Model Artifacts¶

SklearnModelArtifact¶

class bentoml.artifact.SklearnModelArtifact(name, pickle_extension='.pkl')¶

Abstraction for saving/loading scikit learn models using sklearn.externals.joblib

Parameters

name (str) – Name for the artifact
pickle_extension (str) – The extension format for pickled file

Raises

MissingDependencyException – sklean package is required for SklearnModelArtifact

PytorchModelArtifact¶

class bentoml.artifact.PytorchModelArtifact(name, file_extension='.pt')¶

Abstraction for saving/loading objects with torch.save and torch.load

Parameters

name (string) – name of the artifact

Raises

MissingDependencyException – torch package is required for PytorchModelArtifact
InvalidArgument – invalid argument type, model being packed must be instance of torch.nn.Module

KerasModelArtifact¶

class bentoml.artifact.KerasModelArtifact(name, custom_objects=None, model_extension='.h5', store_as_json_and_weights=False)¶

Abstraction for saving/loading Keras model

Parameters

name (string) – name of the artifact
custom_objects (dict) – dictionary of Keras custom objects for model
store_as_json_and_weights (bool) – flag allowing storage of the Keras model as JSON and weights

Raises

MissingDependencyException – keras or tensorflow.keras package is required for KerasModelArtifact
InvalidArgument – invalid argument type, model being packed must be instance of keras.engine.network.Network, tf.keras.models.Model, or their aliases

FastaiModelArtifact¶

class bentoml.artifact.FastaiModelArtifact(name)¶

Saving and Loading FastAI Model

Parameters

name (str) – Name for the fastai model

Raises

MissingDependencyException – Require fastai package to use Fast ai model artifact
InvalidArgument – invalid argument type, model being packed must be instance of fastai.basic_train.Learner

TensorflowSavedModelArtifact¶

class bentoml.artifact.TensorflowSavedModelArtifact(name)¶

Abstraction for saving/loading Tensorflow model in tf.saved_model format

Parameters: name (string) – name of the artifact
Raises: MissingDependencyException – tensorflow package is required for TensorflowSavedModelArtifact

Example usage:

>>> import tensorflow as tf
>>>
>>> # Option 1: custom model with specific method call
>>> class Adder(tf.Module):
>>>     @tf.function(input_signature=[tf.TensorSpec(shape=None, dtype=tf.float32)])
>>>     def add(self, x):
>>>         return x + x + 1.
>>> model_to_save = Adder()
>>> # ... compiling, training, etc
>>>
>>> # Option 2: Sequential model (direct call only)
>>> model_to_save = tf.keras.Sequential([
>>>     tf.keras.layers.Flatten(input_shape=(28, 28)),
>>>     tf.keras.layers.Dense(128, activation='relu'),
>>>     tf.keras.layers.Dense(10, activation='softmax')
>>> ])
>>> # ... compiling, training, etc
>>>
>>> import bentoml
>>> from bentoml.handlers import JsonHandler
>>> from bentoml.artifact import TensorflowSavedModelArtifact
>>>
>>> @bentoml.env(pip_dependencies=["tensorflow"])
>>> @bentoml.artifacts([TensorflowSavedModelArtifact('model')])
>>> class TfModelService(bentoml.BentoService):
>>>
>>>     @bentoml.api(JsonHandler)
>>>     def predict(self, json):
>>>         input_data = json['input']
>>>         prediction = self.artifacts.model.add(input_data)
>>>         # prediction = self.artifacts.model(input_data)  # if Sequential mode
>>>         return prediction.numpy()
>>>
>>> svc = TfModelService()
>>>
>>> # Option 1: pack directly with Tensorflow trackable object
>>> svc.pack('model', model_to_save)
>>>
>>> # Option 2: save to file path then pack
>>> tf.saved_model.save(model_to_save, '/tmp/adder/1')
>>> svc.pack('model', '/tmp/adder/1')

XgboostModelArtifact¶

class bentoml.artifact.XgboostModelArtifact(name, model_extension='.model')¶

Abstraction for save/load object with Xgboost.

Parameters

name (string) – name of the artifact
model_extension (string) – Extension name for saved xgboost model

Raises

ImportError – xgboost package is required for using XgboostModelArtifact
TypeError – invalid argument type, model being packed must be instance of xgboost.core.Booster

LightGBMModelArtifact¶

class bentoml.artifact.LightGBMModelArtifact(name, model_extension='.txt')¶

Abstraction for save/load object with LightGBM.

Parameters

name (string) – name of the artifact
model_extension (string) – Extension name for saved xgboost model

Raises

MissingDependencyException – lightgbm package is required for using LightGBMModelArtifact
InvalidArgument – invalid argument type, model being packed must be instance of lightgbm.Booster

H2oModelArtifact¶

class bentoml.artifact.H2oModelArtifact(name)¶

Abstraction for saving/loading objects with h2o.save_model and h2o.load_model

Parameters: name (str) – Name for this h2o artifact..
Raises: MissingDependencyException – h2o package is required to use H2o model artifact

PickleArtifact¶

class bentoml.artifact.PickleArtifact(name, pickle_module=<module 'bentoml.utils.cloudpickle' from '/home/docs/checkouts/readthedocs.org/user_builds/bentoml/checkouts/latest/bentoml/utils/cloudpickle.py'>, pickle_extension='.pkl')¶

Abstraction for saving/loading python objects with pickle serialization

Parameters

name (str) – Name for the artifact
pickle_module (module|str) – The python module will be used for pickle and unpickle artifact, default pickle module in BentoML’s fork of cloudpickle, which is identical to the Apache Spark fork
pickle_extension (str) – The extension format for pickled file.

TextFileArtifact¶

class bentoml.artifact.TextFileArtifact(name, file_extension='.txt', encoding='utf8')¶

Abstraction for saving/loading string to/from text files

Parameters

name (str) – Name of the artifact
file_extension (str, optional) – The file extention used for the saved text file. Defaults to “.txt”
encoding (str) – The encoding will be used for saving/loading text. Defaults to “utf8”