ads.model package
Subpackages
- ads.model.common package
- ads.model.deployment package
- Subpackages
- Submodules
- ads.model.deployment.model_deployer module
ModelDeployer
ModelDeployer.config
ModelDeployer.ds_client
ModelDeployer.ds_composite_client
ModelDeployer.deploy()
ModelDeployer.get_model_deployment()
ModelDeployer.get_model_deployment_state()
ModelDeployer.delete()
ModelDeployer.list_deployments()
ModelDeployer.show_deployments()
ModelDeployer.delete()
ModelDeployer.deploy()
ModelDeployer.deploy_from_model_uri()
ModelDeployer.get_model_deployment()
ModelDeployer.get_model_deployment_state()
ModelDeployer.list_deployments()
ModelDeployer.show_deployments()
ModelDeployer.update()
- ads.model.deployment.model_deployment module
LogNotConfiguredError
ModelDeployment
ModelDeployment.config
ModelDeployment.properties
ModelDeployment.workflow_state_progress
ModelDeployment.workflow_steps
ModelDeployment.dsc_model_deployment
ModelDeployment.state
ModelDeployment.created_by
ModelDeployment.lifecycle_state
ModelDeployment.lifecycle_details
ModelDeployment.time_created
ModelDeployment.display_name
ModelDeployment.description
ModelDeployment.freeform_tags
ModelDeployment.defined_tags
ModelDeployment.runtime
ModelDeployment.infrastructure
ModelDeployment.deploy()
ModelDeployment.delete()
ModelDeployment.update()
ModelDeployment.activate()
ModelDeployment.deactivate()
ModelDeployment.list()
ModelDeployment.with_display_name()
ModelDeployment.with_description()
ModelDeployment.with_freeform_tags()
ModelDeployment.with_defined_tags()
ModelDeployment.with_runtime()
ModelDeployment.with_infrastructure()
ModelDeployment.from_dict()
ModelDeployment.from_id()
ModelDeployment.sync()
ModelDeployment.CONST_CREATED_BY
ModelDeployment.CONST_DEFINED_TAG
ModelDeployment.CONST_DESCRIPTION
ModelDeployment.CONST_DISPLAY_NAME
ModelDeployment.CONST_FREEFORM_TAG
ModelDeployment.CONST_ID
ModelDeployment.CONST_INFRASTRUCTURE
ModelDeployment.CONST_LIFECYCLE_DETAILS
ModelDeployment.CONST_LIFECYCLE_STATE
ModelDeployment.CONST_MODEL_DEPLOYMENT_URL
ModelDeployment.CONST_RUNTIME
ModelDeployment.CONST_TIME_CREATED
ModelDeployment.access_log
ModelDeployment.activate()
ModelDeployment.attribute_map
ModelDeployment.created_by
ModelDeployment.deactivate()
ModelDeployment.defined_tags
ModelDeployment.delete()
ModelDeployment.deploy()
ModelDeployment.description
ModelDeployment.display_name
ModelDeployment.freeform_tags
ModelDeployment.from_dict()
ModelDeployment.from_id()
ModelDeployment.infrastructure
ModelDeployment.initialize_spec_attributes
ModelDeployment.kind
ModelDeployment.lifecycle_details
ModelDeployment.lifecycle_state
ModelDeployment.list()
ModelDeployment.list_df()
ModelDeployment.logs()
ModelDeployment.model_deployment_id
ModelDeployment.model_input_serializer
ModelDeployment.predict()
ModelDeployment.predict_log
ModelDeployment.runtime
ModelDeployment.show_logs()
ModelDeployment.state
ModelDeployment.status
ModelDeployment.sync()
ModelDeployment.time_created
ModelDeployment.to_dict()
ModelDeployment.type
ModelDeployment.update()
ModelDeployment.url
ModelDeployment.watch()
ModelDeployment.with_defined_tags()
ModelDeployment.with_description()
ModelDeployment.with_display_name()
ModelDeployment.with_freeform_tags()
ModelDeployment.with_infrastructure()
ModelDeployment.with_runtime()
ModelDeploymentFailedError
ModelDeploymentLogType
ModelDeploymentMode
- ads.model.deployment.model_deployment_properties module
ModelDeploymentProperties
ModelDeploymentProperties.swagger_types
ModelDeploymentProperties.model_id
ModelDeploymentProperties.model_uri
ModelDeploymentProperties.with_prop()
ModelDeploymentProperties.with_instance_configuration()
ModelDeploymentProperties.with_access_log()
ModelDeploymentProperties.with_predict_log()
ModelDeploymentProperties.build()
ModelDeploymentProperties.build()
ModelDeploymentProperties.sub_properties
ModelDeploymentProperties.to_oci_model()
ModelDeploymentProperties.to_update_deployment()
ModelDeploymentProperties.with_access_log()
ModelDeploymentProperties.with_category_log()
ModelDeploymentProperties.with_instance_configuration()
ModelDeploymentProperties.with_logging_configuration()
ModelDeploymentProperties.with_predict_log()
ModelDeploymentProperties.with_prop()
- Module contents
- ads.model.extractor package
- Submodules
- ads.model.extractor.keras_extractor module
- ads.model.extractor.lightgbm_extractor module
- ads.model.extractor.model_info_extractor module
ModelInfoExtractor
ModelInfoExtractor.framework()
ModelInfoExtractor.algorithm()
ModelInfoExtractor.version()
ModelInfoExtractor.hyperparameter()
ModelInfoExtractor.info()
ModelInfoExtractor.algorithm()
ModelInfoExtractor.framework()
ModelInfoExtractor.hyperparameter()
ModelInfoExtractor.info()
ModelInfoExtractor.version()
normalize_hyperparameter()
- ads.model.extractor.model_info_extractor_factory module
- ads.model.extractor.pytorch_extractor module
- ads.model.extractor.sklearn_extractor module
- ads.model.extractor.spark_extractor module
- ads.model.extractor.tensorflow_extractor module
TensorflowExtractor
TensorflowExtractor.model
TensorflowExtractor.estimator
TensorflowExtractor.framework()
TensorflowExtractor.algorithm()
TensorflowExtractor.version()
TensorflowExtractor.hyperparameter()
TensorflowExtractor.algorithm
TensorflowExtractor.framework
TensorflowExtractor.hyperparameter
TensorflowExtractor.version
- ads.model.extractor.xgboost_extractor module
- Module contents
- ads.model.framework package
- Submodules
- ads.model.framework.huggingface_model module
HuggingFacePipelineModel
HuggingFacePipelineModel.algorithm
HuggingFacePipelineModel.artifact_dir
HuggingFacePipelineModel.auth
HuggingFacePipelineModel.estimator
HuggingFacePipelineModel.framework
HuggingFacePipelineModel.hyperparameter
HuggingFacePipelineModel.metadata_custom
HuggingFacePipelineModel.metadata_provenance
HuggingFacePipelineModel.metadata_taxonomy
HuggingFacePipelineModel.model_artifact
HuggingFacePipelineModel.model_deployment
HuggingFacePipelineModel.model_file_name
HuggingFacePipelineModel.model_id
HuggingFacePipelineModel.properties
HuggingFacePipelineModel.runtime_info
HuggingFacePipelineModel.schema_input
HuggingFacePipelineModel.schema_output
HuggingFacePipelineModel.serialize
HuggingFacePipelineModel.version
HuggingFacePipelineModel.delete_deployment()
HuggingFacePipelineModel.deploy()
HuggingFacePipelineModel.from_model_artifact()
HuggingFacePipelineModel.from_model_catalog()
HuggingFacePipelineModel.introspect()
HuggingFacePipelineModel.predict()
HuggingFacePipelineModel.prepare()
HuggingFacePipelineModel.reload()
HuggingFacePipelineModel.save()
HuggingFacePipelineModel.summary_status()
HuggingFacePipelineModel.verify()
HuggingFacePipelineModel.delete()
HuggingFacePipelineModel.delete_deployment()
HuggingFacePipelineModel.deploy()
HuggingFacePipelineModel.evaluate()
HuggingFacePipelineModel.from_id()
HuggingFacePipelineModel.from_model_artifact()
HuggingFacePipelineModel.from_model_catalog()
HuggingFacePipelineModel.from_model_deployment()
HuggingFacePipelineModel.get_data_serializer()
HuggingFacePipelineModel.get_model_serializer()
HuggingFacePipelineModel.introspect()
HuggingFacePipelineModel.metadata_custom
HuggingFacePipelineModel.metadata_provenance
HuggingFacePipelineModel.metadata_taxonomy
HuggingFacePipelineModel.model_deployment_id
HuggingFacePipelineModel.model_id
HuggingFacePipelineModel.model_input_serializer_type
HuggingFacePipelineModel.model_save_serializer_type
HuggingFacePipelineModel.populate_metadata()
HuggingFacePipelineModel.populate_schema()
HuggingFacePipelineModel.predict()
HuggingFacePipelineModel.prepare()
HuggingFacePipelineModel.prepare_save_deploy()
HuggingFacePipelineModel.reload()
HuggingFacePipelineModel.reload_runtime_info()
HuggingFacePipelineModel.restart_deployment()
HuggingFacePipelineModel.save()
HuggingFacePipelineModel.schema_input
HuggingFacePipelineModel.schema_output
HuggingFacePipelineModel.serialize_model()
HuggingFacePipelineModel.set_model_input_serializer()
HuggingFacePipelineModel.set_model_save_serializer()
HuggingFacePipelineModel.summary_status()
HuggingFacePipelineModel.update()
HuggingFacePipelineModel.update_deployment()
HuggingFacePipelineModel.upload_artifact()
HuggingFacePipelineModel.verify()
- ads.model.framework.lightgbm_model module
LightGBMModel
LightGBMModel.algorithm
LightGBMModel.artifact_dir
LightGBMModel.auth
LightGBMModel.estimator
LightGBMModel.framework
LightGBMModel.hyperparameter
LightGBMModel.metadata_custom
LightGBMModel.metadata_provenance
LightGBMModel.metadata_taxonomy
LightGBMModel.model_artifact
LightGBMModel.model_deployment
LightGBMModel.model_file_name
LightGBMModel.model_id
LightGBMModel.properties
LightGBMModel.runtime_info
LightGBMModel.schema_input
LightGBMModel.schema_output
LightGBMModel.serialize
LightGBMModel.version
LightGBMModel.delete_deployment()
LightGBMModel.deploy()
LightGBMModel.from_model_artifact()
LightGBMModel.from_model_catalog()
LightGBMModel.introspect()
LightGBMModel.predict()
LightGBMModel.prepare()
LightGBMModel.reload()
LightGBMModel.save()
LightGBMModel.summary_status()
LightGBMModel.verify()
LightGBMModel.delete()
LightGBMModel.delete_deployment()
LightGBMModel.deploy()
LightGBMModel.evaluate()
LightGBMModel.from_id()
LightGBMModel.from_model_artifact()
LightGBMModel.from_model_catalog()
LightGBMModel.from_model_deployment()
LightGBMModel.get_data_serializer()
LightGBMModel.get_model_serializer()
LightGBMModel.introspect()
LightGBMModel.metadata_custom
LightGBMModel.metadata_provenance
LightGBMModel.metadata_taxonomy
LightGBMModel.model_deployment_id
LightGBMModel.model_id
LightGBMModel.model_input_serializer_type
LightGBMModel.model_save_serializer_type
LightGBMModel.populate_metadata()
LightGBMModel.populate_schema()
LightGBMModel.predict()
LightGBMModel.prepare()
LightGBMModel.prepare_save_deploy()
LightGBMModel.reload()
LightGBMModel.reload_runtime_info()
LightGBMModel.restart_deployment()
LightGBMModel.save()
LightGBMModel.schema_input
LightGBMModel.schema_output
LightGBMModel.serialize_model()
LightGBMModel.set_model_input_serializer()
LightGBMModel.set_model_save_serializer()
LightGBMModel.summary_status()
LightGBMModel.update()
LightGBMModel.update_deployment()
LightGBMModel.upload_artifact()
LightGBMModel.verify()
- ads.model.framework.pytorch_model module
PyTorchModel
PyTorchModel.algorithm
PyTorchModel.artifact_dir
PyTorchModel.auth
PyTorchModel.estimator
PyTorchModel.framework
PyTorchModel.hyperparameter
PyTorchModel.metadata_custom
PyTorchModel.metadata_provenance
PyTorchModel.metadata_taxonomy
PyTorchModel.model_artifact
PyTorchModel.model_deployment
PyTorchModel.model_file_name
PyTorchModel.model_id
PyTorchModel.properties
PyTorchModel.runtime_info
PyTorchModel.schema_input
PyTorchModel.schema_output
PyTorchModel.serialize
PyTorchModel.version
PyTorchModel.delete_deployment()
PyTorchModel.deploy()
PyTorchModel.from_model_artifact()
PyTorchModel.from_model_catalog()
PyTorchModel.introspect()
PyTorchModel.predict()
PyTorchModel.prepare()
PyTorchModel.reload()
PyTorchModel.save()
PyTorchModel.summary_status()
PyTorchModel.verify()
PyTorchModel.delete()
PyTorchModel.delete_deployment()
PyTorchModel.deploy()
PyTorchModel.evaluate()
PyTorchModel.from_id()
PyTorchModel.from_model_artifact()
PyTorchModel.from_model_catalog()
PyTorchModel.from_model_deployment()
PyTorchModel.get_data_serializer()
PyTorchModel.get_model_serializer()
PyTorchModel.introspect()
PyTorchModel.metadata_custom
PyTorchModel.metadata_provenance
PyTorchModel.metadata_taxonomy
PyTorchModel.model_deployment_id
PyTorchModel.model_id
PyTorchModel.model_input_serializer_type
PyTorchModel.model_save_serializer_type
PyTorchModel.populate_metadata()
PyTorchModel.populate_schema()
PyTorchModel.predict()
PyTorchModel.prepare()
PyTorchModel.prepare_save_deploy()
PyTorchModel.reload()
PyTorchModel.reload_runtime_info()
PyTorchModel.restart_deployment()
PyTorchModel.save()
PyTorchModel.schema_input
PyTorchModel.schema_output
PyTorchModel.serialize_model()
PyTorchModel.set_model_input_serializer()
PyTorchModel.set_model_save_serializer()
PyTorchModel.summary_status()
PyTorchModel.update()
PyTorchModel.update_deployment()
PyTorchModel.upload_artifact()
PyTorchModel.verify()
- ads.model.framework.sklearn_model module
SklearnModel
SklearnModel.algorithm
SklearnModel.artifact_dir
SklearnModel.auth
SklearnModel.estimator
SklearnModel.framework
SklearnModel.hyperparameter
SklearnModel.metadata_custom
SklearnModel.metadata_provenance
SklearnModel.metadata_taxonomy
SklearnModel.model_artifact
SklearnModel.model_deployment
SklearnModel.model_file_name
SklearnModel.model_id
SklearnModel.properties
SklearnModel.runtime_info
SklearnModel.schema_input
SklearnModel.schema_output
SklearnModel.serialize
SklearnModel.version
SklearnModel.delete_deployment()
SklearnModel.deploy()
SklearnModel.from_model_artifact()
SklearnModel.from_model_catalog()
SklearnModel.introspect()
SklearnModel.predict()
SklearnModel.prepare()
SklearnModel.reload()
SklearnModel.save()
SklearnModel.summary_status()
SklearnModel.verify()
SklearnModel.delete()
SklearnModel.delete_deployment()
SklearnModel.deploy()
SklearnModel.evaluate()
SklearnModel.from_id()
SklearnModel.from_model_artifact()
SklearnModel.from_model_catalog()
SklearnModel.from_model_deployment()
SklearnModel.get_data_serializer()
SklearnModel.get_model_serializer()
SklearnModel.introspect()
SklearnModel.metadata_custom
SklearnModel.metadata_provenance
SklearnModel.metadata_taxonomy
SklearnModel.model_deployment_id
SklearnModel.model_id
SklearnModel.model_input_serializer_type
SklearnModel.model_save_serializer_type
SklearnModel.populate_metadata()
SklearnModel.populate_schema()
SklearnModel.predict()
SklearnModel.prepare()
SklearnModel.prepare_save_deploy()
SklearnModel.reload()
SklearnModel.reload_runtime_info()
SklearnModel.restart_deployment()
SklearnModel.save()
SklearnModel.schema_input
SklearnModel.schema_output
SklearnModel.serialize_model()
SklearnModel.set_model_input_serializer()
SklearnModel.set_model_save_serializer()
SklearnModel.summary_status()
SklearnModel.update()
SklearnModel.update_deployment()
SklearnModel.upload_artifact()
SklearnModel.verify()
- ads.model.framework.spark_model module
SparkPipelineModel
SparkPipelineModel.algorithm
SparkPipelineModel.artifact_dir
SparkPipelineModel.auth
SparkPipelineModel.estimator
SparkPipelineModel.framework
SparkPipelineModel.hyperparameter
SparkPipelineModel.metadata_custom
SparkPipelineModel.metadata_provenance
SparkPipelineModel.metadata_taxonomy
SparkPipelineModel.model_artifact
SparkPipelineModel.model_file_name
SparkPipelineModel.model_id
SparkPipelineModel.properties
SparkPipelineModel.runtime_info
SparkPipelineModel.schema_input
SparkPipelineModel.schema_output
SparkPipelineModel.serialize
SparkPipelineModel.version
SparkPipelineModel.delete_deployment()
SparkPipelineModel.deploy()
SparkPipelineModel.from_model_artifact()
SparkPipelineModel.from_model_catalog()
SparkPipelineModel.introspect()
SparkPipelineModel.predict()
SparkPipelineModel.prepare()
SparkPipelineModel.reload()
SparkPipelineModel.save()
SparkPipelineModel.summary_status()
SparkPipelineModel.verify()
SparkPipelineModel.delete()
SparkPipelineModel.delete_deployment()
SparkPipelineModel.deploy()
SparkPipelineModel.evaluate()
SparkPipelineModel.from_id()
SparkPipelineModel.from_model_artifact()
SparkPipelineModel.from_model_catalog()
SparkPipelineModel.from_model_deployment()
SparkPipelineModel.get_data_serializer()
SparkPipelineModel.get_model_serializer()
SparkPipelineModel.introspect()
SparkPipelineModel.metadata_custom
SparkPipelineModel.metadata_provenance
SparkPipelineModel.metadata_taxonomy
SparkPipelineModel.model_deployment_id
SparkPipelineModel.model_id
SparkPipelineModel.model_input_serializer_type
SparkPipelineModel.model_save_serializer_type
SparkPipelineModel.populate_metadata()
SparkPipelineModel.populate_schema()
SparkPipelineModel.predict()
SparkPipelineModel.prepare()
SparkPipelineModel.prepare_save_deploy()
SparkPipelineModel.reload()
SparkPipelineModel.reload_runtime_info()
SparkPipelineModel.restart_deployment()
SparkPipelineModel.save()
SparkPipelineModel.schema_input
SparkPipelineModel.schema_output
SparkPipelineModel.serialize_model()
SparkPipelineModel.set_model_input_serializer()
SparkPipelineModel.set_model_save_serializer()
SparkPipelineModel.summary_status()
SparkPipelineModel.update()
SparkPipelineModel.update_deployment()
SparkPipelineModel.upload_artifact()
SparkPipelineModel.verify()
- ads.model.framework.tensorflow_model module
TensorFlowModel
TensorFlowModel.algorithm
TensorFlowModel.artifact_dir
TensorFlowModel.auth
TensorFlowModel.estimator
TensorFlowModel.framework
TensorFlowModel.hyperparameter
TensorFlowModel.metadata_custom
TensorFlowModel.metadata_provenance
TensorFlowModel.metadata_taxonomy
TensorFlowModel.model_artifact
TensorFlowModel.model_deployment
TensorFlowModel.model_file_name
TensorFlowModel.model_id
TensorFlowModel.properties
TensorFlowModel.runtime_info
TensorFlowModel.schema_input
TensorFlowModel.schema_output
TensorFlowModel.serialize
TensorFlowModel.version
TensorFlowModel.delete_deployment()
TensorFlowModel.deploy()
TensorFlowModel.from_model_artifact()
TensorFlowModel.from_model_catalog()
TensorFlowModel.introspect()
TensorFlowModel.predict()
TensorFlowModel.prepare()
TensorFlowModel.reload()
TensorFlowModel.save()
TensorFlowModel.summary_status()
TensorFlowModel.verify()
TensorFlowModel.delete()
TensorFlowModel.delete_deployment()
TensorFlowModel.deploy()
TensorFlowModel.evaluate()
TensorFlowModel.from_id()
TensorFlowModel.from_model_artifact()
TensorFlowModel.from_model_catalog()
TensorFlowModel.from_model_deployment()
TensorFlowModel.get_data_serializer()
TensorFlowModel.get_model_serializer()
TensorFlowModel.introspect()
TensorFlowModel.metadata_custom
TensorFlowModel.metadata_provenance
TensorFlowModel.metadata_taxonomy
TensorFlowModel.model_deployment_id
TensorFlowModel.model_id
TensorFlowModel.model_input_serializer_type
TensorFlowModel.model_save_serializer_type
TensorFlowModel.populate_metadata()
TensorFlowModel.populate_schema()
TensorFlowModel.predict()
TensorFlowModel.prepare()
TensorFlowModel.prepare_save_deploy()
TensorFlowModel.reload()
TensorFlowModel.reload_runtime_info()
TensorFlowModel.restart_deployment()
TensorFlowModel.save()
TensorFlowModel.schema_input
TensorFlowModel.schema_output
TensorFlowModel.serialize_model()
TensorFlowModel.set_model_input_serializer()
TensorFlowModel.set_model_save_serializer()
TensorFlowModel.summary_status()
TensorFlowModel.update()
TensorFlowModel.update_deployment()
TensorFlowModel.upload_artifact()
TensorFlowModel.verify()
- ads.model.framework.xgboost_model module
XGBoostModel
XGBoostModel.algorithm
XGBoostModel.artifact_dir
XGBoostModel.auth
XGBoostModel.estimator
XGBoostModel.framework
XGBoostModel.hyperparameter
XGBoostModel.metadata_custom
XGBoostModel.metadata_provenance
XGBoostModel.metadata_taxonomy
XGBoostModel.model_artifact
XGBoostModel.model_deployment
XGBoostModel.model_file_name
XGBoostModel.model_id
XGBoostModel.properties
XGBoostModel.runtime_info
XGBoostModel.schema_input
XGBoostModel.schema_output
XGBoostModel.serialize
XGBoostModel.version
XGBoostModel.delete_deployment()
XGBoostModel.deploy()
XGBoostModel.from_model_artifact()
XGBoostModel.from_model_catalog()
XGBoostModel.introspect()
XGBoostModel.predict()
XGBoostModel.prepare()
XGBoostModel.reload()
XGBoostModel.save()
XGBoostModel.summary_status()
XGBoostModel.verify()
XGBoostModel.delete()
XGBoostModel.delete_deployment()
XGBoostModel.deploy()
XGBoostModel.evaluate()
XGBoostModel.from_id()
XGBoostModel.from_model_artifact()
XGBoostModel.from_model_catalog()
XGBoostModel.from_model_deployment()
XGBoostModel.get_data_serializer()
XGBoostModel.get_model_serializer()
XGBoostModel.introspect()
XGBoostModel.metadata_custom
XGBoostModel.metadata_provenance
XGBoostModel.metadata_taxonomy
XGBoostModel.model_deployment_id
XGBoostModel.model_id
XGBoostModel.model_input_serializer_type
XGBoostModel.model_save_serializer_type
XGBoostModel.populate_metadata()
XGBoostModel.populate_schema()
XGBoostModel.predict()
XGBoostModel.prepare()
XGBoostModel.prepare_save_deploy()
XGBoostModel.reload()
XGBoostModel.reload_runtime_info()
XGBoostModel.restart_deployment()
XGBoostModel.save()
XGBoostModel.schema_input
XGBoostModel.schema_output
XGBoostModel.serialize_model()
XGBoostModel.set_model_input_serializer()
XGBoostModel.set_model_save_serializer()
XGBoostModel.summary_status()
XGBoostModel.update()
XGBoostModel.update_deployment()
XGBoostModel.upload_artifact()
XGBoostModel.verify()
- Module contents
- ads.model.model_artifact_boilerplate package
- ads.model.runtime package
- Submodules
- ads.model.runtime.env_info module
- ads.model.runtime.model_deployment_details module
- ads.model.runtime.model_provenance_details module
ModelProvenanceDetails
ModelProvenanceDetails.project_ocid
ModelProvenanceDetails.tenancy_ocid
ModelProvenanceDetails.training_code
ModelProvenanceDetails.training_compartment_ocid
ModelProvenanceDetails.training_conda_env
ModelProvenanceDetails.training_region
ModelProvenanceDetails.training_resource_ocid
ModelProvenanceDetails.user_ocid
ModelProvenanceDetails.vm_image_internal_id
TrainingCode
- ads.model.runtime.runtime_info module
- ads.model.runtime.utils module
- Module contents
- ads.model.service package
- Submodules
- ads.model.service.oci_datascience_model module
ModelArtifactNotFoundError
ModelNotSavedError
ModelProvenanceNotFoundError
ModelWithActiveDeploymentError
OCIDataScienceModel
OCIDataScienceModel.create()
OCIDataScienceModel.create_model_provenance()
OCIDataScienceModel.get_model_provenance()
OCIDataScienceModel.get_artifact_info()
OCIDataScienceModel.create_model_artifact()
OCIDataScienceModel.import_model_artifact()
OCIDataScienceModel.update()
OCIDataScienceModel.delete()
OCIDataScienceModel.model_deployment()
OCIDataScienceModel.from_id()
OCIDataScienceModel.create()
OCIDataScienceModel.create_model_artifact()
OCIDataScienceModel.create_model_provenance()
OCIDataScienceModel.delete()
OCIDataScienceModel.export_model_artifact()
OCIDataScienceModel.from_id()
OCIDataScienceModel.get_artifact_info()
OCIDataScienceModel.get_model_artifact_content()
OCIDataScienceModel.get_model_provenance()
OCIDataScienceModel.import_model_artifact()
OCIDataScienceModel.model_deployment()
OCIDataScienceModel.update()
OCIDataScienceModel.update_model_provenance()
check_for_model_id()
- ads.model.service.oci_datascience_model_version_set module
- Module contents
- ads.model.transformer 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, ignore_conda_error: 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:
- 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, ignore_conda_error: Optional[bool] = False)
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:
- 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', auth: Optional[dict] = None, ignore_conda_error: bool = False) 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. Defaults to environment variable CONDA_BUCKET_NS.
bucketname ((str, optional)) – The bucketname of service pack. Defaults to environment variable CONDA_BUCKET_NAME.
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.
- Raises:
ValueError – If neither slug or conda_env_uri is provided.
- Returns:
A RuntimeInfo instance.
- Return type:
- 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 data_deserializer: str
- 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.artifact_downloader module
- class ads.model.artifact_downloader.ArtifactDownloader(dsc_model: OCIDataScienceModel, target_dir: str, force_overwrite: Optional[bool] = False)
Bases:
ABC
The abstract class to download model artifacts.
Initializes ArtifactDownloader instance.
- Parameters:
dsc_model (OCIDataScienceModel) – The data scince model instance.
target_dir (str) – The target location of model after download.
force_overwrite (bool) – Overwrite target_dir if exists.
- PROGRESS_STEPS_COUNT = 1
- download()
Downloads model artifacts.
- Return type:
None
- Raises:
ValueError – If target directory does not exist.
- class ads.model.artifact_downloader.LargeArtifactDownloader(dsc_model: OCIDataScienceModel, target_dir: str, auth: Optional[Dict] = None, force_overwrite: Optional[bool] = False, region: Optional[str] = None, bucket_uri: Optional[str] = None, overwrite_existing_artifact: Optional[bool] = True, remove_existing_artifact: Optional[bool] = True)
Bases:
ArtifactDownloader
Initializes LargeArtifactDownloader instance.
- Parameters:
dsc_model (OCIDataScienceModel) – The data scince model instance.
target_dir (str) – The target location of model after download.
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.) – Overwrite target_dir if exists.
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.
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.
- PROGRESS_STEPS_COUNT = 4
- class ads.model.artifact_downloader.SmallArtifactDownloader(dsc_model: OCIDataScienceModel, target_dir: str, force_overwrite: Optional[bool] = False)
Bases:
ArtifactDownloader
Initializes ArtifactDownloader instance.
- Parameters:
dsc_model (OCIDataScienceModel) – The data scince model instance.
target_dir (str) – The target location of model after download.
force_overwrite (bool) – Overwrite target_dir if exists.
- PROGRESS_STEPS_COUNT = 3
ads.model.artifact_uploader module
- class ads.model.artifact_uploader.ArtifactUploader(dsc_model: OCIDataScienceModel, artifact_path: str)
Bases:
ABC
The abstract class to upload model artifacts.
Initializes ArtifactUploader instance.
- Parameters:
dsc_model (OCIDataScienceModel) – The data scince model instance.
artifact_path (str) – The model artifact location.
- PROGRESS_STEPS_COUNT = 3
- upload()
Uploads model artifacts.
- class ads.model.artifact_uploader.LargeArtifactUploader(dsc_model: OCIDataScienceModel, artifact_path: str, bucket_uri: str, auth: Optional[Dict] = None, region: Optional[str] = None, overwrite_existing_artifact: Optional[bool] = True, remove_existing_artifact: Optional[bool] = True)
Bases:
ArtifactUploader
Initializes LargeArtifactUploader instance.
- Parameters:
dsc_model (OCIDataScienceModel) – The data scince model instance.
artifact_path (str) – The model artifact location.
bucket_uri (str) – 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/.
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.
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.
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.
- PROGRESS_STEPS_COUNT = 4
- class ads.model.artifact_uploader.SmallArtifactUploader(dsc_model: OCIDataScienceModel, artifact_path: str)
Bases:
ArtifactUploader
Initializes ArtifactUploader instance.
- Parameters:
dsc_model (OCIDataScienceModel) – The data scince model instance.
artifact_path (str) – The model artifact location.
- PROGRESS_STEPS_COUNT = 1
ads.model.base_properties module
- class ads.model.base_properties.BaseProperties
Bases:
Serializable
Represents base properties class.
- with_prop(name: str, value: Any) BaseProperties
Sets property value.
- with_dict(obj_dict: Dict) BaseProperties
Populates properties values from dict.
- with_env() BaseProperties
Populates properties values from environment variables.
- to_dict() Dict
Serializes instance of class into a dictionary.
- with_config(config: ads.config.ConfigSection) BaseProperties
Sets properties values from the config profile.
- from_dict(obj_dict: Dict[str, Any]) 'BaseProperties'
Creates an instance of the properties class from a dictionary.
- from_config(uri: str, profile: str, auth: Optional[Dict] = None) "BaseProperties":
Loads properties from the config file.
- to_config(uri: str, profile: str, force_overwrite: Optional[bool] = False, auth: Optional[Dict] = None) None
Saves properties to the config file.
- classmethod from_config(uri: str, profile: str, auth: Optional[Dict] = None) BaseProperties
Loads properties from the config file.
- Parameters:
uri (str) – The URI of the config file. Can be local path or OCI object storage URI.
profile (str) – The config profile name.
auth ((Dict, optional). Defaults to None.) – The default authentication 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:
Instance of the BaseProperties.
- Return type:
- classmethod from_dict(obj_dict: Dict[str, Any]) BaseProperties
Creates an instance of the properties class from a dictionary.
- Parameters:
obj_dict (Dict[str, Any]) – List of properties and values in dictionary format.
- Returns:
Instance of the BaseProperties.
- Return type:
- to_config(uri: str, profile: str, force_overwrite: Optional[bool] = False, auth: Optional[Dict] = None) None
Saves properties to the config file.
- Parameters:
uri (str) – The URI of the config file. Can be local path or OCI object storage URI.
profile (str) – The config profile name.
force_overwrite ((bool, optional). Defaults to False.) – Whether to overwrite existing files or not.
auth ((Dict, optional). Defaults to None.) – The default authentication 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:
Nothing
- Return type:
None
- to_dict(**kwargs)
Serializes instance of class into a dictionary.
- Returns:
A dictionary.
- Return type:
Dict
- with_config(config: ConfigSection) BaseProperties
Sets properties values from the config profile.
- Returns:
Instance of the BaseProperties.
- Return type:
- with_dict(obj_dict: Dict[str, Any]) BaseProperties
Sets properties from a dict.
- Parameters:
obj_dict (Dict[str, Any]) – List of properties and values in dictionary format.
- Returns:
Instance of the BaseProperties.
- Return type:
- Raises:
TypeError – If input object has a wrong type.
- with_env() BaseProperties
Sets properties values from environment variables.
- Returns:
Instance of the BaseProperties.
- Return type:
- with_prop(name: str, value: Any) BaseProperties
Sets property value.
- Parameters:
name (str) – Property name.
value – Property value.
- Returns:
Instance of the BaseProperties.
- Return type:
- class ads.model.generic_model.DataScienceModelType
Bases:
str
- MODEL = 'datasciencemodel'
- MODEL_DEPLOYMENT = 'datasciencemodeldeployment'
- class ads.model.generic_model.FrameworkSpecificModel(estimator: Optional[Callable] = None, artifact_dir: Optional[str] = None, properties: Optional[ModelProperties] = None, auth: Optional[Dict] = None, serialize: bool = True, model_save_serializer: Optional[SERDE] = None, model_input_serializer: Optional[SERDE] = None, **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.
model_save_serializer ((SERDE or str, optional). Defaults to None.) – Instance of ads.model.SERDE. Used for serialize/deserialize model.
model_input_serializer ((SERDE or str, optional). Defaults to None.) – Instance of ads.model.SERDE. Used for serialize/deserialize model input.
- 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: Optional[Callable] = None, artifact_dir: Optional[str] = None, properties: Optional[ModelProperties] = None, auth: Optional[Dict] = None, serialize: bool = True, model_save_serializer: Optional[SERDE] = None, model_input_serializer: Optional[SERDE] = None, **kwargs: dict)
Bases:
MetadataMixin
,Introspectable
,EvaluatorMixin
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:
- metadata_provenance
The model provenance metadata.
- Type:
- metadata_taxonomy
The model taxonomy metadata.
- Type:
- model_artifact
This is built by calling prepare.
- Type:
- model_deployment
A ModelDeployment instance.
- Type:
- model_file_name
Name of the serialized model.
- Type:
str
- model_id
The model ID.
- Type:
str
- model_input_serializer
Instance of ads.model.SERDE. Used for serialize/deserialize data.
- Type:
- properties
ModelProperties object required to save and deploy model.
- Type:
- runtime_info
A RuntimeInfo instance.
- Type:
- 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.
- set_model_input_serializer(serde)
Registers serializer used for serializing data passed in verify/predict.
- 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="dbexp_p38_cpu_v1", ... inference_python_version="3.8", ... 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.
model_save_serializer ((SERDE or str, optional). Defaults to None.) – Instance of ads.model.SERDE. Used for serialize/deserialize model.
model_input_serializer ((SERDE or str, optional). Defaults to None.) – Instance of ads.model.SERDE. Used for serialize/deserialize model input.
- 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, deployment_image: Optional[str] = None, **kwargs: Dict) ModelDeployment
Deploys a model. The model needs to be saved to the model catalog at first. You can deploy the model on either conda or container runtime. The customized runtime allows you to bring your own service container. To deploy model on container runtime, make sure to build the container and push it to OCIR. For more information, see https://docs.oracle.com/en-us/iaas/data-science/using/mod-dep-byoc.htm.
Example
# This is an example to deploy model on container runtime >>> model = GenericModel(estimator=estimator, artifact_dir=tempfile.mkdtemp()) >>> model.summary_status() >>> model.prepare( … model_file_name=”toy_model.pkl”, … ignore_conda_error=True, # set ignore_conda_error=True for container runtime … force_overwrite=True … ) >>> model.verify() >>> model.save() >>> model.deploy( … deployment_image=”iad.ocir.io/<namespace>/<image>:<tag>”, … entrypoint=[“python”, “/opt/ds/model/deployed_model/api.py”], … server_port=5000, … health_check_port=5000, … environment_variables={“key”:”value”} … )
- 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
deployment_image ((str, optional). Defaults to None.) – The OCIR path of docker container image. Required for deploying model on container runtime.
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.
- image_digest: (str, optional). Defaults to None.
The digest of docker container image.
- cmd: (List, optional). Defaults to empty.
The command line arguments for running docker container image.
- entrypoint: (List, optional). Defaults to empty.
The entrypoint for running docker container image.
- server_port: (int, optional). Defaults to 8080.
The server port for docker container image.
- health_check_port: (int, optional). Defaults to 8080.
The health check port for docker container image.
- deployment_mode: (str, optional). Defaults to HTTPS_ONLY.
The deployment mode. Allowed values are: HTTPS_ONLY and STREAM_ONLY.
- input_stream_ids: (List, optional). Defaults to empty.
The input stream ids. Required for STREAM_ONLY mode.
- output_stream_ids: (List, optional). Defaults to empty.
The output stream ids. Required for STREAM_ONLY mode.
- environment_variables: (Dict, optional). Defaults to empty.
The environment variables for 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:
- 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, ignore_conda_error: Optional[bool] = False, **kwargs) Self
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:
Self
- 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, ignore_conda_error: Optional[bool] = False, **kwargs: dict) Self
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.
ignore_conda_error ((bool, optional). Defaults to False.) – Parameter to ignore error when collecting conda information.
- Returns:
An instance of GenericModel class.
- Return type:
Self
- 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, ignore_conda_error: Optional[bool] = False, **kwargs) Self
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.
ignore_conda_error ((bool, optional). Defaults to False.) – Parameter to ignore error when collecting conda information.
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:
Self
- 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, ignore_conda_error: Optional[bool] = False, **kwargs) Self
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.
ignore_conda_error ((bool, optional). Defaults to False.) – Parameter to ignore error when collecting conda information.
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:
Self
- get_data_serializer()
Gets data serializer.
- Returns:
object
- Return type:
ads.model.Serializer object.
- get_model_serializer()
Gets model serializer.
- 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
- model_input_serializer_type
alias of
ModelInputSerializerType
- model_save_serializer_type
alias of
ModelSerializerType
- 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, ignore_conda_error: bool = False, **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).
ignore_conda_error ((bool, optional). Defaults to False.) – Parameter to ignore error when collecting conda information.
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:
- 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, ignore_conda_error: bool = False, 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, deployment_image: Optional[str] = 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).
ignore_conda_error ((bool, optional). Defaults to False.) – Parameter to ignore error when collecting conda information.
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.
deployment_image ((str, optional). Defaults to None.) – The OCIR path of docker container image. Required for deploying model on container runtime.
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.
- image_digest: (str, optional). Defaults to None.
The digest of docker container image.
- cmd: (List, optional). Defaults to empty.
The command line arguments for running docker container image.
- entrypoint: (List, optional). Defaults to empty.
The entrypoint for running docker container image.
- server_port: (int, optional). Defaults to 8080.
The server port for docker container image.
- health_check_port: (int, optional). Defaults to 8080.
The health check port for docker container image.
- deployment_mode: (str, optional). Defaults to HTTPS_ONLY.
The deployment mode. Allowed values are: HTTPS_ONLY and STREAM_ONLY.
- input_stream_ids: (List, optional). Defaults to empty.
The input stream ids. Required for STREAM_ONLY mode.
- output_stream_ids: (List, optional). Defaults to empty.
The output stream ids. Required for STREAM_ONLY mode.
- environment_variables: (Dict, optional). Defaults to empty.
The environment variables for model deployment.
- 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:
- 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:
- 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:
- 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.
- timeout: (int, optional). Defaults to 10 seconds.
The connection timeout in seconds for the client.
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
- set_model_input_serializer(model_input_serializer: Union[str, SERDE])
Registers serializer used for serializing data passed in verify/predict.
Examples
>>> generic_model.set_model_input_serializer(GenericModel.model_input_serializer_type.CLOUDPICKLE)
>>> # Register serializer by passing the name of it. >>> generic_model.set_model_input_serializer("cloudpickle")
>>> # Example of creating customized model input serializer and registing it. >>> from ads.model import SERDE >>> from ads.model.generic_model import GenericModel
>>> class MySERDE(SERDE): ... def __init__(self): ... super().__init__() ... def serialize(self, data): ... serialized_data = 1 ... return serialized_data ... def deserialize(self, data): ... deserialized_data = 2 ... return deserialized_data
>>> class Toy: ... def predict(self, x): ... return x ** 2
>>> generic_model = GenericModel( ... estimator=Toy(), ... artifact_dir=tempfile.mkdtemp(), ... model_input_serializer=MySERDE() ... )
>>> # Or register the serializer after creating model instance. >>> generic_model.set_model_input_serializer(MySERDE())
- Parameters:
model_input_serializer ((str, or ads.model.SERDE)) – name of the serializer, or instance of SERDE.
- set_model_save_serializer(model_save_serializer: Union[str, SERDE])
Registers serializer used for saving model.
Examples
>>> generic_model.set_model_save_serializer(GenericModel.model_save_serializer_type.CLOUDPICKLE)
>>> # Register serializer by passing the name of it. >>> generic_model.set_model_save_serializer("cloudpickle")
>>> # Example of creating customized model save serializer and registing it. >>> from ads.model import SERDE >>> from ads.model.generic_model import GenericModel
>>> class MySERDE(SERDE): ... def __init__(self): ... super().__init__() ... def serialize(self, data): ... serialized_data = 1 ... return serialized_data ... def deserialize(self, data): ... deserialized_data = 2 ... return deserialized_data
>>> class Toy: ... def predict(self, x): ... return x ** 2
>>> generic_model = GenericModel( ... estimator=Toy(), ... artifact_dir=tempfile.mkdtemp(), ... model_save_serializer=MySERDE() ... )
>>> # Or register the serializer after creating model instance. >>> generic_model.set_model_save_serializer(MySERDE())
- Parameters:
model_save_serializer ((ads.model.SERDE or str)) – name of the serializer or instance of SERDE.
- 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:
- 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:
- 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.
is_json_payload (bool) – Defaults to False. Indicate whether to send data with a application/json MIME TYPE.
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.ModelDeploymentRuntimeType
Bases:
object
- CONDA = 'conda'
- CONTAINER = 'container'
- 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_introspect module
The module that helps to minimize the number of errors of the model post-deployment process. The model provides a simple testing harness to ensure that model artifacts are thoroughly tested before being saved to the model catalog.
Classes
- ModelIntrospect
Class to introspect model artifacts.
Examples
>>> model_introspect = ModelIntrospect(artifact=model_artifact)
>>> model_introspect()
... Test key Test name Result Message
... ----------------------------------------------------------------------------
... test_key_1 test_name_1 Passed test passed
... test_key_2 test_name_2 Not passed some error occured
>>> model_introspect.status
... Passed
- class ads.model.model_introspect.Introspectable
Bases:
ABC
Base class that represents an introspectable object.
- exception ads.model.model_introspect.IntrospectionNotPassed
Bases:
ValueError
- class ads.model.model_introspect.ModelIntrospect(artifact: Introspectable)
Bases:
object
Class to introspect model artifacts.
- Parameters:
status (str) – Returns the current status of model introspection. The possible variants: Passed, Not passed, Not tested.
failures (int) – Returns the number of failures of introspection result.
- run(self) None
Invokes model artifacts introspection.
- to_dataframe(self) pd.DataFrame
Serializes model introspection result into a DataFrame.
Examples
>>> model_introspect = ModelIntrospect(artifact=model_artifact) >>> result = model_introspect() ... Test key Test name Result Message ... ---------------------------------------------------------------------------- ... test_key_1 test_name_1 Passed test passed ... test_key_2 test_name_2 Not passed some error occured
Initializes the Model Introspect.
- Parameters:
artifact (Introspectable) – The instance of ModelArtifact object.
- Raises:
ValueError – If model artifact object not provided.:
TypeError – If provided input paramater not a ModelArtifact instance.:
- property failures: int
Calculates the number of failures.
- Returns:
The number of failures.
- Return type:
int
- run() DataFrame
Invokes introspection.
- Returns:
The introspection result in a DataFrame format.
- Return type:
pd.DataFrame
- property status: str
Gets the current status of model introspection.
- to_dataframe() DataFrame
Serializes model introspection result into a DataFrame.
- Returns:
The model introspection result in a DataFrame representation.
- Return type:
pandas.DataFrame
- class ads.model.model_introspect.PrintItem(key: str = '', case: str = '', result: str = '', message: str = '')
Bases:
object
Class represents the model introspection print item.
- case: str = ''
- key: str = ''
- message: str = ''
- result: str = ''
- to_list() List[str]
Converts instance to a list representation.
- Returns:
The instance in a list representation.
- Return type:
List[str]
ads.model.model_metadata module
- class ads.model.model_metadata.Framework
Bases:
str
- BERT = 'bert'
- CUML = 'cuml'
- EMCEE = 'emcee'
- ENSEMBLE = 'ensemble'
- FLAIR = 'flair'
- GENSIM = 'gensim'
- H20 = 'h2o'
- KERAS = 'keras'
- LIGHT_GBM = 'lightgbm'
- MXNET = 'mxnet'
- NLTK = 'nltk'
- ORACLE_AUTOML = 'oracle_automl'
- OTHER = 'other'
- PROPHET = 'prophet'
- PYMC3 = 'pymc3'
- PYOD = 'pyod'
- PYSTAN = 'pystan'
- PYTORCH = 'pytorch'
- SCIKIT_LEARN = 'scikit-learn'
- SKTIME = 'sktime'
- SPACY = 'spacy'
- SPARK = 'pyspark'
- STATSMODELS = 'statsmodels'
- TENSORFLOW = 'tensorflow'
- TRANSFORMERS = 'transformers'
- WORD2VEC = 'word2vec'
- XGBOOST = 'xgboost'
- class ads.model.model_metadata.MetadataCustomCategory
Bases:
str
- OTHER = 'Other'
- PERFORMANCE = 'Performance'
- TRAINING_AND_VALIDATION_DATASETS = 'Training and Validation Datasets'
- TRAINING_ENV = 'Training Environment'
- TRAINING_PROFILE = 'Training Profile'
- class ads.model.model_metadata.MetadataCustomKeys
Bases:
str
- CLIENT_LIBRARY = 'ClientLibrary'
- CONDA_ENVIRONMENT = 'CondaEnvironment'
- CONDA_ENVIRONMENT_PATH = 'CondaEnvironmentPath'
- ENVIRONMENT_TYPE = 'EnvironmentType'
- MODEL_ARTIFACTS = 'ModelArtifacts'
- MODEL_FILE_NAME = 'ModelFileName'
- MODEL_SERIALIZATION_FORMAT = 'ModelSerializationFormat'
- SLUG_NAME = 'SlugName'
- TRAINING_DATASET = 'TrainingDataset'
- TRAINING_DATASET_NUMBER_OF_COLS = 'TrainingDatasetNumberOfCols'
- TRAINING_DATASET_NUMBER_OF_ROWS = 'TrainingDatasetNumberOfRows'
- TRAINING_DATASET_SIZE = 'TrainingDatasetSize'
- VALIDATION_DATASET = 'ValidationDataset'
- VALIDATION_DATASET_NUMBER_OF_COLS = 'ValidationDataSetNumberOfCols'
- VALIDATION_DATASET_NUMBER_OF_ROWS = 'ValidationDatasetNumberOfRows'
- VALIDATION_DATASET_SIZE = 'ValidationDatasetSize'
- class ads.model.model_metadata.MetadataCustomPrintColumns
Bases:
str
- CATEGORY = 'Category'
- DESCRIPTION = 'Description'
- KEY = 'Key'
- VALUE = 'Value'
- exception ads.model.model_metadata.MetadataDescriptionTooLong(key: str, length: int)
Bases:
ValueError
Maximum allowed length of metadata description has been exceeded. See https://docs.oracle.com/en-us/iaas/data-science/using/models_saving_catalog.htm for more details.
- exception ads.model.model_metadata.MetadataSizeTooLarge(size: int)
Bases:
ValueError
Maximum allowed size for model metadata has been exceeded. See https://docs.oracle.com/en-us/iaas/data-science/using/models_saving_catalog.htm for more details.
- class ads.model.model_metadata.MetadataTaxonomyKeys
Bases:
str
- ALGORITHM = 'Algorithm'
- ARTIFACT_TEST_RESULT = 'ArtifactTestResults'
- FRAMEWORK = 'Framework'
- FRAMEWORK_VERSION = 'FrameworkVersion'
- HYPERPARAMETERS = 'Hyperparameters'
- USE_CASE_TYPE = 'UseCaseType'
- class ads.model.model_metadata.MetadataTaxonomyPrintColumns
Bases:
str
- KEY = 'Key'
- VALUE = 'Value'
- exception ads.model.model_metadata.MetadataValueTooLong(key: str, length: int)
Bases:
ValueError
Maximum allowed length of metadata value has been exceeded. See https://docs.oracle.com/en-us/iaas/data-science/using/models_saving_catalog.htm for more details.
- class ads.model.model_metadata.ModelCustomMetadata
Bases:
ModelMetadata
Class that represents Model Custom Metadata.
- get(self, key: str) ModelCustomMetadataItem
Returns the model metadata item by provided key.
- reset(self) None
Resets all model metadata items to empty values.
- to_dataframe(self) pd.DataFrame
Returns the model metadata list in a data frame format.
- size(self) int
Returns the size of the model metadata in bytes.
- validate(self) bool
Validates metadata.
- to_dict(self)
Serializes model metadata into a dictionary.
- from_dict(cls) ModelCustomMetadata
Constructs model metadata from dictionary.
- to_yaml(self)
Serializes model metadata into a YAML.
- add(self, key: str, value: str, description: str = '', category: str = MetadataCustomCategory.OTHER, replace: bool = False) None:
Adds a new model metadata item. Replaces existing one if replace flag is True.
- remove(self, key: str) None
Removes a model metadata item by key.
- clear(self) None
Removes all metadata items.
- isempty(self) bool
Checks if metadata is empty.
- to_json(self)
Serializes model metadata into a JSON.
- to_json_file(self, file_path: str, storage_options: dict = None) None
Saves the metadata to a local file or object storage.
Examples
>>> metadata_custom = ModelCustomMetadata() >>> metadata_custom.add(key="format", value="pickle") >>> metadata_custom.add(key="note", value="important note", description="some description") >>> metadata_custom["format"].description = "some description" >>> metadata_custom.to_dataframe() Key Value Description Category ---------------------------------------------------------------------------- 0 format pickle some description user defined 1 note important note some description user defined >>> metadata_custom metadata: - category: user defined description: some description key: format value: pickle - category: user defined description: some description key: note value: important note >>> metadata_custom.remove("format") >>> metadata_custom metadata: - category: user defined description: some description key: note value: important note >>> metadata_custom.to_dict() {'metadata': [{ 'key': 'note', 'value': 'important note', 'category': 'user defined', 'description': 'some description' }]} >>> metadata_custom.reset() >>> metadata_custom metadata: - category: None description: None key: note value: None >>> metadata_custom.clear() >>> metadata_custom.to_dataframe() Key Value Description Category ----------------------------------------------------------------------------
Initializes custom model metadata.
- add(key: str, value: str, description: str = '', category: str = 'Other', replace: bool = False) None
Adds a new model metadata item. Overrides the existing one if replace flag is True.
- Parameters:
key (str) – The metadata item key.
value (str) – The metadata item value.
description (str) – The metadata item description.
category (str) – The metadata item category.
replace (bool) – Overrides the existing metadata item if replace flag is True.
- Returns:
Nothing.
- Return type:
None
- Raises:
TypeError – If provided key is not a string. If provided description not a string.
ValueError – If provided key is empty. If provided value is empty. If provided value cannot be serialized to JSON. If item with provided key is already registered and replace flag is False. If provided category is not supported.
MetadataValueTooLong – If the length of provided value exceeds 255 charracters.
MetadataDescriptionTooLong – If the length of provided description exceeds 255 charracters.
- clear() None
Removes all metadata items.
- Returns:
Nothing.
- Return type:
None
- classmethod from_dict(data: Dict) ModelCustomMetadata
Constructs an instance of ModelCustomMetadata from a dictionary.
- Parameters:
data (Dict) – Model metadata in a dictionary format.
- Returns:
An instance of model custom metadata.
- Return type:
- Raises:
ValueError – In case of the wrong input data format.
- isempty() bool
Checks if metadata is empty.
- Returns:
True if metadata is empty, False otherwise.
- Return type:
bool
- remove(key: str) None
Removes a model metadata item.
- Parameters:
key (str) – The key of the metadata item that should be removed.
- Returns:
Nothing.
- Return type:
None
- set_training_data(path: str, data_size: Optional[str] = None)
Adds training_data path and data size information into model custom metadata.
- Parameters:
path (str) – The path where the training_data is stored.
data_size (str) – The size of the training_data.
- Returns:
Nothing.
- Return type:
None
- set_validation_data(path: str, data_size: Optional[str] = None)
Adds validation_data path and data size information into model custom metadata.
- Parameters:
path (str) – The path where the validation_data is stored.
data_size (str) – The size of the validation_data.
- Returns:
Nothing.
- Return type:
None
- to_dataframe() DataFrame
Returns the model metadata list in a data frame format.
- Returns:
The model metadata in a dataframe format.
- Return type:
pandas.DataFrame
- class ads.model.model_metadata.ModelCustomMetadataItem(key: str, value: Optional[str] = None, description: Optional[str] = None, category: Optional[str] = None)
Bases:
ModelTaxonomyMetadataItem
Class that represents model custom metadata item.
- key
The model metadata item key.
- Type:
str
- value
The model metadata item value.
- Type:
str
- description
The model metadata item description.
- Type:
str
- category
The model metadata item category.
- Type:
str
- reset(self) None
Resets model metadata item.
- to_dict(self) dict
Serializes model metadata item to dictionary.
- from_dict(cls) ModelCustomMetadataItem
Constructs model metadata item from dictionary.
- to_yaml(self)
Serializes model metadata item to YAML.
- size(self) int
Returns the size of the metadata in bytes.
- update(self, value: str = '', description: str = '', category: str = '') None
Updates metadata item information.
- to_json(self) JSON
Serializes metadata item into a JSON.
- to_json_file(self, file_path: str, storage_options: dict = None) None
Saves the metadata item value to a local file or object storage.
- validate(self) bool
Validates metadata item.
- property category: str
- property description: str
- reset() None
Resets model metadata item.
Resets value, description and category to None.
- Returns:
Nothing.
- Return type:
None
- update(value: str, description: str, category: str) None
Updates metadata item.
- Parameters:
value (str) – The value of model metadata item.
description (str) – The description of model metadata item.
category (str) – The category of model metadata item.
- Returns:
Nothing.
- Return type:
None
- validate() bool
Validates metadata item.
- Returns:
True if validation passed.
- Return type:
bool
- Raises:
ValueError – If invalid category provided.
MetadataValueTooLong – If value exceeds the length limit.
- class ads.model.model_metadata.ModelMetadata
Bases:
ABC
The base abstract class representing model metadata.
- get(self, key: str) ModelMetadataItem
Returns the model metadata item by provided key.
- reset(self) None
Resets all model metadata items to empty values.
- to_dataframe(self) pd.DataFrame
Returns the model metadata list in a data frame format.
- size(self) int
Returns the size of the model metadata in bytes.
- validate(self) bool
Validates metadata.
- to_dict(self)
Serializes model metadata into a dictionary.
- from_dict(cls) ModelMetadata
Constructs model metadata from dictionary.
- to_yaml(self)
Serializes model metadata into a YAML.
- to_json(self)
Serializes model metadata into a JSON.
- to_json_file(self, file_path: str, storage_options: dict = None) None
Saves the metadata to a local file or object storage.
Initializes Model Metadata.
- abstract classmethod from_dict(data: Dict) ModelMetadata
Constructs an instance of ModelMetadata from a dictionary.
- Parameters:
data (Dict) – Model metadata in a dictionary format.
- Returns:
An instance of model metadata.
- Return type:
- get(key: str) ModelMetadataItem
Returns the model metadata item by provided key.
- Parameters:
key (str) – The key of model metadata item.
- Returns:
The model metadata item.
- Return type:
- Raises:
ValueError – If provided key is empty or metadata item not found.
- property keys: Tuple[str]
Returns all registered metadata keys.
- Returns:
The list of metadata keys.
- Return type:
Tuple[str]
- reset() None
Resets all model metadata items to empty values.
Resets value, description and category to None for every metadata item.
- size() int
Returns the size of the model metadata in bytes.
- Returns:
The size of model metadata in bytes.
- Return type:
int
- abstract to_dataframe() DataFrame
Returns the model metadata list in a data frame format.
- Returns:
The model metadata in a dataframe format.
- Return type:
pandas.DataFrame
- to_dict()
Serializes model metadata into a dictionary.
- Returns:
The model metadata in a dictionary representation.
- Return type:
Dict
- to_json()
Serializes model metadata into a JSON.
- Returns:
The model metadata in a JSON representation.
- Return type:
JSON
- to_json_file(file_path: str, storage_options: Optional[dict] = None) None
Saves the metadata to a local file or object storage.
- Parameters:
file_path (str) – The file path to store the data. “oci://bucket_name@namespace/folder_name/” “oci://bucket_name@namespace/folder_name/metadata.json” “path/to/local/folder” “path/to/local/folder/metadata.json”
storage_options (dict. Default None) – Parameters passed on to the backend filesystem class. Defaults to options set using DatasetFactory.set_default_storage().
- Returns:
Nothing.
- Return type:
None
- Raises:
ValueError – When file path is empty.:
TypeError – When file path not a string.:
Examples
>>> metadata = ModelTaxonomyMetadataItem() >>> storage_options = {"config": oci.config.from_file(os.path.join("~/.oci", "config"))} >>> storage_options {'log_requests': False, 'additional_user_agent': '', 'pass_phrase': None, 'user': '<user-id>', 'fingerprint': '05:15:2b:b1:46:8a:32:ec:e2:69:5b:32:01:**:**:**)', 'tenancy': '<tenancy-id>', 'region': 'us-ashburn-1', 'key_file': '/home/datascience/.oci/oci_api_key.pem'} >>> metadata.to_json_file(file_path = 'oci://bucket_name@namespace/folder_name/metadata_taxonomy.json', storage_options=storage_options) >>> metadata_item.to_json_file("path/to/local/folder/metadata_taxonomy.json")
- to_yaml()
Serializes model metadata into a YAML.
- Returns:
The model metadata in a YAML representation.
- Return type:
Yaml
- validate() bool
Validates model metadata.
- Returns:
True if metadata is valid.
- Return type:
bool
- validate_size() bool
Validates model metadata size.
Validates the size of metadata. Throws an error if the size of the metadata exceeds expected value.
- Returns:
True if metadata size is valid.
- Return type:
bool
- Raises:
MetadataSizeTooLarge – If the size of the metadata exceeds expected value.
- class ads.model.model_metadata.ModelMetadataItem
Bases:
ABC
The base abstract class representing model metadata item.
- to_dict(self) Dict
Serializes model metadata item to dictionary.
- from_dict(cls, data: Dict) ModelMetadataItem
Constructs an instance of ModelMetadataItem from a dictionary.
- to_yaml(self)
Serializes model metadata item to YAML.
- size(self) int
Returns the size of the metadata in bytes.
- to_json(self) JSON
Serializes metadata item to JSON.
- to_json_file(self, file_path: str, storage_options: dict = None) None
Saves the metadata item value to a local file or object storage.
- validate(self) bool
Validates metadata item.
- classmethod from_dict(data: Dict) ModelMetadataItem
Constructs an instance of ModelMetadataItem from a dictionary.
- Parameters:
data (Dict) – Metadata item in a dictionary format.
- Returns:
An instance of model metadata item.
- Return type:
- size() int
Returns the size of the model metadata in bytes.
- Returns:
The size of model metadata in bytes.
- Return type:
int
- to_dict() dict
Serializes model metadata item to dictionary.
- Returns:
The dictionary representation of model metadata item.
- Return type:
dict
- to_json()
Serializes metadata item into a JSON.
- Returns:
The metadata item in a JSON representation.
- Return type:
JSON
- to_json_file(file_path: str, storage_options: Optional[dict] = None) None
Saves the metadata item value to a local file or object storage.
- Parameters:
file_path (str) – The file path to store the data. “oci://bucket_name@namespace/folder_name/” “oci://bucket_name@namespace/folder_name/result.json” “path/to/local/folder” “path/to/local/folder/result.json”
storage_options (dict. Default None) – Parameters passed on to the backend filesystem class. Defaults to options set using DatasetFactory.set_default_storage().
- Returns:
Nothing.
- Return type:
None
- Raises:
ValueError – When file path is empty.:
TypeError – When file path not a string.:
Examples
>>> metadata_item = ModelCustomMetadataItem(key="key1", value="value1") >>> storage_options = {"config": oci.config.from_file(os.path.join("~/.oci", "config"))} >>> storage_options {'log_requests': False, 'additional_user_agent': '', 'pass_phrase': None, 'user': '<user-id>', 'fingerprint': '05:15:2b:b1:46:8a:32:ec:e2:69:5b:32:01:**:**:**)', 'tenancy': '<tenency-id>', 'region': 'us-ashburn-1', 'key_file': '/home/datascience/.oci/oci_api_key.pem'} >>> metadata_item.to_json_file(file_path = 'oci://bucket_name@namespace/folder_name/file.json', storage_options=storage_options) >>> metadata_item.to_json_file("path/to/local/folder/file.json")
- to_yaml()
Serializes model metadata item to YAML.
- Returns:
The model metadata item in a YAML representation.
- Return type:
Yaml
- abstract validate() bool
Validates metadata item.
- Returns:
True if validation passed.
- Return type:
bool
- class ads.model.model_metadata.ModelProvenanceMetadata(repo: Optional[str] = None, git_branch: Optional[str] = None, git_commit: Optional[str] = None, repository_url: Optional[str] = None, training_script_path: Optional[str] = None, training_id: Optional[str] = None, artifact_dir: Optional[str] = None)
Bases:
DataClassSerializable
ModelProvenanceMetadata class.
Examples
>>> provenance_metadata = ModelProvenanceMetadata.fetch_training_code_details() ModelProvenanceMetadata(repo=<git.repo.base.Repo '/home/datascience/.git'>, git_branch='master', git_commit='99ad04c31803f1d4ffcc3bf4afbd6bcf69a06af2', repository_url='file:///home/datascience', "", "") >>> provenance_metadata.assert_path_not_dirty("your_path", ignore=False)
- artifact_dir: str = None
- assert_path_not_dirty(path: str, ignore: bool)
Checks if all the changes in this path has been commited.
- Parameters:
path ((str)) – path.
(bool) (ignore) – whether to ignore the changes or not.
- Raises:
ChangesNotCommitted – if there are changes not being commited.:
- Returns:
Nothing.
- Return type:
None
- classmethod fetch_training_code_details(training_script_path: Optional[str] = None, training_id: Optional[str] = None, artifact_dir: Optional[str] = None)
Fetches the training code details: repo, git_branch, git_commit, repository_url, training_script_path and training_id.
- Parameters:
training_script_path ((str, optional). Defaults to None.) – Training script path.
training_id ((str, optional). Defaults to None.) – The training OCID for model.
artifact_dir (str) – artifact directory to store the files needed for deployment.
- Returns:
A ModelProvenanceMetadata instance.
- Return type:
- classmethod from_dict(data: Dict[str, str]) ModelProvenanceMetadata
Constructs an instance of ModelProvenanceMetadata from a dictionary.
- Parameters:
data (Dict[str,str]) – Model provenance metadata in dictionary format.
- Returns:
An instance of ModelProvenanceMetadata.
- Return type:
- git_branch: str = None
- git_commit: str = None
- repo: str = None
- repository_url: str = None
- to_dict() dict
Serializes model provenance metadata into a dictionary.
- Returns:
The dictionary representation of the model provenance metadata.
- Return type:
Dict
- training_id: str = None
- training_script_path: str = None
- class ads.model.model_metadata.ModelTaxonomyMetadata
Bases:
ModelMetadata
Class that represents Model Taxonomy Metadata.
- get(self, key: str) ModelTaxonomyMetadataItem
Returns the model metadata item by provided key.
- reset(self) None
Resets all model metadata items to empty values.
- to_dataframe(self) pd.DataFrame
Returns the model metadata list in a data frame format.
- size(self) int
Returns the size of the model metadata in bytes.
- validate(self) bool
Validates metadata.
- to_dict(self)
Serializes model metadata into a dictionary.
- from_dict(cls) ModelTaxonomyMetadata
Constructs model metadata from dictionary.
- to_yaml(self)
Serializes model metadata into a YAML.
- to_json(self)
Serializes model metadata into a JSON.
- to_json_file(self, file_path: str, storage_options: dict = None) None
Saves the metadata to a local file or object storage.
Examples
>>> metadata_taxonomy = ModelTaxonomyMetadata() >>> metadata_taxonomy.to_dataframe() Key Value -------------------------------------------- 0 UseCaseType binary_classification 1 Framework sklearn 2 FrameworkVersion 0.2.2 3 Algorithm algorithm 4 Hyperparameters {}
>>> metadata_taxonomy.reset() >>> metadata_taxonomy.to_dataframe() Key Value -------------------------------------------- 0 UseCaseType None 1 Framework None 2 FrameworkVersion None 3 Algorithm None 4 Hyperparameters None
>>> metadata_taxonomy metadata: - key: UseCaseType category: None description: None value: None
Initializes Model Metadata.
- classmethod from_dict(data: Dict) ModelTaxonomyMetadata
Constructs an instance of ModelTaxonomyMetadata from a dictionary.
- Parameters:
data (Dict) – Model metadata in a dictionary format.
- Returns:
An instance of model taxonomy metadata.
- Return type:
- Raises:
ValueError – In case of the wrong input data format.
- to_dataframe() DataFrame
Returns the model metadata list in a data frame format.
- Returns:
The model metadata in a dataframe format.
- Return type:
pandas.DataFrame
- class ads.model.model_metadata.ModelTaxonomyMetadataItem(key: str, value: Optional[str] = None)
Bases:
ModelMetadataItem
Class that represents model taxonomy metadata item.
- key
The model metadata item key.
- Type:
str
- value
The model metadata item value.
- Type:
str
- reset(self) None
Resets model metadata item.
- to_dict(self) Dict
Serializes model metadata item to dictionary.
- from_dict(cls) ModelTaxonomyMetadataItem
Constructs model metadata item from dictionary.
- to_yaml(self)
Serializes model metadata item to YAML.
- size(self) int
Returns the size of the metadata in bytes.
- update(self, value: str = '') None
Updates metadata item information.
- to_json(self) JSON
Serializes metadata item into a JSON.
- to_json_file(self, file_path: str, storage_options: dict = None) None
Saves the metadata item value to a local file or object storage.
- validate(self) bool
Validates metadata item.
- property key: str
- reset() None
Resets model metadata item.
Resets value to None.
- Returns:
Nothing.
- Return type:
None
- update(value: str) None
Updates metadata item value.
- Parameters:
value (str) – The value of model metadata item.
- Returns:
Nothing.
- Return type:
None
- validate() bool
Validates metadata item.
- Returns:
True if validation passed.
- Return type:
bool
- Raises:
ValueError – If invalid UseCaseType provided. If invalid Framework provided.
- property value: str
- class ads.model.model_metadata.UseCaseType
Bases:
str
- ANOMALY_DETECTION = 'anomaly_detection'
- BINARY_CLASSIFICATION = 'binary_classification'
- CLUSTERING = 'clustering'
- DIMENSIONALITY_REDUCTION = 'dimensionality_reduction/representation'
- IMAGE_CLASSIFICATION = 'image_classification'
- MULTINOMIAL_CLASSIFICATION = 'multinomial_classification'
- NER = 'ner'
- OBJECT_LOCALIZATION = 'object_localization'
- OTHER = 'other'
- RECOMMENDER = 'recommender'
- REGRESSION = 'regression'
- SENTIMENT_ANALYSIS = 'sentiment_analysis'
- TIME_SERIES_FORECASTING = 'time_series_forecasting'
- TOPIC_MODELING = 'topic_modeling'
ads.model.model_metadata_mixin module
- class ads.model.model_metadata_mixin.MetadataMixin
Bases:
object
MetadataMixin class which populates the custom metadata, taxonomy metadata, input/output schema and provenance metadata.
- populate_metadata(use_case_type: Optional[str] = None, data_sample: Optional[ADSData] = 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, ignore_conda_error: bool = False)
Populates input schema and output schema. If the schema exceeds the limit of 32kb, save as json files to the artifact directory.
- Parameters:
use_case_type ((str, optional). Defaults to None.) – The use case type of the model.
data_sample ((ADSData, optional). Defaults to None.) – A sample of the data that will be used to generate intput_schema and output_schema.
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 None.) – The training model OCID.
ignore_pending_changes (bool. Defaults to False.) – Ignore the pending changes in git.
max_col_num ((int, optional). Defaults to utils.DATA_SCHEMA_MAX_COL_NUM.) – The maximum number of columns allowed in auto generated schema.
- Returns:
Nothing.
- Return type:
None
- populate_schema(data_sample: Optional[ADSData] = None, X_sample: Optional[Union[List, Tuple, DataFrame, Series, ndarray]] = None, y_sample: Optional[Union[List, Tuple, DataFrame, Series, ndarray]] = None, max_col_num: int = 2000)
Populate input and output schemas. If the schema exceeds the limit of 32kb, save as json files to the artifact dir.
- Parameters:
data_sample (ADSData) – A sample of the data that will be used to generate input_schema and output_schema.
X_sample (Union[list, tuple, pd.Series, np.ndarray, pd.DataFrame]) – A sample of input data that will be used to generate the input schema.
y_sample (Union[list, tuple, pd.Series, np.ndarray, pd.DataFrame]) – A sample of output data that will be used to generate the output schema.
max_col_num ((int, optional). Defaults to utils.DATA_SCHEMA_MAX_COL_NUM.) – The maximum number of columns allowed in auto generated schema.
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, deployment_image: Optional[str] = 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_image: str = 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.model_version_set module
- class ads.model.model_version_set.ModelVersionSet(spec: Optional[Dict] = None, **kwargs)
Bases:
Builder
Represents Model Version Set.
- id
Model version set OCID.
- Type:
str
- project_id
Project OCID.
- Type:
str
- compartment_id
Compartment OCID.
- Type:
str
- name
Model version set name.
- Type:
str
- description
Model version set description.
- Type:
str
- freeform_tags
Model version set freeform tags.
- Type:
Dict[str, str]
- defined_tags
Model version set defined tags.
- Type:
Dict[str, Dict[str, object]]
- details_link
Link to details page in OCI console.
- Type:
str
- create(self, \*\*kwargs) 'ModelVersionSet'
Creates a model version set.
- update(self, \*\*kwargs) 'ModelVersionSet'
Updates a model version set.
- delete(self, delete_model: Optional[bool] = False) "ModelVersionSet":
Removes a model version set.
- to_dict(self) dict
Serializes model version set to a dictionary.
- from_id(cls, id: str) 'ModelVersionSet'
Gets an existing model version set by OCID.
- from_ocid(cls, ocid: str) 'ModelVersionSet'
Gets an existing model version set by OCID.
- from_name(cls, name: str) 'ModelVersionSet'
Gets an existing model version set by name.
- from_dict(cls, config: dict) 'ModelVersionSet'
Load a model version set instance from a dictionary of configurations.
Examples
>>> mvs = (ModelVersionSet() ... .with_compartment_id(os.environ["PROJECT_COMPARTMENT_OCID"]) ... .with_project_id(os.environ["PROJECT_OCID"]) ... .with_name("test_experiment") ... .with_description("Experiment number one")) >>> mvs.create() >>> mvs.model_add(model_ocid, version_label="Version label 1") >>> mvs.model_list() >>> mvs.details_link ... https://console.<region>.oraclecloud.com/data-science/model-version-sets/<ocid> >>> mvs.delete()
Initializes a model version set.
- Parameters:
spec ((Dict, optional). Defaults to None.) – Object specification.
kwargs (Dict) –
Specification as keyword arguments. If ‘spec’ contains the same key as the one in kwargs, the value from kwargs will be used.
project_id: str
compartment_id: str
name: str
description: str
defined_tags: Dict[str, Dict[str, object]]
freeform_tags: Dict[str, str]
- CONST_COMPARTMENT_ID = 'compartmentId'
- CONST_DEFINED_TAG = 'definedTags'
- CONST_DESCRIPTION = 'description'
- CONST_FREEFORM_TAG = 'freeformTags'
- CONST_ID = 'id'
- CONST_NAME = 'name'
- CONST_PROJECT_ID = 'projectId'
- LIFECYCLE_STATE_ACTIVE = 'ACTIVE'
- LIFECYCLE_STATE_DELETED = 'DELETED'
- LIFECYCLE_STATE_DELETING = 'DELETING'
- LIFECYCLE_STATE_FAILED = 'FAILED'
- attribute_map = {'compartmentId': 'compartment_id', 'definedTags': 'defined_tags', 'description': 'description', 'freeformTags': 'freeform_tags', 'id': 'id', 'name': 'name', 'projectId': 'project_id'}
- property compartment_id: str
- create(**kwargs) ModelVersionSet
Creates a model version set.
- Parameters:
kwargs – Additional keyword arguments.
- Returns:
The ModelVersionSet instance (self)
- Return type:
- property defined_tags: Dict[str, Dict[str, object]]
- delete(delete_model: Optional[bool] = False) ModelVersionSet
Removes a model version set.
- Parameters:
delete_model ((bool, optional). Defaults to False.) – By default, this parameter is false. A model version set can only be deleted if all the models associate with it are already in the DELETED state. You can optionally specify the deleteRelatedModels boolean query parameters to true, which deletes all associated models for you.
- Returns:
The ModelVersionSet instance (self).
- Return type:
- property description: str
- property details_link: str
Link to details page in OCI console.
- Returns:
Link to details page in OCI console.
- Return type:
str
- property freeform_tags: Dict[str, str]
- classmethod from_dict(config: dict) ModelVersionSet
Load a model version set instance from a dictionary of configurations.
- Parameters:
config (dict) – A dictionary of configurations.
- Returns:
The model version set instance.
- Return type:
- classmethod from_dsc_model_version_set(dsc_model_version_set: DataScienceModelVersionSet) ModelVersionSet
Initialize a ModelVersionSet instance from a DataScienceModelVersionSet.
- Parameters:
dsc_model_version_set (DataScienceModelVersionSet) – An instance of DataScienceModelVersionSet.
- Returns:
An instance of ModelVersionSet.
- Return type:
- classmethod from_id(id: str) ModelVersionSet
Gets an existing model version set by OCID.
- Parameters:
id (str) – The model version set OCID.
- Returns:
An instance of ModelVersionSet.
- Return type:
- classmethod from_name(name: str, compartment_id: Optional[str] = None) ModelVersionSet
Gets an existing model version set by name.
- Parameters:
name (str) – The model version set name.
compartment_id ((str, optional). Defaults to None.) – Compartment OCID of the OCI resources. If compartment_id is not specified, the value will be taken from environment variables.
- Returns:
An instance of ModelVersionSet.
- Return type:
- classmethod from_ocid(ocid: str) ModelVersionSet
Gets an existing model version set by OCID.
- Parameters:
id (str) – The model version set OCID.
- Returns:
An instance of ModelVersionSet.
- Return type:
- property id: Optional[str]
The OCID of the model version set.
- property kind: str
The kind of the object as showing in YAML.
- Returns:
“modelVersionSet”
- Return type:
str
- classmethod list(compartment_id: Optional[str] = None, **kwargs) List[ModelVersionSet]
List model version sets in a given compartment.
- Parameters:
compartment_id (str) – The OCID of compartment.
kwargs – Additional keyword arguments for filtering model version sets.
- Returns:
The list of model version sets.
- Return type:
List[ModelVersionSet]
- model_add(model_id: str, version_label: Optional[str] = None, **kwargs) None
Adds new model to model version set.
- Parameters:
model_id (str) – The OCID of the model which needs to be associated with the model version set.
version_label (str) – The model version label.
kwargs – Additional keyword arguments.
- Returns:
Nothing.
- Return type:
None
- Raises:
ModelVersionSetNotSaved – If model version set has not been saved yet.:
- models(**kwargs) List[DataScienceModel]
Gets list of models associated with a model version set.
- Parameters:
kwargs –
- project_id: str
Project OCID.
- lifecycle_state: str
Filter results by the specified lifecycle state. Must be a valid state for the resource type. Allowed values are: “ACTIVE”, “DELETED”, “FAILED”, “INACTIVE”
Can be any attribute that oci.data_science.data_science_client.DataScienceClient.list_models. accepts.
- Returns:
List of models associated with the model version set.
- Return type:
List[DataScienceModel]
- Raises:
ModelVersionSetNotSaved – If model version set has not been saved yet.:
- property name: str
- property project_id: str
- property status: Optional[str]
Status of the model version set.
- Returns:
Status of the model version set.
- Return type:
str
- to_dict() dict
Serializes model version set to a dictionary.
- Returns:
The model version set serialized as a dictionary.
- Return type:
dict
- update() ModelVersionSet
Updates a model version set.
- Returns:
The ModelVersionSet instance (self).
- Return type:
- with_compartment_id(compartment_id: str) ModelVersionSet
Sets the compartment OCID.
- Parameters:
compartment_id (str) – The compartment OCID.
- Returns:
The ModelVersionSet instance (self)
- Return type:
- with_defined_tags(**kwargs: Dict[str, Dict[str, object]]) ModelVersionSet
Sets defined tags.
- Returns:
The ModelVersionSet instance (self)
- Return type:
- with_description(description: str) ModelVersionSet
Sets the description.
- Parameters:
description (str) – The description of the model version set.
- Returns:
The ModelVersionSet instance (self)
- Return type:
- with_freeform_tags(**kwargs: Dict[str, str]) ModelVersionSet
Sets freeform tags.
- Returns:
The ModelVersionSet instance (self)
- Return type:
- with_name(name: str) ModelVersionSet
Sets the name of the model version set.
- Parameters:
name (str) – The name of the model version set.
- Returns:
The ModelVersionSet instance (self)
- Return type:
- with_project_id(project_id: str) ModelVersionSet
Sets the project OCID.
- Parameters:
project_id (str) – The project OCID.
- Returns:
The ModelVersionSet instance (self)
- Return type:
- ads.model.model_version_set.experiment(name: str, create_if_not_exists: Optional[bool] = True, **kwargs: Dict)
Context manager helping to operate with model version set.
- Parameters:
name (str) – The name of the model version set.
create_if_not_exists ((bool, optional). Defaults to True.) – Creates model version set if not exists.
kwargs ((Dict, optional).) –
- compartment_id: (str, optional). Defaults to value from the environment variables.
The compartment OCID.
- project_id: (str, optional). Defaults to value from the environment variables.
The project OCID.
- description: (str, optional). Defaults to None.
The description of the model version set.
- Yields:
ModelVersionSet – The model version set object.
Module contents
- class ads.model.AutoMLModel(estimator: Callable, artifact_dir: str, properties: Optional[ModelProperties] = None, auth: Dict = None, model_save_serializer: Optional[SERDE] = None, model_input_serializer: Optional[SERDE] = None, **kwargs)
Bases:
FrameworkSpecificModel
AutoMLModel class for estimators from AutoML framework.
- algorithm
“ensemble”, the algorithm name 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
A trained automl estimator/model using oracle automl.
- Type:
Callable
- framework
“oracle_automl”, the framework name of the estimator.
- Type:
str
- hyperparameter
The hyperparameters of the estimator.
- Type:
dict
- metadata_custom
The model custom metadata.
- Type:
- metadata_provenance
The model provenance metadata.
- Type:
- metadata_taxonomy
The model taxonomy metadata.
- Type:
- model_artifact
This is built by calling prepare.
- Type:
- model_deployment
A ModelDeployment instance.
- Type:
- model_file_name
Name of the serialized model. Default to “model.pkl”.
- Type:
str
- model_id
The model ID.
- Type:
str
- properties
ModelProperties object required to save and deploy model. For more details, check https://accelerated-data-science.readthedocs.io/en/latest/ads.model.html#module-ads.model.model_properties.
- Type:
- runtime_info
A RuntimeInfo instance.
- Type:
- 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.
- 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.
- 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 >>> import logging >>> import warnings >>> from ads.automl.driver import AutoML >>> from ads.automl.provider import OracleAutoMLProvider >>> from ads.dataset.dataset_browser import DatasetBrowser >>> from ads.model.framework.automl_model import AutoMLModel >>> from ads.model.model_metadata import UseCaseType >>> ds = DatasetBrowser.sklearn().open("wine").set_target("target") >>> train, test = ds.train_test_split(test_size=0.1, random_state = 42)
>>> ml_engine = OracleAutoMLProvider(n_jobs=-1, loglevel=logging.ERROR) >>> oracle_automl = AutoML(train, provider=ml_engine) >>> model, baseline = oracle_automl.train( ... model_list=['LogisticRegression', 'DecisionTreeClassifier'], ... random_state = 42, ... time_budget = 500 ... )
>>> automl_model.prepare(inference_conda_env=inference_conda_env, force_overwrite=True) >>> automl_model.verify(...) >>> automl_model.save() >>> model_deployment = automl_model.deploy(wait_for_completion=False)
Initiates a AutoMLModel instance.
- Parameters:
estimator (Callable) – Any model object generated by automl framework.
artifact_dir (str) – Directory for generate artifact.
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.
model_save_serializer ((SERDE or str, optional). Defaults to None.) – Instance of ads.model.SERDE. Used for serialize/deserialize model.
model_input_serializer ((SERDE, optional). Defaults to None.) – Instance of ads.model.SERDE. Used for serialize/deserialize data.
- Returns:
AutoMLModel instance.
- Return type:
- Raises:
TypeError – If the input model is not an AutoML model.
- class ads.model.DataScienceModel(spec: Optional[Dict] = None, **kwargs)
Bases:
Builder
Represents a Data Science Model.
- id
Model ID.
- Type:
str
- project_id
Project OCID.
- Type:
str
- compartment_id
Compartment OCID.
- Type:
str
- name
Model name.
- Type:
str
- description
Model description.
- Type:
str
- freeform_tags
Model freeform tags.
- Type:
Dict[str, str]
- defined_tags
Model defined tags.
- Type:
Dict[str, Dict[str, object]]
- input_schema
Model input schema.
- Type:
ads.feature_engineering.Schema
- output_schema
Model output schema.
- Type:
ads.feature_engineering.Schema, Dict
- defined_metadata_list
Model defined metadata.
- Type:
- custom_metadata_list
Model custom metadata.
- Type:
- provenance_metadata
Model provenance metadata.
- Type:
- artifact
The artifact location. Can be either path to folder with artifacts or path to zip archive.
- Type:
str
- status
Model status.
- Type:
Union[str, None]
- model_version_set_id
Model version set ID
- Type:
str
- version_label
Model version label
- Type:
str
- create(self, \*\*kwargs) 'DataScienceModel'
Creates model.
- delete(self, delete_associated_model_deployment: Optional[bool] = False) "DataScienceModel":
Removes model.
- to_dict(self) dict
Serializes model to a dictionary.
- from_id(cls, id: str) 'DataScienceModel'
Gets an existing model by OCID.
- from_dict(cls, config: dict) 'DataScienceModel'
Loads model instance from a dictionary of configurations.
- upload_artifact(self, ...) None
Uploads model artifacts to the model catalog.
- download_artifact(self, ...) None
Downloads model artifacts from the model catalog.
- update(self, \*\*kwargs) 'DataScienceModel'
Updates datascience model in model catalog.
- list(cls, compartment_id: str = None, \*\*kwargs) List['DataScienceModel']
Lists datascience models in a given compartment.
- sync(self):
Sync up a datascience model with OCI datascience model.
- with_project_id(self, project_id: str) 'DataScienceModel'
Sets the project ID.
- with_description(self, description: str) 'DataScienceModel'
Sets the description.
- with_compartment_id(self, compartment_id: str) 'DataScienceModel'
Sets the compartment ID.
- with_display_name(self, name: str) 'DataScienceModel'
Sets the name.
- with_freeform_tags(self, \*\*kwargs: Dict[str, str]) 'DataScienceModel'
Sets freeform tags.
- with_defined_tags(self, \*\*kwargs: Dict[str, Dict[str, object]]) 'DataScienceModel'
Sets defined tags.
- with_input_schema(self, schema: Union[Schema, Dict]) 'DataScienceModel'
Sets the model input schema.
- with_output_schema(self, schema: Union[Schema, Dict]) 'DataScienceModel'
Sets the model output schema.
- with_defined_metadata_list(self, metadata: Union[ModelTaxonomyMetadata, Dict]) 'DataScienceModel'
Sets model taxonomy (defined) metadata.
- with_custom_metadata_list(self, metadata: Union[ModelCustomMetadata, Dict]) 'DataScienceModel'
Sets model custom metadata.
- with_provenance_metadata(self, metadata: Union[ModelProvenanceMetadata, Dict]) 'DataScienceModel'
Sets model provenance metadata.
- with_artifact(self, uri: str)
Sets the artifact location. Can be a local.
- with_model_version_set_id(self, model_version_set_id: str):
Sets the model version set ID.
- with_version_label(self, version_label: str):
Sets the model version label.
Examples
>>> ds_model = (DataScienceModel() ... .with_compartment_id(os.environ["NB_SESSION_COMPARTMENT_OCID"]) ... .with_project_id(os.environ["PROJECT_OCID"]) ... .with_display_name("TestModel") ... .with_description("Testing the test model") ... .with_freeform_tags(tag1="val1", tag2="val2") ... .with_artifact("/path/to/the/model/artifacts/")) >>> ds_model.create() >>> ds_model.status() >>> ds_model.with_description("new description").update() >>> ds_model.download_artifact("/path/to/dst/folder/") >>> ds_model.delete() >>> DataScienceModel.list()
Initializes datascience model.
- Parameters:
spec ((Dict, optional). Defaults to None.) – Object specification.
kwargs (Dict) –
Specification as keyword arguments. If ‘spec’ contains the same key as the one in kwargs, the value from kwargs will be used.
project_id: str
compartment_id: str
name: str
description: str
defined_tags: Dict[str, Dict[str, object]]
freeform_tags: Dict[str, str]
input_schema: Union[ads.feature_engineering.Schema, Dict]
output_schema: Union[ads.feature_engineering.Schema, Dict]
defined_metadata_list: Union[ModelTaxonomyMetadata, Dict]
custom_metadata_list: Union[ModelCustomMetadata, Dict]
provenance_metadata: Union[ModelProvenanceMetadata, Dict]
artifact: str
- CONST_ARTIFACT = 'artifact'
- CONST_COMPARTMENT_ID = 'compartmentId'
- CONST_CUSTOM_METADATA = 'customMetadataList'
- CONST_DEFINED_METADATA = 'definedMetadataList'
- CONST_DEFINED_TAG = 'definedTags'
- CONST_DESCRIPTION = 'description'
- CONST_DISPLAY_NAME = 'displayName'
- CONST_FREEFORM_TAG = 'freeformTags'
- CONST_ID = 'id'
- CONST_INPUT_SCHEMA = 'inputSchema'
- CONST_MODEL_VERSION_LABEL = 'versionLabel'
- CONST_MODEL_VERSION_SET_ID = 'modelVersionSetId'
- CONST_OUTPUT_SCHEMA = 'outputSchema'
- CONST_PROJECT_ID = 'projectId'
- CONST_PROVENANCE_METADATA = 'provenanceMetadata'
- property artifact: str
- attribute_map = {'artifact': 'artifact', 'compartmentId': 'compartment_id', 'customMetadataList': 'custom_metadata_list', 'definedMetadataList': 'defined_metadata_list', 'definedTags': 'defined_tags', 'description': 'description', 'displayName': 'display_name', 'freeformTags': 'freeform_tags', 'id': 'id', 'inputSchema': 'input_schema', 'modelVersionSetId': 'model_version_set_id', 'outputSchema': 'output_schema', 'projectId': 'project_id', 'provenanceMetadata': 'provenance_metadata', 'versionLabel': 'version_label'}
- property compartment_id: str
- create(**kwargs) DataScienceModel
Creates datascience model.
- Parameters:
kwargs –
Additional kwargs arguments. Can be any attribute that oci.data_science.models.Model accepts.
In addition can be also provided the attributes listed below.
- 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.
- 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 variable.
- auth: (Dict, optional). Defaults to None.
The default authentication 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.
- timeout: (int, optional). Defaults to 10 seconds.
The connection timeout in seconds for the client.
- Returns:
The DataScienceModel instance (self)
- Return type:
- Raises:
ValueError – If compartment id not provided. If project id not provided.
- property custom_metadata_list: ModelCustomMetadata
Returns model custom metadatda.
- property defined_metadata_list: ModelTaxonomyMetadata
Returns model taxonomy (defined) metadatda.
- property defined_tags: Dict[str, Dict[str, object]]
- delete(delete_associated_model_deployment: Optional[bool] = False) DataScienceModel
Removes model from the model catalog.
- Parameters:
delete_associated_model_deployment ((bool, optional). Defaults to False.) – Whether associated model deployments need to be deleted or not.
- Returns:
The DataScienceModel instance (self).
- Return type:
- property description: str
- property display_name: str
- download_artifact(target_dir: str, auth: Optional[Dict] = None, force_overwrite: Optional[bool] = False, bucket_uri: Optional[str] = None, region: Optional[str] = None, overwrite_existing_artifact: Optional[bool] = True, remove_existing_artifact: Optional[bool] = True, timeout: Optional[int] = None)
Downloads model artifacts from the model catalog.
- Parameters:
target_dir (str) – The target location of model artifacts.
auth ((Dict, optional). Defaults to None.) – The default authentication 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.) – Overwrite target directory if exists.
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/.
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.
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.
timeout ((int, optional). Defaults to 10 seconds.) – The connection timeout in seconds for the client.
- Raises:
ModelArtifactSizeError – If model artifacts size greater than 2GB and temporary OS bucket uri not provided.
- property freeform_tags: Dict[str, str]
- classmethod from_dict(config: Dict) DataScienceModel
Loads model instance from a dictionary of configurations.
- Parameters:
config (Dict) – A dictionary of configurations.
- Returns:
The model instance.
- Return type:
- classmethod from_id(id: str) DataScienceModel
Gets an existing model by OCID.
- Parameters:
id (str) – The model OCID.
- Returns:
An instance of DataScienceModel.
- Return type:
- property id: Optional[str]
The model OCID.
- property input_schema: Schema
Returns model input schema.
- Returns:
Model input schema.
- Return type:
ads.feature_engineering.Schema
- property kind: str
The kind of the object as showing in a YAML.
- classmethod list(compartment_id: Optional[str] = None, project_id: Optional[str] = None, **kwargs) List[DataScienceModel]
Lists datascience models in a given compartment.
- Parameters:
compartment_id ((str, optional). Defaults to None.) – The compartment OCID.
project_id ((str, optional). Defaults to None.) – The project OCID.
kwargs – Additional keyword arguments for filtering models.
- Returns:
The list of the datascience models.
- Return type:
List[DataScienceModel]
- classmethod list_df(compartment_id: Optional[str] = None, project_id: Optional[str] = None, **kwargs) DataFrame
Lists datascience models in a given compartment.
- Parameters:
compartment_id ((str, optional). Defaults to None.) – The compartment OCID.
project_id ((str, optional). Defaults to None.) – The project OCID.
kwargs – Additional keyword arguments for filtering models.
- Returns:
The list of the datascience models in a pandas dataframe format.
- Return type:
pandas.DataFrame
- property model_version_set_id: str
- property output_schema: Schema
Returns model output schema.
- Returns:
Model output schema.
- Return type:
ads.feature_engineering.Schema
- property project_id: str
- property provenance_metadata: ModelProvenanceMetadata
Returns model provenance metadatda.
- property status: Optional[str]
Status of the model.
- Returns:
Status of the model.
- Return type:
str
- sync()
Sync up a datascience model with OCI datascience model.
- to_dict() Dict
Serializes model to a dictionary.
- Returns:
The model serialized as a dictionary.
- Return type:
dict
- update(**kwargs) DataScienceModel
Updates datascience model in model catalog.
- Parameters:
kwargs – Additional kwargs arguments. Can be any attribute that oci.data_science.models.Model accepts.
- Returns:
The DataScienceModel instance (self).
- Return type:
- upload_artifact(bucket_uri: Optional[str] = None, auth: Optional[Dict] = None, region: Optional[str] = None, overwrite_existing_artifact: Optional[bool] = True, remove_existing_artifact: Optional[bool] = True, timeout: Optional[int] = None) None
Uploads model artifacts to the model catalog.
- Parameters:
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/.
auth ((Dict, optional). Defaults to None.) – The default authentication 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.
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.
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.
timeout ((int, optional). Defaults to 10 seconds.) – The connection timeout in seconds for the client.
- property version_label: str
- with_artifact(uri: str)
Sets the artifact location. Can be a local.
- Parameters:
uri (str) – Path to artifact directory or to the ZIP archive. It could contain a serialized model(required) as well as any files needed for deployment. The content of the source folder will be zipped and uploaded to the model catalog.
Examples
>>> .with_artifact(uri="./model1/") >>> .with_artifact(uri="./model1.zip")
- with_compartment_id(compartment_id: str) DataScienceModel
Sets the compartment ID.
- Parameters:
compartment_id (str) – The compartment ID.
- Returns:
The DataScienceModel instance (self)
- Return type:
- with_custom_metadata_list(metadata: Union[ModelCustomMetadata, Dict]) DataScienceModel
Sets model custom metadata.
- Parameters:
metadata (Union[ModelCustomMetadata, Dict]) – The custom metadata.
- Returns:
The DataScienceModel instance (self)
- Return type:
- with_defined_metadata_list(metadata: Union[ModelTaxonomyMetadata, Dict]) DataScienceModel
Sets model taxonomy (defined) metadata.
- Parameters:
metadata (Union[ModelTaxonomyMetadata, Dict]) – The defined metadata.
- Returns:
The DataScienceModel instance (self)
- Return type:
- with_defined_tags(**kwargs: Dict[str, Dict[str, object]]) DataScienceModel
Sets defined tags.
- Returns:
The DataScienceModel instance (self)
- Return type:
- with_description(description: str) DataScienceModel
Sets the description.
- Parameters:
description (str) – The description of the model.
- Returns:
The DataScienceModel instance (self)
- Return type:
- with_display_name(name: str) DataScienceModel
Sets the name.
- Parameters:
name (str) – The name.
- Returns:
The DataScienceModel instance (self)
- Return type:
- with_freeform_tags(**kwargs: Dict[str, str]) DataScienceModel
Sets freeform tags.
- Returns:
The DataScienceModel instance (self)
- Return type:
- with_input_schema(schema: Union[Schema, Dict]) DataScienceModel
Sets the model input schema.
- Parameters:
schema (Union[ads.feature_engineering.Schema, Dict]) – The model input schema.
- Returns:
The DataScienceModel instance (self)
- Return type:
- with_model_version_set_id(model_version_set_id: str)
Sets the model version set ID.
- Parameters:
urmodel_version_set_idi (str) – The Model version set OCID.
- with_output_schema(schema: Union[Schema, Dict]) DataScienceModel
Sets the model output schema.
- Parameters:
schema (Union[ads.feature_engineering.Schema, Dict]) – The model output schema.
- Returns:
The DataScienceModel instance (self)
- Return type:
- with_project_id(project_id: str) DataScienceModel
Sets the project ID.
- Parameters:
project_id (str) – The project ID.
- Returns:
The DataScienceModel instance (self)
- Return type:
- with_provenance_metadata(metadata: Union[ModelProvenanceMetadata, Dict]) DataScienceModel
Sets model provenance metadata.
- Parameters:
provenance_metadata (Union[ModelProvenanceMetadata, Dict]) – The provenance metadata.
- Returns:
The DataScienceModel instance (self)
- Return type:
- with_version_label(version_label: str)
Sets the model version label.
- Parameters:
version_label (str) – The model version label.
- class ads.model.GenericModel(estimator: Optional[Callable] = None, artifact_dir: Optional[str] = None, properties: Optional[ModelProperties] = None, auth: Optional[Dict] = None, serialize: bool = True, model_save_serializer: Optional[SERDE] = None, model_input_serializer: Optional[SERDE] = None, **kwargs: dict)
Bases:
MetadataMixin
,Introspectable
,EvaluatorMixin
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:
- metadata_provenance
The model provenance metadata.
- Type:
- metadata_taxonomy
The model taxonomy metadata.
- Type:
- model_artifact
This is built by calling prepare.
- Type:
- model_deployment
A ModelDeployment instance.
- Type:
- model_file_name
Name of the serialized model.
- Type:
str
- model_id
The model ID.
- Type:
str
- model_input_serializer
Instance of ads.model.SERDE. Used for serialize/deserialize data.
- Type:
- properties
ModelProperties object required to save and deploy model.
- Type:
- runtime_info
A RuntimeInfo instance.
- Type:
- 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.
- set_model_input_serializer(serde)
Registers serializer used for serializing data passed in verify/predict.
- 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="dbexp_p38_cpu_v1", ... inference_python_version="3.8", ... 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.
model_save_serializer ((SERDE or str, optional). Defaults to None.) – Instance of ads.model.SERDE. Used for serialize/deserialize model.
model_input_serializer ((SERDE or str, optional). Defaults to None.) – Instance of ads.model.SERDE. Used for serialize/deserialize model input.
- 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, deployment_image: Optional[str] = None, **kwargs: Dict) ModelDeployment
Deploys a model. The model needs to be saved to the model catalog at first. You can deploy the model on either conda or container runtime. The customized runtime allows you to bring your own service container. To deploy model on container runtime, make sure to build the container and push it to OCIR. For more information, see https://docs.oracle.com/en-us/iaas/data-science/using/mod-dep-byoc.htm.
Example
# This is an example to deploy model on container runtime >>> model = GenericModel(estimator=estimator, artifact_dir=tempfile.mkdtemp()) >>> model.summary_status() >>> model.prepare( … model_file_name=”toy_model.pkl”, … ignore_conda_error=True, # set ignore_conda_error=True for container runtime … force_overwrite=True … ) >>> model.verify() >>> model.save() >>> model.deploy( … deployment_image=”iad.ocir.io/<namespace>/<image>:<tag>”, … entrypoint=[“python”, “/opt/ds/model/deployed_model/api.py”], … server_port=5000, … health_check_port=5000, … environment_variables={“key”:”value”} … )
- 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
deployment_image ((str, optional). Defaults to None.) – The OCIR path of docker container image. Required for deploying model on container runtime.
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.
- image_digest: (str, optional). Defaults to None.
The digest of docker container image.
- cmd: (List, optional). Defaults to empty.
The command line arguments for running docker container image.
- entrypoint: (List, optional). Defaults to empty.
The entrypoint for running docker container image.
- server_port: (int, optional). Defaults to 8080.
The server port for docker container image.
- health_check_port: (int, optional). Defaults to 8080.
The health check port for docker container image.
- deployment_mode: (str, optional). Defaults to HTTPS_ONLY.
The deployment mode. Allowed values are: HTTPS_ONLY and STREAM_ONLY.
- input_stream_ids: (List, optional). Defaults to empty.
The input stream ids. Required for STREAM_ONLY mode.
- output_stream_ids: (List, optional). Defaults to empty.
The output stream ids. Required for STREAM_ONLY mode.
- environment_variables: (Dict, optional). Defaults to empty.
The environment variables for 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:
- 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, ignore_conda_error: Optional[bool] = False, **kwargs) Self
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:
Self
- 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, ignore_conda_error: Optional[bool] = False, **kwargs: dict) Self
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.
ignore_conda_error ((bool, optional). Defaults to False.) – Parameter to ignore error when collecting conda information.
- Returns:
An instance of GenericModel class.
- Return type:
Self
- 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, ignore_conda_error: Optional[bool] = False, **kwargs) Self
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.
ignore_conda_error ((bool, optional). Defaults to False.) – Parameter to ignore error when collecting conda information.
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:
Self
- 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, ignore_conda_error: Optional[bool] = False, **kwargs) Self
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.
ignore_conda_error ((bool, optional). Defaults to False.) – Parameter to ignore error when collecting conda information.
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:
Self
- get_data_serializer()
Gets data serializer.
- Returns:
object
- Return type:
ads.model.Serializer object.
- get_model_serializer()
Gets model serializer.
- 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