mlrun.package.packagers.default_packager.DefaultPackager

mlrun.package.packagers.default_packager.DefaultPackager#

class mlrun.package.packagers.default_packager.DefaultPackager[source]#

Bases: Packager

A default packager that handles all types and packs them as pickle files.

The default packager implements all the required methods and has a default logic that should satisfy most use cases. To work with this class, don't override the abstract class methods, but instead follow the guidelines below:

The class variable PACKABLE_OBJECT_TYPE: The type of object this packager can pack and unpack (used in the is_packable method).
The class variable PACK_SUBCLASSES: A flag that indicates whether to pack all subclasses of the PACKABLE_OBJECT_TYPE (used in the is_packable method). Default is False.
The class variable DEFAULT_PACKING_ARTIFACT_TYPE: The default artifact type to pack as. It is returned from the method get_default_packing_artifact_type.
The class variable DEFAULT_UNPACKING_ARTIFACT_TYPE: The default artifact type to unpack from. It is returned from the method get_default_unpacking_artifact_type.
The abstract class method pack(): This method is implemented to get the object and send it to the relevant packing method by the given artifact type using the following naming: "pack_<artifact_type>". (If the artifact type was not provided, it uses the default). For example: if the artifact type is x then the class method pack_x must be implemented. The signature of each pack class method must be:
```
def pack_x(self, obj: Any, key: str, ...) -> Union[Tuple[Artifact, dict], dict]:
    pass
```
Where 'x' is the artifact type, 'obj' is the object to pack, key is the key to name the artifact and ... are additional, custom, log hint configurations. The returned values are the packed artifact and the instructions for unpacking it, or in the case of result, the dictionary of the result with its key and value. configurations are sent by the user and shouldn't be mandatory, meaning they should have a default value (otherwise, the user has to add them to every log hint).
The abstract class method unpack(): The method is implemented to get a DataItem and send it to the relevant unpacking method by the artifact type using the following naming: "unpack_<artifact_type>". (If the artifact type was not provided, it uses the default). For example: if the artifact type stored within the DataItem is x then the class method unpack_x must be implemented. The signature of each unpack class method must be:
```
def unpack_x(self, data_item: mlrun.DataItem, ...) -> Any:
    pass
```
Where 'x' is the artifact type, 'data_item' is the artifact's data item to unpack, ... are the instructions that were originally returned from pack_x. (Each instruction must be optional (have a default value) to support objects from this type that were not packaged but custom-logged.) The returned value is the unpacked object.
The abstract class method is_packable(): The method is implemented to automatically validate
the object type and artifact type by the following rules:
- Object type validation: Checks if the given object type matches the variable PACKABLE_OBJECT_TYPE with respect to the PACK_SUBCLASSES class variable.
- Artifact type validation: Checks if the given artifact type is in the list returned from get_supported_artifact_types.
The abstract class method is_unpackable(): The method is left as implemented in Packager.
The abstract class method get_supported_artifact_types(): The method is implemented to look for all pack + unpack class methods implemented to collect the supported artifact types. If PackagerX has pack_y, unpack_y and pack_z, unpack_z that means the artifact types supported are y and z.
The abstract class method get_default_packing_artifact_type(): The method is implemented to return the new class variable DEFAULT_PACKING_ARTIFACT_TYPE. You can still override the method if the default artifact type you need could change according to the object that's about to be packed.
The abstract class method get_default_unpacking_artifact_type(): The method is implemented to return the new class variable DEFAULT_UNPACKING_ARTIFACT_TYPE. You can still override the method if the default artifact type you need could change according to the data item that's about to be unpacked.

Important to remember

From the Packager docstring:

Linking artifacts ("extra data"): In order to link between packages (using the extra data or metrics spec attributes of an artifact), use the key as if it exists and as value ellipses (...). The manager links all packages once it is done packing.

For example, given extra data keys in the log hint as extra_data, set them to an artifact as follows:
```
artifact = Artifact(key="my_artifact")
artifact.spec.extra_data = {key: ... for key in extra_data}
```
Clearing outputs: Some packagers may produce files and temporary directories that should be deleted after the artifact is logged. The packager can mark paths of files and directories to delete after logging using the class method add_future_clearing_path.

For example, in the following packager's pack method, you can write a text file, create an artifact, and then mark the text file to be deleted once the artifact is logged:
```
with open("./some_file.txt", "w") as file:
    file.write("Pack me")
artifact = Artifact(key="my_artifact")
self.add_future_clearing_path(path="./some_file.txt")
return artifact, None
```

Packager Summary

Packing Type: Any type

Packing Sub-Classes: False

Priority: Default priority (5)

Default Artifact Types:

Packing: object
Unpacking: object

Artifact Types:

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 an object 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 = 'object'#: The default artifact type to pack as.

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

PACKABLE_OBJECT_TYPE: type = Ellipsis#: The type of object this packager can pack and unpack.

PACK_SUBCLASSES = False#: A flag for indicating whether to also pack all subclasses of the PACKABLE_OBJECT_TYPE.

PRIORITY: int = Ellipsis#: 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.
`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 this packager can pack an object of the provided type as the provided artifact type.
`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_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 an object as a result.
`unpack`(data_item[, artifact_type, instructions])	Unpack the data item's artifact by the provided type using the given instructions.
`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[source]#

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[source]#

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][source]#

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 this packager can pack an object of the provided type as the provided artifact type.

The method is implemented to validate the object's type and artifact type by checking if the given object type matches the variable PACKABLE_OBJECT_TYPE with respect to the PACK_SUBCLASSES class variable. If it does, it checks if the given artifact type is in the list returned from get_supported_artifact_types.

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[mlrun.artifacts.base.Artifact, dict] | dict[source]#

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_object(obj: Any, key: str, pickle_module_name: str = 'cloudpickle') → tuple[mlrun.artifacts.base.Artifact, dict][source]#

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: Any, key: str) → dict[source]#

Pack an object as a result.

Parameters:

obj -- The object 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[source]#

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_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[source]#

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.default_packager.DefaultPackager

Contents

mlrun.package.packagers.default_packager.DefaultPackager#