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
if TYPE_CHECKING:
from bentoml.types import ModelSignature
from bentoml.types import ModelSignatureDict
try:
import catboost as cb
except ImportError: # pragma: no cover
raise MissingDependencyException(
"'catboost' is required in order to use module 'bentoml.catboost', install catboost with 'pip install catboost'. For more information, refer to https://catboost.ai/en/docs/concepts/installation."
)
MODULE_NAME = "bentoml.catboost"
MODEL_FILENAME = "saved_model.cbm"
DEFAULT_MODEL_TRAINING_CLASS_NAME = "CatBoost"
API_VERSION = "v1"
logger = logging.getLogger(__name__)
[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.catboost.get("my_catboost_model")
"""
model = bentoml.models.get(tag_like)
if model.info.module not in (MODULE_NAME, __name__):
raise NotFound(
f"Model {model.tag} was saved with module {model.info.module}, not loading with {MODULE_NAME}."
)
return model
[docs]def load_model(bento_model: str | Tag | bentoml.Model) -> cb.CatBoost:
"""
Load the CatBoost 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:
:obj:`~catboost.CatBoost`: The CatBoost 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.catboost.load_model("my_catboost_model")
""" # noqa: LN001
if not isinstance(bento_model, bentoml.Model):
bento_model = get(bento_model)
if bento_model.info.module not in (MODULE_NAME, __name__):
raise NotFound(
f"Model {bento_model.tag} was saved with module {bento_model.info.module}, not loading with {MODULE_NAME}."
)
model_file = bento_model.path_of(MODEL_FILENAME)
cb_class_name: str = bento_model.info.options.training_class_name # type: ignore
cb_class: t.Type[cb.CatBoost] = getattr(cb, cb_class_name)
if not issubclass(cb_class, cb.CatBoost):
raise BentoMLException(f"{cb_class_name} is not a valid CatBoost class.")
cb_instance = cb_class()
booster: cb.CatBoost = cb_instance.load_model(fname=model_file)
return booster
@attr.define
class CatBoostOptions(ModelOptions):
training_class_name: str = attr.field(factory=str)
[docs]def save_model(
name: Tag | str,
model: cb.CatBoost,
*,
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 CatBoost 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 CatBoost 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 (:code:`List[ModuleType]`, `optional`, default to :code:`None`):
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:
:obj:`~bentoml.Tag`: A :obj:`tag` with a format `name:version` where `name` is the
user-defined model's name, and a generated `version` by BentoML.
Example:
.. code-block:: python
import bentoml
import numpy as np
from catboost import CatBoostClassifier, Pool
# initialize data
train_data = np.random.randint(0, 100, size=(100, 10))
train_labels = np.random.randint(0, 2, size=(100))
test_data = catboost_pool = Pool(train_data, train_labels)
model = CatBoostClassifier(iterations=2,
depth=2,
learning_rate=1,
loss_function='Logloss',
verbose=True)
# train the model
model.fit(train_data, train_labels)
# save the model to the BentoML model store
bento_model = bentoml.catboost.save_model("my_catboost_model", model)
"""
if not isinstance(model, cb.CatBoost):
raise TypeError(f"Given model ({model}) is not a catboost.CatBoost.")
context: ModelContext = ModelContext(
framework_name="catboost",
framework_versions={"catboost": get_pkg_version("catboost")},
)
if signatures is None:
signatures = {
"predict": {"batchable": False},
}
logger.info(
'Using the default model signature for CatBoost (%s) for model "%s".',
signatures,
name,
)
options = CatBoostOptions(
training_class_name=model.__class__.__name__,
)
with bentoml.models._create( # type: ignore
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=options,
) as bento_model:
model.save_model(bento_model.path_of(MODEL_FILENAME)) # type: ignore (incomplete CatBoost 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 CatBoostRunnable(bentoml.Runnable):
SUPPORTED_RESOURCES = ("nvidia.com/gpu", "cpu")
SUPPORTS_CPU_MULTI_THREADING = True
predict_params: t.Dict[str, t.Any]
def __init__(self):
super().__init__()
self.model = load_model(bento_model)
self.predict_params = {
"task_type": "CPU",
}
# check for resources
available_gpus = os.getenv("CUDA_VISIBLE_DEVICES", "")
if available_gpus not in ("", "-1"):
self.predict_params["task_type"] = "GPU"
else:
nthreads = os.getenv("OMP_NUM_THREADS")
if nthreads is not None and nthreads != "":
nthreads = max(int(nthreads), 1)
else:
nthreads = -1
self.predict_params["thread_count"] = nthreads
self.predict_fns: dict[str, t.Callable[..., t.Any]] = {}
for method_name in bento_model.info.signatures:
try:
self.predict_fns[method_name] = getattr(self.model, method_name)
except AttributeError:
raise InvalidArgument(
f"No method with name {method_name} found for CatBoost model of type {self.model.__class__}"
)
def add_runnable_method(method_name: str, options: ModelSignature):
def _run(self: CatBoostRunnable, input_data: t.Any) -> t.Any:
if not isinstance(input_data, cb.Pool):
input_data = cb.Pool(input_data)
res = self.predict_fns[method_name](input_data, **self.predict_params)
return np.asarray(res) # type: ignore (incomplete np types)
CatBoostRunnable.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 bento_model.info.signatures.items():
add_runnable_method(method_name, options)
return CatBoostRunnable