ParametersRecord

class ParametersRecord(*args: Any, **kwargs: dict[str, Any])

Bases: ArrayRecord

Deprecated class ParametersRecord, use ArrayRecord instead.

This class exists solely for backward compatibility with legacy code that previously used ParametersRecord. It has been renamed to ArrayRecord.

Warning

ParametersRecord is deprecated and will be removed in a future release. Use ArrayRecord instead.

Examples

Legacy (deprecated) usage:

from flwr.common import ParametersRecord

record = ParametersRecord()

Updated usage:

from flwr.common import ArrayRecord

record = ArrayRecord()

Methods

clear()
count_bytes()	Return number of Bytes stored in this object.
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])
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()

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.

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.

items() → a set-like object providing a view on D's items.

keys() → a set-like object providing a view on D's keys.

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.