ConfigsRecord

class ConfigsRecord(configs_dict: dict[str, int | float | str | bytes | bool | list[int] | list[float] | list[str] | list[bytes] | list[bool]] | None = None, keep_input: bool = True)

Bases: TypedDict[str, int | float | str | bytes | bool | list[int] | list[float] | list[str] | list[bytes] | list[bool]]

Configs record.

A ConfigsRecord is a Python dictionary designed to ensure that each key-value pair adheres to specified data types. A ConfigsRecord is one of the types of records that a flwr.common.RecordSet supports and can therefore be used to construct common.Message objects.

Parameters:

• configs_dict (Optional[Dict[str, ConfigsRecordValues]]) – A dictionary that stores basic types (i.e. str, int, float, bytes as defined in ConfigsScalar) and lists of such types (see ConfigsScalarList).

• keep_input (bool (default: True)) – A boolean indicating whether config passed should be deleted from the input dictionary immediately after adding them to the record. When set to True, the data is duplicated in memory. If memory is a concern, set it to False.

Examples

The usage of a ConfigsRecord is envisioned for sending configuration values telling the target node how to perform a certain action (e.g. train/evaluate a model ). You can use standard Python built-in types such as float, str , bytes. All types allowed are defined in flwr.common.ConfigsRecordValues. While lists are supported, we encourage you to use a ParametersRecord instead if these are of high dimensionality.

Let’s see some examples of how to construct a ConfigsRecord from scratch:

>>> from flwr.common import ConfigsRecord
>>>
>>> # A `ConfigsRecord` is a specialized Python dictionary
>>> record = ConfigsRecord({"lr": 0.1, "batch-size": 128})
>>> # You can add more content to an existing record
>>> record["compute-average"] = True
>>> # It also supports lists
>>> record["loss-fn-coefficients"] = [0.4, 0.25, 0.35]
>>> # And string values (among other types)
>>> record["path-to-S3"] = "s3://bucket_name/folder1/fileA.json"

Just like the other types of records in a flwr.common.RecordSet, types are enforced. If you need to add a custom data structure or object, we recommend to serialise it into bytes and save it as such (bytes are allowed in a ConfigsRecord)

Methods

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

This function counts booleans as occupying 1 Byte.

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

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.