Source code for bentoml._internal.frameworks.sklearn

from __future__ import annotations

import logging
import typing as t
from types import ModuleType
from typing import TYPE_CHECKING

import bentoml
from bentoml import Tag
from bentoml.exceptions import MissingDependencyException
from bentoml.exceptions import NotFound
from bentoml.models import Model
from bentoml.models import ModelContext

from ..types import LazyType
from ..utils.pkg import get_pkg_version

    from sklearn.base import BaseEstimator
    from sklearn.pipeline import Pipeline

    from bentoml.types import ModelSignature

    from .. import external_typing as ext
    from ..models.model import ModelSignaturesType

    SklearnModel: t.TypeAlias = BaseEstimator | Pipeline

    import joblib
    from joblib import parallel_backend
except ImportError:  # pragma: no cover
        from sklearn.utils._joblib import joblib
        from sklearn.utils._joblib import parallel_backend
    except ImportError:
        raise MissingDependencyException(
            "scikit-learn is required in order to use the module 'bentoml.sklearn', install scikit-learn with 'pip install scikit-learn'. For more information, refer to"

MODULE_NAME = "bentoml.sklearn"
MODEL_FILENAME = "saved_model.pkl"

logger = logging.getLogger(__name__)

[docs]def get(tag_like: str | Tag) -> Model: model = bentoml.models.get(tag_like) if not in (MODULE_NAME, __name__): raise NotFound( f"Model {model.tag} was saved with module {}, not loading with {MODULE_NAME}." ) return model
[docs]def load_model(bento_model: str | Tag | Model) -> SklearnModel: """ Load the scikit-learn model with the given tag from the local BentoML model store. Args: bento_model: Either the tag of the model to get from the store, or a BentoML `~bentoml.Model` instance to load the model from. Returns: The scikit-learn model loaded from the model store or BentoML :obj:`~bentoml.Model`. Example: .. code-block:: python import bentoml sklearn = bentoml.sklearn.load_model('my_model:latest') """ # noqa if not isinstance(bento_model, Model): bento_model = get(bento_model) if not in (MODULE_NAME, __name__): raise NotFound( f"Model {bento_model.tag} was saved with module {}, not loading with {MODULE_NAME}." ) model_file = bento_model.path_of(MODEL_FILENAME) return joblib.load(model_file)
[docs]def save_model( name: Tag | str, model: SklearnModel, *, signatures: ModelSignaturesType | None = None, labels: t.Dict[str, str] | None = None, custom_objects: t.Dict[str, t.Any] | None = None, external_modules: t.List[ModuleType] | None = None, metadata: t.Dict[str, t.Any] | None = None, ) -> bentoml.Model: """ Save a model instance to BentoML modelstore. Args: name: Name for given model instance. This should pass Python identifier check. model: Instance of model to be saved. signatures: Methods to expose for running inference on the target model. Signatures are used for creating Runner instances when serving model with bentoml.Service labels: user-defined labels for managing models, e.g. team=nlp, stage=dev custom_objects: user-defined additional python objects to be saved alongside the model, e.g. a tokenizer instance, preprocessor function, model configuration json external_modules: user-defined additional python modules to be saved alongside the model or custom objects, e.g. a tokenizer module, preprocessor module, model configuration module metadata: Custom metadata for given model. Returns: :obj:`~bentoml.Tag`: A :obj:`tag` with a format ``name:version`` where ``name`` is the user-defined model's name, and a generated ``version``. Examples: .. code-block:: python import bentoml from sklearn.datasets import load_iris from sklearn.neighbors import KNeighborsClassifier model = KNeighborsClassifier() iris = load_iris() X =[:, :4] Y =, Y) bento_model = bentoml.sklearn.save_model('kneighbors', model) """ # noqa if not ( LazyType("sklearn.base.BaseEstimator").isinstance(model) or LazyType("sklearn.pipeline.Pipeline").isinstance(model) ): raise TypeError( f"Given model ({model}) is not a sklearn.base.BaseEstimator or sklearn.pipeline.Pipeline." ) context = ModelContext( framework_name="sklearn", framework_versions={"scikit-learn": get_pkg_version("scikit-learn")}, ) if signatures is None: signatures = {"predict": {"batchable": False}} 'Using the default model signature for scikit-learn (%s) for model "%s".', signatures, name, ) with bentoml.models.create( name, module=MODULE_NAME, api_version=API_VERSION, labels=labels, custom_objects=custom_objects, external_modules=external_modules, metadata=metadata, context=context, signatures=signatures, ) as bento_model: joblib.dump(model, bento_model.path_of(MODEL_FILENAME)) return bento_model
def get_runnable(bento_model: Model): """ Private API: use :obj:`~bentoml.Model.to_runnable` instead. """ class SklearnRunnable(bentoml.Runnable): SUPPORTED_RESOURCES = ("cpu",) SUPPORTS_CPU_MULTI_THREADING = True def __init__(self): super().__init__() self.model = load_model(bento_model) def add_runnable_method(method_name: str, options: ModelSignature): def _run( self: SklearnRunnable, input_data: ext.NpNDArray | ext.PdDataFrame, *args: t.Any, **kwargs: t.Any, ) -> ext.NpNDArray: # TODO: set inner_max_num_threads and n_jobs param here base on strategy env vars with parallel_backend(backend="loky"): return getattr(self.model, method_name)(input_data, *args, **kwargs) SklearnRunnable.add_method( _run, name=method_name, batchable=options.batchable, batch_dim=options.batch_dim, input_spec=options.input_spec, output_spec=options.output_spec, ) for method_name, options in add_runnable_method(method_name, options) return SklearnRunnable