mlem.api.save()

Saves given object to a given path

def save(
    obj: Any,
    path: str,
    project: Optional[str] = None,
    sample_data=None,
    fs: Union[str, AbstractFileSystem] = None,
    index: bool = None,
    external: Optional[bool] = None,
    description: str = None,
    params: Dict[str, str] = None,
    labels: List[str] = None,
    update: bool = False,
) -> MlemObject

Usage:

from mlem.api import save

save(obj, path, index=False, external=True)

Description

Saves a given object to a given path. The path can belong to different file systems (eg: S3). The function returns and saves the object as a MLEM Object.

Parameters

• obj (required) - Object to dump
• path (required) - If not located on LocalFileSystem, then should be uri or fs argument should be provided
• project (optional) - path to mlem project
• sample_data (optional) - If the object is a model or function, you can provide input data sample, so MLEM will include it's schema in the model's metafile
• fs (optional) - FileSystem for the path argument
• index (optional) - Whether to add object to mlem project index
• external (optional) - Save result directly to path (not inside .mlem/)
• description (optional) - description for object
• params (optional) - arbitrary params for object
• labels (optional) - labels for object
• update (optional) - whether to keep old description/labels/params if new values were not provided

Exceptions

• MlemObjectNotFound - Thrown if we can't find MLEM object

Example: Save a trained model with MLEM

import os
from sklearn.datasets import load_iris
from sklearn.tree import DecisionTreeClassifier
from pandas import DataFrame
from mlem.api import save

train, target = load_iris(return_X_y=True)
train = DataFrame(train)
train.columns = train.columns.astype(str)
model = DecisionTreeClassifier().fit(train, target)
path = os.path.join(os.getcwd(), "saved-model")

save(model, path, sample_data=train, index=False)