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https://github.com/wassname/ray.git
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Moving Local Mode to C++ (#7670)
This commit is contained in:
ijrsvt
+36
-65
@@ -15,7 +15,6 @@ import sys
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import threading
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import time
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import traceback
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import random
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# Ray modules
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import ray.cloudpickle as pickle
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@@ -54,7 +53,6 @@ from ray.utils import (
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is_cython,
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setup_logger,
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)
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from ray.local_mode_manager import LocalModeManager
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SCRIPT_MODE = 0
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WORKER_MODE = 1
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@@ -264,6 +262,10 @@ class Worker:
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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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@@ -291,10 +293,6 @@ class Worker:
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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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Raises:
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Exception if running in LOCAL_MODE and any of the object IDs do not
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exist in the emulated object store.
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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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@@ -303,9 +301,6 @@ class Worker:
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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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if self.mode == LOCAL_MODE:
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return self.local_mode_manager.get_objects(object_ids)
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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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@@ -438,9 +433,11 @@ def get_gpu_ids():
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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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if _mode() == LOCAL_MODE:
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raise RuntimeError("ray.get_gpu_ids() currently does not work in "
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"local_mode.")
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logger.info("ray.get_gpu_ids() currently does not work in LOCAL "
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"MODE.")
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all_resource_ids = global_worker.core_worker.resource_ids()
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assigned_ids = [
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@@ -600,8 +597,8 @@ def init(address=None,
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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): True if the code should be executed serially
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without Ray. This is useful for debugging.
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local_mode (bool): True if the code should 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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@@ -705,17 +702,13 @@ def init(address=None,
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_internal_config["free_objects_period_milliseconds"] = 1000
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global _global_node
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if driver_mode == LOCAL_MODE:
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# If starting Ray in LOCAL_MODE, don't start any other processes.
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_global_node = ray.node.LocalNode()
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elif redis_address is None:
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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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object_id_seed=object_id_seed,
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local_mode=local_mode,
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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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@@ -1122,12 +1115,11 @@ def connect(node,
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ray._raylet.set_internal_config(internal_config)
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if mode is not LOCAL_MODE:
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# Create a Redis client to primary.
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# The Redis client can safely be shared between threads. However,
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# that is not true of Redis pubsub clients. See the documentation at
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# https://github.com/andymccurdy/redis-py#thread-safety.
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worker.redis_client = node.create_redis_client()
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# Create a Redis client to primary.
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# The Redis client can safely be shared between threads. However,
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# that is not true of Redis pubsub clients. See the documentation at
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# https://github.com/andymccurdy/redis-py#thread-safety.
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worker.redis_client = node.create_redis_client()
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# Initialize some fields.
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if mode is WORKER_MODE:
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@@ -1136,12 +1128,6 @@ def connect(node,
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job_id = JobID.nil()
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# TODO(qwang): Rename this to `worker_id_str` or type to `WorkerID`
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worker.worker_id = _random_string()
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setproctitle.setproctitle("ray::IDLE")
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elif mode is LOCAL_MODE:
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if job_id is None:
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job_id = JobID.from_int(random.randint(1, 65535))
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worker.worker_id = ray.utils.compute_driver_id_from_job(
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job_id).binary()
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else:
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# This is the code path of driver mode.
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if job_id is None:
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@@ -1155,6 +1141,9 @@ def connect(node,
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worker.worker_id = ray.utils.compute_driver_id_from_job(
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job_id).binary()
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if mode is not SCRIPT_MODE and setproctitle:
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setproctitle.setproctitle("ray::IDLE")
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if not isinstance(job_id, JobID):
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raise TypeError("The type of given job id must be JobID.")
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@@ -1163,12 +1152,6 @@ def connect(node,
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worker.node = node
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worker.set_mode(mode)
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# If running Ray in LOCAL_MODE, there is no need to create call
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# create_worker or to start the worker service.
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if mode == LOCAL_MODE:
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worker.local_mode_manager = LocalModeManager()
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return
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# For driver's check that the version information matches the version
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# information that the Ray cluster was started with.
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try:
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@@ -1249,9 +1232,9 @@ def connect(node,
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(log_stderr_file
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if log_stderr_file is not None else sys.stderr).name)
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worker.redis_client.hmset(b"Workers:" + worker.worker_id, worker_dict)
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else:
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raise ValueError("Invalid worker mode. Expected DRIVER or WORKER.")
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elif not LOCAL_MODE:
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raise ValueError(
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"Invalid worker mode. Expected DRIVER, WORKER or LOCAL.")
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redis_address, redis_port = node.redis_address.split(":")
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gcs_options = ray._raylet.GcsClientOptions(
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redis_address,
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@@ -1259,15 +1242,9 @@ def connect(node,
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node.redis_password,
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)
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worker.core_worker = ray._raylet.CoreWorker(
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(mode == SCRIPT_MODE),
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node.plasma_store_socket_name,
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node.raylet_socket_name,
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job_id,
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gcs_options,
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node.get_logs_dir_path(),
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node.node_ip_address,
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node.node_manager_port,
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)
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(mode == SCRIPT_MODE), node.plasma_store_socket_name,
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node.raylet_socket_name, job_id, gcs_options, node.get_logs_dir_path(),
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node.node_ip_address, node.node_manager_port, mode == LOCAL_MODE)
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if driver_object_store_memory is not None:
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worker.core_worker.set_object_store_client_options(
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@@ -1276,9 +1253,10 @@ def connect(node,
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# Put something in the plasma store so that subsequent plasma store
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# accesses will be faster. Currently the first access is always slow, and
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# we don't want the user to experience this.
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temporary_object_id = ray.ObjectID.from_random()
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worker.put_object(1, object_id=temporary_object_id)
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ray.internal.free([temporary_object_id])
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if mode != LOCAL_MODE:
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temporary_object_id = ray.ObjectID.from_random()
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worker.put_object(1, object_id=temporary_object_id)
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ray.internal.free([temporary_object_id])
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# Start the import thread
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worker.import_thread = import_thread.ImportThread(worker, mode,
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@@ -1540,16 +1518,13 @@ def put(value, weakref=False):
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worker = global_worker
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worker.check_connected()
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with profiling.profile("ray.put"):
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if worker.mode == LOCAL_MODE:
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object_id = worker.local_mode_manager.put_object(value)
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else:
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try:
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object_id = worker.put_object(value, pin_object=not weakref)
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except ObjectStoreFullError:
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logger.info(
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"Put failed since the value was either too large or the "
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"store was full of pinned objects.")
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raise
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try:
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object_id = worker.put_object(value, pin_object=not weakref)
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except ObjectStoreFullError:
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logger.info(
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"Put failed since the value was either too large or the "
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"store was full of pinned objects.")
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raise
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return object_id
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@@ -1624,10 +1599,6 @@ def wait(object_ids, num_returns=1, timeout=None):
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worker.check_connected()
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# TODO(swang): Check main thread.
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with profiling.profile("ray.wait"):
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# When Ray is run in LOCAL_MODE, all functions are run immediately,
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# so all objects in object_id are ready.
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if worker.mode == LOCAL_MODE:
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return object_ids[:num_returns], object_ids[num_returns:]
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# TODO(rkn): This is a temporary workaround for
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# https://github.com/ray-project/ray/issues/997. However, it should be
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