mlrun.package.packagers.numpy_packagers.NumPyNDArrayListPackager

mlrun.package.packagers.numpy_packagers.NumPyNDArrayListPackager#

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

Bases: _NumPyNDArrayCollectionPackager

list[numpy.ndarray] packager.

Packager Summary

Packing Type: typing.List[numpy.ndarray]

Packing Sub-Classes: False

Priority: 4

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 list 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 pack all subclasses of the PACKABLE_OBJECT_TYPE as well.
`PRIORITY`	The priority of this packager in the packagers collection of the manager (lower is better).

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]

alias of List[ndarray] .. autoattribute:: PACKABLE_OBJECT_TYPE .. autoattribute:: PACK_SUBCLASSES .. autoattribute:: PRIORITY

Methods

`__init__`()
`add_future_clearing_path`(path[, ...])	Mark a path to be cleared by this packager's manager post logging the packaged artifacts.
`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_future_clearing_path_list`()	Get the packager's future clearing path list.
`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 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.
`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__()#

classmethod add_future_clearing_path(path: Union[str, Path], add_temp_paths_only: bool = True)#

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

Parameters:

path – The path to clear.
add_temp_paths_only – Whether to add only temporary files. When running locally on local files DataItem.local() will return the local given path which should not be deleted. This flag helps to avoid deleting files in that scenario.

classmethod 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.

classmethod 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 will be used when a data item is sent for unpacking without it being a package, but a simple url or an old / manually logged artifact.

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

classmethod get_future_clearing_path_list() → List[str]#

Get the packager’s future clearing path list.

Returns:: The clearing path list.

classmethod get_supported_artifact_types() → List[str]#

Get all the supported artifact types on this packager.

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

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

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.

classmethod is_unpackable(data_item: DataItem, type_hint: Type, artifact_type: Optional[str] = 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 does match, it will look 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.

classmethod pack(obj: Any, key: Optional[str] = None, artifact_type: Optional[str] = None, configurations: Optional[dict] = None) → Union[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 will be 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.

classmethod pack_file(obj: Union[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.

classmethod 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 it’s pickling instructions.

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

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.

classmethod unpack(data_item: DataItem, artifact_type: Optional[str] = None, instructions: Optional[dict] = 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 will be 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.

classmethod unpack_file(data_item: DataItem, file_format: Optional[str] = None) → List[ndarray][source]#

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.

Returns:

The unpacked array.

classmethod unpack_object(data_item: DataItem, pickle_module_name: str = 'cloudpickle', object_module_name: Optional[str] = None, python_version: Optional[str] = None, pickle_module_version: Optional[str] = None, object_module_version: Optional[str] = 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 the current interpreter object module version match the pickled object version before unpickling the object.
python_version – The python version in which the original object was pickled. Used to verify the current interpreter python version match the pickled object version before unpickling the object.
pickle_module_version – The pickle module version. Used to verify the current interpreter module version match the one who 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.NumPyNDArrayListPackager

Contents

mlrun.package.packagers.numpy_packagers.NumPyNDArrayListPackager#