mlrun.package.log_hint.LogHint

class mlrun.package.log_hint.LogHint(*, key: str, tag: str = '', itemized: Any = False, artifact_type: str | None = None, packing_kwargs: dict | None = None, labels: dict[str, str] | None = None, artifact_path: str | None = None, extra_data: dict = None, metrics: dict = None)

Bases: BaseModel

A log hint is a configuration to log an object returned from an MLRun function. Log hints are passed to the function's run() method via the returns argument.

Create a new model by parsing and validating input data from keyword arguments.

Raises ValidationError if the input data cannot be parsed to form a valid model.

Attributes

key	The artifact key to log the object under.
tag	The artifact tag to log the object under.
itemized	Determines if collections (lists or dicts) should be unbundled and logged as individual items.
artifact_type	The artifact type to log the object as.
packing_kwargs	Additional keyword arguments to pass to the packager's pack when packing the object for logging.
labels	Labels to add to the logged artifact.
artifact_path	The artifact path to log the object to.
extra_data	Extra data to log alongside the artifact.
metrics	Metrics to log alongside the model artifact (only for model artifacts).

key: str

The artifact key to log the object under.

tag: str

The artifact tag to log the object under. Default is an empty string.

itemized: Any

Determines if collections (lists or dicts) should be unbundled and logged as individual items.

When itemized is enabled, the packager performs an unbundling process: instead of logging a collection as a single unit, it breaks it down into separate artifacts. Each item is logged under the primary key using either an index suffix (for sequences) or a sub-key suffix (for maps), inheriting the original log hint configuration.

Accepts the following types:

• bool - True recursively unbundles the object all the way down. False (default) logs the collection as a single, opaque artifact.

• int - Specifies the maximum depth of unbundling. For example, 1 itemizes the top-level collection but logs nested collections as single units.

artifact_type: str | None

The artifact type to log the object as. If None is given, the default artifact type for the object's type will be used. Default is None.

Common artifact types are listed in mlrun.package.ArtifactType.

packing_kwargs: dict | None

Additional keyword arguments to pass to the packager's pack when packing the object for logging. To know which keyword arguments are supported, check the relevant packager (according to the returned object type) pack method (according to the given artifact type) documentation.

labels: dict[str, str] | None

Labels to add to the logged artifact.

artifact_path: str | None

The artifact path to log the object to. If None, the default artifact path will be used. Default is None.

extra_data: dict

Extra data to log alongside the artifact. To link to another package, write the key and a '...' as the value. For more information, see the 'Linking artifacts' section at the Packager or DefaultPackager documentation.

metrics: dict

Metrics to log alongside the model artifact (only for model artifacts). To link to another package, write the key and a '...' as the value. For more information, see the 'Linking artifacts' section at the Packager or DefaultPackager documentation.

Methods

__init__(**data)	Create a new model by parsing and validating input data from keyword arguments.
construct([_fields_set])	Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
copy(*[, include, exclude, update, deep])	Duplicate a model, optionally choose which fields to include, exclude and change.
dict(*[, include, exclude, by_alias, ...])	Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
from_orm(obj)
json(*[, include, exclude, by_alias, ...])	Generate a JSON representation of the model, include and exclude arguments as per dict().
parse_file(path, *[, content_type, ...])
parse_obj(obj)	Override the default model_validate method to add support for parsing log hints from the old dictionary format.
parse_raw(b, *[, content_type, encoding, ...])
schema([by_alias, ref_template])
schema_json(*[, by_alias, ref_template])
update_forward_refs(**localns)	Try to update ForwardRefs on fields based on this Model, globalns and localns.
validate(value)

__init__(**data: Any) → None

Create a new model by parsing and validating input data from keyword arguments.

Raises ValidationError if the input data cannot be parsed to form a valid model.

classmethod construct(_fields_set: 'SetStr' | None = None, **values: Any) → Model

Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed. Behaves as if Config.extra = 'allow' was set since it adds all passed values

copy(*, include: 'AbstractSetIntStr' | 'MappingIntStrAny' | None = None, exclude: 'AbstractSetIntStr' | 'MappingIntStrAny' | None = None, update: 'DictStrAny' | None = None, deep: bool = False) → Model

Duplicate a model, optionally choose which fields to include, exclude and change.

Parameters:

• include -- fields to include in new model

• exclude -- fields to exclude from new model, as with values this takes precedence over include

• update -- values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data

• deep -- set to True to make a deep copy of the model

Returns:

new model instance

dict(*, include: 'AbstractSetIntStr' | 'MappingIntStrAny' | None = None, exclude: 'AbstractSetIntStr' | 'MappingIntStrAny' | None = None, by_alias: bool = False, skip_defaults: bool | None = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) → DictStrAny

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

classmethod from_orm(obj: Any) → Model

json(*, include: 'AbstractSetIntStr' | 'MappingIntStrAny' | None = None, exclude: 'AbstractSetIntStr' | 'MappingIntStrAny' | None = None, by_alias: bool = False, skip_defaults: bool | None = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Callable[[Any], Any] | None = None, models_as_dict: bool = True, **dumps_kwargs: Any) → str

Generate a JSON representation of the model, include and exclude arguments as per dict().

encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().

classmethod parse_file(path: str | Path, *, content_type: str = None, encoding: str = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model

classmethod parse_obj(obj: Any) → Self

Override the default model_validate method to add support for parsing log hints from the old dictionary format.

Note: This override is temporary and will be removed in MLRun 1.13.0, at which point only the new LogHint format will be supported for parsing.

Parameters:

obj -- The object to validate and parse into a LogHint instance. This can be in the old dictionary format or the new LogHint format.

Returns:

An instance of LogHint created from the input object.

classmethod parse_raw(b: StrBytes, *, content_type: str = None, encoding: str = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model

classmethod schema(by_alias: bool = True, ref_template: str = '#/definitions/{model}') → DictStrAny

classmethod schema_json(*, by_alias: bool = True, ref_template: str = '#/definitions/{model}', **dumps_kwargs: Any) → str

classmethod update_forward_refs(**localns: Any) → None

Try to update ForwardRefs on fields based on this Model, globalns and localns.

classmethod validate(value: Any) → Model