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: Optional[str] = None, 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, optional). Defaults to None.) – 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.

classmethod from_uri(uri: str, artifact_dir: str, model_file_name: Optional[str] = None, 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, optional). Defaults to None) – 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, model_file_name: Optional[str] = None, **kwargs)

Prepares score.py file.

Parameters:
  • jinja_template_filename (str.) – The jinja template file name.

  • model_file_name ((str, optional). Defaults to None.) – The file name of the serialized model.

  • **kwargs ((dict)) – use_torch_script: bool

Return type:

None

Raises:

ValueError – If model_file_name not provided.

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.DataScienceModelType

Bases: str

MODEL = 'datasciencemodel'
MODEL_DEPLOYMENT = 'datasciencemodeldeployment'
class ads.model.generic_model.FrameworkSpecificModel(estimator: Callable, artifact_dir: Optional[str] = None, properties: Optional[ModelProperties] = None, auth: Optional[Dict] = None, serialize: bool = True, **kwargs: dict)

Bases: GenericModel

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.

predict(data: Optional[Any] = None, auto_serialize_data: bool = True, **kwargs) Dict[str, Any]

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

Examples

>>> uri = "https://github.com/pytorch/hub/raw/master/images/dog.jpg"
>>> prediction = model.predict(image=uri)['prediction']
>>> # examples on storage options
>>> prediction = model.predict(
...        image="oci://<bucket>@<tenancy>/myimage.png",
...        storage_options=ads.auth.default_signer()
... )['prediction']
Parameters:
  • data (Any) – Data for the prediction for onnx models, for local serialization method, data can be the data types that each framework support.

  • auto_serialize_data (bool.) – Whether to auto serialize input data. Defauls to False for GenericModel, and True for other frameworks. data required to be json serializable if auto_serialize_data=False. If auto_serialize_data set to True, data will be serialized before sending to model deployment endpoint.

  • kwargs

    content_type: str, used to indicate the media type of the resource. image: PIL.Image Object or uri for the image.

    A valid string path for image file can be local path, http(s), oci, s3, gs.

    storage_options: dict

    Passed to fsspec.open for a particular storage connection. Please see fsspec (https://filesystem-spec.readthedocs.io/en/latest/api.html#fsspec.open) for more details.

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.

verify(data: Optional[Any] = None, reload_artifacts: bool = True, auto_serialize_data: bool = True, **kwargs) Dict[str, Any]

Test if deployment works in local environment.

Examples

>>> uri = "https://github.com/pytorch/hub/raw/master/images/dog.jpg"
>>> prediction = model.verify(image=uri)['prediction']
>>> # examples on storage options
>>> prediction = model.verify(
...        image="oci://<bucket>@<tenancy>/myimage.png",
...        storage_options=ads.auth.default_signer()
... )['prediction']
Parameters:
  • data (Any) – Data used to test if deployment works in local environment.

  • reload_artifacts (bool. Defaults to True.) – Whether to reload artifacts or not.

  • auto_serialize_data (bool.) – Whether to auto serialize input data. Defauls to False for GenericModel, and True for other frameworks. data required to be json serializable if auto_serialize_data=False. if auto_serialize_data set to True, data will be serialized before sending to model deployment endpoint.

  • kwargs

    content_type: str, used to indicate the media type of the resource. image: PIL.Image Object or uri for the image.

    A valid string path for image file can be local path, http(s), oci, s3, gs.

    storage_options: dict

    Passed to fsspec.open for a particular storage connection. Please see fsspec (https://filesystem-spec.readthedocs.io/en/latest/api.html#fsspec.open) for more details.

Returns:

A dictionary which contains prediction results.

Return type:

Dict

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

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, ..., \*\*kwargs)

Loads model from the specified folder, or zip/tar archive.

from_model_catalog(model_id, ..., \*\*kwargs)

Loads model from model catalog.

from_model_deployment(model_deployment_id, ..., \*\*kwargs)

Loads model from model deployment.

update_deployment(model_deployment_id, ..., \*\*kwargs)

Updates a model deployment.

from_id(ocid, ..., \*\*kwargs)

Loads model from model OCID or model deployment OCID.

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.

restart_deployment(...)

Restarts the model deployment.

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.

upload_artifact(...)

Uploads model artifacts to the provided uri.

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="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()
>>> # Update access log id, freeform tags and description for the model deployment
>>> model.update_deployment(
>>>     properties=ModelDeploymentProperties(
>>>         access_log_id=<log_ocid>,
>>>         description="Description for Custom Model",
>>>         freeform_tags={"key": "value"},
>>>     )
>>> )
>>> model.predict(2)
>>> # Uncomment the line below to delete the model and the associated model deployment
>>> # model.delete(delete_associated_model_deployment = True)

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.

classmethod delete(model_id: Optional[str] = None, delete_associated_model_deployment: Optional[bool] = False, delete_model_artifact: Optional[bool] = False, artifact_dir: Optional[str] = None, **kwargs: Dict) None

Deletes a model from Model Catalog.

Parameters:
  • model_id ((str, optional). Defaults to None.) – The model OCID to be deleted. If the method called on instance level, then self.model_id will be used.

  • delete_associated_model_deployment ((bool, optional). Defaults to False.) – Whether associated model deployments need to be deleted or not.

  • delete_model_artifact ((bool, optional). Defaults to False.) – Whether associated model artifacts need to be deleted or not.

  • artifact_dir ((str, optional). Defaults to None) – The local path to the model artifacts folder. If the method called on instance level, the self.artifact_dir will be used by default.

Return type:

None

Raises:

ValueError – If model_id not provided.

delete_deployment(wait_for_completion: bool = True) None

Deletes the current deployment.

Parameters:

wait_for_completion ((bool, optional). Defaults to True.) – Whether to wait till completion.

Return type:

None

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, deployment_memory_in_gbs: Optional[float] = None, deployment_ocpus: Optional[float] = 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. If a display_name is not provided in kwargs, a randomly generated easy to remember name with timestamp will be generated, like ‘strange-spider-2022-08-17-23:55.02’.

  • 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_memory_in_gbs ((float, optional). Defaults to None.) – Specifies the size of the memory of the model deployment instance in GBs.

  • deployment_ocpus ((float, optional). Defaults to None.) – Specifies the ocpus count of the model deployment instance.

  • 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 10 seconds.

    Poll interval in seconds.

    freeform_tags: (Dict[str, str], optional). Defaults to None.

    Freeform tags of the model deployment.

    defined_tags: (Dict[str, dict[str, object]], optional). Defaults to None.

    Defined tags of the model deployment.

    Also can be any keyword argument for initializing the ads.model.deployment.ModelDeploymentProperties. See ads.model.deployment.ModelDeploymentProperties() for details.

Returns:

The ModelDeployment instance.

Return type:

ModelDeployment

Raises:

ValueError – If model_id is not specified.

classmethod from_id(ocid: str, model_file_name: Optional[str] = None, artifact_dir: Optional[str] = None, auth: Optional[Dict] = None, force_overwrite: Optional[bool] = False, properties: Optional[Union[ModelProperties, Dict]] = None, bucket_uri: Optional[str] = None, remove_existing_artifact: Optional[bool] = True, **kwargs) GenericModel

Loads model from model OCID or model deployment OCID.

Parameters:
  • ocid (str) – The model OCID or model deployment OCID.

  • model_file_name ((str, optional). Defaults to None.) – The name of the serialized model.

  • artifact_dir ((str, optional). Defaults to None.) – 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.

  • bucket_uri ((str, optional). Defaults to None.) – The OCI Object Storage URI where model artifacts will be copied to. The bucket_uri is only necessary for downloading large artifacts with size is greater than 2GB. Example: oci://<bucket_name>@<namespace>/prefix/.

  • remove_existing_artifact ((bool, optional). Defaults to True.) – Wether artifacts uploaded to object storage bucket need to be removed or not.

  • 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_artifact(uri: str, model_file_name: Optional[str] = None, artifact_dir: Optional[str] = None, 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, optional). Defaults to None.) – The serialized model file name. Will be extracted from artifacts if not provided.

  • artifact_dir ((str, optional). Defaults to None.) – 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.

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: Optional[str] = None, artifact_dir: Optional[str] = None, auth: Optional[Dict] = None, force_overwrite: Optional[bool] = False, properties: Optional[Union[ModelProperties, Dict]] = None, bucket_uri: Optional[str] = None, remove_existing_artifact: Optional[bool] = True, **kwargs) GenericModel

Loads model from model catalog.

Parameters:
  • model_id (str) – The model OCID.

  • model_file_name ((str, optional). Defaults to None.) – The name of the serialized model.

  • artifact_dir ((str, optional). Defaults to None.) – 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.

  • bucket_uri ((str, optional). Defaults to None.) – The OCI Object Storage URI where model artifacts will be copied to. The bucket_uri is only necessary for downloading large artifacts with size is greater than 2GB. Example: oci://<bucket_name>@<namespace>/prefix/.

  • remove_existing_artifact ((bool, optional). Defaults to True.) – Wether artifacts uploaded to object storage bucket need to be removed or not.

  • 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.

    region: (str, optional). Defaults to None.

    The destination Object Storage bucket region. By default the value will be extracted from the OCI_REGION_METADATA environment variables.

Returns:

An instance of GenericModel class.

Return type:

GenericModel

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

Loads model from model deployment.

Parameters:
  • model_deployment_id (str) – The model deployment OCID.

  • model_file_name ((str, optional). Defaults to None.) – The name of the serialized model.

  • artifact_dir ((str, optional). Defaults to None.) – 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.

  • bucket_uri ((str, optional). Defaults to None.) – The OCI Object Storage URI where model artifacts will be copied to. The bucket_uri is only necessary for downloading large artifacts with size is greater than 2GB. Example: oci://<bucket_name>@<namespace>/prefix/.

  • remove_existing_artifact ((bool, optional). Defaults to True.) – Wether artifacts uploaded to object storage bucket need to be removed or not.

  • 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.

    region: (str, optional). Defaults to None.

    The destination Object Storage bucket region. By default the value will be extracted from the OCI_REGION_METADATA environment variables.

Returns:

An instance of GenericModel class.

Return type:

GenericModel

get_data_serializer(data: any, data_type: Optional[str] = None)

The data_serializer_class class is set in init and used here. Frameworks should subclass the InputDataSerializer class, then set that as the self.data_serializer_class. Frameworks should avoid overwriting this method whenever possible.

Parameters:
  • data ((Any)) – data to be passed to model for prediction.

  • data_type (str) – Type of the data.

Returns:

Serialized data.

Return type:

data

introspect() DataFrame

Conducts instrospection.

Returns:

A pandas DataFrame which contains the instrospection results.

Return type:

pandas.DataFrame

property metadata_custom
property metadata_provenance
property metadata_taxonomy
property model_deployment_id
property model_id
predict(data: Optional[Any] = None, auto_serialize_data: bool = False, **kwargs) Dict[str, Any]

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

Examples

>>> uri = "https://github.com/pytorch/hub/raw/master/images/dog.jpg"
>>> prediction = model.predict(image=uri)['prediction']
>>> # examples on storage options
>>> prediction = model.predict(
...        image="oci://<bucket>@<tenancy>/myimage.png",
...        storage_options=ads.auth.default_signer()
... )['prediction']
Parameters:
  • data (Any) – Data for the prediction for onnx models, for local serialization method, data can be the data types that each framework support.

  • auto_serialize_data (bool.) – Whether to auto serialize input data. Defauls to False for GenericModel, and True for other frameworks. data required to be json serializable if auto_serialize_data=False. If auto_serialize_data set to True, data will be serialized before sending to model deployment endpoint.

  • kwargs

    content_type: str, used to indicate the media type of the resource. image: PIL.Image Object or uri for the image.

    A valid string path for image file can be local path, http(s), oci, s3, gs.

    storage_options: dict

    Passed to fsspec.open for a particular storage connection. Please see fsspec (https://filesystem-spec.readthedocs.io/en/latest/api.html#fsspec.open) for more details.

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, DataFrame, Series, ndarray]] = None, y_sample: Optional[Union[list, tuple, DataFrame, Series, ndarray]] = None, training_script_path: Optional[str] = None, training_id: Optional[str] = None, ignore_pending_changes: bool = True, max_col_num: int = 2000, **kwargs: Dict) GenericModel

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, optional). Defaults to None.) – Name of the serialized model. Will be auto generated if not provided.

  • 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 – If files already exist but force_overwrite is False.

  • ValueError – If inference_python_version is not provided, but also cannot be found through manifest file.

Returns:

An instance of GenericModel class.

Return type:

GenericModel

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, DataFrame, Series, ndarray]] = None, y_sample: Optional[Union[list, tuple, DataFrame, Series, ndarray]] = 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, deployment_memory_in_gbs: Optional[float] = None, deployment_ocpus: Optional[float] = None, bucket_uri: Optional[str] = None, overwrite_existing_artifact: Optional[bool] = True, remove_existing_artifact: Optional[bool] = True, model_version_set: Optional[Union[str, ModelVersionSet]] = None, version_label: Optional[str] = None, **kwargs: Dict) ModelDeployment

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, optional). Defaults to None.) – 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. If a model_display_name is not provided in kwargs, a randomly generated easy to remember name with timestamp will be generated, like ‘strange-spider-2022-08-17-23:55.02’.

  • 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.

  • deployment_display_name ((str, optional). Defaults to None.) – The name of the model deployment. If a deployment_display_name is not provided in kwargs, a randomly generated easy to remember name with timestamp will be generated, like ‘strange-spider-2022-08-17-23:55.02’.

  • 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

  • deployment_memory_in_gbs ((float, optional). Defaults to None.) – Specifies the size of the memory of the model deployment instance in GBs.

  • deployment_ocpus ((float, optional). Defaults to None.) – Specifies the ocpus count of the model deployment instance.

  • bucket_uri ((str, optional). Defaults to None.) – The OCI Object Storage URI where model artifacts will be copied to. The bucket_uri is only necessary for downloading large artifacts with size is greater than 2GB. Example: oci://<bucket_name>@<namespace>/prefix/.

  • overwrite_existing_artifact ((bool, optional). Defaults to True.) – Overwrite target bucket artifact if exists.

  • remove_existing_artifact ((bool, optional). Defaults to True.) – Wether artifacts uploaded to object storage bucket need to be removed or not.

  • model_version_set ((Union[str, ModelVersionSet], optional). Defaults to None.) – The Model version set OCID, or name, or ModelVersionSet instance.

  • version_label ((str, optional). Defaults to None.) – The model version lebel.

  • 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 10 seconds.

    Poll interval in seconds.

    freeform_tags: (Dict[str, str], optional). Defaults to None.

    Freeform tags of the model deployment.

    defined_tags: (Dict[str, dict[str, object]], optional). Defaults to None.

    Defined tags of the model deployment.

    region: (str, optional). Defaults to None.

    The destination Object Storage bucket region. By default the value will be extracted from the OCI_REGION_METADATA environment variables.

    Also can be any keyword argument for initializing the ads.model.deployment.ModelDeploymentProperties. See ads.model.deployment.ModelDeploymentProperties() for details.

Returns:

The ModelDeployment instance.

Return type:

ModelDeployment

Raises:
  • FileExistsError – If files already exist but force_overwrite is False.

  • ValueError – If inference_python_version is not provided, but also cannot be found through manifest file.

reload() GenericModel

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

Returns:

An instance of GenericModel class.

Return type:

GenericModel

reload_runtime_info() None

Reloads the model artifact file: runtime.yaml.

Returns:

Nothing.

Return type:

None

restart_deployment(max_wait_time: int = 1200, poll_interval: int = 10) ModelDeployment

Restarts the current deployment.

Parameters:
  • max_wait_time ((int, optional). Defaults to 1200 seconds.) – Maximum amount of time to wait for activate or deactivate in seconds. Total amount of time to wait for restart deployment is twice as the value. Negative implies infinite wait time.

  • poll_interval ((int, optional). Defaults to 10 seconds.) – Poll interval in seconds.

Returns:

The ModelDeployment instance.

Return type:

ModelDeployment

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, bucket_uri: Optional[str] = None, overwrite_existing_artifact: Optional[bool] = True, remove_existing_artifact: Optional[bool] = True, model_version_set: Optional[Union[str, ModelVersionSet]] = None, version_label: Optional[str] = None, **kwargs) str

Saves model artifacts to the model catalog.

Parameters:
  • display_name ((str, optional). Defaults to None.) – The name of the model. If a display_name is not provided in kwargs, randomly generated easy to remember name with timestamp will be generated, like ‘strange-spider-2022-08-17-23:55.02’.

  • 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.

  • bucket_uri ((str, optional). Defaults to None.) – The OCI Object Storage URI where model artifacts will be copied to. The bucket_uri is only necessary for uploading large artifacts which size is greater than 2GB. Example: oci://<bucket_name>@<namespace>/prefix/.

  • overwrite_existing_artifact ((bool, optional). Defaults to True.) – Overwrite target bucket artifact if exists.

  • remove_existing_artifact ((bool, optional). Defaults to True.) – Wether artifacts uploaded to object storage bucket need to be removed or not.

  • model_version_set ((Union[str, ModelVersionSet], optional). Defaults to None.) – The model version set OCID, or model version set name, or ModelVersionSet instance.

  • version_label ((str, optional). Defaults to None.) – The model version lebel.

  • 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.

    region: (str, optional). Defaults to None.

    The destination Object Storage bucket region. By default the value will be extracted from the OCI_REGION_METADATA environment variables.

    Also can be any attribute that oci.data_science.models.Model accepts.

Raises:

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

Returns:

The model id.

Return type:

str

property schema_input
property schema_output
serialize_model(as_onnx: bool = False, initial_types: Optional[List[Tuple]] = None, force_overwrite: bool = False, X_sample: Optional[any] = None, **kwargs)

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

update(**kwargs) GenericModel

Updates model metadata in the Model Catalog. Updates only metadata information. The model artifacts are immutable and cannot be updated.

Parameters:

kwargs

display_name: (str, optional). Defaults to None.

The name of the model.

description: (str, optional). Defaults to None.

The description of the model.

freeform_tagsDict(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.

version_label: (str, optional). Defaults to None.

The model version lebel.

Additional kwargs arguments. Can be any attribute that oci.data_science.models.Model accepts.

Returns:

An instance of GenericModel (self).

Return type:

GenericModel

Raises:

ValueError – if model not saved to the Model Catalog.

classmethod update_deployment(model_deployment_id: Optional[str] = None, properties: Optional[Union[ModelDeploymentProperties, dict]] = None, wait_for_completion: bool = True, max_wait_time: int = 1200, poll_interval: int = 10, **kwargs) ModelDeployment

Updates a model deployment.

You can update model_deployment_configuration_details and change instance_shape and model_id when the model deployment is in the ACTIVE lifecycle state. The bandwidth_mbps or instance_count can only be updated while the model deployment is in the INACTIVE state. Changes to the bandwidth_mbps or instance_count will take effect the next time the ActivateModelDeployment action is invoked on the model deployment resource.

Examples

>>> # Update access log id, freeform tags and description for the model deployment
>>> model.update_deployment(
>>>     properties=ModelDeploymentProperties(
>>>         access_log_id=<log_ocid>,
>>>         description="Description for Custom Model",
>>>         freeform_tags={"key": "value"},
>>>     )
>>> )
Parameters:
  • model_deployment_id (str.) – The model deployment OCID. Defaults to None. If the method called on instance level, then self.model_deployment.model_deployment_id will be used.

  • properties (ModelDeploymentProperties or dict) – The properties for updating the deployment.

  • wait_for_completion (bool) – Flag set for whether to wait for deployment to complete before proceeding. Defaults to True.

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

  • poll_interval (int) – Poll interval in seconds (Defaults to 10).

  • kwargs

    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:

An instance of ModelDeployment class.

Return type:

ModelDeployment

upload_artifact(uri: str, auth: Optional[Dict] = None, force_overwrite: Optional[bool] = False) None

Uploads model artifacts to the provided uri. The artifacts will be zipped before uploading.

Parameters:
  • uri (str) – The destination location for the model artifacts, which can be a local path or OCI object storage URI. Examples: >>> upload_artifact(uri=”/some/local/folder/”) >>> upload_artifact(uri=”oci://bucket@namespace/prefix/”)

  • 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) – Overwrite target_dir if exists.

verify(data: Optional[Any] = None, reload_artifacts: bool = True, auto_serialize_data: bool = False, **kwargs) Dict[str, Any]

Test if deployment works in local environment.

Examples

>>> uri = "https://github.com/pytorch/hub/raw/master/images/dog.jpg"
>>> prediction = model.verify(image=uri)['prediction']
>>> # examples on storage options
>>> prediction = model.verify(
...        image="oci://<bucket>@<tenancy>/myimage.png",
...        storage_options=ads.auth.default_signer()
... )['prediction']
Parameters:
  • data (Any) – Data used to test if deployment works in local environment.

  • reload_artifacts (bool. Defaults to True.) – Whether to reload artifacts or not.

  • auto_serialize_data (bool.) – Whether to auto serialize input data. Defauls to False for GenericModel, and True for other frameworks. data required to be json serializable if auto_serialize_data=False. if auto_serialize_data set to True, data will be serialized before sending to model deployment endpoint.

  • kwargs

    content_type: str, used to indicate the media type of the resource. image: PIL.Image Object or uri for the image.

    A valid string path for image file can be local path, http(s), oci, s3, gs.

    storage_options: dict

    Passed to fsspec.open for a particular storage connection. Please see fsspec (https://filesystem-spec.readthedocs.io/en/latest/api.html#fsspec.open) for more details.

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, bucket_uri: Optional[str] = None, remove_existing_artifact: Optional[bool] = None, overwrite_existing_artifact: Optional[bool] = 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, deployment_memory_in_gbs: Optional[Union[float, int]] = None, deployment_ocpus: Optional[Union[float, int]] = None)

Bases: BaseProperties

Represents properties required to save and deploy model.

bucket_uri: str = None
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_memory_in_gbs: Union[float, int] = None
deployment_ocpus: Union[float, int] = None
deployment_predict_log_id: str = None
inference_conda_env: str = None
inference_python_version: str = None
overwrite_existing_artifact: bool = None
project_id: str = None
remove_existing_artifact: bool = 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

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

Bases: PyTorchExtractor

Module contents