mlrun.package.packagers.numpy_packagers.NumPyNDArrayListPackager

class mlrun.package.packagers.numpy_packagers.NumPyNDArrayListPackager

Bases: _NumPyNDArrayCollectionPackager

list[numpy.ndarray] packager.

Packager Summary

Packing Type: list[numpy.ndarray]

Packing Sub-Classes: False

Priority: Default priority (5)

Default Artifact Types:

• Packing: file

• Unpacking: file

Packing 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 list of numpy arrays as a result.

Unpacking Artifact Types:

• file - Unpack a numppy array list from file.

  • file_format - The file format to use for reading the arrays list. Default is None - will be read by thefile 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.

• object - 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 beraised.

  • 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 objectmodule 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 thatthe current interpreter python version matches the pickled object version beforeunpickling the object.

  • pickle_module_version - The pickle module version. Used to verify that the current interpreter moduleversion 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.

Attributes

BUNDLE_FROM_DICT	Whether the PACKABLE_OBJECT_TYPE can be used as a bundle and be initialized from a dictionary.
BUNDLE_FROM_LIST	Whether the PACKABLE_OBJECT_TYPE can be used as a bundle and be initialized from a list.
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	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.

BUNDLE_FROM_DICT = False

Whether the PACKABLE_OBJECT_TYPE can be used as a bundle and be initialized from a dictionary.

BUNDLE_FROM_LIST = False

Whether the PACKABLE_OBJECT_TYPE can be used as a bundle and be initialized from a list.

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 list[ndarray]

PACK_SUBCLASSES = False

Whether to also pack all subclasses of the PACKABLE_OBJECT_TYPE.

PRIORITY: int = 4

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.

Returns:

The clearing path list.

priority

Get the packager's priority.

Returns:

The packager's priority.

Methods

__init__()
add_future_clearing_path(path)	Mark a path to be cleared by this packager's manager after logging the packaged artifacts.
bundle(collection)	Initialize a bundle object with the collection given using this packager.
can_bundle(bundle_hint, collection_type)	Check if the packager can be used to initialize a bundle (a collection of packages) of the required type with the provided collection type.
can_unbundle(bundled_object)	Check if the packager can unbundle a bundled object of the provided type.
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 (union of packing and unpacking).
get_supported_packing_artifact_types()	Get the supported artifact types for packing by scanning for pack_* methods.
get_supported_unpacking_artifact_types()	Get the supported artifact types for unpacking by scanning for unpack_* methods.
is_packable(obj[, artifact_type, configurations])	Check if the object provided is a list 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 list of numpy arrays as a result.
unbundle(bundled_object)	Unbundle the given object into a collection of objects (for later pack them each separately).
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 list 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.

bundle(collection: dict | list) → Any

Initialize a bundle object with the collection given using this packager.

Parameters:

collection -- The collection of objects to bundle.

Returns:

The bundled object.

Raises:

NotImplementedError -- In case the packager does not support bundling.

can_bundle(bundle_hint: type, collection_type: type[dict] | type[list]) → bool

Check if the packager can be used to initialize a bundle (a collection of packages) of the required type with the provided collection type.

The method is implemented to validate the bundle type by checking if the given type matches the variable PACKABLE_OBJECT_TYPE with respect to the PACK_SUBCLASSES class variable. If it does, it checks if the given collection type's flag is set (either BUNDLE_FROM_LIST or BUNDLE_FROM_DICT).

Parameters:

• bundle_hint -- The bundle type hint to check if the PACKABLE_OBJECT_TYPE matches to.

• collection_type -- The available collection type that will be used in the bundle type's constructor.

Returns:

True if it can be used as a bundle and False otherwise.

can_unbundle(bundled_object: Any)

Check if the packager can unbundle a bundled object of the provided type.

The method is implemented to automatically checks if the packager can be used as a bundle (either class variables BUNDLE_FROM_LIST or BUNDLE_FROM_DICT are true) and then checks that the bundle type matches the class variable PACKABLE_OBJECT_TYPE with respect to the PACK_SUBCLASSES class variable.

Parameters:

bundled_object -- The bundled object to check.

Returns:

True if it can unbundle and False otherwise.

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 (union of packing and unpacking).

Returns:

A list of all the supported artifact types.

get_supported_packing_artifact_types() → list[str]

Get the supported artifact types for packing by scanning for pack_* methods.

Returns:

A list of artifact types discovered from pack_* method names.

get_supported_unpacking_artifact_types() → list[str]

Get the supported artifact types for unpacking by scanning for unpack_* methods.

Returns:

A list of artifact types discovered from unpack_* method names.

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

Check if the object provided is a list 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_unpacking_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: list[ndarray], key: str) → dict

Pack a list of numpy arrays as a result.

Parameters:

• obj -- The arrays list to pack and log.

• key -- The result's key.

Returns:

The result dictionary.

unbundle(bundled_object: Any) → dict | list

Unbundle the given object into a collection of objects (for later pack them each separately).

Returns:

The unbundled collection of objects - a list or dict.

Raises:

NotImplementedError -- In case the packager does not support bundling.

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) → list[ndarray]

Unpack a numppy array list from file.

Parameters:

• data_item -- The data item to unpack.

• file_format -- The file format to use for reading the arrays list. 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.