ads.model.serde package#
Submodules#
ads.model.serde.common module#
- class ads.model.serde.common.Deserializer[source]#
Bases:
object
Abstract base class for creation of new deserializers.
- class ads.model.serde.common.SERDE[source]#
Bases:
Serializer
,Deserializer
A layer contains two groups which can interact with each other to serialize and deserialize supported data structure using supported data format.
- deserialize(**kwargs)#
Deserialize data/model into original type.
- Returns:
object
- Return type:
deserialized data/model.
- name = ''#
- serialize(**kwargs)#
Serialize data/model into specific type.
- Returns:
object
- Return type:
Serialized data/model.
ads.model.serde.model_input module#
- class ads.model.serde.model_input.CloudpickleModelInputDeserializer(name='cloudpickle')[source]#
Bases:
ModelInputDeserializer
Use cloudpickle to deserialize data into its original type.
- class ads.model.serde.model_input.CloudpickleModelInputSERDE[source]#
Bases:
CloudpickleModelInputSerializer
,CloudpickleModelInputDeserializer
- deserialize(data)#
Deserialize data into its original type.
- Parameters:
(object) (data) –
- Returns:
object
- Return type:
deserialized data used for a prediction.
- name = 'cloudpickle'#
- serialize(data)#
Serialize data into bytes.
- Parameters:
(object) (data) –
- Returns:
object
- Return type:
Serialized data used for a request.
- class ads.model.serde.model_input.CloudpickleModelInputSerializer[source]#
Bases:
ModelInputSerializer
Serialize data of various formats to bytes.
- class ads.model.serde.model_input.JsonModelInputDeserializer(name='json')[source]#
Bases:
ModelInputDeserializer
ADS data deserializer. Deserialize data to into its original type.
- class ads.model.serde.model_input.JsonModelInputSERDE[source]#
Bases:
JsonModelInputSerializer
,JsonModelInputDeserializer
- name = 'json'#
- serialize(data: Dict | str | List | ndarray | Series | DataFrame)#
Serialize data into a dictionary containing serialized input data and original data type information.
- Parameters:
- Returns:
A dictionary containing serialized input data and original data type information.
- Return type:
Dict
- Raises:
TypeError – if provided data type is not supported.
- class ads.model.serde.model_input.JsonModelInputSerializer[source]#
Bases:
ModelInputSerializer
ADS data serializer. Serialize data of various formats to into a dictionary containing serialized input data and original data type information.
Examples
>>> from ads.model.serde.model_input import JsonModelInputSerializer
>>> # numpy array will be converted to base64 encoded string, >>> # while `data_type` will record its original type: `numpy.ndarray` >>> import numpy as np >>> input_data = np.array([1, 2, 3]) >>> serialized_data = JsonModelInputSerializer().serialize(data=input_data) >>> serialized_data { 'data': 'k05VTVBZAQB2AHsnZGVzY3InOiAnPGk4JywgJ2ZvcnRyYW5fb3JkZXInOiBGYWxzZS wgJ3NoYXBlJzogKDMsKSwgfSAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgI CAgICAgICAgICAgICAgICAgICAgIAoBAAAAAAAAAAIAAAAAAAAAAwAAAAAAAAA=', 'data_type': "<class 'numpy.ndarray'>" }
>>> # `pd.core.frame.DataFrame` will be converted to json by `.to_json()` >>> # while `data_type` will record its original type: `pandas.core.frame.DataFrame` >>> import pandas as pd >>> df = pd.DataFrame(data={'col1': [1, 2], 'col2': [3, 4]}) >>> serialized_data = JsonModelInputSerializer().serialize(data=df) >>> serialized_data { 'data': '{"col1":{"0":1,"1":2},"col2":{"0":3,"1":4}}', 'data_type': "<class 'pandas.core.frame.DataFrame'>" }
>>> # `pandas.core.series.Series` will be converted to list by `.tolist()` >>> # while `data_type` will record its original type: `pandas.core.series.Series` >>> ser = pd.Series(data={'a': 1, 'b': 2, 'c': 3}, index=['a', 'b', 'c']) >>> serialized_data = JsonModelInputSerializer().serialize(data=ser) >>> serialized_data { 'data': [1, 2, 3], 'data_type': "<class 'pandas.core.series.Series'>" }
>>> # `torch.Tensor` will be converted to base64 encoded string, >>> # while `data_type` will record its original type: `torch.Tensor` >>> import torch >>> tt = torch.tensor([[1, 2, 3], [4, 5, 6]]) >>> serialized_data = JsonModelInputSerializer().serialize(data=tt) >>> serialized_data { 'data': 'UEsDBAAACAgAAAAAAAAAAAAAAAAAAAAAAAAQABIAYXJjaGl2ZS9kYXRhLnBrbEZCDgBaWlpaW lpaWlpaWlpaWoACY3RvcmNoLl91dGlscwpfcmVidWlsZF90ZW5zb3JfdjIKcQAoKFgHAAAAc3RvcmFnZXEB Y3RvcmNoCkxvbmdTdG9yYWdlCnECWAEAAAAwcQNYAwAAAGNwdXEESwZ0cQVRSwBLAksDhnEGSwNLAYZxB4l jY29sbGVjdGlvbnMKT3JkZXJlZERpY3QKcQgpUnEJdHEKUnELLlBLBwim2iAhmQAAAJkAAABQSwMEAAAICA AAAAAAAAAAAAAAAAAAAAAAAA4AKwBhcmNoaXZlL2RhdGEvMEZCJwBaWlpaWlpaWlpaWlpaWlpaWlpaWlpaWl paWlpaWlpaWlpaWlpaWloBAAAAAAAAAAIAAAAAAAAAAwAAAAAAAAAEAAAAAAAAAAUAAAAAAAAABgAAAAAAA ABQSwcI9z/uVjAAAAAwAAAAUEsDBAAACAgAAAAAAAAAAAAAAAAAAAAAAAAPABMAYXJjaGl2ZS92ZXJzaW9u RkIPAFpaWlpaWlpaWlpaWlpaWjMKUEsHCNGeZ1UCAAAAAgAAAFBLAQIAAAAACAgAAAAAAACm2iAhmQAAAJk AAAAQAAAAAAAAAAAAAAAAAAAAAABhcmNoaXZlL2RhdGEucGtsUEsBAgAAAAAICAAAAAAAAPc/7lYwAAAAMA AAAA4AAAAAAAAAAAAAAAAA6QAAAGFyY2hpdmUvZGF0YS8wUEsBAgAAAAAICAAAAAAAANGeZ1UCAAAAAgAAA A8AAAAAAAAAAAAAAAAAgAEAAGFyY2hpdmUvdmVyc2lvblBLBgYsAAAAAAAAAB4DLQAAAAAAAAAAAAMAAAAA AAAAAwAAAAAAAAC3AAAAAAAAANIBAAAAAAAAUEsGBwAAAACJAgAAAAAAAAEAAABQSwUGAAAAAAMAAwC3AAA A0gEAAAAA', 'data_type': "<class 'torch.Tensor'>" }
>>> # `tensorflow.Tensor` will be converted to base64 encoded string, >>> # while `data_type` will record its original type: `tensorflow.python.framework.ops.EagerTensor`. >>> import torch >>> c = tf.constant([[1.0, 2.0], [3.0, 4.0]]) >>> serialized_data = JsonModelInputSerializer().serialize(data=c) >>> serialized_data { 'data': 'k05VTVBZAQB2AHsnZGVzY3InOiAnPGY0JywgJ2ZvcnRyYW5fb3JkZXInOiBGYWxzZSwgJ3NoYXB lJzogKDIsIDIpLCB9ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICA gICAgICAgIAoAAIA/AAAAQAAAQEAAAIBA', 'data_type': "<class 'tensorflow.python.framework.ops.EagerTensor'>" }
>>> # dict, str, list, tuple will be saved as orignal type >>> # and `data_type` will record its type. >>> mystring = "this is a string." >>> serialized_data = JsonModelInputSerializer().serialize(data=mystring) >>> serialized_data { 'data': 'this is a string.', 'data_type': "<class 'str'>" }
- serialize(data: Dict | str | List | ndarray | Series | DataFrame)[source]#
Serialize data into a dictionary containing serialized input data and original data type information.
- Parameters:
- Returns:
A dictionary containing serialized input data and original data type information.
- Return type:
Dict
- Raises:
TypeError – if provided data type is not supported.
- class ads.model.serde.model_input.ModelInputDeserializer(name='customized')[source]#
Bases:
Deserializer
Abstract base class for creation of new data deserializers.
- class ads.model.serde.model_input.ModelInputSerializer[source]#
Bases:
Serializer
Abstract base class for creation of new data serializers.
- class ads.model.serde.model_input.ModelInputSerializerFactory[source]#
Bases:
object
Data Serializer Factory.
Examples
>>> serializer, deserializer = ModelInputSerializerFactory.get("cloudpickle")
- classmethod get(se: str = 'json')[source]#
Gets data serializer and corresponding deserializer.
- Parameters:
(str) (se) – The name of the required serializer.
- Raises:
ValueError: – Raises when input is unsupported format.
- Returns:
Intance of `ads.model.serde.common.SERDE”.
- Return type:
serde (ads.model.serde.common.SERDE)
- class ads.model.serde.model_input.ModelInputSerializerType[source]#
Bases:
object
- CLOUDPICKLE = 'cloudpickle'#
- JSON = 'json'#
- class ads.model.serde.model_input.SparkModelInputDeserializer(name='spark')[source]#
Bases:
ModelInputDeserializer
- class ads.model.serde.model_input.SparkModelInputSERDE[source]#
Bases:
SparkModelInputSerializer
,SparkModelInputDeserializer
- deserialize()#
Not implement. See spark template.
- name = 'spark'#
- class ads.model.serde.model_input.SparkModelInputSerializer[source]#
Bases:
JsonModelInputSerializer
[An internal class] Defines the contract for input data to spark pipeline models.
ads.model.serde.model_serializer module#
- class ads.model.serde.model_serializer.CloudPickleModelSerializer(model_file_suffix='pkl')[source]#
Bases:
ModelSerializer
Uses Cloudpickle to save model.
- serialize(estimator, model_path, **kwargs)[source]#
Uses cloudpickle.dump to save model. See https://docs.python.org/3/library/pickle.html#pickle.dump for more details.
- Parameters:
estimator – The model to be saved.
model_path – The file object or path of the model in which it is to be stored.
kwargs –
- model_save: (dict, optional).
The dictionary where contains the availiable options to be passed to cloudpickle.dump.
- class ads.model.serde.model_serializer.CloudpickleModelSaveSERDE(model_file_suffix='pkl')[source]#
Bases:
CloudPickleModelSerializer
,ModelDeserializer
- deserialize(**kwargs)#
Deserialize data/model into original type.
- Returns:
object
- Return type:
deserialized data/model.
- name = 'cloudpickle'#
- serialize(estimator, model_path, **kwargs)#
Uses cloudpickle.dump to save model. See https://docs.python.org/3/library/pickle.html#pickle.dump for more details.
- Parameters:
estimator – The model to be saved.
model_path – The file object or path of the model in which it is to be stored.
kwargs –
- model_save: (dict, optional).
The dictionary where contains the availiable options to be passed to cloudpickle.dump.
- class ads.model.serde.model_serializer.HuggingFaceModelSerializer(model_file_suffix='')[source]#
Bases:
ModelSerializer
Save HuggingFace Pipeline.
- class ads.model.serde.model_serializer.HuggingFacePipelineSaveSERDE(model_file_suffix='')[source]#
Bases:
HuggingFaceModelSerializer
,ModelDeserializer
- deserialize(**kwargs)#
Deserialize data/model into original type.
- Returns:
object
- Return type:
deserialized data/model.
- name = 'huggingface'#
- serialize(estimator, model_path, **kwargs)#
Serialize data/model into specific type.
- Returns:
object
- Return type:
Serialized data/model.
- class ads.model.serde.model_serializer.HuggingFaceSerializerType[source]#
Bases:
object
- HUGGINGFACE = 'huggingface'#
- class ads.model.serde.model_serializer.JobLibModelSerializer(model_file_suffix='joblib')[source]#
Bases:
ModelSerializer
Uses Joblib to save model.
- serialize(estimator, model_path, **kwargs)[source]#
Uses joblib.dump to save model. See https://joblib.readthedocs.io/en/latest/generated/joblib.dump.html for more details.
- Parameters:
estimator – The model to be saved.
model_path – The file object or path of the model in which it is to be stored.
kwargs –
- model_save: (dict, optional).
The dictionary where contains the availiable options to be passed to joblib.dump.
- class ads.model.serde.model_serializer.JoblibModelSaveSERDE(model_file_suffix='joblib')[source]#
Bases:
JobLibModelSerializer
,ModelDeserializer
- deserialize(**kwargs)#
Deserialize data/model into original type.
- Returns:
object
- Return type:
deserialized data/model.
- name = 'joblib'#
- serialize(estimator, model_path, **kwargs)#
Uses joblib.dump to save model. See https://joblib.readthedocs.io/en/latest/generated/joblib.dump.html for more details.
- Parameters:
estimator – The model to be saved.
model_path – The file object or path of the model in which it is to be stored.
kwargs –
- model_save: (dict, optional).
The dictionary where contains the availiable options to be passed to joblib.dump.
- class ads.model.serde.model_serializer.LightGBMModelSaveSERDE(model_file_suffix='txt')[source]#
Bases:
LightGBMModelSerializer
,ModelDeserializer
- deserialize(**kwargs)#
Deserialize data/model into original type.
- Returns:
object
- Return type:
deserialized data/model.
- name = 'lightgbm'#
- serialize(estimator, model_path, **kwargs)#
Serialize data/model into specific type.
- Returns:
object
- Return type:
Serialized data/model.
- class ads.model.serde.model_serializer.LightGBMModelSerializer(model_file_suffix='txt')[source]#
Bases:
ModelSerializer
Save LightGBM Model through save_model into txt.
- class ads.model.serde.model_serializer.LightGBMModelSerializerType[source]#
Bases:
object
- LIGHTGBM = 'lightgbm'#
- ONNX = 'lightgbm_onnx'#
- class ads.model.serde.model_serializer.LightGBMOnnxModelSaveSERDE[source]#
Bases:
LightGBMOnnxModelSerializer
,ModelDeserializer
- deserialize(**kwargs)#
Deserialize data/model into original type.
- Returns:
object
- Return type:
deserialized data/model.
- name = 'lightgbm_onnx'#
- serialize(estimator, model_path, initial_types: List[Tuple] | None = None, X_sample: Dict | str | List | Tuple | ndarray | Series | DataFrame | None = None, **kwargs)#
Save model into onnx format.
- Parameters:
estimator – The model to be saved.
model_path – The file object or path of the model in which it is to be stored.
initial_types – (List[Tuple], optional) a python list. Each element is a tuple of a variable name and a data type.
X_sample – (any, optional). Defaults to None. Contains model inputs such that model(X_sample) is a valid invocation of the model, used to valid model input type.
- class ads.model.serde.model_serializer.LightGBMOnnxModelSerializer[source]#
Bases:
OnnxModelSerializer
Converts LightGBM model into onnx format.
- serialize(estimator, model_path, initial_types: List[Tuple] | None = None, X_sample: Dict | str | List | Tuple | ndarray | Series | DataFrame | None = None, **kwargs)#
Save model into onnx format.
- Parameters:
estimator – The model to be saved.
model_path – The file object or path of the model in which it is to be stored.
initial_types – (List[Tuple], optional) a python list. Each element is a tuple of a variable name and a data type.
X_sample – (any, optional). Defaults to None. Contains model inputs such that model(X_sample) is a valid invocation of the model, used to valid model input type.
- class ads.model.serde.model_serializer.ModelDeserializer[source]#
Bases:
Deserializer
Base class for creation of new model deserializers.
- class ads.model.serde.model_serializer.ModelSerializer(model_file_suffix)[source]#
Bases:
Serializer
Base class for creation of new model serializers.
- serialize(**kwargs)#
Serialize data/model into specific type.
- Returns:
object
- Return type:
Serialized data/model.
- class ads.model.serde.model_serializer.ModelSerializerFactory[source]#
Bases:
object
Model Serializer Factory.
- Returns:
model_save_serde
- Return type:
Intance of ads.model.SERDE”.
- class ads.model.serde.model_serializer.ModelSerializerType[source]#
Bases:
object
- CLOUDPICKLE = 'cloudpickle'#
- ONNX = 'onnx'#
- class ads.model.serde.model_serializer.OnnxModelSaveSERDE(model_file_suffix='onnx')[source]#
Bases:
OnnxModelSerializer
,ModelDeserializer
- deserialize(**kwargs)#
Deserialize data/model into original type.
- Returns:
object
- Return type:
deserialized data/model.
- name = 'onnx'#
- serialize(estimator, model_path, initial_types: List[Tuple] | None = None, X_sample: Dict | str | List | Tuple | ndarray | Series | DataFrame | None = None, **kwargs)#
Save model into onnx format.
- Parameters:
estimator – The model to be saved.
model_path – The file object or path of the model in which it is to be stored.
initial_types – (List[Tuple], optional) a python list. Each element is a tuple of a variable name and a data type.
X_sample – (any, optional). Defaults to None. Contains model inputs such that model(X_sample) is a valid invocation of the model, used to valid model input type.
- class ads.model.serde.model_serializer.OnnxModelSerializer(model_file_suffix='onnx')[source]#
Bases:
ModelSerializer
Base class for creation of onnx converter for each model framework.
- serialize(estimator, model_path, initial_types: List[Tuple] | None = None, X_sample: Dict | str | List | Tuple | ndarray | Series | DataFrame | None = None, **kwargs)[source]#
Save model into onnx format.
- Parameters:
estimator – The model to be saved.
model_path – The file object or path of the model in which it is to be stored.
initial_types – (List[Tuple], optional) a python list. Each element is a tuple of a variable name and a data type.
X_sample – (any, optional). Defaults to None. Contains model inputs such that model(X_sample) is a valid invocation of the model, used to valid model input type.
- class ads.model.serde.model_serializer.PyTorchModelSaveSERDE(model_file_suffix='pt')[source]#
Bases:
PyTorchModelSerializer
,ModelDeserializer
- deserialize(**kwargs)#
Deserialize data/model into original type.
- Returns:
object
- Return type:
deserialized data/model.
- name = 'torch'#
- serialize(estimator, model_path, **kwarg)#
Serialize data/model into specific type.
- Returns:
object
- Return type:
Serialized data/model.
- class ads.model.serde.model_serializer.PyTorchModelSerializer(model_file_suffix='pt')[source]#
Bases:
ModelSerializer
Save PyTorch Model using torch.save(). See https://pytorch.org/docs/stable/generated/torch.save.html for more details.
- class ads.model.serde.model_serializer.PyTorchModelSerializerType[source]#
Bases:
object
- ONNX = 'torch_onnx'#
- TORCH = 'torch'#
- TORCHSCRIPT = 'torchscript'#
- class ads.model.serde.model_serializer.PyTorchOnnxModelSaveSERDE[source]#
Bases:
PytorchOnnxModelSerializer
,ModelDeserializer
- deserialize(**kwargs)#
Deserialize data/model into original type.
- Returns:
object
- Return type:
deserialized data/model.
- name = 'torch_onnx'#
- serialize(estimator, model_path: str, X_sample: Dict | str | List | Tuple | ndarray | Series | DataFrame | None = None, **kwargs)#
Exports the given Pytorch model into ONNX format.
- Parameters:
path (str, default to None) – Path to save the serialized model.
onnx_args ((tuple or torch.Tensor), default to None) – Contains model inputs such that model(onnx_args) is a valid invocation of the model. Can be structured either as: 1) ONLY A TUPLE OF ARGUMENTS; 2) A TENSOR; 3) A TUPLE OF ARGUMENTS ENDING WITH A DICTIONARY OF NAMED ARGUMENTS
X_sample (Union[list, tuple, pd.Series, np.ndarray, pd.DataFrame]. Defaults to None.) – A sample of input data that will be used to generate input schema and detect onnx_args.
kwargs –
- input_names: (List[str], optional). Defaults to [“input”].
Names to assign to the input nodes of the graph, in order.
- output_names: (List[str], optional). Defaults to [“output”].
Names to assign to the output nodes of the graph, in order.
- dynamic_axes: (dict, optional). Defaults to None.
Specify axes of tensors as dynamic (i.e. known only at run-time).
- Returns:
Nothing
- Return type:
None
- Raises:
AssertionError – if onnx module is not support by the current version of torch
ValueError – if X_sample is not provided if path is not provided
- class ads.model.serde.model_serializer.PytorchOnnxModelSerializer[source]#
Bases:
OnnxModelSerializer
Converts Pytorch model into onnx format.
- serialize(estimator, model_path: str, X_sample: Dict | str | List | Tuple | ndarray | Series | DataFrame | None = None, **kwargs)[source]#
Exports the given Pytorch model into ONNX format.
- Parameters:
path (str, default to None) – Path to save the serialized model.
onnx_args ((tuple or torch.Tensor), default to None) – Contains model inputs such that model(onnx_args) is a valid invocation of the model. Can be structured either as: 1) ONLY A TUPLE OF ARGUMENTS; 2) A TENSOR; 3) A TUPLE OF ARGUMENTS ENDING WITH A DICTIONARY OF NAMED ARGUMENTS
X_sample (Union[list, tuple, pd.Series, np.ndarray, pd.DataFrame]. Defaults to None.) – A sample of input data that will be used to generate input schema and detect onnx_args.
kwargs –
- input_names: (List[str], optional). Defaults to [“input”].
Names to assign to the input nodes of the graph, in order.
- output_names: (List[str], optional). Defaults to [“output”].
Names to assign to the output nodes of the graph, in order.
- dynamic_axes: (dict, optional). Defaults to None.
Specify axes of tensors as dynamic (i.e. known only at run-time).
- Returns:
Nothing
- Return type:
None
- Raises:
AssertionError – if onnx module is not support by the current version of torch
ValueError – if X_sample is not provided if path is not provided
- class ads.model.serde.model_serializer.SklearnModelSerializerType[source]#
Bases:
object
- CLOUDPICKLE = 'cloudpickle'#
- JOBLIB = 'joblib'#
- ONNX = 'sklearn_onnx'#
- class ads.model.serde.model_serializer.SklearnOnnxModelSaveSERDE[source]#
Bases:
SklearnOnnxModelSerializer
,ModelDeserializer
- deserialize(**kwargs)#
Deserialize data/model into original type.
- Returns:
object
- Return type:
deserialized data/model.
- static is_either_numerical_or_string_dataframe(data: DataFrame) bool #
Check whether all the columns are either numerical or string for dataframe.
- name = 'sklearn_onnx'#
- serialize(estimator, model_path, initial_types: List[Tuple] | None = None, X_sample: Dict | str | List | Tuple | ndarray | Series | DataFrame | None = None, **kwargs)#
Save model into onnx format.
- Parameters:
estimator – The model to be saved.
model_path – The file object or path of the model in which it is to be stored.
initial_types – (List[Tuple], optional) a python list. Each element is a tuple of a variable name and a data type.
X_sample – (any, optional). Defaults to None. Contains model inputs such that model(X_sample) is a valid invocation of the model, used to valid model input type.
- class ads.model.serde.model_serializer.SklearnOnnxModelSerializer[source]#
Bases:
OnnxModelSerializer
Converts Skearn Model into Onnx.
- static is_either_numerical_or_string_dataframe(data: DataFrame) bool [source]#
Check whether all the columns are either numerical or string for dataframe.
- serialize(estimator, model_path, initial_types: List[Tuple] | None = None, X_sample: Dict | str | List | Tuple | ndarray | Series | DataFrame | None = None, **kwargs)#
Save model into onnx format.
- Parameters:
estimator – The model to be saved.
model_path – The file object or path of the model in which it is to be stored.
initial_types – (List[Tuple], optional) a python list. Each element is a tuple of a variable name and a data type.
X_sample – (any, optional). Defaults to None. Contains model inputs such that model(X_sample) is a valid invocation of the model, used to valid model input type.
- class ads.model.serde.model_serializer.SparkModelSaveSERDE(model_file_suffix='')[source]#
Bases:
SparkModelSerializer
,ModelDeserializer
- deserialize(**kwargs)#
Deserialize data/model into original type.
- Returns:
object
- Return type:
deserialized data/model.
- name = 'spark'#
- serialize(estimator, model_path, **kwargs)#
Serialize data/model into specific type.
- Returns:
object
- Return type:
Serialized data/model.
- class ads.model.serde.model_serializer.SparkModelSerializer(model_file_suffix='')[source]#
Bases:
ModelSerializer
Save Spark Model.
- class ads.model.serde.model_serializer.SparkModelSerializerType[source]#
Bases:
object
- SPARK = 'spark'#
- class ads.model.serde.model_serializer.TensorFlowModelSaveSERDE(model_file_suffix='h5')[source]#
Bases:
TensorFlowModelSerializer
,ModelDeserializer
- deserialize(**kwargs)#
Deserialize data/model into original type.
- Returns:
object
- Return type:
deserialized data/model.
- name = 'tf'#
- serialize(estimator, model_path, **kwargs)#
Serialize data/model into specific type.
- Returns:
object
- Return type:
Serialized data/model.
- class ads.model.serde.model_serializer.TensorFlowModelSerializer(model_file_suffix='h5')[source]#
Bases:
ModelSerializer
Save Tensorflow Model.
- class ads.model.serde.model_serializer.TensorFlowOnnxModelSaveSERDE[source]#
Bases:
TensorFlowOnnxModelSerializer
,ModelDeserializer
- deserialize(**kwargs)#
Deserialize data/model into original type.
- Returns:
object
- Return type:
deserialized data/model.
- name = 'tf_onnx'#
- class ads.model.serde.model_serializer.TensorFlowOnnxModelSerializer[source]#
Bases:
OnnxModelSerializer
Converts Tensorflow model into onnx format.
- class ads.model.serde.model_serializer.TensorflowModelSerializerType[source]#
Bases:
object
- ONNX = 'tf_onnx'#
- TENSORFLOW = 'tf'#
- class ads.model.serde.model_serializer.TorchScriptModelSaveSERDE(model_file_suffix='pt')[source]#
Bases:
TorchScriptModelSerializer
,ModelDeserializer
- deserialize(**kwargs)#
Deserialize data/model into original type.
- Returns:
object
- Return type:
deserialized data/model.
- name = 'torchscript'#
- serialize(estimator, model_path, **kwargs)#
Serialize data/model into specific type.
- Returns:
object
- Return type:
Serialized data/model.
- class ads.model.serde.model_serializer.TorchScriptModelSerializer(model_file_suffix='pt')[source]#
Bases:
ModelSerializer
Save PyTorch Model using torchscript. See https://pytorch.org/tutorials/beginner/saving_loading_models.html#export-load-model-in-torchscript-format for more details.
- class ads.model.serde.model_serializer.XgboostJsonModelSaveSERDE(model_file_suffix='json')[source]#
Bases:
XgboostJsonModelSerializer
,ModelDeserializer
- deserialize(**kwargs)#
Deserialize data/model into original type.
- Returns:
object
- Return type:
deserialized data/model.
- name = 'xgboost'#
- serialize(estimator, model_path, **kwargs)#
Save Xgboost Model through xgboost.save_model .See https://xgboost.readthedocs.io/en/stable/python/python_api.html#xgboost.Booster.save_model for more details.
- Parameters:
estimator – The model to be saved.
model_path – The file object or path of the model in which it is to be stored.
- class ads.model.serde.model_serializer.XgboostJsonModelSerializer(model_file_suffix='json')[source]#
Bases:
ModelSerializer
Save Xgboost Model through xgboost.save_model into JSON.
- serialize(estimator, model_path, **kwargs)[source]#
Save Xgboost Model through xgboost.save_model .See https://xgboost.readthedocs.io/en/stable/python/python_api.html#xgboost.Booster.save_model for more details.
- Parameters:
estimator – The model to be saved.
model_path – The file object or path of the model in which it is to be stored.
- class ads.model.serde.model_serializer.XgboostModelSerializerType[source]#
Bases:
object
- ONNX = 'xgboost_onnx'#
- XGBOOST = 'xgboost'#
- class ads.model.serde.model_serializer.XgboostOnnxModelSaveSERDE[source]#
Bases:
XgboostOnnxModelSerializer
,ModelDeserializer
- deserialize(**kwargs)#
Deserialize data/model into original type.
- Returns:
object
- Return type:
deserialized data/model.
- name = 'xgboost_onnx'#
- serialize(estimator, model_path, initial_types: List[Tuple] | None = None, X_sample: Dict | str | List | Tuple | ndarray | Series | DataFrame | None = None, **kwargs)#
Save model into onnx format.
- Parameters:
estimator – The model to be saved.
model_path – The file object or path of the model in which it is to be stored.
initial_types – (List[Tuple], optional) a python list. Each element is a tuple of a variable name and a data type.
X_sample – (any, optional). Defaults to None. Contains model inputs such that model(X_sample) is a valid invocation of the model, used to valid model input type.
- class ads.model.serde.model_serializer.XgboostOnnxModelSerializer[source]#
Bases:
OnnxModelSerializer
Converts Xgboost model into onnx format.
- serialize(estimator, model_path, initial_types: List[Tuple] | None = None, X_sample: Dict | str | List | Tuple | ndarray | Series | DataFrame | None = None, **kwargs)#
Save model into onnx format.
- Parameters:
estimator – The model to be saved.
model_path – The file object or path of the model in which it is to be stored.
initial_types – (List[Tuple], optional) a python list. Each element is a tuple of a variable name and a data type.
X_sample – (any, optional). Defaults to None. Contains model inputs such that model(X_sample) is a valid invocation of the model, used to valid model input type.
- class ads.model.serde.model_serializer.XgboostTxtModelSaveSERDE(model_file_suffix='txt')[source]#
Bases:
XgboostTxtModelSerializer
,ModelDeserializer
- deserialize(**kwargs)#
Deserialize data/model into original type.
- Returns:
object
- Return type:
deserialized data/model.
- name = 'xgboost_txt'#
- serialize(estimator, model_path, **kwargs)#
Save Xgboost Model through xgboost.save_model .See https://xgboost.readthedocs.io/en/stable/python/python_api.html#xgboost.Booster.save_model for more details.
- Parameters:
estimator – The model to be saved.
model_path – The file object or path of the model in which it is to be stored.
- class ads.model.serde.model_serializer.XgboostTxtModelSerializer(model_file_suffix='txt')[source]#
Bases:
ModelSerializer
Save Xgboost Model through xgboost.save_model into txt.
- serialize(estimator, model_path, **kwargs)[source]#
Save Xgboost Model through xgboost.save_model .See https://xgboost.readthedocs.io/en/stable/python/python_api.html#xgboost.Booster.save_model for more details.
- Parameters:
estimator – The model to be saved.
model_path – The file object or path of the model in which it is to be stored.
- class ads.model.serde.model_serializer.XgboostUbjModelSaveSERDE(model_file_suffix='ubj')[source]#
Bases:
XgboostUbjModelSerializer
,ModelDeserializer
- deserialize(**kwargs)#
Deserialize data/model into original type.
- Returns:
object
- Return type:
deserialized data/model.
- name = 'xgboost_ubj'#
- serialize(estimator, model_path, **kwargs)#
Save Xgboost Model through xgboost.save_model .See https://xgboost.readthedocs.io/en/stable/python/python_api.html#xgboost.Booster.save_model for more details.
- Parameters:
estimator – The model to be saved.
model_path – The file object or path of the model in which it is to be stored.
- class ads.model.serde.model_serializer.XgboostUbjModelSerializer(model_file_suffix='ubj')[source]#
Bases:
ModelSerializer
Save Xgboost Model through xgboost.save_model into binary JSON.
- serialize(estimator, model_path, **kwargs)[source]#
Save Xgboost Model through xgboost.save_model .See https://xgboost.readthedocs.io/en/stable/python/python_api.html#xgboost.Booster.save_model for more details.
- Parameters:
estimator – The model to be saved.
model_path – The file object or path of the model in which it is to be stored.