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mlem.api.build()

Pack model into something useful, such as docker image, Python package or something else.

def build(
    builder: Union[str, MlemBuilder],
    model: Union[str, MlemModel],
    **builder_kwargs,
)

Usage:

from mlem.api import build

build("pip", "rf", target="build", package_name="example_mlem_get_started")

Description

This API is the underlying mechanism for the mlem build command and allows us to programmatically create ship-able assets from MlemModels such as pip-ready packages, Docker images, etc.

$ mlem types builder pip
Type mlem.contrib.pip.base.PipBuilder
MlemABC parent type: builder
MlemABC type: pip
MlemObject type name: builder
Create a directory python package
Fields:
[required] package_name: str
        Name of python package
[required] target: str
        Path to save result
[not required] templates_dir: List[str] = []
        list of directories to look for jinja templates
[not required] templates_dir.0: str = None
        Element of templates_dir
[not required] python_version: str = None
        Required python version
[not required] short_description: str = ""
        short_description
[not required] url: str = ""
        url
[not required] email: str = ""
        author's email
[not required] author: str = ""
        author's name
[not required] version: str = "0.0.0"
        package version
[not required] additional_setup_kwargs: Dict[str, any] = {}
        additional parameters for setup()
[not required] additional_setup_kwargs.key: any = None
        Element of additional_setup_kwargs

Parameters

  • builder (required) - Builder to use.
  • model (required) - The model to build.
  • builder_kwargs (required) - Additional keyword arguments to pass to the builder.

Returns

The result of the build, different for different builders.

Exceptions

None

Examples

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

from mlem.contrib.docker import DockerImageBuilder
from mlem.contrib.docker.base import DockerImage
from mlem.contrib.fastapi import FastAPIServer

from mlem.api import build

train, target = load_iris(return_X_y=True)
model = DecisionTreeClassifier().fit(train, target)
model_meta = MlemModel.from_obj(model)

built = build(
    DockerImageBuilder(
        server=FastAPIServer(),
        image=DockerImage(name="pack_docker_test_image"),
        force_overwrite=True,
    ),
    model_meta,
)
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