mlem.api.load_meta()

Load MlemObject.

def load_meta(
    path: Union[str, os.PathLike],
    project: Optional[str] = None,
    rev: Optional[str] = None,
    follow_links: bool = True,
    load_value: bool = False,
    fs: Optional[AbstractFileSystem] = None,
    try_migrations: bool = False,
    *,
    force_type: Optional[Type[T]] = None,
) -> T

Usage:

import os
from mlem.api import load_meta

out_path = os.path.join(os.getcwd(), "saved-model")
loaded = load_meta(out_path)

Description

Loads a MlemObject from a given path. This differs from load since the latter loads the actual Python object incorporated within MlemObject. In fact, load uses load_meta beneath and uses its get_value() method to get the underlying Python object.

Parameters

• path (required) - Path to the object. Could be local path or path inside a git repo.
• project (optional) - URL to project if object is located there.
• rev (optional) - revision, could be git commit SHA, branch name or tag.
• follow_links (optional) - If object we read is a MLEM link, whether to load theactual object link points to. Defaults to True.
• load_value (optional) - Load actual python object incorporated in MlemObject. Defaults to False.
• fs (optional) - filesystem to load from. If not provided, will be inferred from path
• try_migrations (optional) - If loading older versions of metadata, try to apply migrations
• force_type (optional) - type of meta to be loaded. Defaults to MlemObject (any mlem meta)

Returns

MlemObject: Saved MlemObject

Exceptions

• WrongMetaType - Thrown if the loaded meta object has a different type than what is expected (force_type or MlemObject)

Examples

import os
from sklearn.datasets import load_iris
from sklearn.tree import DecisionTreeClassifier

from mlem.core.objects import MlemModel
from mlem.api import load_meta

train, _ = load_iris(return_X_y=True)
out_path = os.path.join(os.getcwd(), "saved-model")
meta = load_meta(out_path, load_value=True, force_type=MlemModel)

model = meta.get_value()
assert isinstance(model, DecisionTreeClassifier)
model.predict(train)