import ray
from typing import Any, Dict, Hashable, List, Tuple
from deephyper.evaluator.storage._storage import Storage
from deephyper.evaluator.storage._memory_storage import MemoryStorage
[docs]class RayStorage(Storage):
"""Storage class using Ray actors. The RayStorage is wrapping the MemoryStorage class to be a Ray actor.
Args:
address (str, optional): address of the Ray-head. Defaults to ``"auto"``, to connect to the local head node.
"""
ray_storage_counter = 0
def __init__(self, address="auto") -> None:
super().__init__()
self.address = address
self.actor_name = f"{RayStorage.ray_storage_counter}"
RayStorage.ray_storage_counter += 1
self.memory_storage_actor = None
def _connect(self):
if self.memory_storage_actor is None:
self.memory_storage_actor = (
ray.remote(MemoryStorage)
.options(name=self.actor_name, namespace="deephyper")
.remote()
)
else:
self.memory_storage_actor = ray.get_actor(self.actor_name)
self.connected = True
def __getstate__(self):
state = {
"connected": self.connected,
"actor_name": self.actor_name,
"address": self.address,
}
return state
def __setstate__(self, newstate):
self.__dict__.update(newstate)
if not (ray.is_initialized()):
ray.init(address=self.address)
self.memory_storage_actor = ray.get_actor(
self.actor_name, namespace="deephyper"
)
[docs] def create_new_search(self) -> Hashable:
"""Create a new search in the store and returns its identifier.
Returns:
Hashable: The identifier of the search.
"""
return ray.get(self.memory_storage_actor.create_new_search.remote())
[docs] def create_new_job(self, search_id: Hashable) -> Hashable:
"""Creates a new job in the store and returns its identifier.
Args:
search_id (Hashable): The identifier of the search in which a new job
is created.
Returns:
Hashable: The created identifier of the job.
"""
return ray.get(self.memory_storage_actor.create_new_job.remote(search_id))
[docs] def store_search_value(
self, search_id: Hashable, key: Hashable, value: Any
) -> None:
"""Stores the value corresponding to key for search_id.
Args:
search_id (Hashable): The identifier of the job.
key (Hashable): A key to use to store the value.
value (Any): The value to store.
"""
ray.get(
self.memory_storage_actor.store_search_value.remote(search_id, key, value)
)
[docs] def load_search_value(self, search_id: Hashable, key: Hashable) -> Any:
"""Loads the value corresponding to key for search_id.
Args:
search_id (Hashable): The identifier of the job.
key (Hashable): A key to use to access the value.
"""
return ray.get(
self.memory_storage_actor.load_search_value.remote(search_id, key)
)
[docs] def store_job(self, job_id: Hashable, key: Hashable, value: Any) -> None:
"""Stores the value corresponding to key for job_id.
Args:
job_id (Hashable): The identifier of the job.
key (Hashable): A key to use to store the value.
value (Any): The value to store.
"""
ray.get(self.memory_storage_actor.store_job.remote(job_id, key, value))
[docs] def store_job_in(
self, job_id: Hashable, args: Tuple = None, kwargs: Dict = None
) -> None:
"""Stores the input arguments of the executed job.
Args:
job_id (Hashable): The identifier of the job.
args (Optional[Tuple], optional): The positional arguments. Defaults to None.
kwargs (Optional[Dict], optional): The keyword arguments. Defaults to None.
"""
ray.get(self.memory_storage_actor.store_job_in.remote(job_id, args, kwargs))
[docs] def store_job_out(self, job_id: Hashable, value: Any) -> None:
"""Stores the output value of the executed job.
Args:
job_id (Hashable): The identifier of the job.
value (Any): The value to store.
"""
ray.get(self.memory_storage_actor.store_job_out.remote(job_id, value))
[docs] def load_all_search_ids(self) -> List[Hashable]:
"""Loads the identifiers of all recorded searches.
Returns:
List[Hashable]: A list of identifiers of all the recorded searches.
"""
return ray.get(self.memory_storage_actor.load_all_search_ids.remote())
[docs] def load_all_job_ids(self, search_id: Hashable) -> List[Hashable]:
"""Loads the identifiers of all recorded jobs in the search.
Args:
search_id (Hashable): The identifier of the search.
Returns:
List[Hashable]: A list of identifiers of all the jobs.
"""
return ray.get(self.memory_storage_actor.load_all_job_ids.remote(search_id))
[docs] def load_search(self, search_id: Hashable) -> dict:
"""Loads the data of a search.
Args:
search_id (Hashable): The identifier of the search.
Returns:
dict: The corresponding data of the search.
"""
return ray.get(self.memory_storage_actor.load_search.remote(search_id))
[docs] def load_job(self, job_id: Hashable) -> dict:
"""Loads the data of a job.
Args:
job_id (Hashable): The identifier of the job.
Returns:
dict: The corresponding data of the job.
"""
return ray.get(self.memory_storage_actor.load_job.remote(job_id))
[docs] def load_out_from_all_jobs(self, search_id: Hashable) -> List[Any]:
"""Loads the output value from all jobs.
Args:
search_id (Hashable): The identifier of the search.
Returns:
List[Any]: A list of all the retrieved output values.
"""
return ray.get(
self.memory_storage_actor.load_out_from_all_jobs.remote(search_id)
)
[docs] def load_jobs(self, job_ids: List[Hashable]) -> dict:
"""Load all data from a given list of jobs' identifiers.
Args:
job_ids (list): The list of job identifiers.
Returns:
dict: A dictionnary of the retrieved values where the keys are the identifier of jobs.
"""
return ray.get(self.memory_storage_actor.load_jobs.remote(job_ids))
