Source code for bentoml._internal.frameworks.xgboost

from __future__ import annotations

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

import attr
import numpy as np

import bentoml
from bentoml import Tag
from bentoml.exceptions import BentoMLException
from bentoml.exceptions import InvalidArgument
from bentoml.exceptions import MissingDependencyException
from bentoml.exceptions import NotFound
from bentoml.models import ModelOptions

from ..models.model import ModelContext
from ..utils.pkg import get_pkg_version

    from bentoml.types import ModelSignature
    from bentoml.types import ModelSignatureDict

    from .. import external_typing as ext

    import xgboost as xgb
except ImportError:  # pragma: no cover
    raise MissingDependencyException(
        "'xgboost' is required in order to use module 'bentoml.xgboost', install xgboost with 'pip install xgboost'. For more information, refer to"

    from xgboost import XGBModel
except ImportError:  # pragma: no cover
    # if sklearn is not installed, XGBoost will not expose XGBModel, so make
    # a dummy class ourselves
    class XGBModel:

MODULE_NAME = "bentoml.xgboost"
MODEL_FILENAME = "saved_model.ubj"

logger = logging.getLogger(__name__)

class XGBoostOptions(ModelOptions):
    model_class: str | None = None

[docs]def get(tag_like: str | Tag) -> bentoml.Model: """ Get the BentoML model with the given tag. Args: tag_like (``str`` ``|`` :obj:`~bentoml.Tag`): The tag of the model to retrieve from the model store. Returns: :obj:`~bentoml.Model`: A BentoML :obj:`~bentoml.Model` with the matching tag. Example: .. code-block:: python import bentoml # target model must be from the BentoML model store model = bentoml.xgboost.get("my_xgboost_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 | bentoml.Model, ) -> xgb.Booster | xgb.XGBModel: """ Load the XGBoost model with the given tag from the local BentoML model store. Args: bento_model (``str`` ``|`` :obj:`~bentoml.Tag` ``|`` :obj:`~bentoml.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 XGBoost model loaded from the model store or BentoML :obj:`~bentoml.Model`. Example: .. code-block:: python import bentoml # target model must be from the BentoML model store booster = bentoml.xgboost.load_model("my_xgboost_model") """ # noqa: LN001 if not isinstance(bento_model, bentoml.Model): bento_model = get(bento_model) assert isinstance(bento_model, bentoml.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) model_api_version = if model_api_version == "v1": model = xgb.Booster(model_file=model_file) else: if model_api_version != "v2": logger.warning( "Got an XGBoost model with an unsupported version '%s', unexpected errors may occur.", model_api_version, ) model_class = t.cast(XGBoostOptions, if model_class is None: raise BentoMLException( f"Model '{bento_model.tag}' is missing the required 'model_class' option. This should not be possible; please file an issue if you encounter this error." ) try: xgb_class: type[xgb.XGBModel] | type[xgb.Booster] = getattr( xgb, model_class ) except AttributeError: if model_class != "Booster": raise BentoMLException( f"Model '{bento_model.tag}' is an XGBoost Scikit-Learn model, but sklearn is not installed." ) from None else: raise BentoMLException( "xgboost.Booster could not be found, your XGBoost installation may be corrupted. Ensure there is no file named '' that may be being loaded instead of the XGBoost library." ) from None model: xgb.Booster | xgb.XGBModel = xgb_class() model.load_model(model_file) return model
[docs]def save_model( name: Tag | str, model: xgb.Booster | xgb.XGBModel, *, signatures: dict[str, ModelSignatureDict] | None = None, labels: dict[str, str] | None = None, custom_objects: dict[str, t.Any] | None = None, external_modules: t.List[ModuleType] | None = None, metadata: dict[str, t.Any] | None = None, ) -> bentoml.Model: """ Save an XGBoost model instance to the BentoML model store. Args: name: The name to give to the model in the BentoML store. This must be a valid :obj:`~bentoml.Tag` name. model: The XGBoost model to be saved. signatures: Signatures of predict methods to be used. If not provided, the signatures default to ``{"predict": {"batchable": False}}``. See :obj:`~bentoml.types.ModelSignature` for more details. labels: A default set of management labels to be associated with the model. An example is ``{"training-set": "data-1"}``. custom_objects: Custom objects to be saved with the model. An example is ``{"my-normalizer": normalizer}``. Custom objects are currently serialized with cloudpickle, but this implementation is subject to change. 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: Metadata to be associated with the model. An example is ``{"max_depth": 2}``. Metadata is intended for display in model management UI and therefore must be a default Python type, such as ``str`` or ``int``. Returns: A BentoML tag with the user-defined name and a generated version. Example: .. code-block:: python import xgboost as xgb import bentoml # read in data dtrain = xgb.DMatrix('demo/data/agaricus.txt.train') dtest = xgb.DMatrix('demo/data/agaricus.txt.test') # specify parameters via map param = dict(max_depth=2, eta=1, objective='binary:logistic') num_round = 2 bst = xgb.train(param, dtrain, num_round) ... # `save` the booster to BentoML modelstore: bento_model = bentoml.xgboost.save_model("my_xgboost_model", bst, booster_params=param) """ # noqa: LN001 if isinstance(model, xgb.Booster): model_class = "Booster" elif isinstance(model, XGBModel): model_class = model.__class__.__name__ else: raise TypeError(f"Given model ({model}) is not a xgboost.Booster.") context: ModelContext = ModelContext( framework_name="xgboost", framework_versions={"xgboost": get_pkg_version("xgboost")}, ) if signatures is None: signatures = { "predict": {"batchable": False}, } 'Using the default model signature for xgboost (%s) for model "%s".', signatures, name, ) with bentoml.models.create( name, module=MODULE_NAME, api_version=API_VERSION, signatures=signatures, labels=labels, custom_objects=custom_objects, external_modules=external_modules, metadata=metadata, context=context, options=XGBoostOptions(model_class=model_class), ) as bento_model: model.save_model(bento_model.path_of(MODEL_FILENAME)) # type: ignore (incomplete XGBoost types) return bento_model
def get_runnable(bento_model: bentoml.Model) -> t.Type[bentoml.Runnable]: """ Private API: use :obj:`~bentoml.Model.to_runnable` instead. """ class XGBoostRunnable(bentoml.Runnable): SUPPORTED_RESOURCES = ("", "cpu") SUPPORTS_CPU_MULTI_THREADING = True def __init__(self): super().__init__() self.model = load_model(bento_model) self.booster = ( self.model if isinstance(self.model, xgb.Booster) else self.model.get_booster() ) # check for resources if os.getenv("CUDA_VISIBLE_DEVICES") not in (None, "", "-1"): self.booster.set_param({"predictor": "gpu_predictor", "gpu_id": 0}) # type: ignore (incomplete XGBoost types) else: nthreads = os.getenv("OMP_NUM_THREADS") if nthreads is not None and nthreads != "": nthreads = max(int(nthreads), 1) else: nthreads = 1 self.booster.set_param({"predictor": "cpu_predictor", "nthread": nthreads}) # type: ignore (incomplete XGBoost types) self.predict_fns: dict[str, t.Callable[..., t.Any]] = {} for method_name in try: self.predict_fns[method_name] = getattr(self.model, method_name) except AttributeError: raise InvalidArgument( f"No method with name {method_name} found for XGBoost model of type {self.model.__class__}" ) def add_runnable_method(method_name: str, options: ModelSignature): def _run( self: XGBoostRunnable, input_data: ext.NpNDArray | ext.PdDataFrame, # TODO: add support for DMatrix *args: t.Any, **kwargs: t.Any, ) -> ext.NpNDArray: if isinstance(self.model, xgb.Booster): inp = xgb.DMatrix(input_data) else: inp = input_data res = self.predict_fns[method_name](inp, *args, **kwargs) return np.asarray(res) # type: ignore (incomplete np types) XGBoostRunnable.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 XGBoostRunnable