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[api] API deprecations and cleanups for 1.0 (internal_config and Checkpointable actor) (#10333)

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* remove

* internal config updates, remove Checkpointable

* Lower object timeout default

* remove json

* Fix flaky test

* Fix unit test
This commit is contained in:
Stephanie Wang
2020-08-27 10:19:53 -07:00
committed by GitHub
parent 0aec4cbccb
commit f75dfd60a3
56 changed files with 239 additions and 1267 deletions
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python/ray/actor.py
-146
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@@ -2,8 +2,6 @@ import inspect
import logging
import weakref
from abc import ABCMeta, abstractmethod
from collections import namedtuple
import ray.ray_constants as ray_constants
import ray._raylet
import ray.signature as signature
@@ -854,11 +852,6 @@ def modify_class(cls):
"classes. In Python 2, you must declare the class with "
"'class ClassName(object):' instead of 'class ClassName:'.")
if issubclass(cls, Checkpointable) and inspect.isabstract(cls):
raise TypeError(
"A checkpointable actor class should implement all abstract "
"methods in the `Checkpointable` interface.")
# Modify the class to have an additional method that will be used for
# terminating the worker.
class Class(cls):
@@ -869,20 +862,6 @@ def modify_class(cls):
if worker.mode != ray.LOCAL_MODE:
ray.actor.exit_actor()
def __ray_checkpoint__(self):
"""Save a checkpoint.
This task saves the current state of the actor, the current task
frontier according to the raylet, and the checkpoint index
(number of tasks executed so far).
"""
worker = ray.worker.global_worker
if not isinstance(self, ray.actor.Checkpointable):
raise TypeError(
"__ray_checkpoint__.remote() may only be called on actors "
"that implement ray.actor.Checkpointable")
return worker._save_actor_checkpoint()
Class.__module__ = cls.__module__
Class.__name__ = cls.__name__
@@ -951,128 +930,3 @@ def exit_actor():
assert False, "This process should have terminated."
else:
raise TypeError("exit_actor called on a non-actor worker.")
CheckpointContext = namedtuple(
"CheckpointContext",
[
# Actor's ID.
"actor_id",
# Number of tasks executed since last checkpoint.
"num_tasks_since_last_checkpoint",
# Time elapsed since last checkpoint, in milliseconds.
"time_elapsed_ms_since_last_checkpoint",
],
)
"""A namedtuple that contains information about actor's last checkpoint."""
Checkpoint = namedtuple(
"Checkpoint",
[
# ID of this checkpoint.
"checkpoint_id",
# The timestamp at which this checkpoint was saved,
# represented as milliseconds elapsed since Unix epoch.
"timestamp",
],
)
"""A namedtuple that represents a checkpoint."""
class Checkpointable(metaclass=ABCMeta):
"""An interface that indicates an actor can be checkpointed."""
@abstractmethod
def should_checkpoint(self, checkpoint_context):
"""Whether this actor needs to be checkpointed.
This method will be called after every task. You should implement this
callback to decide whether this actor needs to be checkpointed at this
time, based on the checkpoint context, or any other factors.
Args:
checkpoint_context: A namedtuple that contains info about last
checkpoint.
Returns:
A boolean value that indicates whether this actor needs to be
checkpointed.
"""
pass
@abstractmethod
def save_checkpoint(self, actor_id, checkpoint_id):
"""Save a checkpoint to persistent storage.
If `should_checkpoint` returns true, this method will be called. You
should implement this callback to save actor's checkpoint and the given
checkpoint id to persistent storage.
Args:
actor_id: Actor's ID.
checkpoint_id: ID of this checkpoint. You should save it together
with actor's checkpoint data. And it will be used by the
`load_checkpoint` method.
Returns:
None.
"""
pass
@abstractmethod
def load_checkpoint(self, actor_id, available_checkpoints):
"""Load actor's previous checkpoint, and restore actor's state.
This method will be called when an actor is restarted, after
actor's constructor.
If the actor needs to restore from previous checkpoint, this function
should restore actor's state and return the checkpoint ID. Otherwise,
it should do nothing and return None.
Note, this method must return one of the checkpoint IDs in the
`available_checkpoints` list, or None. Otherwise, an exception will be
raised.
Args:
actor_id: Actor's ID.
available_checkpoints: A list of `Checkpoint` namedtuples that
contains all available checkpoint IDs and their timestamps,
sorted by timestamp in descending order.
Returns:
The ID of the checkpoint from which the actor was resumed, or None
if the actor should restart from the beginning.
"""
pass
@abstractmethod
def checkpoint_expired(self, actor_id, checkpoint_id):
"""Delete an expired checkpoint.
This method will be called when an checkpoint is expired. You should
implement this method to delete your application checkpoint data.
Note, the maximum number of checkpoints kept in the backend can be
configured at `RayConfig.num_actor_checkpoints_to_keep`.
Args:
actor_id: ID of the actor.
checkpoint_id: ID of the checkpoint that has expired.
Returns:
None.
"""
pass
def get_checkpoints_for_actor(actor_id):
"""Get the available checkpoints for the given actor ID, return a list
sorted by checkpoint timestamp in descending order.
"""
checkpoint_info = ray.state.state.actor_checkpoint_info(actor_id)
if checkpoint_info is None:
return []
checkpoints = [
Checkpoint(checkpoint_id, timestamp) for checkpoint_id, timestamp in
zip(checkpoint_info["CheckpointIds"], checkpoint_info["Timestamps"])
]
return sorted(
checkpoints,
key=lambda checkpoint: checkpoint.timestamp,
reverse=True,
)