the exports that occurred before the call to psubscribe.
with worker.lock:
export_keys = worker.redis_client.lrange("Exports", 0, -1)
for key in export_keys:
num_imported += 1
# Handle the driver case first.
if mode != WORKER_MODE:
if key.startswith(b"FunctionsToRun"):
fetch_and_execute_function_to_run(key, worker=worker)
# Continue because FunctionsToRun are the only things that the
# driver should import.
continue
if key.startswith(b"RemoteFunction"):
fetch_and_register_remote_function(key, worker=worker)
elif key.startswith(b"FunctionsToRun"):
fetch_and_execute_function_to_run(key, worker=worker)
elif key.startswith(b"ActorClass"):
# If this worker is an actor that is supposed to construct this
# class, fetch the actor and class information and construct
# the class.
class_id = key.split(b":", 1)[1]
if (worker.actor_id != NIL_ACTOR_ID and
worker.class_id == class_id):
worker.fetch_and_register_actor(key, worker)
else:
raise Exception("This code should be unreachable.")
try:
for msg in worker.import_pubsub_client.listen():
with worker.lock:
if msg["type"] == "psubscribe":
continue
assert msg["data"] == b"rpush"
num_imports = worker.redis_client.llen("Exports")
assert num_imports >= num_imported
for i in range(num_imported, num_imports):
num_imported += 1
key = worker.redis_client.lindex("Exports", i)
# Handle the driver case first.
if mode != WORKER_MODE:
if key.startswith(b"FunctionsToRun"):
with log_span("ray:import_function_to_run",
worker=worker):
fetch_and_execute_function_to_run(
key, worker=worker)
# Continue because FunctionsToRun are the only things
# that the driver should import.
continue
if key.startswith(b"RemoteFunction"):
with log_span("ray:import_remote_function",
worker=worker):
fetch_and_register_remote_function(key,
worker=worker)
elif key.startswith(b"FunctionsToRun"):
with log_span("ray:import_function_to_run",
worker=worker):
fetch_and_execute_function_to_run(key,
worker=worker)
elif key.startswith(b"Actor"):
# Only get the actor if the actor ID matches the actor
# ID of this worker.
actor_id, = worker.redis_client.hmget(key, "actor_id")
if worker.actor_id == actor_id:
worker.fetch_and_register["Actor"](key, worker)
else:
raise Exception("This code should be unreachable.")
except redis.ConnectionError:
# When Redis terminates the listen call will throw a ConnectionError,
# which we catch here.
pass
def connect(info, object_id_seed=None, mode=WORKER_MODE, worker=global_worker,
actor_id=NIL_ACTOR_ID):
"""Connect this worker to the local scheduler, to Plasma, and to Redis.
Args:
info (dict): A dictionary with address of the Redis server and the
sockets of the plasma store, plasma manager, and local scheduler.
object_id_seed: A seed to use to make the generation of object IDs
deterministic.
mode: The mode of the worker. One of SCRIPT_MODE, WORKER_MODE,
PYTHON_MODE, and SILENT_MODE.
actor_id: The ID of the actor running on this worker. If this worker is
not an actor, then this is NIL_ACTOR_ID.
"""
check_main_thread()
# Do some basic checking to make sure we didn't call ray.init twice.
error_message = "Perhaps you called ray.init twice by accident?"
assert not worker.connected, error_message
assert worker.cached_functions_to_run is not None, error_message
assert worker.cached_remote_functions is not None, error_message
# Initialize some fields.
worker.worker_id = random_string()
worker.actor_id = actor_id
worker.connected = True
worker.set_mode(mode)
# The worker.events field is used to aggregate logging information and
# display it in the web UI. Note that Python lists protected by the GIL,
# which is important because we will append to this field from multiple
# threads.
worker.events = []
# If running Ray in PYTHON_MODE, there is no need to create call
# create_worker or to start the worker service.
if mode == PYTHON_MODE:
return
# Set the node IP address.
worker.node_ip_address = info["node_ip_address"]
worker.redis_address = info["redis_address"]
# Create a Redis client.
redis_ip_address, redis_port = info["redis_address"].split(":")
worker.redis_client = redis.StrictRedis(host=redis_ip_address,
port=int(redis_port))
worker.lock = threading.Lock()
# Check the RedirectOutput key in Redis and based on its value redirect
# worker output and error to their own files.
if mode == WORKER_MODE:
# This key is set in services.py when Redis is started.
redirect_worker_output_val = worker.redis_client.get("RedirectOutput")
if (redirect_worker_output_val is not None and
int(redirect_worker_output_val) == 1):
redirect_worker_output = 1
else:
redirect_worker_output = 0
if redirect_worker_output:
log_stdout_file, log_stderr_file = services.new_log_files("worker",
True)
sys.stdout = log_stdout_file
sys.stderr = log_stderr_file
services.record_log_files_in_redis(info["redis_address"],
info["node_ip_address"],
[log_stdout_file,
log_stderr_file])
# Create an object for interfacing with the global state.
global_state._initialize_global_state(redis_ip_address, int(redis_port))
# Register the worker with Redis.
if mode in [SCRIPT_MODE, SILENT_MODE]:
# The concept of a driver is the same as the concept of a "job".
# Register the driver/job with Redis here.
import __main__ as main
driver_info = {
"node_ip_address": worker.node_ip_address,
"driver_id": worker.worker_id,
"start_time": time.time(),
"plasma_store_socket": info["store_socket_name"],
"plasma_manager_socket": info["manager_socket_name"],
"local_scheduler_socket": info["local_scheduler_socket_name"]}
driver_info["name"] = (main.__file__ if hasattr(main, "__file__")
else "INTERACTIVE MODE")
worker.redis_client.hmset(b"Drivers:" + worker.worker_id, driver_info)
if not worker.redis_client.exists("webui"):
worker.redis_client.hmset("webui", {"url": info["webui_url"]})
is_worker = False
elif mode == WORKER_MODE:
# Register the worker with Redis.
worker_dict = {
"node_ip_address": worker.node_ip_address,
"plasma_store_socket": info["store_socket_name"],
"plasma_manager_socket": info["manager_socket_name"],
"local_scheduler_socket": info["local_scheduler_socket_name"]}
if redirect_worker_output:
worker_dict["stdout_file"] = os.path.abspath(log_stdout_file.name)
worker_dict["stderr_file"] = os.path.abspath(log_stderr_file.name)
worker.redis_client.hmset(b"Workers:" + worker.worker_id, worker_dict)
is_worker = True
else:
raise Exception("This code should be unreachable.")
# Create an object store client.
worker.plasma_client = plasma.connect(info["store_socket_name"],
info["manager_socket_name"],
64)
# Create the local scheduler client.
if worker.actor_id != NIL_ACTOR_ID:
num_gpus = int(worker.redis_client.hget(b"Actor:" + actor_id,
"num_gpus"))
else:
num_gpus = 0
worker.local_scheduler_client = ray.local_scheduler.LocalSchedulerClient(
info["local_scheduler_socket_name"], worker.worker_id, worker.actor_id,
is_worker, num_gpus)
# If this is a driver, set the current task ID, the task driver ID, and set
# the task index to 0.
if mode in [SCRIPT_MODE, SILENT_MODE]:
# If the user provided an object_id_seed, then set the current task ID
# deterministically based on that seed (without altering the state of
# the user's random number generator). Otherwise, set the current task
# ID randomly to avoid object ID collisions.
numpy_state = np.random.get_state()
if object_id_seed is not None:
np.random.seed(object_id_seed)
else:
# Try to use true randomness.
np.random.seed(None)
worker.current_task_id = ray.local_scheduler.ObjectID(
np.random.bytes(20))
# When tasks are executed on remote workers in the context of multiple
# drivers, the task driver ID is used to keep track of which driver is
# responsible for the task so that error messages will be propagated to
# the correct driver.
worker.task_driver_id = ray.local_scheduler.ObjectID(worker.worker_id)
# Reset the state of the numpy random number generator.
np.random.set_state(numpy_state)
# Set other fields needed for computing task IDs.
worker.task_index = 0
worker.put_index = 0
# Create an entry for the driver task in the task table. This task is
# added immediately with status RUNNING. This allows us to push errors
# related to this driver task back to the driver. For example, if the
# driver creates an object that is later evicted, we should notify the
# user that we're unable to reconstruct the object, since we cannot
# rerun the driver.
driver_task = ray.local_scheduler.Task(
worker.task_driver_id,
ray.local_scheduler.ObjectID(NIL_FUNCTION_ID),
[],
0,
worker.current_task_id,
worker.task_index,
ray.local_scheduler.ObjectID(NIL_ACTOR_ID),
worker.actor_counters[actor_id],
[0, 0, 0])
global_state._execute_command(
driver_task.task_id(),
"RAY.TASK_TABLE_ADD",
driver_task.task_id().id(),
TASK_STATUS_RUNNING,
NIL_LOCAL_SCHEDULER_ID,
ray.local_scheduler.task_to_string(driver_task))
# Set the driver's current task ID to the task ID assigned to the
# driver task.
worker.current_task_id = driver_task.task_id()
# If this is an actor, get the ID of the corresponding class for the actor.
if worker.actor_id != NIL_ACTOR_ID:
actor_key = b"Actor:" + worker.actor_id
class_id = worker.redis_client.hget(actor_key, "class_id")
worker.class_id = class_id
# Initialize the serialization library. This registers some classes, and so
# it must be run before we export all of the cached remote functions.
_initialize_serialization()
# Start a thread to import exports from the driver or from other workers.
# Note that the driver also has an import thread, which is used only to
# import custom class definitions from calls to _register_class that happen
# under the hood on workers.
t = threading.Thread(target=import_thread, args=(worker, mode))
# Making the thread a daemon causes it to exit when the main thread exits.
t.daemon = True
t.start()
# If this is a driver running in SCRIPT_MODE, start a thread to print error
# messages asynchronously in the background. Ideally the scheduler would
# push messages to the driver's worker service, but we ran into bugs when
# trying to properly shutdown the driver's worker service, so we are
# temporarily using this implementation which constantly queries the
# scheduler for new error messages.
if mode == SCRIPT_MODE:
t = threading.Thread(target=print_error_messages, args=(worker,))
# Making the thread a daemon causes it to exit when the main thread
# exits.
t.daemon = True
t.start()
if mode in [SCRIPT_MODE, SILENT_MODE]:
# Add the directory containing the script that is running to the Python
# paths of the workers. Also add the current directory. Note that this
# assumes that the directory structures on the machines in the clusters
# are the same.
script_directory = os.path.abspath(os.path.dirname(sys.argv[0]))
current_directory = os.path.abspath(os.path.curdir)
worker.run_function_on_all_workers(
lambda worker_info: sys.path.insert(1, script_directory))
worker.run_function_on_all_workers(
lambda worker_info: sys.path.insert(1, current_directory))
# TODO(rkn): Here we first export functions to run, then remote
# functions. The order matters. For example, one of the functions to
# run may set the Python path, which is needed to import a module used
# to define a remote function. We may want to change the order to
# simply be the order in which the exports were defined on the driver.
# In addition, we will need to retain the ability to decide what the
# first few exports are (mostly to set the Python path). Additionally,
# note that the first exports to be defined on the driver will be the
# ones defined in separate modules that are imported by the driver.
# Export cached functions_to_run.
for function in worker.cached_functions_to_run:
worker.run_function_on_all_workers(function)
# Export cached remote functions to the workers.
for info in worker.cached_remote_functions:
(function_id, func_name, func,
func_invoker, function_properties) = info
export_remote_function(function_id, func_name, func, func_invoker,
function_properties, worker)
worker.cached_functions_to_run = None
worker.cached_remote_functions = None
def disconnect(worker=global_worker):
"""Disconnect this worker from the scheduler and object store."""
# 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.connected = False
worker.cached_functions_to_run = []
worker.cached_remote_functions = []
worker.serialization_context = pyarrow.SerializationContext()
def register_class(cls, pickle=False, worker=global_worker):
raise Exception("The function ray.register_class is deprecated. It should "
"be safe to remove any calls to this function.")
def _register_class(cls, pickle=False, worker=global_worker):
"""Enable serialization and deserialization for a particular class.
This method runs the register_class function defined below on every worker,
which will enable ray to properly serialize and deserialize objects of
this class.
Args:
cls (type): The class that ray should serialize.
pickle (bool): If False then objects of this class will be serialized
by turning their __dict__ fields into a dictionary. If True, then
objects of this class will be serialized using pickle.
Raises:
Exception: An exception is raised if pickle=False and the class cannot
be efficiently serialized by Ray.
"""
class_id = random_string()
def register_class_for_serialization(worker_info):
worker_info["worker"].serialization_context.register_type(
cls, class_id, pickle=pickle)
if not pickle:
# Raise an exception if cls cannot be serialized efficiently by Ray.
serialization.check_serializable(cls)
worker.run_function_on_all_workers(register_class_for_serialization)
else:
# Since we are pickling objects of this class, we don't actually need
# to ship the class definition.
register_class_for_serialization({"worker": worker})
class RayLogSpan(object):
"""An object used to enable logging a span of events with a with statement.
Attributes:
event_type (str): The type of the event being logged.
contents: Additional information to log.
"""
def __init__(self, event_type, contents=None, worker=global_worker):
"""Initialize a RayLogSpan object."""
self.event_type = event_type
self.contents = contents
self.worker = worker
def __enter__(self):
"""Log the beginning of a span event."""
log(event_type=self.event_type,
contents=self.contents,
kind=LOG_SPAN_START,
worker=self.worker)
def __exit__(self, type, value, tb):
"""Log the end of a span event. Log any exception that occurred."""
if type is None:
log(event_type=self.event_type, kind=LOG_SPAN_END,
worker=self.worker)
else:
log(event_type=self.event_type,
contents={"type": str(type),
"value": value,
"traceback": traceback.format_exc()},
kind=LOG_SPAN_END,
worker=self.worker)
def log_span(event_type, contents=None, worker=global_worker):
return RayLogSpan(event_type, contents=contents, worker=worker)
def log_event(event_type, contents=None, worker=global_worker):
log(event_type, kind=LOG_POINT, contents=contents, worker=worker)
def log(event_type, kind, contents=None, worker=global_worker):
"""Log an event to the global state store.
This adds the event to a buffer of events locally. The buffer can be
flushed and written to the global state store by calling flush_log().
Args:
event_type (str): The type of the event.
contents: More general data to store with the event.
kind (int): Either LOG_POINT, LOG_SPAN_START, or LOG_SPAN_END. This is
LOG_POINT if the event being logged happens at a single point in
time. It is LOG_SPAN_START if we are starting to log a span of
time, and it is LOG_SPAN_END if we are finishing logging a span of
time.
"""
# TODO(rkn): This code currently takes around half a microsecond. Since we
# call it tens of times per task, this adds up. We will need to redo the
# logging code, perhaps in C.
contents = {} if contents is None else contents
assert isinstance(contents, dict)
# Make sure all of the keys and values in the dictionary are strings.
contents = {str(k): str(v) for k, v in contents.items()}
worker.events.append((time.time(), event_type, kind, contents))
def flush_log(worker=global_worker):
"""Send the logged worker events to the global state store."""
event_log_key = b"event_log:" + worker.worker_id
event_log_value = json.dumps(worker.events)
worker.local_scheduler_client.log_event(event_log_key,
event_log_value,
time.time())
worker.events = []
def get(object_ids, worker=global_worker):
"""Get a remote object or a list of remote objects from the object store.
This method blocks until the object corresponding to the object ID 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). If object_ids is a list, then the objects
corresponding to each object in the list will be returned.
Args:
object_ids: Object ID of the object to get or a list of object IDs to
get.
Returns:
A Python object or a list of Python objects.
"""
check_connected(worker)
with log_span("ray:get", worker=worker):
check_main_thread()
if worker.mode == PYTHON_MODE:
# In PYTHON_MODE, ray.get is the identity operation (the input will
# actually be a value not an objectid).
return object_ids
if isinstance(object_ids, list):
values = worker.get_object(object_ids)
for i, value in enumerate(values):
if isinstance(value, RayTaskError):
raise RayGetError(object_ids[i], value)
return values
else:
value = worker.get_object([object_ids])[0]
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(object_ids, value)
return value
def put(value, worker=global_worker):
"""Store an object in the object store.
Args:
value: The Python object to be stored.
Returns:
The object ID assigned to this value.
"""
check_connected(worker)
with log_span("ray:put", worker=worker):
check_main_thread()
if worker.mode == PYTHON_MODE:
# In PYTHON_MODE, ray.put is the identity operation.
return value
object_id = worker.local_scheduler_client.compute_put_id(
worker.current_task_id, worker.put_index)
worker.put_object(object_id, value)
worker.put_index += 1
return object_id
def wait(object_ids, num_returns=1, timeout=None, worker=global_worker):
"""Return a list of IDs that are ready and a list of IDs that are not.
If timeout is set, the function returns either when the requested number of
IDs are ready or when the timeout is reached, whichever occurs first. If it
is not set, the function simply waits until that number of objects is ready
and returns that exact number of objectids.
This method returns two lists. The first list consists of object IDs that
correspond to objects that are stored in the object store. The second list
corresponds to the rest of the object IDs (which may or may not be ready).
Args:
object_ids (List[ObjectID]): List of object IDs for objects that may or
may not be ready. Note that these IDs must be unique.
num_returns (int): The number of object IDs that should be returned.
timeout (int): The maximum amount of time in milliseconds to wait
before returning.
Returns:
A list of object IDs that are ready and a list of the remaining object
IDs.
"""
check_connected(worker)
with log_span("ray:wait", worker=worker):
check_main_thread()
object_id_strs = [plasma.ObjectID(object_id.id())
for object_id in object_ids]
timeout = timeout if timeout is not None else 2 ** 30
ready_ids, remaining_ids = worker.plasma_client.wait(object_id_strs,
timeout,
num_returns)
ready_ids = [ray.local_scheduler.ObjectID(object_id.binary())
for object_id in ready_ids]
remaining_ids = [ray.local_scheduler.ObjectID(object_id.binary())
for object_id in remaining_ids]
return ready_ids, remaining_ids
def format_error_message(exception_message, task_exception=False):
"""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")
if task_exception:
# For errors that occur inside of tasks, 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 _submit_task(function_id, 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(function_id, 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 export_remote_function(function_id, func_name, func, func_invoker,
function_properties, worker=global_worker):
check_main_thread()
if _mode(worker) not in [SCRIPT_MODE, SILENT_MODE]:
raise Exception("export_remote_function can only be called on a "
"driver.")
worker.function_properties[
worker.task_driver_id.id()][function_id.id()] = function_properties
task_driver_id = worker.task_driver_id
key = b"RemoteFunction:" + task_driver_id.id() + b":" + function_id.id()
# Work around limitations of Python pickling.
func_name_global_valid = func.__name__ in func.__globals__
func_name_global_value = func.__globals__.get(func.__name__)
# Allow the function to reference itself as a global variable
func.__globals__[func.__name__] = func_invoker
try:
pickled_func = pickle.dumps(func)
finally:
# Undo our changes
if func_name_global_valid:
func.__globals__[func.__name__] = func_name_global_value
else:
del func.__globals__[func.__name__]
worker.redis_client.hmset(key, {
"driver_id": worker.task_driver_id.id(),
"function_id": function_id.id(),
"name": func_name,
"module": func.__module__,
"function": pickled_func,
"num_return_vals": function_properties.num_return_vals,
"num_cpus": function_properties.num_cpus,
"num_gpus": function_properties.num_gpus,
"num_custom_resource": function_properties.num_custom_resource,
"max_calls": function_properties.max_calls})
worker.redis_client.rpush("Exports", key)
def in_ipython():
"""Return true if we are in an IPython interpreter and false otherwise."""
try:
__IPYTHON__
return True
except NameError:
return False
def compute_function_id(func_name, func):
"""Compute an function ID for a function.
Args:
func_name: The name of the function (this includes the module name plus
the function name).
func: The actual function.
Returns:
This returns the function ID.
"""
function_id_hash = hashlib.sha1()
# Include the function name in the hash.
function_id_hash.update(func_name.encode("ascii"))
# If we are running a script or are in IPython, include the source code in
# the hash. If we are in a regular Python interpreter we skip this part
# because the source code is not accessible.
import __main__ as main
if hasattr(main, "__file__") or in_ipython():
function_id_hash.update(inspect.getsource(func).encode("ascii"))
# Compute the function ID.
function_id = function_id_hash.digest()
assert len(function_id) == 20
function_id = FunctionID(function_id)
return function_id
def remote(*args, **kwargs):
"""This decorator is used to define remote functions and to define actors.
Args:
num_return_vals (int): The number of object IDs that a call to this
function should return.
num_cpus (int): The number of CPUs needed to execute this function.
num_gpus (int): The number of GPUs needed to execute this function.
num_custom_resource (int): The quantity of a user-defined custom
resource that is needed to execute this function. This flag is
experimental and is subject to changes in the future.
max_calls (int): The maximum number of tasks of this kind that can be
run on a worker before the worker needs to be restarted.
checkpoint_interval (int): The number of tasks to run between
checkpoints of the actor state.
"""
worker = global_worker
def make_remote_decorator(num_return_vals, num_cpus, num_gpus,
num_custom_resource, max_calls,
checkpoint_interval, func_id=None):
def remote_decorator(func_or_class):
if inspect.isfunction(func_or_class):
function_properties = FunctionProperties(
num_return_vals=num_return_vals,
num_cpus=num_cpus,
num_gpus=num_gpus,
num_custom_resource=num_custom_resource,
max_calls=max_calls)
return remote_function_decorator(func_or_class,
function_properties)
if inspect.isclass(func_or_class):
return worker.make_actor(func_or_class, num_cpus, num_gpus,
checkpoint_interval)
raise Exception("The @ray.remote decorator must be applied to "
"either a function or to a class.")
def remote_function_decorator(func, function_properties):
func_name = "{}.{}".format(func.__module__, func.__name__)
if func_id is None:
function_id = compute_function_id(func_name, func)
else:
function_id = func_id
def func_call(*args, **kwargs):
"""This runs immediately when a remote function is called."""
check_connected()
check_main_thread()
args = signature.extend_args(function_signature, args, kwargs)
if _mode() == PYTHON_MODE:
# In 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.
result = func(*copy.deepcopy(args))
return result
objectids = _submit_task(function_id, 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."""
result = func(*arguments)
return result
def func_invoker(*args, **kwargs):
"""This is 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.is_remote = True
func_name = "{}.{}".format(func.__module__, func.__name__)
func_invoker.func_name = func_name
if sys.version_info >= (3, 0):
func_invoker.__doc__ = func.__doc__
else:
func_invoker.func_doc = func.func_doc
signature.check_signature_supported(func)
function_signature = signature.extract_signature(func)
# Everything ready - export the function
if worker.mode in [SCRIPT_MODE, SILENT_MODE]:
export_remote_function(function_id, func_name, func,
func_invoker, function_properties)
elif worker.mode is None:
worker.cached_remote_functions.append((function_id, func_name,
func, func_invoker,
function_properties))
return func_invoker
return remote_decorator
num_return_vals = (kwargs["num_return_vals"] if "num_return_vals"
in kwargs else 1)
num_cpus = kwargs["num_cpus"] if "num_cpus" in kwargs else 1
num_gpus = kwargs["num_gpus"] if "num_gpus" in kwargs else 0
num_custom_resource = (kwargs["num_custom_resource"]
if "num_custom_resource" in kwargs else 0)
max_calls = kwargs["max_calls"] if "max_calls" in kwargs else 0
checkpoint_interval = (kwargs["checkpoint_interval"]
if "checkpoint_interval" in kwargs else -1)
if _mode() == WORKER_MODE:
if "function_id" in kwargs:
function_id = kwargs["function_id"]
return make_remote_decorator(num_return_vals, num_cpus, num_gpus,
num_custom_resource, max_calls,
checkpoint_interval, function_id)
if len(args) == 1 and len(kwargs) == 0 and callable(args[0]):
# This is the case where the decorator is just @ray.remote.
return make_remote_decorator(
num_return_vals, num_cpus,
num_gpus, num_custom_resource,
max_calls, checkpoint_interval)(args[0])
else:
# This is the case where the decorator is something like
# @ray.remote(num_return_vals=2).
error_string = ("The @ray.remote decorator must be applied either "
"with no arguments and no parentheses, for example "
"'@ray.remote', or it must be applied using some of "
"the arguments 'num_return_vals', 'num_cpus', "
"'num_gpus', num_custom_resource, or 'max_calls', "
"like '@ray.remote(num_return_vals=2)'.")
assert (len(args) == 0 and
("num_return_vals" in kwargs or
"num_cpus" in kwargs or
"num_gpus" in kwargs or
"num_custom_resource" in kwargs or
"max_calls" in kwargs or
"checkpoint_interval" in kwargs)), error_string
for key in kwargs:
assert key in ["num_return_vals", "num_cpus",
"num_gpus", "num_custom_resource", "max_calls",
"checkpoint_interval"], error_string
assert "function_id" not in kwargs
return make_remote_decorator(num_return_vals, num_cpus, num_gpus,
num_custom_resource, max_calls,
checkpoint_interval)
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