mlrun.package.packagers.numpy_packagers.NumPyNDArrayDictPackager

mlrun.package.packagers.numpy_packagers.NumPyNDArrayDictPackager#

class mlrun.package.packagers.numpy_packagers.NumPyNDArrayDictPackager[source]#

Bases: _NumPyNDArrayCollectionPackager

dict[str, numpy.ndarray] packager.

Packager Summary

Packing Type: typing.Dict[str, numpy.ndarray]

Packing Sub-Classes: False

Priority: Default priority (5)

Default Artifact Types:

Packing: file
Unpacking: file

Artifact Types:

file - Pack an array collection as a file by the given format.
- file_format - The file format to save as. Default is npy.
- save_kwargs - Additional keyword arguments to pass to the numpy save functions.
object - Pack a python object, pickling it into a pkl file and store it in an artifact.
- pickle_module_name - The pickle module name to use for serializing the object.
result - Pack a dictionary of numpy arrays as a result.

Attributes

`DEFAULT_PACKING_ARTIFACT_TYPE`	The default artifact type to pack as.
`DEFAULT_UNPACKING_ARTIFACT_TYPE`	The default artifact type to unpack from.
`PACKABLE_OBJECT_TYPE`	The type of object this packager can pack and unpack.
`PACK_SUBCLASSES`	A flag for indicating whether to also pack all subclasses of the PACKABLE_OBJECT_TYPE.
`PRIORITY`	The priority of this packager in the packagers collection of the manager (lower is better).
`future_clearing_path_list`	Get the packager's future clearing path list.
`priority`	Get the packager's priority.

DEFAULT_PACKING_ARTIFACT_TYPE = 'file'#: The default artifact type to pack as.

DEFAULT_UNPACKING_ARTIFACT_TYPE = 'file'#: The default artifact type to unpack from.

PACKABLE_OBJECT_TYPE#

The type of object this packager can pack and unpack.

alias of Dict[str, ndarray]

alias of Dict[str, ndarray] .. autoattribute:: PACKABLE_OBJECT_TYPE .. autoattribute:: PACK_SUBCLASSES .. autoattribute:: PRIORITY .. autoattribute:: future_clearing_path_list .. autoattribute:: priority

Methods

`__init__`()
`add_future_clearing_path`(path)	Mark a path to be cleared by this packager's manager after logging the packaged artifacts.
`get_data_item_local_path`(data_item[, ...])	Get the local path to the item handled by the data item provided.
`get_default_packing_artifact_type`(obj)	Get the default artifact type for packing an object of this packager.
`get_default_unpacking_artifact_type`(data_item)	Get the default artifact type used for unpacking a data item holding an object of this packager.
`get_supported_artifact_types`()	Get all the supported artifact types on this packager.
`is_packable`(obj[, artifact_type, configurations])	Check if the object provided is a dictionary of numpy arrays.
`is_unpackable`(data_item, type_hint[, ...])	Check if this packager can unpack an input according to the user-given type hint and the provided artifact type.
`pack`(obj[, key, artifact_type, configurations])	Pack an object as the given artifact type using the provided configurations.
`pack_file`(obj, key[, file_format])	Pack an array collection as a file by the given format.
`pack_object`(obj, key[, pickle_module_name])	Pack a python object, pickling it into a pkl file and store it in an artifact.
`pack_result`(obj, key)	Pack a dictionary of numpy arrays as a result.
`unpack`(data_item[, artifact_type, instructions])	Unpack the data item's artifact by the provided type using the given instructions.
`unpack_file`(data_item[, file_format, ...])	Unpack a numppy array dictionary from file.
`unpack_object`(data_item[, ...])	Unpack the data item's object, unpickle it using the instructions, and return.

__init__()#

add_future_clearing_path(path: str | Path)#

Mark a path to be cleared by this packager's manager after logging the packaged artifacts.

Parameters:: path -- The path to clear post logging the artifacts.

get_data_item_local_path(data_item: DataItem, add_to_future_clearing_path: bool | None = None) → str#

Get the local path to the item handled by the data item provided. The local path can be the same as the data item in case the data item points to a local path, or will be downloaded to a temporary directory and return this newly created temporary local path.

Parameters:

data_item -- The data item to get its item local path.
add_to_future_clearing_path -- Whether to add the local path to the future clearing paths list. If None, it will add the path to the list only if the data item is not of kind 'file', meaning it represents a local file and hence we don't want to delete it post running automatically. We wish to delete it only if the local path is temporary (and that will be in case kind is not 'file', so it is being downloaded to a temporary directory).

Returns:

The data item local path.

get_default_packing_artifact_type(obj: Any) → str#

Get the default artifact type for packing an object of this packager.

Parameters:: obj -- The about-to-be packed object.
Returns:: The default artifact type.

get_default_unpacking_artifact_type(data_item: DataItem) → str#

Get the default artifact type used for unpacking a data item holding an object of this packager. The method is used when a data item is sent for unpacking without it being a package, but is a simple url or an old / manually logged artifact.

Parameters:: data_item -- The about-to-be unpacked data item.
Returns:: The default artifact type.

get_supported_artifact_types() → List[str]#

Get all the supported artifact types on this packager.

Returns:: A list of all the supported artifact types.

is_packable(obj: Any, artifact_type: str | None = None, configurations: dict | None = None) → bool[source]#

Check if the object provided is a dictionary of numpy arrays.

Parameters:

obj -- The object to pack.
artifact_type -- The artifact type to log the object as.
configurations -- The log hint configurations passed by the user.

Returns:

True if packable and False otherwise.

is_unpackable(data_item: DataItem, type_hint: Type, artifact_type: str | None = None) → bool#

Check if this packager can unpack an input according to the user-given type hint and the provided artifact type.

The default implementation tries to match the packable object type of this packager to the given type hint. If it matches, it looks for the artifact type in the list returned from get_supported_artifact_types.

Parameters:

data_item -- The input data item to check if unpackable.
type_hint -- The type hint of the input to unpack (the object type to be unpacked).
artifact_type -- The artifact type to unpack the object as.

Returns:

True if unpackable and False otherwise.

pack(obj: Any, key: str | None = None, artifact_type: str | None = None, configurations: dict | None = None) → Tuple[Artifact, dict] | dict#

Pack an object as the given artifact type using the provided configurations.

Parameters:

obj -- The object to pack.
key -- The key of the artifact.
artifact_type -- Artifact type to log to MLRun. If passing None, the default artifact type is used.
configurations -- Log hints configurations to pass to the packing method.

Returns:

If the packed object is an artifact, a tuple of the packed artifact and unpacking instructions dictionary. If the packed object is a result, a dictionary containing the result key and value.

pack_file(obj: List[ndarray] | Dict[str, ndarray], key: str, file_format: str = 'npz', **save_kwargs) → Tuple[Artifact, dict]#

Pack an array collection as a file by the given format.

Parameters:

obj -- The aray collection to pack.
key -- The key to use for the artifact.
file_format -- The file format to save as. Default is npy.
save_kwargs -- Additional keyword arguments to pass to the numpy save functions.

Returns:

The packed artifact and instructions.

pack_object(obj: Any, key: str, pickle_module_name: str = 'cloudpickle') → Tuple[Artifact, dict]#

Pack a python object, pickling it into a pkl file and store it in an artifact.

Parameters:

obj -- The object to pack and log.
key -- The artifact's key.
pickle_module_name -- The pickle module name to use for serializing the object.

Returns:

The artifacts and its pickling instructions.

pack_result(obj: Dict[str, ndarray], key: str) → dict[source]#

Pack a dictionary of numpy arrays as a result.

Parameters:

obj -- The arrays dictionary to pack and log.
key -- The result's key.

Returns:

The result dictionary.

unpack(data_item: DataItem, artifact_type: str | None = None, instructions: dict | None = None) → Any#

Unpack the data item's artifact by the provided type using the given instructions.

Parameters:

data_item -- The data input to unpack.
artifact_type -- The artifact type to unpack the data item as. If passing None, the default artifact type is used.
instructions -- Additional instructions noted in the package to pass to the unpacking method.

Returns:

The unpacked data item's object.

Raises:

MLRunPackageUnpackingError -- In case the packager could not unpack the data item.

unpack_file(data_item: DataItem, file_format: str | None = None, allow_pickle: bool = False) → Dict[str, ndarray][source]#

Unpack a numppy array dictionary from file.

Parameters:

data_item -- The data item to unpack.
file_format -- The file format to use for reading the arrays dictionary. Default is None - will be read by the file extension.
allow_pickle -- Whether to allow loading pickled arrays in case of object type arrays. Only relevant to 'npz' format. Default is False for security reasons.

Returns:

The unpacked array.

unpack_object(data_item: DataItem, pickle_module_name: str = 'cloudpickle', object_module_name: str | None = None, python_version: str | None = None, pickle_module_version: str | None = None, object_module_version: str | None = None) → Any#

Unpack the data item's object, unpickle it using the instructions, and return.

Warnings of mismatching python and module versions between the original pickling interpreter and this one may be raised.

Parameters:

data_item -- The data item holding the pkl file.
pickle_module_name -- Module to use for unpickling the object.
object_module_name -- The original object's module. Used to verify that the current interpreter object module version matches the pickled object version before unpickling the object.
python_version -- The python version in which the original object was pickled. Used to verify that the current interpreter python version matches the pickled object version before unpickling the object.
pickle_module_version -- The pickle module version. Used to verify that the current interpreter module version matches the one that pickled the object before unpickling it.
object_module_version -- The original object's module version to match to the interpreter's module version.

Returns:

The un-pickled python object.

mlrun.package.packagers.numpy_packagers.NumPyNDArrayDictPackager

Contents

mlrun.package.packagers.numpy_packagers.NumPyNDArrayDictPackager#