ParametersRecord¶
- class ParametersRecord(*args: Any, **kwargs: dict[str, Any])[source]¶
Bases:
ArrayRecord
Deprecated class
ParametersRecord
, useArrayRecord
instead.This class exists solely for backward compatibility with legacy code that previously used
ParametersRecord
. It has been renamed toArrayRecord
.Warning
ParametersRecord
is deprecated and will be removed in a future release. UseArrayRecord
instead.Examples
Legacy (deprecated) usage:
from flwr.common import ParametersRecord record = ParametersRecord()
Updated usage:
from flwr.common import ArrayRecord record = ArrayRecord()
Methods
clear
()Return number of Bytes stored in this object.
deflate
()Deflate the ArrayRecord.
from_array_dict
(array_dict, *[, keep_input])Create ArrayRecord from a dictionary of
Array
.from_numpy_ndarrays
(ndarrays, *[, keep_input])Create ArrayRecord from a list of NumPy
ndarray
.from_torch_state_dict
(state_dict, *[, ...])Create ArrayRecord from PyTorch
state_dict
.get
(k[,d])inflate
(object_content[, children])Inflate an ArrayRecord from bytes.
items
()keys
()pop
(k[,d])If key is not found, d is returned if given, otherwise KeyError is raised.
popitem
()as a 2-tuple; but raise KeyError if D is empty.
setdefault
(k[,d])to_numpy_ndarrays
(*[, keep_input])Return the ArrayRecord as a list of NumPy
ndarray
.to_torch_state_dict
(*[, keep_input])Return the ArrayRecord as a PyTorch
state_dict
.update
([E, ]**F)If E present and has a .keys() method, does: for k in E: D[k] = E[k] If E present and lacks .keys() method, does: for (k, v) in E: D[k] = v In either case, this is followed by: for k, v in F.items(): D[k] = v
values
()Attributes
- property children: dict[str, InflatableObject]¶
Return a dictionary of Arrays with their Object IDs as keys.
- clear() None. Remove all items from D. ¶
- count_bytes() int ¶
Return number of Bytes stored in this object.
Note that a small amount of Bytes might also be included in this counting that correspond to metadata of the serialized object (e.g. of NumPy array) needed for deseralization.
- deflate() bytes ¶
Deflate the ArrayRecord.
- classmethod from_array_dict(array_dict: OrderedDict[str, Array], *, keep_input: bool = True) ArrayRecord ¶
Create ArrayRecord from a dictionary of
Array
.
- classmethod from_numpy_ndarrays(ndarrays: list[ndarray[Any, dtype[Any]]], *, keep_input: bool = True) ArrayRecord ¶
Create ArrayRecord from a list of NumPy
ndarray
.
- classmethod from_torch_state_dict(state_dict: OrderedDict[str, torch.Tensor], *, keep_input: bool = True) ArrayRecord ¶
Create ArrayRecord from PyTorch
state_dict
.
- get(k[, d]) D[k] if k in D, else d. d defaults to None. ¶
- classmethod inflate(object_content: bytes, children: dict[str, InflatableObject] | None = None) ArrayRecord ¶
Inflate an ArrayRecord from bytes.
- Parameters:
object_content (bytes) – The deflated object content of the ArrayRecord.
children (Optional[dict[str, InflatableObject]] (default: None)) – Dictionary of children InflatableObjects mapped to their Object IDs. These children enable the full inflation of the ArrayRecord.
- Returns:
The inflated ArrayRecord.
- Return type:
- items() a set-like object providing a view on D's items. ¶
- keys() a set-like object providing a view on D's keys. ¶
- property object_id: str¶
Get object_id.
- pop(k[, d]) v, remove specified key and return the corresponding value. ¶
If key is not found, d is returned if given, otherwise KeyError is raised.
- popitem() (k, v), remove and return some (key, value) pair ¶
as a 2-tuple; but raise KeyError if D is empty.
- setdefault(k[, d]) D.get(k,d), also set D[k]=d if k not in D ¶
- to_numpy_ndarrays(*, keep_input: bool = True) list[ndarray[Any, dtype[Any]]] ¶
Return the ArrayRecord as a list of NumPy
ndarray
.
- to_torch_state_dict(*, keep_input: bool = True) OrderedDict[str, torch.Tensor] ¶
Return the ArrayRecord as a PyTorch
state_dict
.
- update([E, ]**F) None. Update D from mapping/iterable E and F. ¶
If E present and has a .keys() method, does: for k in E: D[k] = E[k] If E present and lacks .keys() method, does: for (k, v) in E: D[k] = v In either case, this is followed by: for k, v in F.items(): D[k] = v
- values() an object providing a view on D's values. ¶