nvflare.app_common.decomposers.numpy_decomposers module

Decomposers for types from app_common and Machine Learning libraries.

class Float32ScalarDecomposer[source]

Bases: NumpyScalarDecomposer

supported_type()[source]

Returns the type/class supported by this decomposer.

Returns:

The class (not instance) of supported type

class Float64ScalarDecomposer[source]

Bases: NumpyScalarDecomposer

supported_type()[source]

Returns the type/class supported by this decomposer.

Returns:

The class (not instance) of supported type

class Int32ScalarDecomposer[source]

Bases: NumpyScalarDecomposer

supported_type()[source]

Returns the type/class supported by this decomposer.

Returns:

The class (not instance) of supported type

class Int64ScalarDecomposer[source]

Bases: NumpyScalarDecomposer

supported_type()[source]

Returns the type/class supported by this decomposer.

Returns:

The class (not instance) of supported type

class NumpyArrayDecomposer[source]

Bases: ViaFileDecomposer

dump_to_file(items: dict, path: str, fobs_ctx: dict)[source]

Dump the items to the file with the specified path

Parameters:

  • items – a dict of items of target object type to be dumped to file

  • path – the path to the file.

  • fobs_ctx – FOBS Context

Returns: a tuple of (file name, meta info)

The “path” is a temporary file name. You should create the file with the specified name. However, some frameworks (e.g. numpy) may add a special suffix to the name. In this case, you must return the modified name.

The “items” is a dict of target objects. The dict contains all objects of the target type in one payload. The dict could be very big. You must create a file to contain all the objects.

get_bytes_dot() → int[source]

Get the Datum Object Type to be used for bytes datum

Returns: the DOT for bytes datum

get_file_dot() → int[source]

Get the Datum Object Type to be used for file ref datum

Returns: the DOT for file ref datum

load_from_file(path: str, fobs_ctx: dict, meta: dict | None = None) → Any[source]

Load target object items from the specified file

Parameters:

  • path – the absolute path to the file to be loaded.

  • fobs_ctx – FOBS Context.

  • meta – meta info of the file.

Returns: a dict of target objects.

You must not delete the file after loading. Management of the file is done by the ViaFile class.

native_decompose(target: ndarray, manager: DatumManager | None = None) → bytes[source]
native_recompose(data: bytes, manager: DatumManager | None = None) → ndarray[source]
supported_type()[source]

Returns the type/class supported by this decomposer.

Returns:

The class (not instance) of supported type

class NumpyScalarDecomposer[source]

Bases: Decomposer, ABC

Decomposer base class for all numpy types with item method.

decompose(target: Any, manager: DatumManager | None = None) → Any[source]

Decompose the target into types supported by msgpack or classes with decomposers registered.

Msgpack supports primitives, bytes, memoryview, lists, dicts.

Parameters:

  • target – The instance to be serialized

  • manager – Datum manager to store externalized datum

Returns:

The decomposed serializable objects

recompose(data: Any, manager: DatumManager | None = None) → ndarray[source]

Reconstruct the object from decomposed components.

Parameters:

  • data – The decomposed component

  • manager – Datum manager to internalize datum

Returns:

The reconstructed object

register()[source]