ads.model.framework other package

Submodules

ads.model.artifact module

exception ads.model.artifact.AritfactFolderStructureError(required_files: Tuple[str]): Bases: Exception

exception ads.model.artifact.ArtifactNestedFolderError(folder: str): Bases: Exception

exception ads.model.artifact.ArtifactRequiredFilesError(required_files: Tuple[str]): Bases: Exception

class ads.model.artifact.ModelArtifact(artifact_dir: str, model_file_name: str, reload: Optional[bool] = False)

Bases: object

The class that represents model artifacts. It is designed to help to generate and manage model artifacts.

Initializes a ModelArtifact instance.

Parameters

artifact_dir (str) – The local artifact folder to store the files needed for deployment.
model_file_name (str) – The file name of the serialized model.
reload ((bool, optional). Defaults to False.) – Determine whether will reload the Model into the env.

Returns

A ModelArtifact instance.

Return type

ModelArtifact

Raises

ValueError – If artifact_dir not provided. If model_file_name not provided.

classmethod from_uri(uri: str, artifact_dir: str, model_file_name: str, force_overwrite: Optional[bool] = False, auth: Optional[Dict] = None)

Constructs a ModelArtifact object from the existing model artifacts.

Parameters

uri (str) – The URI of source artifact folder or achive. Can be local path or OCI object storage URI.
artifact_dir (str) – The local artifact folder to store the files needed for deployment.
model_file_name ((str)) – The file name of the serialized model.
force_overwrite ((bool, optional). Defaults to False.) – Whether to overwrite existing files or not.
auth ((Dict, optional). Defaults to None.) – The default authetication is set using ads.set_auth API. If you need to override the default, use the ads.common.auth.api_keys or ads.common.auth.resource_principal to create appropriate authentication signer and kwargs required to instantiate IdentityClient object.

Returns

A ModelArtifact instance

Return type

ModelArtifact

Raises

ValueError – If uri is equal to artifact_dir, and it not exists.

prepare_runtime_yaml(inference_conda_env: str, inference_python_version: Optional[str] = None, training_conda_env: Optional[str] = None, training_python_version: Optional[str] = None, force_overwrite: bool = False, namespace: str = 'id19sfcrra6z', bucketname: str = 'service-conda-packs') → None

Generate a runtime yaml file and save it to the artifact directory.

Parameters

inference_conda_env ((str, optional). Defaults to None.) – The object storage path of conda pack which will be used in deployment. Can be either slug or object storage path of the conda pack. You can only pass in slugs if the conda pack is a service pack.
inference_python_version ((str, optional). Defaults to None.) – The python version which will be used in deployment.
training_conda_env ((str, optional). Defaults to None.) – The object storage path of conda pack used during training. Can be either slug or object storage path of the conda pack. You can only pass in slugs if the conda pack is a service pack.
training_python_version ((str, optional). Defaults to None.) – The python version used during training.
force_overwrite ((bool, optional). Defaults to False.) – Whether to overwrite existing files.
namespace ((str, optional)) – The namespace of region.
bucketname ((str, optional)) – The bucketname of service pack.

Raises

ValueError – If neither slug or conda_env_uri is provided.

Returns

A RuntimeInfo instance.

Return type

RuntimeInfo

prepare_score_py(jinja_template_filename: str)

write score.py file.

Parameters: jinja_template_filename (str.) – The jinja template file name.
Returns: Nothing
Return type: None

reload()

Syncs the score.py to reload the model and predict function.

Returns: Nothing
Return type: None

ads.model.generic_model module

class ads.model.generic_model.GenericModel(estimator: Callable, artifact_dir: Optional[str] = None, properties: Optional[ModelProperties] = None, auth: Optional[Dict] = None, serialize: bool = True, **kwargs: dict)

Bases: MetadataMixin, Introspectable

Generic Model class which is the base class for all the frameworks including the unsupported frameworks.

algorithm

The algorithm of the model.

Type: str

artifact_dir

Artifact directory to store the files needed for deployment.

Type: str

auth

Default authentication is set using the ads.set_auth API. To override the default, use the ads.common.auth.api_keys or ads.common.auth.resource_principal to create an authentication signer to instantiate an IdentityClient object.

Type: Dict

ds_client

The data science client used by model deployment.

Type: DataScienceClient

estimator

Any model object generated by sklearn framework

Type: Callable

framework

The framework of the model.

Type: str

hyperparameter

The hyperparameters of the estimator.

Type: dict

metadata_custom

The model custom metadata.

Type: ModelCustomMetadata

metadata_provenance

The model provenance metadata.

Type: ModelProvenanceMetadata

metadata_taxonomy

The model taxonomy metadata.

Type: ModelTaxonomyMetadata

model_artifact

This is built by calling prepare.

Type: ModelArtifact

model_deployment

A ModelDeployment instance.

Type: ModelDeployment

model_file_name

Name of the serialized model.

Type: str

model_id

The model ID.

Type: str

properties

ModelProperties object required to save and deploy model.

Type: ModelProperties

runtime_info

A RuntimeInfo instance.

Type: RuntimeInfo

schema_input

Schema describes the structure of the input data.

Type: Schema

schema_output

Schema describes the structure of the output data.

Type: Schema

serialize

Whether to serialize the model to pkl file by default. If False, you need to serialize the model manually, save it under artifact_dir and update the score.py manually.

Type: bool

version

The framework version of the model.

Type: str

delete_deployment(...): Deletes the current model deployment.

deploy(..., \*\*kwargs): Deploys a model.

from_model_artifact(uri, model_file_name, artifact_dir, ..., \*\*kwargs): Loads model from the specified folder, or zip/tar archive.

from_model_catalog(model_id, model_file_name, artifact_dir, ..., \*\*kwargs): Loads model from model catalog.

from_model_deployment(model_deployment_id, model_file_name, artifact_dir, ..., \*\*kwargs): Loads model from model deployment.

introspect(...): Runs model introspection.

predict(data, ...): Returns prediction of input data run against the model deployment endpoint.

prepare(..., \*\*kwargs): Prepare and save the score.py, serialized model and runtime.yaml file.

prepare_save_deploy(..., \*\*kwargs): Shortcut for prepare, save and deploy steps.

reload(...): Reloads the model artifact files: score.py and the runtime.yaml.

save(..., \*\*kwargs): Saves model artifacts to the model catalog.

summary_status(...): Gets a summary table of the current status.

verify(data, ...): Tests if deployment works in local environment.

Examples

>>> import tempfile
>>> from ads.model.generic_model import GenericModel

>>> class Toy:
...     def predict(self, x):
...         return x ** 2
>>> estimator = Toy()

>>> model = GenericModel(estimator=estimator, artifact_dir=tempfile.mkdtemp())
>>> model.summary_status()
>>> model.prepare(inference_conda_env="oci://service-conda-packs@id19sfcrra6z/service_pack/cpu/Data_Exploration_and_Manipulation_for_CPU_Python_3.7/3.0/dataexpl_p37_cpu_v3",
...              inference_python_version="3.7",
...              model_file_name="toy_model.pkl",
...              training_id=None,
...              force_overwrite=True
...            )
>>> model.verify(2)
>>> model.save()
>>> model.deploy()
>>> model.predict(2)
>>> model.delete_deployment()

GenericModel Constructor.

Parameters

estimator ((Callable).) – Trained model.
artifact_dir ((str, optional). Defaults to None.) – Artifact directory to store the files needed for deployment.
properties ((ModelProperties, optional). Defaults to None.) – ModelProperties object required to save and deploy model.
auth ((Dict, optional). Defaults to None.) – The default authetication is set using ads.set_auth API. If you need to override the default, use the ads.common.auth.api_keys or ads.common.auth.resource_principal to create appropriate authentication signer and kwargs required to instantiate IdentityClient object.
serialize ((bool, optional). Defaults to True.) – Whether to serialize the model to pkl file by default. If False, you need to serialize the model manually, save it under artifact_dir and update the score.py manually.

delete_deployment(wait_for_completion: bool = False)

Deletes the current deployment.

Parameters: wait_for_completion ((bool, optional). Defaults to False.) – Whether to wait till completion.
Raises: ValueError – if there is not deployment attached yet.:

deploy(wait_for_completion: Optional[bool] = True, display_name: Optional[str] = None, description: Optional[str] = None, deployment_instance_shape: Optional[str] = None, deployment_instance_count: Optional[int] = None, deployment_bandwidth_mbps: Optional[int] = None, deployment_log_group_id: Optional[str] = None, deployment_access_log_id: Optional[str] = None, deployment_predict_log_id: Optional[str] = None, **kwargs: Dict) → ModelDeployment

Deploys a model. The model needs to be saved to the model catalog at first.

Parameters

wait_for_completion ((bool, optional). Defaults to True.) – Flag set for whether to wait for deployment to complete before proceeding.
display_name ((str, optional). Defaults to None.) – The name of the model.
description ((str, optional). Defaults to None.) – The description of the model.
deployment_instance_shape ((str, optional). Default to VM.Standard2.1.) – The shape of the instance used for deployment.
deployment_instance_count ((int, optional). Defaults to 1.) – The number of instance used for deployment.
deployment_bandwidth_mbps ((int, optional). Defaults to 10.) – The bandwidth limit on the load balancer in Mbps.
deployment_log_group_id ((str, optional). Defaults to None.) – The oci logging group id. The access log and predict log share the same log group.
deployment_access_log_id ((str, optional). Defaults to None.) – The access log OCID for the access logs. https://docs.oracle.com/en-us/iaas/data-science/using/model_dep_using_logging.htm
deployment_predict_log_id ((str, optional). Defaults to None.) – The predict log OCID for the predict logs. https://docs.oracle.com/en-us/iaas/data-science/using/model_dep_using_logging.htm
kwargs –

project_id: (str, optional).
Project OCID. If not specified, the value will be taken from the environment variables.

compartment_id(str, optional).
Compartment OCID. If not specified, the value will be taken from the environment variables.

max_wait_time(int, optional). Defaults to 1200 seconds.
Maximum amount of time to wait in seconds. Negative implies infinite wait time.

poll_interval(int, optional). Defaults to 60 seconds.
Poll interval in seconds.

Returns

The ModelDeployment instance.

Return type

ModelDeployment

Raises

ValueError – If model_id is not specified.

classmethod from_model_artifact(uri: str, model_file_name: str, artifact_dir: str, auth: Optional[Dict] = None, force_overwrite: Optional[bool] = False, properties: Optional[ModelProperties] = None, **kwargs: dict) → GenericModel

Loads model from a folder, or zip/tar archive.

Parameters

uri (str) – The folder path, ZIP file path, or TAR file path. It could contain a seriliazed model(required) as well as any files needed for deployment including: serialized model, runtime.yaml, score.py and etc. The content of the folder will be copied to the artifact_dir folder.
model_file_name (str) – The serialized model file name.
artifact_dir (str) – The artifact directory to store the files needed for deployment.
auth ((Dict, optional). Defaults to None.) – The default authetication is set using ads.set_auth API. If you need to override the default, use the ads.common.auth.api_keys or ads.common.auth.resource_principal to create appropriate authentication signer and kwargs required to instantiate IdentityClient object.
force_overwrite ((bool, optional). Defaults to False.) – Whether to overwrite existing files or not.
properties ((ModelProperties, optional). Defaults to None.) – ModelProperties object required to save and deploy model.

Returns

An instance of GenericModel class.

Return type

GenericModel

Raises

ValueError – If model_file_name not provided.

classmethod from_model_catalog(model_id: str, model_file_name: str, artifact_dir: str, auth: Optional[Dict] = None, force_overwrite: Optional[bool] = False, properties: Optional[Union[ModelProperties, Dict]] = None, **kwargs) → GenericModel

Loads model from model catalog.

Parameters

model_id (str) – The model OCID.
model_file_name ((str)) – The name of the serialized model.
artifact_dir (str) – The artifact directory to store the files needed for deployment. Will be created if not exists.
auth ((Dict, optional). Defaults to None.) – The default authetication is set using ads.set_auth API. If you need to override the default, use the ads.common.auth.api_keys or ads.common.auth.resource_principal to create appropriate authentication signer and kwargs required to instantiate IdentityClient object.
force_overwrite ((bool, optional). Defaults to False.) – Whether to overwrite existing files or not.
properties ((ModelProperties, optional). Defaults to None.) – ModelProperties object required to save and deploy model.
kwargs –

compartment_id(str, optional)
Compartment OCID. If not specified, the value will be taken from the environment variables.

timeout(int, optional). Defaults to 10 seconds.
The connection timeout in seconds for the client.

Returns

An instance of GenericModel class.

Return type

GenericModel

classmethod from_model_deployment(model_deployment_id: str, model_file_name: str, artifact_dir: str, auth: Optional[Dict] = None, force_overwrite: Optional[bool] = False, properties: Optional[Union[ModelProperties, Dict]] = None, **kwargs) → GenericModel

Loads model from model deployment.

Parameters

model_deployment_id (str) – The model deployment OCID.
model_file_name ((str)) – The name of the serialized model.
artifact_dir (str) – The artifact directory to store the files needed for deployment. Will be created if not exists.
auth ((Dict, optional). Defaults to None.) – The default authetication is set using ads.set_auth API. If you need to override the default, use the ads.common.auth.api_keys or ads.common.auth.resource_principal to create appropriate authentication signer and kwargs required to instantiate IdentityClient object.
force_overwrite ((bool, optional). Defaults to False.) – Whether to overwrite existing files or not.
properties ((ModelProperties, optional). Defaults to None.) – ModelProperties object required to save and deploy model.
kwargs –

compartment_id(str, optional)
Compartment OCID. If not specified, the value will be taken from the environment variables.

timeout(int, optional). Defaults to 10 seconds.
The connection timeout in seconds for the client.

Returns

An instance of GenericModel class.

Return type

GenericModel

introspect() → DataFrame

Conducts instrospection.

Returns: A pandas DataFrame which contains the instrospection results.
Return type: pandas.DataFrame

predict(data: Any, **kwargs) → Dict[str, Any]

Returns prediction of input data run against the model deployment endpoint.

Parameters

data (Any) – Data for the prediction for onnx models, for local serialization method, data can be the data types that each framework support.
kwargs –

content_type: str
Used to indicate the media type of the resource. By default, it will be application/octet-stream for bytes input and application/json for other cases. The content-type will be added into headers and passed in the call of model deployment endpoint.

Returns

Dictionary with the predicted values.

Return type

Dict[str, Any]

Raises

NotActiveDeploymentError – If model deployment process was not started or not finished yet.
ValueError – If data is empty or not JSON serializable.

prepare(inference_conda_env: Optional[str] = None, inference_python_version: Optional[str] = None, training_conda_env: Optional[str] = None, training_python_version: Optional[str] = None, model_file_name: Optional[str] = None, as_onnx: bool = False, initial_types: Optional[List[Tuple]] = None, force_overwrite: bool = False, namespace: str = 'id19sfcrra6z', use_case_type: Optional[str] = None, X_sample: Optional[Union[list, tuple, Series, ndarray, DataFrame]] = None, y_sample: Optional[Union[list, tuple, Series, ndarray, DataFrame]] = None, training_script_path: Optional[str] = None, training_id: Optional[str] = None, ignore_pending_changes: bool = True, max_col_num: int = 2000, **kwargs: Dict) → None

Prepare and save the score.py, serialized model and runtime.yaml file.

Parameters

inference_conda_env ((str, optional). Defaults to None.) – Can be either slug or object storage path of the conda pack. You can only pass in slugs if the conda pack is a service pack.
inference_python_version ((str, optional). Defaults to None.) – Python version which will be used in deployment.
training_conda_env ((str, optional). Defaults to None.) – Can be either slug or object storage path of the conda pack. You can only pass in slugs if the conda pack is a service pack. If training_conda_env is not provided, training_conda_env will use the same value of training_conda_env.
training_python_version ((str, optional). Defaults to None.) – Python version used during training.
model_file_name ((str).) – Name of the serialized model.
as_onnx ((bool, optional). Defaults to False.) – Whether to serialize as onnx model.
initial_types ((list[Tuple], optional).) – Defaults to None. Only used for SklearnModel, LightGBMModel and XGBoostModel. Each element is a tuple of a variable name and a type. Check this link http://onnx.ai/sklearn-onnx/api_summary.html#id2 for more explanation and examples for initial_types.
force_overwrite ((bool, optional). Defaults to False.) – Whether to overwrite existing files.
namespace ((str, optional).) – Namespace of region. This is used for identifying which region the service pack is from when you pass a slug to inference_conda_env and training_conda_env.
use_case_type (str) – The use case type of the model. Use it through UserCaseType class or string provided in UseCaseType. For example, use_case_type=UseCaseType.BINARY_CLASSIFICATION or use_case_type=”binary_classification”. Check with UseCaseType class to see all supported types.
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.
y_sample (Union[list, tuple, pd.Series, np.ndarray, pd.DataFrame]. Defaults to None.) – A sample of output data that will be used to generate output schema.
training_script_path (str. Defaults to None.) – Training script path.
training_id ((str, optional). Defaults to value from environment variables.) – The training OCID for model. Can be notebook session or job OCID.
ignore_pending_changes (bool. Defaults to False.) – whether to ignore the pending changes in the git.
max_col_num ((int, optional). Defaults to utils.DATA_SCHEMA_MAX_COL_NUM.) – Do not generate the input schema if the input has more than this number of features(columns).
kwargs –

impute_values: (dict, optional).
The dictionary where the key is the column index(or names is accepted for pandas dataframe) and the value is the impute value for the corresponding column.

Raises

FileExistsError – when files already exist but force_overwrite is False.:
ValueError – when inference_python_version is not provided, but also cannot be found through manifest file.:

Returns

Nothing

Return type

None

prepare_save_deploy(inference_conda_env: Optional[str] = None, inference_python_version: Optional[str] = None, training_conda_env: Optional[str] = None, training_python_version: Optional[str] = None, model_file_name: Optional[str] = None, as_onnx: bool = False, initial_types: Optional[List[Tuple]] = None, force_overwrite: bool = False, namespace: str = 'id19sfcrra6z', use_case_type: Optional[str] = None, X_sample: Optional[Union[list, tuple, Series, ndarray, DataFrame]] = None, y_sample: Optional[Union[list, tuple, Series, ndarray, DataFrame]] = None, training_script_path: Optional[str] = None, training_id: Optional[str] = None, ignore_pending_changes: bool = True, max_col_num: int = 2000, model_display_name: Optional[str] = None, model_description: Optional[str] = None, model_freeform_tags: Optional[dict] = None, model_defined_tags: Optional[dict] = None, ignore_introspection: Optional[bool] = False, wait_for_completion: Optional[bool] = True, deployment_display_name: Optional[str] = None, deployment_description: Optional[str] = None, deployment_instance_shape: Optional[str] = None, deployment_instance_count: Optional[int] = None, deployment_bandwidth_mbps: Optional[int] = None, deployment_log_group_id: Optional[str] = None, deployment_access_log_id: Optional[str] = None, deployment_predict_log_id: Optional[str] = None, **kwargs: Dict)

Shortcut for prepare, save and deploy steps.

Parameters

inference_conda_env ((str, optional). Defaults to None.) – Can be either slug or object storage path of the conda pack. You can only pass in slugs if the conda pack is a service pack.
inference_python_version ((str, optional). Defaults to None.) – Python version which will be used in deployment.
training_conda_env ((str, optional). Defaults to None.) – Can be either slug or object storage path of the conda pack. You can only pass in slugs if the conda pack is a service pack. If training_conda_env is not provided, training_conda_env will use the same value of training_conda_env.
training_python_version ((str, optional). Defaults to None.) – Python version used during training.
model_file_name ((str).) – Name of the serialized model.
as_onnx ((bool, optional). Defaults to False.) – Whether to serialize as onnx model.
initial_types ((list[Tuple], optional).) – Defaults to None. Only used for SklearnModel, LightGBMModel and XGBoostModel. Each element is a tuple of a variable name and a type. Check this link http://onnx.ai/sklearn-onnx/api_summary.html#id2 for more explanation and examples for initial_types.
force_overwrite ((bool, optional). Defaults to False.) – Whether to overwrite existing files.
namespace ((str, optional).) – Namespace of region. This is used for identifying which region the service pack is from when you pass a slug to inference_conda_env and training_conda_env.
use_case_type (str) – The use case type of the model. Use it through UserCaseType class or string provided in UseCaseType. For example, use_case_type=UseCaseType.BINARY_CLASSIFICATION or use_case_type=”binary_classification”. Check with UseCaseType class to see all supported types.
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.
y_sample (Union[list, tuple, pd.Series, np.ndarray, pd.DataFrame]. Defaults to None.) – A sample of output data that will be used to generate output schema.
training_script_path (str. Defaults to None.) – Training script path.
training_id ((str, optional). Defaults to value from environment variables.) – The training OCID for model. Can be notebook session or job OCID.
ignore_pending_changes (bool. Defaults to False.) – whether to ignore the pending changes in the git.
max_col_num ((int, optional). Defaults to utils.DATA_SCHEMA_MAX_COL_NUM.) – Do not generate the input schema if the input has more than this number of features(columns).
model_display_name ((str, optional). Defaults to None.) – The name of the model.
model_description ((str, optional). Defaults to None.) – The description of the model.
model_freeform_tags (Dict(str, str), Defaults to None.) – Freeform tags for the model.
model_defined_tags ((Dict(str, dict(str, object)), optional). Defaults to None.) – Defined tags for the model.
ignore_introspection ((bool, optional). Defaults to None.) – Determine whether to ignore the result of model introspection or not. If set to True, the save will ignore all model introspection errors.
wait_for_completion ((bool, optional). Defaults to True.) – Flag set for whether to wait for deployment to complete before proceeding.
display_name ((str, optional). Defaults to None.) – The name of the model.
description ((str, optional). Defaults to None.) – The description of the model.
deployment_instance_shape ((str, optional). Default to VM.Standard2.1.) – The shape of the instance used for deployment.
deployment_instance_count ((int, optional). Defaults to 1.) – The number of instance used for deployment.
deployment_bandwidth_mbps ((int, optional). Defaults to 10.) – The bandwidth limit on the load balancer in Mbps.
deployment_log_group_id ((str, optional). Defaults to None.) – The oci logging group id. The access log and predict log share the same log group.
deployment_access_log_id ((str, optional). Defaults to None.) – The access log OCID for the access logs. https://docs.oracle.com/en-us/iaas/data-science/using/model_dep_using_logging.htm
deployment_predict_log_id ((str, optional). Defaults to None.) – The predict log OCID for the predict logs. https://docs.oracle.com/en-us/iaas/data-science/using/model_dep_using_logging.htm
kwargs –

impute_values: (dict, optional).
The dictionary where the key is the column index(or names is accepted for pandas dataframe) and the value is the impute value for the corresponding column.

project_id: (str, optional).
Project OCID. If not specified, the value will be taken either from the environment variables or model properties.

compartment_id(str, optional).
Compartment OCID. If not specified, the value will be taken either from the environment variables or model properties.

timeout: (int, optional). Defaults to 10 seconds.
The connection timeout in seconds for the client.

max_wait_time(int, optional). Defaults to 1200 seconds.
Maximum amount of time to wait in seconds. Negative implies infinite wait time.

poll_interval(int, optional). Defaults to 60 seconds.
Poll interval in seconds.

Returns

The ModelDeployment instance.

Return type

ModelDeployment

Raises

FileExistsError – when files already exist but force_overwrite is False.:
ValueError – when inference_python_version is not provided, but also cannot be found through manifest file.:

reload() → None

Reloads the model artifact files: score.py and the runtime.yaml.

Returns: Nothing.
Return type: None

save(display_name: Optional[str] = None, description: Optional[str] = None, freeform_tags: Optional[dict] = None, defined_tags: Optional[dict] = None, ignore_introspection: Optional[bool] = False, **kwargs) → None

Saves model artifacts to the model catalog.

Parameters

display_name ((str, optional). Defaults to None.) – The name of the model.
description ((str, optional). Defaults to None.) – The description of the model.
freeform_tags (Dict(str, str), Defaults to None.) – Freeform tags for the model.
defined_tags ((Dict(str, dict(str, object)), optional). Defaults to None.) – Defined tags for the model.
ignore_introspection ((bool, optional). Defaults to None.) – Determine whether to ignore the result of model introspection or not. If set to True, the save will ignore all model introspection errors.
kwargs –

project_id: (str, optional).
Project OCID. If not specified, the value will be taken either from the environment variables or model properties.

compartment_id(str, optional).
Compartment OCID. If not specified, the value will be taken either from the environment variables or model properties.

timeout: (int, optional). Defaults to 10 seconds.
The connection timeout in seconds for the client.

Raises

RuntimeInfoInconsistencyError – When .runtime_info is not synched with runtime.yaml file.

Returns

model id.

Return type

str

serialize_model(as_onnx: bool = False, initial_types: Optional[List[Tuple]] = None, force_overwrite: bool = False, X_sample: Optional[any] = None)

Serialize and save model using ONNX or model specific method.

Parameters

as_onnx ((boolean, optional)) – If set as True, convert into ONNX model.
initial_types ((List[Tuple], optional)) – a python list. Each element is a tuple of a variable name and a data type.
force_overwrite ((boolean, optional)) – If set as True, overwrite serialized model if exists.
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.

Returns

Nothing

Return type

None

summary_status() → DataFrame

A summary table of the current status.

Returns: The summary stable of the current status.
Return type: pd.DataFrame

verify(data: Any, **kwargs) → Dict[str, Any]

test if deployment works in local environment.

Parameters

data (Any.) – Data used to test if deployment works in local environment.
kwargs – content_type: str, used to indicate the media type of the resource.

Returns

A dictionary which contains prediction results.

Return type

Dict

class ads.model.generic_model.ModelState(value)

Bases: Enum

An enumeration.

AVAILABLE = 'Available'

DONE = 'Done'

NEEDSACTION = 'Needs Action'

NOTAVAILABLE = 'Not Available'

exception ads.model.generic_model.NotActiveDeploymentError(state: str): Bases: Exception

exception ads.model.generic_model.RuntimeInfoInconsistencyError: Bases: Exception

exception ads.model.generic_model.SerializeInputNotImplementedError: Bases: NotImplementedError

exception ads.model.generic_model.SerializeModelNotImplementedError: Bases: NotImplementedError

class ads.model.generic_model.SummaryStatus

Bases: object

SummaryStatus class which track the status of the Model frameworks.

update_action(detail: str, action: str) → None

Updates the action of the summary status table of the corresponding detail.

Parameters

detail ((str)) – Value of the detail in the Details column. Used to locate which row to update.
status ((str)) – New status to be updated for the row specified by detail.

Returns

Nothing.

Return type

None

update_status(detail: str, status: str) → None

Updates the status of the summary status table of the corresponding detail.

Parameters

detail ((str)) – value of the detail in the Details column. Used to locate which row to update.
status ((str)) – new status to be updated for the row specified by detail.

Returns

Nothing.

Return type

None

ads.model.model_properties module

class ads.model.model_properties.ModelProperties(inference_conda_env: Optional[str] = None, inference_python_version: Optional[str] = None, training_conda_env: Optional[str] = None, training_python_version: Optional[str] = None, training_resource_id: Optional[str] = None, training_script_path: Optional[str] = None, training_id: Optional[str] = None, compartment_id: Optional[str] = None, project_id: Optional[str] = None, deployment_instance_shape: Optional[str] = None, deployment_instance_count: Optional[int] = None, deployment_bandwidth_mbps: Optional[int] = None, deployment_log_group_id: Optional[str] = None, deployment_access_log_id: Optional[str] = None, deployment_predict_log_id: Optional[str] = None)

Bases: BaseProperties

Represents properties required to save and deploy model.

compartment_id: str = None

deployment_access_log_id: str = None

deployment_bandwidth_mbps: int = None

deployment_instance_count: int = None

deployment_instance_shape: str = None

deployment_log_group_id: str = None

deployment_predict_log_id: str = None

inference_conda_env: str = None

inference_python_version: str = None

project_id: str = None

training_conda_env: str = None

training_id: str = None

training_python_version: str = None

training_resource_id: str = None

training_script_path: str = None

ads.model.runtime.runtime_info module

class ads.model.runtime.runtime_info.RuntimeInfo(model_artifact_version: str = '', model_deployment: ~ads.model.runtime.model_deployment_details.ModelDeploymentDetails = <factory>, model_provenance: ~ads.model.runtime.model_provenance_details.ModelProvenanceDetails = <factory>)

Bases: DataClassSerializable

RuntimeInfo class which is the data class represenation of the runtime yaml file.

classmethod from_env() → RuntimeInfo

Popolate the RuntimeInfo from environment variables.

Returns: A RuntimeInfo instance.
Return type: RuntimeInfo

model_artifact_version: str = ''

model_deployment: ModelDeploymentDetails

model_provenance: ModelProvenanceDetails

save()

Save the RuntimeInfo object into runtime.yaml file under the artifact directory.

Returns: Nothing.
Return type: None

ads.model.extractor.model_info_extractor_factory module

class ads.model.extractor.model_info_extractor_factory.ModelInfoExtractorFactory

Bases: object

Class that extract Model Taxonomy Metadata for all supported frameworks.

static extract_info(model)

Extracts model taxonomy metadata.

Parameters: model ([ADS model, sklearn, xgboost, lightgbm, keras, oracle_automl]) – The model object
Returns: A dictionary with keys of Framework, FrameworkVersion, Algorithm, Hyperparameters of the model
Return type: ModelTaxonomyMetadata

Examples

>>> from ads.common.model_info_extractor_factory import ModelInfoExtractorFactory
>>> metadata_taxonomy = ModelInfoExtractorFactory.extract_info(model)

ads.model.extractor.model_artifact module

ads.model.extractor.automl_extractor module

class ads.model.extractor.automl_extractor.AutoMLExtractor(model)

Bases: ModelInfoExtractor

Class that extract model metadata from automl models.

model

The model to extract metadata from.

Type: object

estimator

The estimator to extract metadata from.

Type: object

property algorithm

Extracts the algorithm of the model.

Returns: The algorithm of the model.
Return type: object

property framework

Extracts the framework of the model.

Returns: The framework of the model.
Return type: str

property hyperparameter

Extracts the hyperparameters of the model.

Returns: The hyperparameters of the model.
Return type: dict

property version

Extracts the framework version of the model.

Returns: The framework version of the model.
Return type: str

ads.model.extractor.xgboost_extractor module

class ads.model.extractor.xgboost_extractor.XgboostExtractor(model)

Bases: ModelInfoExtractor

Class that extract model metadata from xgboost models.

model

The model to extract metadata from.

Type: object

estimator

The estimator to extract metadata from.

Type: object

framework(self) → str: Returns the framework of the model.

algorithm(self) → object: Returns the algorithm of the model.

version(self) → str: Returns the version of framework of the model.

hyperparameter(self) → dict: Returns the hyperparameter of the model.

property algorithm

Extracts the algorithm of the model.

Returns: The algorithm of the model.
Return type: object

property framework

Extracts the framework of the model.

Returns: The framework of the model.
Return type: str

property hyperparameter

Extracts the hyperparameters of the model.

Returns: The hyperparameters of the model.
Return type: dict

property version

Extracts the framework version of the model.

Returns: The framework version of the model.
Return type: str

ads.model.extractor.lightgbm_extractor module

class ads.model.extractor.lightgbm_extractor.LightgbmExtractor(model)

Bases: ModelInfoExtractor

Class that extract model metadata from lightgbm models.

model

The model to extract metadata from.

Type: object

estimator

The estimator to extract metadata from.

Type: object

framework(self) → str: Returns the framework of the model.

algorithm(self) → object: Returns the algorithm of the model.

version(self) → str: Returns the version of framework of the model.

hyperparameter(self) → dict: Returns the hyperparameter of the model.

property algorithm

Extracts the algorithm of the model.

Returns: The algorithm of the model.
Return type: object

property framework

Extracts the framework of the model.

Returns: The framework of the model.
Return type: str

property hyperparameter

Extracts the hyperparameters of the model.

Returns: The hyperparameters of the model.
Return type: dict

property version

Extracts the framework version of the model.

Returns: The framework version of the model.
Return type: str

ads.model.extractor.model_info_extractor module

class ads.model.extractor.model_info_extractor.ModelInfoExtractor

Bases: ABC

The base abstract class to extract model metadata.

framework(self) → str: Returns the framework of the model.

algorithm(self) → object: Returns the algorithm of the model.

version(self) → str: Returns the version of framework of the model.

hyperparameter(self) → dict: Returns the hyperparameter of the model.

info(self) → dict: Returns the model taxonomy metadata information.

abstract algorithm()

The abstract method to extracts the algorithm of the model.

Returns: The algorithm of the model.
Return type: object

abstract framework()

The abstract method to extracts the framework of the model.

Returns: The framework of the model.
Return type: str

abstract hyperparameter()

The abstract method to extracts the hyperparameters of the model.

Returns: The hyperparameter of the model.
Return type: dict

info()

Extracts the taxonomy metadata of the model.

Returns: The taxonomy metadata of the model.
Return type: dict

abstract version()

The abstract method to extracts the framework version of the model.

Returns: The framework version of the model.
Return type: str

ads.model.extractor.model_info_extractor.normalize_hyperparameter(data: Dict) → dict

Converts all the fields to string to make sure it’s json serializable.

Parameters: data (([Dict])) – The hyperparameter returned by the model.
Returns: Normalized (json serializable) dictionary.
Return type: Dict

ads.model.extractor.sklearn_extractor module

class ads.model.extractor.sklearn_extractor.SklearnExtractor(model)

Bases: ModelInfoExtractor

Class that extract model metadata from sklearn models.

model

The model to extract metadata from.

Type: object

estimator

The estimator to extract metadata from.

Type: object

framework(self) → str: Returns the framework of the model.

algorithm(self) → object: Returns the algorithm of the model.

version(self) → str: Returns the version of framework of the model.

hyperparameter(self) → dict: Returns the hyperparameter of the model.

property algorithm

Extracts the algorithm of the model.

Returns: The algorithm of the model.
Return type: object

property framework

Extracts the framework of the model.

Returns: The framework of the model.
Return type: str

property hyperparameter

Extracts the hyperparameters of the model.

Returns: The hyperparameters of the model.
Return type: dict

property version

Extracts the framework version of the model.

Returns: The framework version of the model.
Return type: str

ads.model.extractor.keras_extractor module

class ads.model.extractor.keras_extractor.KerasExtractor(model)

Bases: ModelInfoExtractor

Class that extract model metadata from keras models.

model

The model to extract metadata from.

Type: object

estimator

The estimator to extract metadata from.

Type: object

property algorithm

Extracts the algorithm of the model.

Returns: The algorithm of the model.
Return type: object

property framework

Extracts the framework of the model.

Returns: The framework of the model.
Return type: str

property hyperparameter

Extracts the hyperparameters of the model.

Returns: The hyperparameters of the model.
Return type: dict

property version

Extracts the framework version of the model.

Returns: The framework version of the model.
Return type: str

ads.model.extractor.tensorflow_extractor module

class ads.model.extractor.tensorflow_extractor.TensorflowExtractor(model)

Bases: ModelInfoExtractor

Class that extract model metadata from tensorflow models.

model

The model to extract metadata from.

Type: object

estimator

The estimator to extract metadata from.

Type: object

framework(self) → str: Returns the framework of the model.

algorithm(self) → object: Returns the algorithm of the model.

version(self) → str: Returns the version of framework of the model.

hyperparameter(self) → dict: Returns the hyperparameter of the model.

property algorithm

Extracts the algorithm of the model.

Returns: The algorithm of the model.
Return type: object

property framework

Extracts the framework of the model.

Returns: The framework of the model.
Return type: str

property hyperparameter

Extracts the hyperparameters of the model.

Returns: The hyperparameters of the model.
Return type: dict

property version

Extracts the framework version of the model.

Returns: The framework version of the model.
Return type: str

ads.model.extractor.pytorch_extractor module

class ads.model.extractor.pytorch_extractor.PytorchExtractor(model)

Bases: ModelInfoExtractor

Class that extract model metadata from pytorch models.

model

The model to extract metadata from.

Type: object

estimator

The estimator to extract metadata from.

Type: object

framework(self) → str: Returns the framework of the model.

algorithm(self) → object: Returns the algorithm of the model.

version(self) → str: Returns the version of framework of the model.

hyperparameter(self) → dict: Returns the hyperparameter of the model.

property algorithm

Extracts the algorithm of the model.

Returns: The algorithm of the model.
Return type: object

property framework

Extracts the framework of the model.

Returns: The framework of the model.
Return type: str

property hyperparameter

Extracts the hyperparameters of the model.

Returns: The hyperparameters of the model.
Return type: dict

property version

Extracts the framework version of the model.

Returns: The framework version of the model.
Return type: str

ads.model.framework other package

Submodules

ads.model.artifact module

ads.model.generic_model module

ads.model.model_properties module

ads.model.runtime.runtime_info module

ads.model.extractor.model_info_extractor_factory module

ads.model.extractor.model_artifact module

ads.model.extractor.automl_extractor module

ads.model.extractor.xgboost_extractor module

ads.model.extractor.lightgbm_extractor module

ads.model.extractor.model_info_extractor module

ads.model.extractor.sklearn_extractor module

ads.model.extractor.keras_extractor module

ads.model.extractor.tensorflow_extractor module

ads.model.extractor.pytorch_extractor module

Module contents