mirror of
https://github.com/vale981/ray
synced 2025-03-08 19:41:38 -05:00
c152730e4a
* . * . * Correct now * No interactivity errors * format * Filtering * lint * . * No more filtering * Removed interactivity * . * . * . * . * . * . * Redirection works * formatting * something broken? * . * Works * formatting * redirect output * formatting * formatting * Fix file descriptor leakage * format * . * . * . * . * . * Refactor * . * Only run on job switch * . * cleanup * . * ... * Review * . * . * . * . * whoops * . * Should fix bug * . * . * addressed comments * formatting * formatting * Fix typo * . * . * . * . Co-authored-by: Ubuntu <ubuntu@ip-172-31-14-33.us-west-2.compute.internal>
1936 lines
78 KiB
Python
1936 lines
78 KiB
Python
from contextlib import contextmanager
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import colorama
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import atexit
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import faulthandler
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import hashlib
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import inspect
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import io
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import json
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import logging
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import os
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import redis
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from six.moves import queue
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import sys
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import threading
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import time
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import traceback
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# Ray modules
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import ray.cloudpickle as pickle
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import ray.gcs_utils
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import ray.memory_monitor as memory_monitor
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import ray.node
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import ray.parameter
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import ray.ray_constants as ray_constants
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import ray.remote_function
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import ray.serialization as serialization
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import ray.services as services
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import ray
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import setproctitle
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import ray.signature
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import ray.state
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from ray import (
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ActorID,
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JobID,
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ObjectID,
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Language,
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)
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from ray import import_thread
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from ray import profiling
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from ray.exceptions import (
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RayConnectionError,
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RayError,
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RayTaskError,
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ObjectStoreFullError,
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)
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from ray.function_manager import FunctionActorManager
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from ray.utils import (_random_string, check_oversized_pickle, is_cython,
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setup_logger, create_and_init_new_worker_log, open_log)
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SCRIPT_MODE = 0
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WORKER_MODE = 1
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LOCAL_MODE = 2
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ERROR_KEY_PREFIX = b"Error:"
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# Logger for this module. It should be configured at the entry point
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# into the program using Ray. Ray provides a default configuration at
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# entry/init points.
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logger = logging.getLogger(__name__)
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class ActorCheckpointInfo:
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"""Information used to maintain actor checkpoints."""
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__slots__ = [
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# Number of tasks executed since last checkpoint.
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"num_tasks_since_last_checkpoint",
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# Timestamp of the last checkpoint, in milliseconds.
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"last_checkpoint_timestamp",
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# IDs of the previous checkpoints.
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"checkpoint_ids",
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]
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def __init__(self, num_tasks_since_last_checkpoint,
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last_checkpoint_timestamp, checkpoint_ids):
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self.num_tasks_since_last_checkpoint = num_tasks_since_last_checkpoint
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self.last_checkpoint_timestamp = last_checkpoint_timestamp
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self.checkpoint_ids = checkpoint_ids
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class Worker:
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"""A class used to define the control flow of a worker process.
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Note:
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The methods in this class are considered unexposed to the user. The
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functions outside of this class are considered exposed.
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Attributes:
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connected (bool): True if Ray has been started and False otherwise.
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node (ray.node.Node): The node this worker is attached to.
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mode: The mode of the worker. One of SCRIPT_MODE, LOCAL_MODE, and
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WORKER_MODE.
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cached_functions_to_run (List): A list of functions to run on all of
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the workers that should be exported as soon as connect is called.
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"""
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def __init__(self):
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"""Initialize a Worker object."""
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self.node = None
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self.mode = None
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self.cached_functions_to_run = []
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self.actor_init_error = None
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self.actors = {}
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# Information used to maintain actor checkpoints.
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self.actor_checkpoint_info = {}
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self.actor_task_counter = 0
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# When the worker is constructed. Record the original value of the
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# CUDA_VISIBLE_DEVICES environment variable.
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self.original_gpu_ids = ray.utils.get_cuda_visible_devices()
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self.memory_monitor = memory_monitor.MemoryMonitor()
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# A dictionary that maps from driver id to SerializationContext
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# TODO: clean up the SerializationContext once the job finished.
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self.serialization_context_map = {}
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self.function_actor_manager = FunctionActorManager(self)
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# This event is checked regularly by all of the threads so that they
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# know when to exit.
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self.threads_stopped = threading.Event()
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# Index of the current session. This number will
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# increment every time when `ray.shutdown` is called.
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self._session_index = 0
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# Functions to run to process the values returned by ray.get. Each
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# postprocessor must take two arguments ("object_ids", and "values").
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self._post_get_hooks = []
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@property
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def connected(self):
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return self.node is not None
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@property
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def node_ip_address(self):
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self.check_connected()
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return self.node.node_ip_address
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@property
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def load_code_from_local(self):
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self.check_connected()
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return self.node.load_code_from_local
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@property
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def current_job_id(self):
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if hasattr(self, "core_worker"):
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return self.core_worker.get_current_job_id()
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return JobID.nil()
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@property
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def actor_id(self):
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if hasattr(self, "core_worker"):
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return self.core_worker.get_actor_id()
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return ActorID.nil()
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@property
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def current_task_id(self):
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return self.core_worker.get_current_task_id()
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@property
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def current_session_and_job(self):
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"""Get the current session index and job id as pair."""
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assert isinstance(self._session_index, int)
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assert isinstance(self.current_job_id, ray.JobID)
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return self._session_index, self.current_job_id
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def mark_actor_init_failed(self, error):
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"""Called to mark this actor as failed during initialization."""
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self.actor_init_error = error
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def reraise_actor_init_error(self):
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"""Raises any previous actor initialization error."""
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if self.actor_init_error is not None:
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raise self.actor_init_error
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def get_serialization_context(self, job_id=None):
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"""Get the SerializationContext of the job that this worker is processing.
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Args:
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job_id: The ID of the job that indicates which job to get
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the serialization context for.
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Returns:
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The serialization context of the given job.
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"""
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# This function needs to be protected by a lock, because it will be
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# called by`register_class_for_serialization`, as well as the import
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# thread, from different threads. Also, this function will recursively
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# call itself, so we use RLock here.
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if job_id is None:
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job_id = self.current_job_id
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with self.lock:
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if job_id not in self.serialization_context_map:
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self.serialization_context_map[
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job_id] = serialization.SerializationContext(self)
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return self.serialization_context_map[job_id]
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def check_connected(self):
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"""Check if the worker is connected.
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Raises:
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Exception: An exception is raised if the worker is not connected.
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"""
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if not self.connected:
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raise RayConnectionError("Ray has not been started yet. You can "
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"start Ray with 'ray.init()'.")
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def set_mode(self, mode):
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"""Set the mode of the worker.
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The mode SCRIPT_MODE should be used if this Worker is a driver that is
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being run as a Python script or interactively in a shell. It will print
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information about task failures.
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The mode WORKER_MODE should be used if this Worker is not a driver. It
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will not print information about tasks.
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The mode LOCAL_MODE should be used if this Worker is a driver and if
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you want to run the driver in a manner equivalent to serial Python for
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debugging purposes. It will not send remote function calls to the
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scheduler and will instead execute them in a blocking fashion.
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Args:
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mode: One of SCRIPT_MODE, WORKER_MODE, and LOCAL_MODE.
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"""
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self.mode = mode
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def put_object(self, value, object_id=None, pin_object=True):
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"""Put value in the local object store with object id `objectid`.
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This assumes that the value for `objectid` has not yet been placed in
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the local object store. If the plasma store is full, the worker will
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automatically retry up to DEFAULT_PUT_OBJECT_RETRIES times. Each
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retry will delay for an exponentially doubling amount of time,
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starting with DEFAULT_PUT_OBJECT_DELAY. After this, exception
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will be raised.
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Args:
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value: The value to put in the object store.
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object_id (object_id.ObjectID): The object ID of the value to be
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put. If None, one will be generated.
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pin_object: If set, the object will be pinned at the raylet.
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Returns:
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object_id.ObjectID: The object ID the object was put under.
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Raises:
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ray.exceptions.ObjectStoreFullError: This is raised if the attempt
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to store the object fails because the object store is full even
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after multiple retries.
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"""
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# Make sure that the value is not an object ID.
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if isinstance(value, ObjectID):
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raise TypeError(
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"Calling 'put' on an ray.ObjectID is not allowed "
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"(similarly, returning an ray.ObjectID from a remote "
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"function is not allowed). If you really want to "
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"do this, you can wrap the ray.ObjectID in a list and "
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"call 'put' on it (or return it).")
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if self.mode == LOCAL_MODE:
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assert object_id is None, ("Local Mode does not support "
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"inserting with an objectID")
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serialized_value = self.get_serialization_context().serialize(value)
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# This *must* be the first place that we construct this python
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# ObjectID because an entry with 0 local references is created when
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# the object is Put() in the core worker, expecting that this python
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# reference will be created. If another reference is created and
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# removed before this one, it will corrupt the state in the
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# reference counter.
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return ray.ObjectID(
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self.core_worker.put_serialized_object(
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serialized_value, object_id=object_id, pin_object=pin_object))
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def deserialize_objects(self, data_metadata_pairs, object_ids):
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context = self.get_serialization_context()
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return context.deserialize_objects(data_metadata_pairs, object_ids)
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def get_objects(self, object_ids, timeout=None):
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"""Get the values in the object store associated with the IDs.
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Return the values from the local object store for object_ids. This will
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block until all the values for object_ids have been written to the
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local object store.
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Args:
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object_ids (List[object_id.ObjectID]): A list of the object IDs
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whose values should be retrieved.
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timeout (float): timeout (float): The maximum amount of time in
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seconds to wait before returning.
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"""
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# Make sure that the values are object IDs.
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for object_id in object_ids:
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if not isinstance(object_id, ObjectID):
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raise TypeError(
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"Attempting to call `get` on the value {}, "
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"which is not an ray.ObjectID.".format(object_id))
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timeout_ms = int(timeout * 1000) if timeout else -1
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data_metadata_pairs = self.core_worker.get_objects(
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object_ids, self.current_task_id, timeout_ms)
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return self.deserialize_objects(data_metadata_pairs, object_ids)
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def run_function_on_all_workers(self, function,
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run_on_other_drivers=False):
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"""Run arbitrary code on all of the workers.
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This function will first be run on the driver, and then it will be
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exported to all of the workers to be run. It will also be run on any
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new workers that register later. If ray.init has not been called yet,
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then cache the function and export it later.
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Args:
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function (Callable): The function to run on all of the workers. It
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takes only one argument, a worker info dict. If it returns
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anything, its return values will not be used.
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run_on_other_drivers: The boolean that indicates whether we want to
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run this function on other drivers. One case is we may need to
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share objects across drivers.
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"""
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# If ray.init has not been called yet, then cache the function and
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# export it when connect is called. Otherwise, run the function on all
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# workers.
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if self.mode is None:
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self.cached_functions_to_run.append(function)
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else:
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# Attempt to pickle the function before we need it. This could
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# fail, and it is more convenient if the failure happens before we
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# actually run the function locally.
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pickled_function = pickle.dumps(function)
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function_to_run_id = hashlib.sha1(pickled_function).digest()
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key = b"FunctionsToRun:" + function_to_run_id
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# First run the function on the driver.
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# We always run the task locally.
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function({"worker": self})
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# Check if the function has already been put into redis.
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function_exported = self.redis_client.setnx(b"Lock:" + key, 1)
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if not function_exported:
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# In this case, the function has already been exported, so
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# we don't need to export it again.
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return
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check_oversized_pickle(pickled_function, function.__name__,
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"function", self)
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# Run the function on all workers.
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self.redis_client.hmset(
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key, {
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"job_id": self.current_job_id.binary(),
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"function_id": function_to_run_id,
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"function": pickled_function,
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"run_on_other_drivers": str(run_on_other_drivers),
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})
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self.redis_client.rpush("Exports", key)
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# TODO(rkn): If the worker fails after it calls setnx and before it
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# successfully completes the hmset and rpush, then the program will
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# most likely hang. This could be fixed by making these three
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# operations into a transaction (or by implementing a custom
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# command that does all three things).
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def main_loop(self):
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"""The main loop a worker runs to receive and execute tasks."""
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def sigterm_handler(signum, frame):
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shutdown(True)
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sys.exit(1)
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ray.utils.set_sigterm_handler(sigterm_handler)
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self.core_worker.run_task_loop()
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sys.exit(0)
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def get_gpu_ids():
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"""Get the IDs of the GPUs that are available to the worker.
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If the CUDA_VISIBLE_DEVICES environment variable was set when the worker
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started up, then the IDs returned by this method will be a subset of the
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IDs in CUDA_VISIBLE_DEVICES. If not, the IDs will fall in the range
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[0, NUM_GPUS - 1], where NUM_GPUS is the number of GPUs that the node has.
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Returns:
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A list of GPU IDs.
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"""
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# TODO(ilr) Handle inserting resources in local mode
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all_resource_ids = global_worker.core_worker.resource_ids()
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assigned_ids = [
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resource_id for resource_id, _ in all_resource_ids.get("GPU", [])
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]
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# If the user had already set CUDA_VISIBLE_DEVICES, then respect that (in
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# the sense that only GPU IDs that appear in CUDA_VISIBLE_DEVICES should be
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# returned).
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if global_worker.original_gpu_ids is not None:
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assigned_ids = [
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global_worker.original_gpu_ids[gpu_id] for gpu_id in assigned_ids
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]
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return assigned_ids
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def get_resource_ids():
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"""Get the IDs of the resources that are available to the worker.
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Returns:
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A dictionary mapping the name of a resource to a list of pairs, where
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each pair consists of the ID of a resource and the fraction of that
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resource reserved for this worker.
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"""
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if _mode() == LOCAL_MODE:
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raise RuntimeError("ray.get_resource_ids() currently does not work in "
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"local_mode.")
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return global_worker.core_worker.resource_ids()
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def get_webui_url():
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"""Get the URL to access the web UI.
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Note that the URL does not specify which node the web UI is on.
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Returns:
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The URL of the web UI as a string.
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"""
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if _global_node is None:
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raise RuntimeError("Ray has not been initialized/connected.")
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return _global_node.webui_url
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global_worker = Worker()
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"""Worker: The global Worker object for this worker process.
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We use a global Worker object to ensure that there is a single worker object
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per worker process.
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"""
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_global_node = None
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"""ray.node.Node: The global node object that is created by ray.init()."""
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def print_failed_task(task_status):
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"""Print information about failed tasks.
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Args:
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task_status (Dict): A dictionary containing the name, operationid, and
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error message for a failed task.
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"""
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logger.error("""
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Error: Task failed
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Function Name: {}
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Task ID: {}
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Error Message: \n{}
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""".format(task_status["function_name"], task_status["operationid"],
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task_status["error_message"]))
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def init(address=None,
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redis_address=None,
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redis_port=None,
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num_cpus=None,
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num_gpus=None,
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memory=None,
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object_store_memory=None,
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resources=None,
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driver_object_store_memory=None,
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redis_max_memory=None,
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log_to_driver=True,
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node_ip_address=ray_constants.NODE_DEFAULT_IP,
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object_id_seed=None,
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local_mode=False,
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redirect_worker_output=None,
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redirect_output=None,
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ignore_reinit_error=False,
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num_redis_shards=None,
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redis_max_clients=None,
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redis_password=ray_constants.REDIS_DEFAULT_PASSWORD,
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plasma_directory=None,
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huge_pages=False,
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include_java=False,
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include_dashboard=None,
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dashboard_host="localhost",
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dashboard_port=ray_constants.DEFAULT_DASHBOARD_PORT,
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job_id=None,
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configure_logging=True,
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logging_level=logging.INFO,
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logging_format=ray_constants.LOGGER_FORMAT,
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plasma_store_socket_name=None,
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raylet_socket_name=None,
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temp_dir=None,
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load_code_from_local=False,
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java_worker_options=None,
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use_pickle=True,
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_internal_config=None,
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lru_evict=False):
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"""
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Connect to an existing Ray cluster or start one and connect to it.
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This method handles two cases; either a Ray cluster already exists and we
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just attach this driver to it or we start all of the processes associated
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with a Ray cluster and attach to the newly started cluster.
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To start Ray and all of the relevant processes, use this as follows:
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.. code-block:: python
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ray.init()
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To connect to an existing Ray cluster, use this as follows (substituting
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in the appropriate address):
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.. code-block:: python
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ray.init(address="123.45.67.89:6379")
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You can also define an environment variable called `RAY_ADDRESS` in
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the same format as the `address` parameter to connect to an existing
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cluster with ray.init().
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Args:
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address (str): The address of the Ray cluster to connect to. If
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this address is not provided, then this command will start Redis,
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a raylet, a plasma store, a plasma manager, and some workers.
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It will also kill these processes when Python exits. If the driver
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is running on a node in a Ray cluster, using `auto` as the value
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tells the driver to detect the the cluster, removing the need to
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specify a specific node address.
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redis_address (str): Deprecated; same as address.
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redis_port (int): The port that the primary Redis shard should listen
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to. If None, then a random port will be chosen.
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num_cpus (int): Number of CPUs the user wishes to assign to each
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raylet.
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num_gpus (int): Number of GPUs the user wishes to assign to each
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raylet.
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resources: A dictionary mapping the names of custom resources to the
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quantities for them available.
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memory: The amount of memory (in bytes) that is available for use by
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workers requesting memory resources. By default, this is
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automatically set based on available system memory.
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object_store_memory: The amount of memory (in bytes) to start the
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object store with. By default, this is automatically set based on
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available system memory, subject to a 20GB cap.
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redis_max_memory: The max amount of memory (in bytes) to allow each
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redis shard to use. Once the limit is exceeded, redis will start
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LRU eviction of entries. This only applies to the sharded redis
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tables (task, object, and profile tables). By default, this is
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autoset based on available system memory, subject to a 10GB cap.
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log_to_driver (bool): If true, the output from all of the worker
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processes on all nodes will be directed to the driver.
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node_ip_address (str): The IP address of the node that we are on.
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object_id_seed (int): Used to seed the deterministic generation of
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object IDs. The same value can be used across multiple runs of the
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same driver in order to generate the object IDs in a consistent
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manner. However, the same ID should not be used for different
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drivers.
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local_mode (bool): If true, the code will be executed serially. This
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is useful for debugging.
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driver_object_store_memory (int): Limit the amount of memory the driver
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can use in the object store for creating objects. By default, this
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is autoset based on available system memory, subject to a 20GB cap.
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ignore_reinit_error: If true, Ray suppresses errors from calling
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ray.init() a second time. Ray won't be restarted.
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num_redis_shards: The number of Redis shards to start in addition to
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the primary Redis shard.
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redis_max_clients: If provided, attempt to configure Redis with this
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maxclients number.
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redis_password (str): Prevents external clients without the password
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from connecting to Redis if provided.
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plasma_directory: A directory where the Plasma memory mapped files
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will be created.
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huge_pages: Boolean flag indicating whether to start the Object
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Store with hugetlbfs support. Requires plasma_directory.
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include_java: Boolean flag indicating whether or not to enable java
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workers.
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include_dashboard: Boolean flag indicating whether or not to start the
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Ray dashboard, which displays the status of the Ray
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cluster. If this argument is None, then the UI will be started if
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the relevant dependencies are present.
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dashboard_host: The host to bind the dashboard server to. Can either be
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localhost (127.0.0.1) or 0.0.0.0 (available from all interfaces).
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By default, this is set to localhost to prevent access from
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external machines.
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dashboard_port: The port to bind the dashboard server to. Defaults to
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8265.
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job_id: The ID of this job.
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configure_logging: True (default) if configuration of logging is
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allowed here. Otherwise, the user may want to configure it
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separately.
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logging_level: Logging level, defaults to logging.INFO. Ignored unless
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"configure_logging" is true.
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logging_format: Logging format, defaults to string containing a
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timestamp, filename, line number, and message. See the source file
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ray_constants.py for details. Ignored unless "configure_logging"
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is true.
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plasma_store_socket_name (str): If provided, specifies the socket
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name used by the plasma store.
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raylet_socket_name (str): If provided, specifies the socket path
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used by the raylet process.
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temp_dir (str): If provided, specifies the root temporary
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directory for the Ray process. Defaults to an OS-specific
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conventional location, e.g., "/tmp/ray".
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load_code_from_local: Whether code should be loaded from a local
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module or from the GCS.
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java_worker_options: Overwrite the options to start Java workers.
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use_pickle: Deprecated.
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_internal_config (str): JSON configuration for overriding
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RayConfig defaults. For testing purposes ONLY.
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lru_evict (bool): If True, when an object store is full, it will evict
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objects in LRU order to make more space and when under memory
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pressure, ray.UnreconstructableError may be thrown. If False, then
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reference counting will be used to decide which objects are safe
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to evict and when under memory pressure, ray.ObjectStoreFullError
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may be thrown.
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Returns:
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Address information about the started processes.
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Raises:
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Exception: An exception is raised if an inappropriate combination of
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arguments is passed in.
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"""
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if not use_pickle:
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raise DeprecationWarning("The use_pickle argument is deprecated.")
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if redis_address is not None:
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raise DeprecationWarning("The redis_address argument is deprecated. "
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"Please use address instead.")
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if "RAY_ADDRESS" in os.environ:
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if redis_address is None and (address is None or address == "auto"):
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address = os.environ["RAY_ADDRESS"]
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else:
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raise RuntimeError(
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"Cannot use both the RAY_ADDRESS environment variable and "
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"the address argument of ray.init simultaneously. If you "
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"use RAY_ADDRESS to connect to a specific Ray cluster, "
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"please call ray.init() or ray.init(address=\"auto\") on the "
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"driver.")
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if redis_address is not None or address is not None:
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redis_address, _, _ = services.validate_redis_address(
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address, redis_address)
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if configure_logging:
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setup_logger(logging_level, logging_format)
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if local_mode:
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driver_mode = LOCAL_MODE
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else:
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driver_mode = SCRIPT_MODE
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if global_worker.connected:
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if ignore_reinit_error:
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logger.error("Calling ray.init() again after it has already been "
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"called.")
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return
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else:
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raise RuntimeError("Maybe you called ray.init twice by accident? "
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"This error can be suppressed by passing in "
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"'ignore_reinit_error=True' or by calling "
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"'ray.shutdown()' prior to 'ray.init()'.")
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# Convert hostnames to numerical IP address.
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if node_ip_address is not None:
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node_ip_address = services.address_to_ip(node_ip_address)
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raylet_ip_address = node_ip_address
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_internal_config = (json.loads(_internal_config)
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if _internal_config else {})
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# Set the internal config options for LRU eviction.
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if lru_evict:
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# Turn off object pinning.
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if _internal_config.get("object_pinning_enabled", False):
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raise Exception(
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"Object pinning cannot be enabled if using LRU eviction.")
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_internal_config["object_pinning_enabled"] = False
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_internal_config["object_store_full_max_retries"] = -1
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_internal_config["free_objects_period_milliseconds"] = 1000
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global _global_node
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if redis_address is None:
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# In this case, we need to start a new cluster.
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ray_params = ray.parameter.RayParams(
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redis_address=redis_address,
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redis_port=redis_port,
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node_ip_address=node_ip_address,
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raylet_ip_address=raylet_ip_address,
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object_id_seed=object_id_seed,
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driver_mode=driver_mode,
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redirect_worker_output=redirect_worker_output,
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redirect_output=redirect_output,
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num_cpus=num_cpus,
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num_gpus=num_gpus,
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resources=resources,
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num_redis_shards=num_redis_shards,
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redis_max_clients=redis_max_clients,
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redis_password=redis_password,
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plasma_directory=plasma_directory,
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huge_pages=huge_pages,
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include_java=include_java,
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include_dashboard=include_dashboard,
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dashboard_host=dashboard_host,
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dashboard_port=dashboard_port,
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memory=memory,
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object_store_memory=object_store_memory,
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redis_max_memory=redis_max_memory,
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plasma_store_socket_name=plasma_store_socket_name,
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raylet_socket_name=raylet_socket_name,
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temp_dir=temp_dir,
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load_code_from_local=load_code_from_local,
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java_worker_options=java_worker_options,
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_internal_config=_internal_config,
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)
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# Start the Ray processes. We set shutdown_at_exit=False because we
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# shutdown the node in the ray.shutdown call that happens in the atexit
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# handler. We still spawn a reaper process in case the atexit handler
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# isn't called.
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_global_node = ray.node.Node(
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head=True,
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shutdown_at_exit=False,
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spawn_reaper=True,
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ray_params=ray_params)
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else:
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# In this case, we are connecting to an existing cluster.
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if num_cpus is not None or num_gpus is not None:
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raise ValueError(
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"When connecting to an existing cluster, num_cpus "
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"and num_gpus must not be provided.")
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if resources is not None:
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raise ValueError("When connecting to an existing cluster, "
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"resources must not be provided.")
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if num_redis_shards is not None:
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raise ValueError("When connecting to an existing cluster, "
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"num_redis_shards must not be provided.")
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if redis_max_clients is not None:
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raise ValueError("When connecting to an existing cluster, "
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"redis_max_clients must not be provided.")
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if memory is not None:
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raise ValueError("When connecting to an existing cluster, "
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"memory must not be provided.")
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if object_store_memory is not None:
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raise ValueError("When connecting to an existing cluster, "
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"object_store_memory must not be provided.")
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if redis_max_memory is not None:
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raise ValueError("When connecting to an existing cluster, "
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"redis_max_memory must not be provided.")
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if plasma_directory is not None:
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raise ValueError("When connecting to an existing cluster, "
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"plasma_directory must not be provided.")
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if huge_pages:
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raise ValueError("When connecting to an existing cluster, "
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"huge_pages must not be provided.")
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if temp_dir is not None:
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raise ValueError("When connecting to an existing cluster, "
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"temp_dir must not be provided.")
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if plasma_store_socket_name is not None:
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raise ValueError("When connecting to an existing cluster, "
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"plasma_store_socket_name must not be provided.")
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if raylet_socket_name is not None:
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raise ValueError("When connecting to an existing cluster, "
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"raylet_socket_name must not be provided.")
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if java_worker_options is not None:
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raise ValueError("When connecting to an existing cluster, "
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"java_worker_options must not be provided.")
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if _internal_config is not None and len(_internal_config) != 0:
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raise ValueError("When connecting to an existing cluster, "
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"_internal_config must not be provided.")
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# In this case, we only need to connect the node.
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ray_params = ray.parameter.RayParams(
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node_ip_address=node_ip_address,
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raylet_ip_address=raylet_ip_address,
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redis_address=redis_address,
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redis_password=redis_password,
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object_id_seed=object_id_seed,
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temp_dir=temp_dir,
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load_code_from_local=load_code_from_local,
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_internal_config=_internal_config)
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_global_node = ray.node.Node(
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ray_params,
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head=False,
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shutdown_at_exit=False,
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spawn_reaper=False,
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connect_only=True)
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connect(
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_global_node,
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mode=driver_mode,
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log_to_driver=log_to_driver,
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worker=global_worker,
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driver_object_store_memory=driver_object_store_memory,
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job_id=job_id)
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for hook in _post_init_hooks:
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hook()
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return _global_node.address_info
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# Functions to run as callback after a successful ray init.
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_post_init_hooks = []
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def shutdown(exiting_interpreter=False):
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"""Disconnect the worker, and terminate processes started by ray.init().
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This will automatically run at the end when a Python process that uses Ray
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exits. It is ok to run this twice in a row. The primary use case for this
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function is to cleanup state between tests.
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Note that this will clear any remote function definitions, actor
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definitions, and existing actors, so if you wish to use any previously
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defined remote functions or actors after calling ray.shutdown(), then you
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need to redefine them. If they were defined in an imported module, then you
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will need to reload the module.
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Args:
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exiting_interpreter (bool): True if this is called by the atexit hook
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and false otherwise. If we are exiting the interpreter, we will
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wait a little while to print any extra error messages.
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"""
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if exiting_interpreter and global_worker.mode == SCRIPT_MODE:
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# This is a duration to sleep before shutting down everything in order
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# to make sure that log messages finish printing.
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time.sleep(0.5)
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disconnect(exiting_interpreter)
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# We need to destruct the core worker here because after this function,
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# we will tear down any processes spawned by ray.init() and the background
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# IO thread in the core worker doesn't currently handle that gracefully.
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if hasattr(global_worker, "core_worker"):
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del global_worker.core_worker
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# Disconnect global state from GCS.
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ray.state.state.disconnect()
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# Shut down the Ray processes.
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global _global_node
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if _global_node is not None:
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_global_node.kill_all_processes(check_alive=False, allow_graceful=True)
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_global_node = None
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# TODO(rkn): Instead of manually resetting some of the worker fields, we
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# should simply set "global_worker" to equal "None" or something like that.
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global_worker.set_mode(None)
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global_worker._post_get_hooks = []
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atexit.register(shutdown, True)
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# TODO(edoakes): this should only be set in the driver.
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def sigterm_handler(signum, frame):
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sys.exit(signum)
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try:
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ray.utils.set_sigterm_handler(sigterm_handler)
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except ValueError:
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logger.warning("Failed to set SIGTERM handler, processes might"
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"not be cleaned up properly on exit.")
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# Define a custom excepthook so that if the driver exits with an exception, we
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# can push that exception to Redis.
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normal_excepthook = sys.excepthook
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def custom_excepthook(type, value, tb):
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# If this is a driver, push the exception to redis.
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if global_worker.mode == SCRIPT_MODE:
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error_message = "".join(traceback.format_tb(tb))
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try:
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global_worker.redis_client.hmset(
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b"Drivers:" + global_worker.worker_id,
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{"exception": error_message})
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except (ConnectionRefusedError, redis.exceptions.ConnectionError):
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logger.warning("Could not push exception to redis.")
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# Call the normal excepthook.
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normal_excepthook(type, value, tb)
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sys.excepthook = custom_excepthook
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# The last time we raised a TaskError in this process. We use this value to
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# suppress redundant error messages pushed from the workers.
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last_task_error_raise_time = 0
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# The max amount of seconds to wait before printing out an uncaught error.
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UNCAUGHT_ERROR_GRACE_PERIOD = 5
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def _set_log_file(file_name, worker_pid, old_obj, setter_func):
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# Line-buffer the output (mode 1).
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f = create_and_init_new_worker_log(file_name, worker_pid)
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# TODO (Alex): Python seems to always flush when writing. If that is no
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# longer true, then we need to manually flush the old buffer.
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# old_obj.flush()
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# TODO (Alex): Flush the c/c++ userspace buffers if necessary.
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# `fflush(stdout); cout.flush();`
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fileno = old_obj.fileno()
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# C++ logging requires redirecting the stdout file descriptor. Note that
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# dup2 will automatically close the old file descriptor before overriding
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# it.
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os.dup2(f.fileno(), fileno)
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# We also manually set sys.stdout and sys.stderr because that seems to
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# have an effect on the output buffering. Without doing this, stdout
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# and stderr are heavily buffered resulting in seemingly lost logging
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# statements. We never want to close the stdout file descriptor, dup2 will
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# close it when necessary and we don't want python's GC to close it.
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setter_func(open_log(fileno, closefd=False))
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return os.path.abspath(f.name)
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def set_log_file(stdout_name, stderr_name):
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"""Sets up logging for the current worker, creating the (fd backed) file and
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flushing buffers as is necessary.
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Args:
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stdout_name (str): The file name that stdout should be written to.
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stderr_name(str): The file name that stderr should be written to.
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Returns:
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(tuple) The absolute paths of the files that stdout and stderr will be
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written to.
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"""
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stdout_path = ""
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stderr_path = ""
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worker_pid = os.getpid()
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# lambda cannot contain assignment
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def stdout_setter(x):
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sys.stdout = x
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def stderr_setter(x):
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sys.stderr = x
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if stdout_name:
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_set_log_file(stdout_name, worker_pid, sys.stdout, stdout_setter)
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# The stderr case should be analogous to the stdout case
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if stderr_name:
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_set_log_file(stderr_name, worker_pid, sys.stderr, stderr_setter)
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return stdout_path, stderr_path
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def print_logs(redis_client, threads_stopped):
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"""Prints log messages from workers on all of the nodes.
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Args:
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redis_client: A client to the primary Redis shard.
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threads_stopped (threading.Event): A threading event used to signal to
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the thread that it should exit.
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"""
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pubsub_client = redis_client.pubsub(ignore_subscribe_messages=True)
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pubsub_client.subscribe(ray.gcs_utils.LOG_FILE_CHANNEL)
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localhost = services.get_node_ip_address()
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try:
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# Keep track of the number of consecutive log messages that have been
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# received with no break in between. If this number grows continually,
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# then the worker is probably not able to process the log messages as
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# rapidly as they are coming in.
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num_consecutive_messages_received = 0
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while True:
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# Exit if we received a signal that we should stop.
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if threads_stopped.is_set():
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return
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|
msg = pubsub_client.get_message()
|
|
if msg is None:
|
|
num_consecutive_messages_received = 0
|
|
threads_stopped.wait(timeout=0.01)
|
|
continue
|
|
num_consecutive_messages_received += 1
|
|
|
|
data = json.loads(ray.utils.decode(msg["data"]))
|
|
|
|
def color_for(data):
|
|
if data["pid"] == "raylet":
|
|
return colorama.Fore.YELLOW
|
|
else:
|
|
return colorama.Fore.CYAN
|
|
|
|
if data["ip"] == localhost:
|
|
for line in data["lines"]:
|
|
print("{}{}(pid={}){} {}".format(
|
|
colorama.Style.DIM, color_for(data), data["pid"],
|
|
colorama.Style.RESET_ALL, line))
|
|
else:
|
|
for line in data["lines"]:
|
|
print("{}{}(pid={}, ip={}){} {}".format(
|
|
colorama.Style.DIM, color_for(data), data["pid"],
|
|
data["ip"], colorama.Style.RESET_ALL, line))
|
|
|
|
if (num_consecutive_messages_received % 100 == 0
|
|
and num_consecutive_messages_received > 0):
|
|
logger.warning(
|
|
"The driver may not be able to keep up with the "
|
|
"stdout/stderr of the workers. To avoid forwarding logs "
|
|
"to the driver, use 'ray.init(log_to_driver=False)'.")
|
|
except (OSError, redis.exceptions.ConnectionError) as e:
|
|
logger.error("print_logs: {}".format(e))
|
|
finally:
|
|
# Close the pubsub client to avoid leaking file descriptors.
|
|
pubsub_client.close()
|
|
|
|
|
|
def print_error_messages_raylet(task_error_queue, threads_stopped):
|
|
"""Prints message received in the given output queue.
|
|
|
|
This checks periodically if any un-raised errors occured in the background.
|
|
|
|
Args:
|
|
task_error_queue (queue.Queue): A queue used to receive errors from the
|
|
thread that listens to Redis.
|
|
threads_stopped (threading.Event): A threading event used to signal to
|
|
the thread that it should exit.
|
|
"""
|
|
|
|
while True:
|
|
# Exit if we received a signal that we should stop.
|
|
if threads_stopped.is_set():
|
|
return
|
|
|
|
try:
|
|
error, t = task_error_queue.get(block=False)
|
|
except queue.Empty:
|
|
threads_stopped.wait(timeout=0.01)
|
|
continue
|
|
# Delay errors a little bit of time to attempt to suppress redundant
|
|
# messages originating from the worker.
|
|
while t + UNCAUGHT_ERROR_GRACE_PERIOD > time.time():
|
|
threads_stopped.wait(timeout=1)
|
|
if threads_stopped.is_set():
|
|
break
|
|
if t < last_task_error_raise_time + UNCAUGHT_ERROR_GRACE_PERIOD:
|
|
logger.debug("Suppressing error from worker: {}".format(error))
|
|
else:
|
|
logger.error(
|
|
"Possible unhandled error from worker: {}".format(error))
|
|
|
|
|
|
def listen_error_messages_raylet(worker, task_error_queue, threads_stopped):
|
|
"""Listen to error messages in the background on the driver.
|
|
|
|
This runs in a separate thread on the driver and pushes (error, time)
|
|
tuples to the output queue.
|
|
|
|
Args:
|
|
worker: The worker class that this thread belongs to.
|
|
task_error_queue (queue.Queue): A queue used to communicate with the
|
|
thread that prints the errors found by this thread.
|
|
threads_stopped (threading.Event): A threading event used to signal to
|
|
the thread that it should exit.
|
|
"""
|
|
worker.error_message_pubsub_client = worker.redis_client.pubsub(
|
|
ignore_subscribe_messages=True)
|
|
# Exports that are published after the call to
|
|
# error_message_pubsub_client.subscribe and before the call to
|
|
# error_message_pubsub_client.listen will still be processed in the loop.
|
|
|
|
# Really we should just subscribe to the errors for this specific job.
|
|
# However, currently all errors seem to be published on the same channel.
|
|
error_pubsub_channel = str(
|
|
ray.gcs_utils.TablePubsub.Value("ERROR_INFO_PUBSUB")).encode("ascii")
|
|
worker.error_message_pubsub_client.subscribe(error_pubsub_channel)
|
|
# worker.error_message_pubsub_client.psubscribe("*")
|
|
|
|
try:
|
|
# Get the errors that occurred before the call to subscribe.
|
|
error_messages = ray.errors()
|
|
for error_message in error_messages:
|
|
logger.error(error_message)
|
|
|
|
while True:
|
|
# Exit if we received a signal that we should stop.
|
|
if threads_stopped.is_set():
|
|
return
|
|
|
|
msg = worker.error_message_pubsub_client.get_message()
|
|
if msg is None:
|
|
threads_stopped.wait(timeout=0.01)
|
|
continue
|
|
gcs_entry = ray.gcs_utils.GcsEntry.FromString(msg["data"])
|
|
assert len(gcs_entry.entries) == 1
|
|
error_data = ray.gcs_utils.ErrorTableData.FromString(
|
|
gcs_entry.entries[0])
|
|
job_id = error_data.job_id
|
|
if job_id not in [
|
|
worker.current_job_id.binary(),
|
|
JobID.nil().binary(),
|
|
]:
|
|
continue
|
|
|
|
error_message = error_data.error_message
|
|
if (error_data.type == ray_constants.TASK_PUSH_ERROR):
|
|
# Delay it a bit to see if we can suppress it
|
|
task_error_queue.put((error_message, time.time()))
|
|
else:
|
|
logger.warning(error_message)
|
|
except (OSError, redis.exceptions.ConnectionError) as e:
|
|
logger.error("listen_error_messages_raylet: {}".format(e))
|
|
finally:
|
|
# Close the pubsub client to avoid leaking file descriptors.
|
|
worker.error_message_pubsub_client.close()
|
|
|
|
|
|
def is_initialized():
|
|
"""Check if ray.init has been called yet.
|
|
|
|
Returns:
|
|
True if ray.init has already been called and false otherwise.
|
|
"""
|
|
return ray.worker.global_worker.connected
|
|
|
|
|
|
def connect(node,
|
|
mode=WORKER_MODE,
|
|
log_to_driver=False,
|
|
worker=global_worker,
|
|
driver_object_store_memory=None,
|
|
job_id=None):
|
|
"""Connect this worker to the raylet, to Plasma, and to Redis.
|
|
|
|
Args:
|
|
node (ray.node.Node): The node to connect.
|
|
mode: The mode of the worker. One of SCRIPT_MODE, WORKER_MODE, and
|
|
LOCAL_MODE.
|
|
log_to_driver (bool): If true, then output from all of the worker
|
|
processes on all nodes will be directed to the driver.
|
|
worker: The ray.Worker instance.
|
|
driver_object_store_memory: Limit the amount of memory the driver can
|
|
use in the object store when creating objects.
|
|
job_id: The ID of job. If it's None, then we will generate one.
|
|
"""
|
|
# 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
|
|
|
|
# Enable nice stack traces on SIGSEGV etc.
|
|
try:
|
|
if not faulthandler.is_enabled():
|
|
faulthandler.enable(all_threads=False)
|
|
except io.UnsupportedOperation:
|
|
pass # ignore
|
|
|
|
# Create a Redis client to primary.
|
|
# The Redis client can safely be shared between threads. However,
|
|
# that is not true of Redis pubsub clients. See the documentation at
|
|
# https://github.com/andymccurdy/redis-py#thread-safety.
|
|
worker.redis_client = node.create_redis_client()
|
|
|
|
# Initialize some fields.
|
|
if mode is WORKER_MODE:
|
|
# We should not specify the job_id if it's `WORKER_MODE`.
|
|
assert job_id is None
|
|
job_id = JobID.nil()
|
|
# TODO(qwang): Rename this to `worker_id_str` or type to `WorkerID`
|
|
worker.worker_id = _random_string()
|
|
else:
|
|
# This is the code path of driver mode.
|
|
if job_id is None:
|
|
# TODO(qwang): use `GcsClient::GenerateJobId()` here.
|
|
job_id = JobID.from_int(
|
|
int(worker.redis_client.incr("JobCounter")))
|
|
# When tasks are executed on remote workers in the context of multiple
|
|
# drivers, the current job ID is used to keep track of which job is
|
|
# responsible for the task so that error messages will be propagated to
|
|
# the correct driver.
|
|
worker.worker_id = ray.utils.compute_driver_id_from_job(
|
|
job_id).binary()
|
|
|
|
if mode is not SCRIPT_MODE and setproctitle:
|
|
setproctitle.setproctitle("ray::IDLE")
|
|
|
|
if not isinstance(job_id, JobID):
|
|
raise TypeError("The type of given job id must be JobID.")
|
|
|
|
# All workers start out as non-actors. A worker can be turned into an actor
|
|
# after it is created.
|
|
worker.node = node
|
|
worker.set_mode(mode)
|
|
|
|
# For driver's check that the version information matches the version
|
|
# information that the Ray cluster was started with.
|
|
try:
|
|
ray.services.check_version_info(worker.redis_client)
|
|
except Exception as e:
|
|
if mode == SCRIPT_MODE:
|
|
raise e
|
|
elif mode == WORKER_MODE:
|
|
traceback_str = traceback.format_exc()
|
|
ray.utils.push_error_to_driver_through_redis(
|
|
worker.redis_client,
|
|
ray_constants.VERSION_MISMATCH_PUSH_ERROR,
|
|
traceback_str,
|
|
job_id=None)
|
|
|
|
worker.lock = threading.RLock()
|
|
|
|
driver_name = ""
|
|
log_stdout_file_path = ""
|
|
log_stderr_file_path = ""
|
|
if mode == SCRIPT_MODE:
|
|
import __main__ as main
|
|
driver_name = (main.__file__
|
|
if hasattr(main, "__file__") else "INTERACTIVE MODE")
|
|
elif mode == WORKER_MODE:
|
|
# Check the RedirectOutput key in Redis and based on its value redirect
|
|
# worker output and error to their own files.
|
|
# 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):
|
|
log_stdout_file_name, log_stderr_file_name = (
|
|
node.get_job_redirected_log_file(worker.worker_id))
|
|
try:
|
|
log_stdout_file_path, log_stderr_file_path = \
|
|
set_log_file(log_stdout_file_name, log_stderr_file_name)
|
|
except IOError:
|
|
raise IOError(
|
|
"Workers must be able to redirect their output at"
|
|
"the file descriptor level.")
|
|
elif not LOCAL_MODE:
|
|
raise ValueError(
|
|
"Invalid worker mode. Expected DRIVER, WORKER or LOCAL.")
|
|
|
|
# TODO (Alex): `current_logging_job` tracks the current job so that we know
|
|
# when to switch log files. If all logging functionaility was moved to c++,
|
|
# the functionaility in `_raylet.pyx::switch_worker_log_if_necessary` could
|
|
# be moved to `CoreWorker::SetCurrentTaskId()`.
|
|
worker.current_logging_job_id = None
|
|
redis_address, redis_port = node.redis_address.split(":")
|
|
gcs_options = ray._raylet.GcsClientOptions(
|
|
redis_address,
|
|
int(redis_port),
|
|
node.redis_password,
|
|
)
|
|
|
|
worker.core_worker = ray._raylet.CoreWorker(
|
|
(mode == SCRIPT_MODE or mode == LOCAL_MODE),
|
|
node.plasma_store_socket_name,
|
|
node.raylet_socket_name,
|
|
job_id,
|
|
gcs_options,
|
|
node.get_logs_dir_path(),
|
|
node.node_ip_address,
|
|
node.node_manager_port,
|
|
node.raylet_ip_address,
|
|
(mode == LOCAL_MODE),
|
|
driver_name,
|
|
log_stdout_file_path,
|
|
log_stderr_file_path,
|
|
)
|
|
|
|
# Create an object for interfacing with the global state.
|
|
# Note, global state should be intialized after `CoreWorker`, because it
|
|
# will use glog, which is intialized in `CoreWorker`.
|
|
ray.state.state._initialize_global_state(
|
|
node.redis_address, redis_password=node.redis_password)
|
|
|
|
if driver_object_store_memory is not None:
|
|
worker.core_worker.set_object_store_client_options(
|
|
"ray_driver_{}".format(os.getpid()), driver_object_store_memory)
|
|
|
|
# Put something in the plasma store so that subsequent plasma store
|
|
# accesses will be faster. Currently the first access is always slow, and
|
|
# we don't want the user to experience this.
|
|
if mode != LOCAL_MODE:
|
|
temporary_object_id = ray.ObjectID.from_random()
|
|
worker.put_object(1, object_id=temporary_object_id)
|
|
ray.internal.free([temporary_object_id])
|
|
|
|
# Start the import thread
|
|
worker.import_thread = import_thread.ImportThread(worker, mode,
|
|
worker.threads_stopped)
|
|
worker.import_thread.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:
|
|
q = queue.Queue()
|
|
worker.listener_thread = threading.Thread(
|
|
target=listen_error_messages_raylet,
|
|
name="ray_listen_error_messages",
|
|
args=(worker, q, worker.threads_stopped))
|
|
worker.printer_thread = threading.Thread(
|
|
target=print_error_messages_raylet,
|
|
name="ray_print_error_messages",
|
|
args=(q, worker.threads_stopped))
|
|
worker.listener_thread.daemon = True
|
|
worker.listener_thread.start()
|
|
worker.printer_thread.daemon = True
|
|
worker.printer_thread.start()
|
|
if log_to_driver:
|
|
worker.logger_thread = threading.Thread(
|
|
target=print_logs,
|
|
name="ray_print_logs",
|
|
args=(worker.redis_client, worker.threads_stopped))
|
|
worker.logger_thread.daemon = True
|
|
worker.logger_thread.start()
|
|
|
|
if mode == SCRIPT_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)
|
|
worker.cached_functions_to_run = None
|
|
|
|
|
|
def disconnect(exiting_interpreter=False):
|
|
"""Disconnect this worker from the raylet and object store."""
|
|
# Reset the list of cached remote functions and actors so that if more
|
|
# remote functions or actors are defined and then connect is called again,
|
|
# the remote functions will be exported. This is mostly relevant for the
|
|
# tests.
|
|
worker = global_worker
|
|
if worker.connected:
|
|
# Shutdown all of the threads that we've started. TODO(rkn): This
|
|
# should be handled cleanly in the worker object's destructor and not
|
|
# in this disconnect method.
|
|
worker.threads_stopped.set()
|
|
if hasattr(worker, "import_thread"):
|
|
worker.import_thread.join_import_thread()
|
|
if hasattr(worker, "listener_thread"):
|
|
worker.listener_thread.join()
|
|
if hasattr(worker, "printer_thread"):
|
|
worker.printer_thread.join()
|
|
if hasattr(worker, "logger_thread"):
|
|
worker.logger_thread.join()
|
|
worker.threads_stopped.clear()
|
|
worker._session_index += 1
|
|
|
|
worker.node = None # Disconnect the worker from the node.
|
|
worker.cached_functions_to_run = []
|
|
worker.serialization_context_map.clear()
|
|
try:
|
|
ray_actor = ray.actor
|
|
except AttributeError:
|
|
ray_actor = None # This can occur during program termination
|
|
if ray_actor is not None:
|
|
ray_actor.ActorClassMethodMetadata.reset_cache()
|
|
|
|
|
|
@contextmanager
|
|
def _changeproctitle(title, next_title):
|
|
setproctitle.setproctitle(title)
|
|
try:
|
|
yield
|
|
finally:
|
|
setproctitle.setproctitle(next_title)
|
|
|
|
|
|
def register_custom_serializer(cls,
|
|
serializer,
|
|
deserializer,
|
|
use_pickle=False,
|
|
use_dict=False,
|
|
class_id=None):
|
|
"""Registers custom functions for efficient object serialization.
|
|
|
|
The serializer and deserializer are used when transferring objects of
|
|
`cls` across processes and nodes. This can be significantly faster than
|
|
the Ray default fallbacks. Wraps `register_custom_serializer` underneath.
|
|
|
|
Args:
|
|
cls (type): The class that ray should use this custom serializer for.
|
|
serializer: The custom serializer that takes in a cls instance and
|
|
outputs a serialized representation. use_pickle and use_dict
|
|
must be False if provided.
|
|
deserializer: The custom deserializer that takes in a serialized
|
|
representation of the cls and outputs a cls instance. use_pickle
|
|
and use_dict must be False if provided.
|
|
use_pickle: Deprecated.
|
|
use_dict: Deprecated.
|
|
class_id (str): Unique ID of the class. Autogenerated if None.
|
|
"""
|
|
worker = global_worker
|
|
worker.check_connected()
|
|
|
|
if use_pickle:
|
|
raise DeprecationWarning(
|
|
"`use_pickle` is no longer a valid parameter and will be removed "
|
|
"in future versions of Ray. If this breaks your application, "
|
|
"see `SerializationContext.register_custom_serializer`.")
|
|
if use_dict:
|
|
raise DeprecationWarning(
|
|
"`use_pickle` is no longer a valid parameter and will be removed "
|
|
"in future versions of Ray. If this breaks your application, "
|
|
"see `SerializationContext.register_custom_serializer`.")
|
|
assert serializer is not None and deserializer is not None
|
|
context = global_worker.get_serialization_context()
|
|
context.register_custom_serializer(
|
|
cls, serializer, deserializer, class_id=class_id)
|
|
|
|
|
|
def show_in_webui(message, key="", dtype="text"):
|
|
"""Display message in dashboard.
|
|
|
|
Display message for the current task or actor in the dashboard.
|
|
For example, this can be used to display the status of a long-running
|
|
computation.
|
|
|
|
Args:
|
|
message (str): Message to be displayed.
|
|
key (str): The key name for the message. Multiple message under
|
|
different keys will be displayed at the same time. Messages
|
|
under the same key will be overriden.
|
|
data_type (str): The type of message for rendering. One of the
|
|
following: text, html.
|
|
"""
|
|
worker = global_worker
|
|
worker.check_connected()
|
|
|
|
acceptable_dtypes = {"text", "html"}
|
|
assert dtype in acceptable_dtypes, "dtype accepts only: {}".format(
|
|
acceptable_dtypes)
|
|
|
|
message_wrapped = {"message": message, "dtype": dtype}
|
|
message_encoded = json.dumps(message_wrapped).encode()
|
|
|
|
worker.core_worker.set_webui_display(key.encode(), message_encoded)
|
|
|
|
|
|
# Global varaible to make sure we only send out the warning once
|
|
blocking_get_inside_async_warned = False
|
|
|
|
|
|
def get(object_ids, timeout=None):
|
|
"""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.
|
|
|
|
This method will issue a warning if it's running inside async context,
|
|
you can use ``await object_id`` instead of ``ray.get(object_id)``. For
|
|
a list of object ids, you can use ``await asyncio.gather(*object_ids)``.
|
|
|
|
Args:
|
|
object_ids: Object ID of the object to get or a list of object IDs to
|
|
get.
|
|
timeout (Optional[float]): The maximum amount of time in seconds to
|
|
wait before returning.
|
|
|
|
Returns:
|
|
A Python object or a list of Python objects.
|
|
|
|
Raises:
|
|
RayTimeoutError: A RayTimeoutError is raised if a timeout is set and
|
|
the get takes longer than timeout to return.
|
|
Exception: An exception is raised if the task that created the object
|
|
or that created one of the objects raised an exception.
|
|
"""
|
|
worker = global_worker
|
|
worker.check_connected()
|
|
|
|
if hasattr(
|
|
worker,
|
|
"core_worker") and worker.core_worker.current_actor_is_asyncio():
|
|
global blocking_get_inside_async_warned
|
|
if not blocking_get_inside_async_warned:
|
|
logger.debug("Using blocking ray.get inside async actor. "
|
|
"This blocks the event loop. Please use `await` "
|
|
"on object id with asyncio.gather if you want to "
|
|
"yield execution to the event loop instead.")
|
|
blocking_get_inside_async_warned = True
|
|
|
|
with profiling.profile("ray.get"):
|
|
is_individual_id = isinstance(object_ids, ray.ObjectID)
|
|
if is_individual_id:
|
|
object_ids = [object_ids]
|
|
|
|
if not isinstance(object_ids, list):
|
|
raise ValueError("'object_ids' must either be an object ID "
|
|
"or a list of object IDs.")
|
|
|
|
global last_task_error_raise_time
|
|
# TODO(ujvl): Consider how to allow user to retrieve the ready objects.
|
|
values = worker.get_objects(object_ids, timeout=timeout)
|
|
for i, value in enumerate(values):
|
|
if isinstance(value, RayError):
|
|
last_task_error_raise_time = time.time()
|
|
if isinstance(value, ray.exceptions.UnreconstructableError):
|
|
worker.core_worker.dump_object_store_memory_usage()
|
|
if isinstance(value, RayTaskError):
|
|
raise value.as_instanceof_cause()
|
|
else:
|
|
raise value
|
|
|
|
# Run post processors.
|
|
for post_processor in worker._post_get_hooks:
|
|
values = post_processor(object_ids, values)
|
|
|
|
if is_individual_id:
|
|
values = values[0]
|
|
return values
|
|
|
|
|
|
def put(value, weakref=False):
|
|
"""Store an object in the object store.
|
|
|
|
The object may not be evicted while a reference to the returned ID exists.
|
|
|
|
Args:
|
|
value: The Python object to be stored.
|
|
weakref: If set, allows the object to be evicted while a reference
|
|
to the returned ID exists. You might want to set this if putting
|
|
a lot of objects that you might not need in the future.
|
|
It allows Ray to more aggressively reclaim memory.
|
|
|
|
Returns:
|
|
The object ID assigned to this value.
|
|
"""
|
|
worker = global_worker
|
|
worker.check_connected()
|
|
with profiling.profile("ray.put"):
|
|
try:
|
|
object_id = worker.put_object(value, pin_object=not weakref)
|
|
except ObjectStoreFullError:
|
|
logger.info(
|
|
"Put failed since the value was either too large or the "
|
|
"store was full of pinned objects.")
|
|
raise
|
|
return object_id
|
|
|
|
|
|
# Global variable to make sure we only send out the warning once.
|
|
blocking_wait_inside_async_warned = False
|
|
|
|
|
|
def wait(object_ids, num_returns=1, timeout=None):
|
|
"""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 object IDs.
|
|
|
|
This method returns two lists. The first list consists of object IDs that
|
|
correspond to objects that are available in the object store. The second
|
|
list corresponds to the rest of the object IDs (which may or may not be
|
|
ready).
|
|
|
|
Ordering of the input list of object IDs is preserved. That is, if A
|
|
precedes B in the input list, and both are in the ready list, then A will
|
|
precede B in the ready list. This also holds true if A and B are both in
|
|
the remaining list.
|
|
|
|
This method will issue a warning if it's running inside an async context.
|
|
Instead of ``ray.wait(object_ids)``, you can use
|
|
``await asyncio.wait(object_ids)``.
|
|
|
|
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 (float): The maximum amount of time in seconds to wait before
|
|
returning.
|
|
|
|
Returns:
|
|
A list of object IDs that are ready and a list of the remaining object
|
|
IDs.
|
|
"""
|
|
worker = global_worker
|
|
|
|
if hasattr(worker,
|
|
"core_worker") and worker.core_worker.current_actor_is_asyncio(
|
|
) and timeout != 0:
|
|
global blocking_wait_inside_async_warned
|
|
if not blocking_wait_inside_async_warned:
|
|
logger.debug("Using blocking ray.wait inside async method. "
|
|
"This blocks the event loop. Please use `await` "
|
|
"on object id with asyncio.wait. ")
|
|
blocking_wait_inside_async_warned = True
|
|
|
|
if isinstance(object_ids, ObjectID):
|
|
raise TypeError("wait() expected a list of ray.ObjectID, got a single "
|
|
"ray.ObjectID")
|
|
|
|
if not isinstance(object_ids, list):
|
|
raise TypeError(
|
|
"wait() expected a list of ray.ObjectID, got {}".format(
|
|
type(object_ids)))
|
|
|
|
if timeout is not None and timeout < 0:
|
|
raise ValueError("The 'timeout' argument must be nonnegative. "
|
|
"Received {}".format(timeout))
|
|
|
|
for object_id in object_ids:
|
|
if not isinstance(object_id, ObjectID):
|
|
raise TypeError("wait() expected a list of ray.ObjectID, "
|
|
"got list containing {}".format(type(object_id)))
|
|
|
|
worker.check_connected()
|
|
# TODO(swang): Check main thread.
|
|
with profiling.profile("ray.wait"):
|
|
|
|
# TODO(rkn): This is a temporary workaround for
|
|
# https://github.com/ray-project/ray/issues/997. However, it should be
|
|
# fixed in Arrow instead of here.
|
|
if len(object_ids) == 0:
|
|
return [], []
|
|
|
|
if len(object_ids) != len(set(object_ids)):
|
|
raise ValueError("Wait requires a list of unique object IDs.")
|
|
if num_returns <= 0:
|
|
raise ValueError(
|
|
"Invalid number of objects to return %d." % num_returns)
|
|
if num_returns > len(object_ids):
|
|
raise ValueError("num_returns cannot be greater than the number "
|
|
"of objects provided to ray.wait.")
|
|
|
|
timeout = timeout if timeout is not None else 10**6
|
|
timeout_milliseconds = int(timeout * 1000)
|
|
ready_ids, remaining_ids = worker.core_worker.wait(
|
|
object_ids,
|
|
num_returns,
|
|
timeout_milliseconds,
|
|
worker.current_task_id,
|
|
)
|
|
return ready_ids, remaining_ids
|
|
|
|
|
|
def get_actor(name):
|
|
"""Get a handle to a detached actor.
|
|
|
|
Gets a handle to a detached actor with the given name. The actor must
|
|
have been created with Actor.options(name="name").remote().
|
|
|
|
Returns:
|
|
ActorHandle to the actor.
|
|
|
|
Raises:
|
|
ValueError if the named actor does not exist.
|
|
"""
|
|
return ray.util.named_actors._get_actor(name)
|
|
|
|
|
|
def kill(actor, no_restart=True):
|
|
"""Kill an actor forcefully.
|
|
|
|
This will interrupt any running tasks on the actor, causing them to fail
|
|
immediately. Any atexit handlers installed in the actor will still be run.
|
|
|
|
If you want to kill the actor but let pending tasks finish,
|
|
you can call ``actor.__ray_terminate__.remote()`` instead to queue a
|
|
termination task.
|
|
|
|
If the actor is a detached actor, subsequent calls to get its handle via
|
|
ray.get_actor will fail.
|
|
|
|
Args:
|
|
actor (ActorHandle): Handle to the actor to kill.
|
|
no_restart (bool): Whether or not this actor should be restarted if
|
|
it's a restartable actor.
|
|
"""
|
|
if not isinstance(actor, ray.actor.ActorHandle):
|
|
raise ValueError("ray.kill() only supported for actors. "
|
|
"Got: {}.".format(type(actor)))
|
|
worker = ray.worker.global_worker
|
|
worker.check_connected()
|
|
worker.core_worker.kill_actor(actor._ray_actor_id, no_restart)
|
|
|
|
|
|
def cancel(object_id, force=False):
|
|
"""Cancels a task according to the following conditions.
|
|
|
|
If the specified task is pending execution, it will not be executed. If
|
|
the task is currently executing, the behavior depends on the ``force``
|
|
flag. When ``force=False``, a KeyboardInterrupt will be raised in Python
|
|
and when ``force=True``, the executing the task will immediately exit. If
|
|
the task is already finished, nothing will happen.
|
|
|
|
Only non-actor tasks can be canceled. Canceled tasks will not be
|
|
retried (max_retries will not be respected).
|
|
|
|
Calling ray.get on a canceled task will raise a RayCancellationError.
|
|
|
|
Args:
|
|
object_id (ObjectID): ObjectID returned by the task
|
|
that should be canceled.
|
|
force (boolean): Whether to force-kill a running task by killing
|
|
the worker that is running the task.
|
|
Raises:
|
|
TypeError: This is also raised for actor tasks.
|
|
"""
|
|
worker = ray.worker.global_worker
|
|
worker.check_connected()
|
|
|
|
if not isinstance(object_id, ray.ObjectID):
|
|
raise TypeError(
|
|
"ray.cancel() only supported for non-actor object IDs. "
|
|
"Got: {}.".format(type(object_id)))
|
|
return worker.core_worker.cancel_task(object_id, force)
|
|
|
|
|
|
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 make_decorator(num_return_vals=None,
|
|
num_cpus=None,
|
|
num_gpus=None,
|
|
memory=None,
|
|
object_store_memory=None,
|
|
resources=None,
|
|
max_calls=None,
|
|
max_retries=None,
|
|
max_restarts=None,
|
|
max_task_retries=None,
|
|
worker=None):
|
|
def decorator(function_or_class):
|
|
if (inspect.isfunction(function_or_class)
|
|
or is_cython(function_or_class)):
|
|
# Set the remote function default resources.
|
|
if max_restarts is not None:
|
|
raise ValueError("The keyword 'max_restarts' is not "
|
|
"allowed for remote functions.")
|
|
if max_task_retries is not None:
|
|
raise ValueError("The keyword 'max_task_retries' is not "
|
|
"allowed for remote functions.")
|
|
|
|
return ray.remote_function.RemoteFunction(
|
|
Language.PYTHON, function_or_class, None, num_cpus, num_gpus,
|
|
memory, object_store_memory, resources, num_return_vals,
|
|
max_calls, max_retries)
|
|
|
|
if inspect.isclass(function_or_class):
|
|
if num_return_vals is not None:
|
|
raise TypeError("The keyword 'num_return_vals' is not "
|
|
"allowed for actors.")
|
|
if max_calls is not None:
|
|
raise TypeError("The keyword 'max_calls' is not "
|
|
"allowed for actors.")
|
|
|
|
return ray.actor.make_actor(function_or_class, num_cpus, num_gpus,
|
|
memory, object_store_memory, resources,
|
|
max_restarts, max_task_retries)
|
|
|
|
raise TypeError("The @ray.remote decorator must be applied to "
|
|
"either a function or to a class.")
|
|
|
|
return decorator
|
|
|
|
|
|
def remote(*args, **kwargs):
|
|
"""Define a remote function or an actor class.
|
|
|
|
This can be used with no arguments to define a remote function or actor as
|
|
follows:
|
|
|
|
.. code-block:: python
|
|
|
|
@ray.remote
|
|
def f():
|
|
return 1
|
|
|
|
@ray.remote
|
|
class Foo:
|
|
def method(self):
|
|
return 1
|
|
|
|
It can also be used with specific keyword arguments:
|
|
|
|
* **num_return_vals:** This is only for *remote functions*. It specifies
|
|
the number of object IDs returned by the remote function invocation.
|
|
* **num_cpus:** The quantity of CPU cores to reserve for this task or for
|
|
the lifetime of the actor.
|
|
* **num_gpus:** The quantity of GPUs to reserve for this task or for the
|
|
lifetime of the actor.
|
|
* **resources:** The quantity of various custom resources to reserve for
|
|
this task or for the lifetime of the actor. This is a dictionary mapping
|
|
strings (resource names) to numbers.
|
|
* **max_calls:** Only for *remote functions*. This specifies the maximum
|
|
number of times that a given worker can execute the given remote function
|
|
before it must exit (this can be used to address memory leaks in
|
|
third-party libraries or to reclaim resources that cannot easily be
|
|
released, e.g., GPU memory that was acquired by TensorFlow). By
|
|
default this is infinite.
|
|
* **max_restarts**: Only for *actors*. This specifies the maximum
|
|
number of times that the actor should be restarted when it dies
|
|
unexpectedly. The minimum valid value is 0 (default), which indicates
|
|
that the actor doesn't need to be restarted. A value of -1
|
|
indicates that an actor should be restarted indefinitely.
|
|
* **max_task_retries**: Only for *actors*. How many times to retry an actor
|
|
task if the task fails due to a system error, e.g., the actor has died.
|
|
If set to -1, the system will retry the failed task until the task
|
|
succeeds, or the actor has reached its max_restarts limit. If set to n >
|
|
0, the system will retry the failed task up to n times, after which the
|
|
task will throw a `RayActorError` exception upon `ray.get`. Note that
|
|
Python exceptions are not considered system errors and will not trigger
|
|
retries.
|
|
* **max_retries**: Only for *remote functions*. This specifies the maximum
|
|
number of times that the remote function should be rerun when the worker
|
|
process executing it crashes unexpectedly. The minimum valid value is 0,
|
|
the default is 4 (default), and a value of -1 indicates infinite retries.
|
|
|
|
This can be done as follows:
|
|
|
|
.. code-block:: python
|
|
|
|
@ray.remote(num_gpus=1, max_calls=1, num_return_vals=2)
|
|
def f():
|
|
return 1, 2
|
|
|
|
@ray.remote(num_cpus=2, resources={"CustomResource": 1})
|
|
class Foo:
|
|
def method(self):
|
|
return 1
|
|
|
|
Remote task and actor objects returned by @ray.remote can also be
|
|
dynamically modified with the same arguments as above using
|
|
``.options()`` as follows:
|
|
|
|
.. code-block:: python
|
|
|
|
@ray.remote(num_gpus=1, max_calls=1, num_return_vals=2)
|
|
def f():
|
|
return 1, 2
|
|
g = f.options(num_gpus=2, max_calls=None)
|
|
|
|
@ray.remote(num_cpus=2, resources={"CustomResource": 1})
|
|
class Foo:
|
|
def method(self):
|
|
return 1
|
|
Bar = Foo.options(num_cpus=1, resources=None)
|
|
|
|
Running remote actors will be terminated when the actor handle to them
|
|
in Python is deleted, which will cause them to complete any outstanding
|
|
work and then shut down. If you want to kill them immediately, you can
|
|
also call ``ray.kill(actor)``.
|
|
"""
|
|
worker = global_worker
|
|
|
|
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_decorator(worker=worker)(args[0])
|
|
|
|
# Parse the keyword arguments from the decorator.
|
|
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', "
|
|
"'memory', 'object_store_memory', 'resources', "
|
|
"'max_calls', or 'max_restarts', like "
|
|
"'@ray.remote(num_return_vals=2, "
|
|
"resources={\"CustomResource\": 1})'.")
|
|
assert len(args) == 0 and len(kwargs) > 0, error_string
|
|
for key in kwargs:
|
|
assert key in [
|
|
"num_return_vals",
|
|
"num_cpus",
|
|
"num_gpus",
|
|
"memory",
|
|
"object_store_memory",
|
|
"resources",
|
|
"max_calls",
|
|
"max_restarts",
|
|
"max_task_retries",
|
|
"max_retries",
|
|
], error_string
|
|
|
|
num_cpus = kwargs["num_cpus"] if "num_cpus" in kwargs else None
|
|
num_gpus = kwargs["num_gpus"] if "num_gpus" in kwargs else None
|
|
resources = kwargs.get("resources")
|
|
if not isinstance(resources, dict) and resources is not None:
|
|
raise TypeError("The 'resources' keyword argument must be a "
|
|
"dictionary, but received type {}.".format(
|
|
type(resources)))
|
|
if resources is not None:
|
|
assert "CPU" not in resources, "Use the 'num_cpus' argument."
|
|
assert "GPU" not in resources, "Use the 'num_gpus' argument."
|
|
|
|
# Handle other arguments.
|
|
num_return_vals = kwargs.get("num_return_vals")
|
|
max_calls = kwargs.get("max_calls")
|
|
max_restarts = kwargs.get("max_restarts")
|
|
max_task_retries = kwargs.get("max_task_retries")
|
|
memory = kwargs.get("memory")
|
|
object_store_memory = kwargs.get("object_store_memory")
|
|
max_retries = kwargs.get("max_retries")
|
|
|
|
return make_decorator(
|
|
num_return_vals=num_return_vals,
|
|
num_cpus=num_cpus,
|
|
num_gpus=num_gpus,
|
|
memory=memory,
|
|
object_store_memory=object_store_memory,
|
|
resources=resources,
|
|
max_calls=max_calls,
|
|
max_restarts=max_restarts,
|
|
max_task_retries=max_task_retries,
|
|
max_retries=max_retries,
|
|
worker=worker)
|