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60 lines
2.0 KiB
Python
60 lines
2.0 KiB
Python
import ray
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import numpy as np
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def get(object_refs):
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"""Get a single or a collection of remote objects from the object store.
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This method is identical to `ray.get` except it adds support for tuples,
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ndarrays and dictionaries.
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Args:
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object_refs: Object ref of the object to get, a list, tuple, ndarray of
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object refs to get or a dict of {key: object ref}.
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Returns:
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A Python object, a list of Python objects or a dict of {key: object}.
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"""
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if isinstance(object_refs, (tuple, np.ndarray)):
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return ray.get(list(object_refs))
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elif isinstance(object_refs, dict):
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keys_to_get = [
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k for k, v in object_refs.items() if isinstance(v, ray.ObjectRef)
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]
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ids_to_get = [
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v for k, v in object_refs.items() if isinstance(v, ray.ObjectRef)
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]
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values = ray.get(ids_to_get)
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result = object_refs.copy()
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for key, value in zip(keys_to_get, values):
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result[key] = value
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return result
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else:
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return ray.get(object_refs)
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def wait(object_refs, num_returns=1, timeout=None):
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"""Return a list of IDs that are ready and a list of IDs that are not.
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This method is identical to `ray.wait` except it adds support for tuples
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and ndarrays.
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Args:
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object_refs (List[ObjectRef], Tuple(ObjectRef), np.array(ObjectRef)):
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List like of object refs for objects that may or may not be ready.
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Note that these IDs must be unique.
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num_returns (int): The number of object refs that should be returned.
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timeout (float): The maximum amount of time in seconds to wait before
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returning.
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Returns:
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A list of object refs that are ready and a list of the remaining object
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IDs.
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"""
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if isinstance(object_refs, (tuple, np.ndarray)):
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return ray.wait(
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list(object_refs), num_returns=num_returns, timeout=timeout)
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return ray.wait(object_refs, num_returns=num_returns, timeout=timeout)
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