Google Cloud Aiplatform SDK

class google.cloud.aiplatform.metadata.schema.google.artifact_schema.ExperimentModel(*, framework_name: str, framework_version: str, model_file: str, uri: str, model_class: Optional[str] = None, predict_schemata: Optional[google.cloud.aiplatform.metadata.schema.utils.PredictSchemata] = None, artifact_id: Optional[str] = None, display_name: Optional[str] = None, schema_version: Optional[str] = None, description: Optional[str] = None, metadata: Optional[Dict] = None, state: Optional[google.cloud.aiplatform_v1.types.artifact.Artifact.State] = State.LIVE)

An artifact representing a Vertex Experiment Model.

Instantiates an ExperimentModel that represents a saved ML model.

• Parameters

  • framework_name (str) – Required. The name of the model’s framework. E.g., ‘sklearn’

  • framework_version (str) – Required. The version of the model’s framework. E.g., ‘1.1.0’

  • model_file (str) – Required. The file name of the model. E.g., ‘model.pkl’

  • uri (str) – Required. The uniform resource identifier of the model artifact directory.

  • model_class (str) – Optional. The class name of the model. E.g., ‘sklearn.linear_model._base.LinearRegression’

  • predict_schemata (PredictSchemata) – Optional. An instance of PredictSchemata which holds instance, parameter and prediction schema uris.

  • artifact_id (str) – Optional. The <resource_id> portion of the Artifact name with the format. This is globally unique in a metadataStore: projects/123/locations/us-central1/metadataStores/<metadata_store_id>/artifacts/<resource_id>.

  • display_name (str) – Optional. The user-defined name of the Artifact.

  • schema_version (str) – Optional. schema_version specifies the version used by the Artifact. If not set, defaults to use the latest version.

  • description (str) – Optional. Describes the purpose of the Artifact to be created.

  • metadata (Dict) – Optional. Contains the metadata information that will be stored in the Artifact.

  • state (google.cloud.gapic.types.Artifact.State) – Optional. The state of this Artifact. This is a property of the Artifact, and does not imply or apture any ongoing process. This property is managed by clients (such as Vertex AI Pipelines), and the system does not prescribe or check the validity of state transitions.

property framework_name(: Optional[str )

The framework name of the saved ML model.

property framework_version(: Optional[str )

The framework version of the saved ML model.

classmethod get(artifact_id: str, *, metadata_store_id: str = 'default', project: Optional[str] = None, location: Optional[str] = None, credentials: Optional[google.auth.credentials.Credentials] = None)

Retrieves an existing ExperimentModel artifact given an artifact id.

• Parameters

  • artifact_id (str) – Required. An artifact id of the ExperimentModel artifact.

  • metadata_store_id (str) – Optional. MetadataStore to retrieve Artifact from. If not set, metadata_store_id is set to “default”. If artifact_id is a fully-qualified resource name, its metadata_store_id overrides this one.

  • project (str) – Optional. Project to retrieve the artifact from. If not set, project set in aiplatform.init will be used.

  • location (str) – Optional. Location to retrieve the Artifact from. If not set, location set in aiplatform.init will be used.

  • credentials (auth_credentials.Credentials) – Optional. Custom credentials to use to retrieve this Artifact. Overrides credentials set in aiplatform.init.

• Returns

  An ExperimentModel class that represents an Artifact resource.

• Raises

  ValueError – if artifact’s schema title is not ‘google.ExperimentModel’.

get_model_info()

Get the model’s info from an experiment model artifact.

• Returns

  A dict of model’s info. This includes model’s class name, framework name, framework version, and input example.

load_model()

Retrieves the original ML model from an ExperimentModel.

Example Usage: \ experiment_model = aiplatform.get_experiment_model("my-sklearn-model") sk_model = experiment_model.load_model() pred_y = model.predict(test_X) ``

• Returns

  The original ML model.

• Raises

  ValueError – if model type is not supported.

property model_class(: Optional[str )

The class name of the saved ML model.

register_model(*, model_id: Optional[str] = None, parent_model: Optional[str] = None, use_gpu: bool = False, is_default_version: bool = True, version_aliases: Optional[Sequence[str]] = None, version_description: Optional[str] = None, display_name: Optional[str] = None, description: Optional[str] = None, labels: Optional[Dict[str, str]] = None, serving_container_image_uri: Optional[str] = None, serving_container_predict_route: Optional[str] = None, serving_container_health_route: Optional[str] = None, serving_container_command: Optional[Sequence[str]] = None, serving_container_args: Optional[Sequence[str]] = None, serving_container_environment_variables: Optional[Dict[str, str]] = None, serving_container_ports: Optional[Sequence[int]] = None, instance_schema_uri: Optional[str] = None, parameters_schema_uri: Optional[str] = None, prediction_schema_uri: Optional[str] = None, explanation_metadata: Optional[google.cloud.aiplatform_v1.types.explanation_metadata.ExplanationMetadata] = None, explanation_parameters: Optional[google.cloud.aiplatform_v1.types.explanation.ExplanationParameters] = None, project: Optional[str] = None, location: Optional[str] = None, credentials: Optional[google.auth.credentials.Credentials] = None, encryption_spec_key_name: Optional[str] = None, staging_bucket: Optional[str] = None, sync: Optional[bool] = True, upload_request_timeout: Optional[float] = None)

Register an ExperimentModel to Model Registry and returns a Model representing the registered Model resource.

Example Usage: \ experiment_model = aiplatform.get_experiment_model("my-sklearn-model") registered_model = experiment_model.register_model() registered_model.deploy(endpoint=my_endpoint) ``

• Parameters

  • model_id (str) – Optional. The ID to use for the registered Model, which will become the final component of the model resource name. This value may be up to 63 characters, and valid characters are [a-z0-9_-]. The first character cannot be a number or hyphen.

  • parent_model (str) – Optional. The resource name or model ID of an existing model that the newly-registered model will be a version of. Only set this field when uploading a new version of an existing model.

  • use_gpu (str) – Optional. Whether or not to use GPUs for the serving container. Only specify this argument when registering a Tensorflow model and ‘serving_container_image_uri’ is not specified.

  • is_default_version (bool) – Optional. When set to True, the newly registered model version will automatically have alias “default” included. Subsequent uses of this model without a version specified will use this “default” version.

    When set to False, the “default” alias will not be moved. Actions targeting the newly-registered model version will need to specifically reference this version by ID or alias.

    New model uploads, i.e. version 1, will always be “default” aliased.

  • version_aliases (Sequence[str]) – Optional. User provided version aliases so that a model version can be referenced via alias instead of auto-generated version ID. A default version alias will be created for the first version of the model.

    The format is [a-z][a-zA-Z0-9-]{0,126}[a-z0-9]

  • version_description (str) – Optional. The description of the model version being uploaded.

  • display_name (str) – Optional. The display name of the Model. The name can be up to 128 characters long and can be consist of any UTF-8 characters.

  • description (str) – Optional. The description of the model.

  • labels (Dict[str, *[str](https://python.readthedocs.io/en/latest/library/stdtypes.html#str)]*) – Optional. The labels with user-defined metadata to organize your Models. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels.

  • serving_container_image_uri (str) – Optional. The URI of the Model serving container. A pre-built container [https://cloud.google.com/vertex-ai/docs/predictions/pre-built-containers](https://cloud.google.com/vertex-ai/docs/predictions/pre-built-containers) is automatically chosen based on the model’s framwork. Set this field to override the default pre-built container.

  • serving_container_predict_route (str) – Optional. An HTTP path to send prediction requests to the container, and which must be supported by it. If not specified a default HTTP path will be used by Vertex AI.

  • serving_container_health_route (str) – Optional. An HTTP path to send health check requests to the container, and which must be supported by it. If not specified a standard HTTP path will be used by Vertex AI.

  • serving_container_command (Sequence[str]) – Optional. The command with which the container is run. Not executed within a shell. The Docker image’s ENTRYPOINT is used if this is not provided. Variable references $(VAR_NAME) are expanded using the container’s environment. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not.

  • serving_container_args (Sequence[str]) – Optional. The arguments to the command. The Docker image’s CMD is used if this is not provided. Variable references $(VAR_NAME) are expanded using the container’s environment. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not.

  • serving_container_environment_variables (Dict[str, *[str](https://python.readthedocs.io/en/latest/library/stdtypes.html#str)]*) – Optional. The environment variables that are to be present in the container. Should be a dictionary where keys are environment variable names and values are environment variable values for those names.

  • serving_container_ports (Sequence[int]) – Optional. Declaration of ports that are exposed by the container. This field is primarily informational, it gives Vertex AI information about the network connections the container uses. Listing or not a port here has no impact on whether the port is actually exposed, any port listening on the default “0.0.0.0” address inside a container will be accessible from the network.

  • instance_schema_uri (str) – Optional. Points to a YAML file stored on Google Cloud Storage describing the format of a single instance, which are used in PredictRequest.instances, ExplainRequest.instances and BatchPredictionJob.input_config. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by AI Platform. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.

  • parameters_schema_uri (str) – Optional. Points to a YAML file stored on Google Cloud Storage describing the parameters of prediction and explanation via PredictRequest.parameters, ExplainRequest.parameters and BatchPredictionJob.model_parameters. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by AI Platform, if no parameters are supported it is set to an empty string. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.

  • prediction_schema_uri (str) – Optional. Points to a YAML file stored on Google Cloud Storage describing the format of a single prediction produced by this Model, which are returned via PredictResponse.predictions, ExplainResponse.explanations, and BatchPredictionJob.output_config. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by AI Platform. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.

  • explanation_metadata (aiplatform.explain.ExplanationMetadata) – Optional. Metadata describing the Model’s input and output for explanation. explanation_metadata is optional while explanation_parameters must be specified when used. For more details, see Ref docs http://tinyurl.com/1igh60kt

  • explanation_parameters (aiplatform.explain.ExplanationParameters) – Optional. Parameters to configure explaining for Model’s predictions. For more details, see Ref docs http://tinyurl.com/1an4zake

  • project – Optional[str]=None, Project to upload this model to. Overrides project set in aiplatform.init.

  • location – Optional[str]=None, Location to upload this model to. Overrides location set in aiplatform.init.

  • credentials – Optional[auth_credentials.Credentials]=None, Custom credentials to use to upload this model. Overrides credentials set in aiplatform.init.

  • encryption_spec_key_name (Optional[str]) – Optional. The Cloud KMS resource identifier of the customer managed encryption key used to protect the model. Has the form projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key. The key needs to be in the same region as where the compute resource is created.

    If set, this Model and all sub-resources of this Model will be secured by this key.

    Overrides encryption_spec_key_name set in aiplatform.init.

  • staging_bucket (str) – Optional. Bucket to stage local model artifacts. Overrides staging_bucket set in aiplatform.init.

  • sync (bool) – Optional. Whether to execute this method synchronously. If False, this method will unblock and it will be executed in a concurrent Future.

  • upload_request_timeout (float) – Optional. The timeout for the upload request in seconds.

• Returns

  Instantiated representation of the registered model resource.

• Return type

  model (aiplatform.Model)

• Raises

  ValueError – If the model doesn’t have a pre-built container that is suitable for its framework and ‘serving_container_image_uri’ is not set.

class google.cloud.aiplatform.Artifact(artifact_name: str, *, metadata_store_id: str = 'default', project: Optional[str] = None, location: Optional[str] = None, credentials: Optional[google.auth.credentials.Credentials] = None)

Bases: google.cloud.aiplatform.metadata.resource._Resource

Metadata Artifact resource for Vertex AI

Retrieves an existing Metadata Artifact given a resource name or ID.

• Parameters

  • artifact_name (str) – Required. A fully-qualified resource name or resource ID of the Artifact. Example: “projects/123/locations/us-central1/metadataStores/default/artifacts/my-resource”. or “my-resource” when project and location are initialized or passed.

  • metadata_store_id (str) – Optional. MetadataStore to retrieve Artifact from. If not set, metadata_store_id is set to “default”. If artifact_name is a fully-qualified resource, its metadata_store_id overrides this one.

  • project (str) – Optional. Project to retrieve the artifact from. If not set, project set in aiplatform.init will be used.

  • location (str) – Optional. Location to retrieve the Artifact from. If not set, location set in aiplatform.init will be used.

  • credentials (auth_credentials.Credentials) – Optional. Custom credentials to use to retrieve this Artifact. Overrides credentials set in aiplatform.init.

client_class()

alias of google.cloud.aiplatform.utils.MetadataClientWithOverride

classmethod create(schema_title: str, *, resource_id: Optional[str] = None, uri: Optional[str] = None, display_name: Optional[str] = None, schema_version: Optional[str] = None, description: Optional[str] = None, metadata: Optional[Dict] = None, state: google.cloud.aiplatform_v1.types.artifact.Artifact.State = State.LIVE, metadata_store_id: Optional[str] = 'default', project: Optional[str] = None, location: Optional[str] = None, credentials: Optional[google.auth.credentials.Credentials] = None)

Creates a new Metadata Artifact.

• Parameters

  • schema_title (str) – Required. schema_title identifies the schema title used by the Artifact.

    Please reference https://cloud.google.com/vertex-ai/docs/ml-metadata/system-schemas.

  • resource_id (str) – Optional. The <resource_id> portion of the Artifact name with the format. This is globally unique in a metadataStore: projects/123/locations/us-central1/metadataStores/<metadata_store_id>/artifacts/<resource_id>.

  • uri (str) – Optional. The uniform resource identifier of the artifact file. May be empty if there is no actual artifact file.

  • display_name (str) – Optional. The user-defined name of the Artifact.

  • schema_version (str) – Optional. schema_version specifies the version used by the Artifact. If not set, defaults to use the latest version.

  • description (str) – Optional. Describes the purpose of the Artifact to be created.

  • metadata (Dict) – Optional. Contains the metadata information that will be stored in the Artifact.

  • state (google.cloud.gapic.types.Artifact.State) – Optional. The state of this Artifact. This is a property of the Artifact, and does not imply or capture any ongoing process. This property is managed by clients (such as Vertex AI Pipelines), and the system does not prescribe or check the validity of state transitions.

  • metadata_store_id (str) – Optional. The <metadata_store_id> portion of the resource name with the format: projects/123/locations/us-central1/metadataStores/<metadata_store_id>/artifacts/<resource_id> If not provided, the MetadataStore’s ID will be set to “default”.

  • project (str) – Optional. Project used to create this Artifact. Overrides project set in aiplatform.init.

  • location (str) – Optional. Location used to create this Artifact. Overrides location set in aiplatform.init.

  • credentials (auth_credentials.Credentials) – Optional. Custom credentials used to create this Artifact. Overrides credentials set in aiplatform.init.

• Returns

  Instantiated representation of the managed Metadata Artifact.

• Return type

  Artifact

property create_time(: [datetime.datetime](https://python.readthedocs.io/en/latest/library/datetime.html#datetime.datetime )

Time this resource was created.

delete(sync: bool = True)

Deletes this Vertex AI resource. WARNING: This deletion is permanent.

• Parameters

  sync (bool) – Whether to execute this deletion synchronously. If False, this method will be executed in concurrent Future and any downstream object will be immediately returned and synced when the Future has completed.

property display_name(: [str](https://python.readthedocs.io/en/latest/library/stdtypes.html#str )

Display name of this resource.

property encryption_spec(: Optional[[google.cloud.aiplatform_v1.types.encryption_spec.EncryptionSpec](../aiplatform_v1/types.md#google.cloud.aiplatform_v1.types.EncryptionSpec)_ )

Customer-managed encryption key options for this Vertex AI resource.

If this is set, then all resources created by this Vertex AI resource will be encrypted with the provided encryption key.

property gca_resource(: [proto.message.Message](https://proto-plus-python.readthedocs.io/en/latest/reference/message.html#proto.message.Message )

The underlying resource proto representation.

classmethod get(resource_id: str, metadata_store_id: str = 'default', project: Optional[str] = None, location: Optional[str] = None, credentials: Optional[google.auth.credentials.Credentials] = None)

Retrieves a Metadata resource.

• Parameters

  • resource_id (str) – Required. The <resource_id> portion of the resource name with the format: projects/123/locations/us-central1/metadataStores/<metadata_store_id>/<resource_noun>/<resource_id>.

  • metadata_store_id (str) – The <metadata_store_id> portion of the resource name with the format: projects/123/locations/us-central1/metadataStores/<metadata_store_id>/<resource_noun>/<resource_id> If not provided, the MetadataStore’s ID will be set to “default”.

  • project (str) – Project used to retrieve or create this resource. Overrides project set in aiplatform.init.

  • location (str) – Location used to retrieve or create this resource. Overrides location set in aiplatform.init.

  • credentials (auth_credentials.Credentials) – Custom credentials used to retrieve or create this resource. Overrides credentials set in aiplatform.init.

• Returns

  Instantiated representation of the managed Metadata resource or None if no resource was found.

• Return type

  resource (_Resource)

classmethod get_or_create(resource_id: str, schema_title: str, display_name: Optional[str] = None, schema_version: Optional[str] = None, description: Optional[str] = None, metadata: Optional[Dict] = None, metadata_store_id: str = 'default', project: Optional[str] = None, location: Optional[str] = None, credentials: Optional[google.auth.credentials.Credentials] = None)

Retrieves or Creates (if it does not exist) a Metadata resource.

• Parameters

  • resource_id (str) – Required. The <resource_id> portion of the resource name with the format: projects/123/locations/us-central1/metadataStores/<metadata_store_id>/<resource_noun>/<resource_id>.

  • schema_title (str) – Required. schema_title identifies the schema title used by the resource.

  • display_name (str) – Optional. The user-defined name of the resource.

  • schema_version (str) – Optional. schema_version specifies the version used by the resource. If not set, defaults to use the latest version.

  • description (str) – Optional. Describes the purpose of the resource to be created.

  • metadata (Dict) – Optional. Contains the metadata information that will be stored in the resource.

  • metadata_store_id (str) – The <metadata_store_id> portion of the resource name with the format: projects/123/locations/us-central1/metadataStores/<metadata_store_id>/<resource_noun>/<resource_id> If not provided, the MetadataStore’s ID will be set to “default”.

  • project (str) – Project used to retrieve or create this resource. Overrides project set in aiplatform.init.

  • location (str) – Location used to retrieve or create this resource. Overrides location set in aiplatform.init.

  • credentials (auth_credentials.Credentials) – Custom credentials used to retrieve or create this resource. Overrides credentials set in aiplatform.init.

• Returns

  Instantiated representation of the managed Metadata resource.

• Return type

  resource (_Resource)

classmethod get_with_uri(uri: str, *, metadata_store_id: Optional[str] = 'default', project: Optional[str] = None, location: Optional[str] = None, credentials: Optional[google.auth.credentials.Credentials] = None)

Get an Artifact by it’s uri.

If more than one Artifact with this uri is in the metadata store then the Artifact with the latest create_time is returned.

• Parameters

  • uri (str) – Required. Uri of the Artifact to retrieve.

  • metadata_store_id (str) – Optional. MetadataStore to retrieve Artifact from. If not set, metadata_store_id is set to “default”. If artifact_name is a fully-qualified resource, its metadata_store_id overrides this one.

  • project (str) – Optional. Project to retrieve the artifact from. If not set, project set in aiplatform.init will be used.

  • location (str) – Optional. Location to retrieve the Artifact from. If not set, location set in aiplatform.init will be used.

  • credentials (auth_credentials.Credentials) – Optional. Custom credentials to use to retrieve this Artifact. Overrides credentials set in aiplatform.init.

• Returns

  Artifact with given uri.

• Return type

  Artifact

• Raises

  ValueError – If no Artifact exists with the provided uri.

property labels(: Dict[str, str )

User-defined labels containing metadata about this resource.

Read more about labels at https://goo.gl/xmQnxf

property lineage_console_uri(: [str](https://python.readthedocs.io/en/latest/library/stdtypes.html#str )

Cloud console uri to view this Artifact Lineage.

classmethod list(filter: Optional[str] = None, metadata_store_id: str = 'default', project: Optional[str] = None, location: Optional[str] = None, credentials: Optional[google.auth.credentials.Credentials] = None, order_by: Optional[str] = None)

List resources that match the list filter in target metadataStore.

• Parameters

  • filter (str) – Optional. A query to filter available resources for matching results.

  • metadata_store_id (str) – The <metadata_store_id> portion of the resource name with the format: projects/123/locations/us-central1/metadataStores/<metadata_store_id>/<resource_noun>/<resource_id> If not provided, the MetadataStore’s ID will be set to “default”.

  • project (str) – Project used to create this resource. Overrides project set in aiplatform.init.

  • location (str) – Location used to create this resource. Overrides location set in aiplatform.init.

  • credentials (auth_credentials.Credentials) – Custom credentials used to create this resource. Overrides credentials set in aiplatform.init.

  • order_by (str) – Optional. How the list of messages is ordered. Specify the values to order by and an ordering operation. The default sorting order is ascending. To specify descending order for a field, users append a ” desc” suffix; for example: “foo desc, bar”. Subfields are specified with a . character, such as foo.bar. see https://google.aip.dev/132#ordering for more details.

• Returns

  a list of managed Metadata resource.

• Return type

  resources (sequence[_Resource])

property name(: [str](https://python.readthedocs.io/en/latest/library/stdtypes.html#str )

Name of this resource.

property resource_name(: [str](https://python.readthedocs.io/en/latest/library/stdtypes.html#str )

Full qualified resource name.

property state(: Optional[[google.cloud.aiplatform_v1.types.artifact.Artifact.State](../aiplatform_v1/types.md#google.cloud.aiplatform_v1.types.Artifact.State)_ )

The State for this Artifact.

sync_resource()

Syncs local resource with the resource in metadata store.

to_dict()

Returns the resource proto as a dictionary.

update(metadata: Optional[Dict] = None, description: Optional[str] = None, credentials: Optional[google.auth.credentials.Credentials] = None, location: Optional[str] = None)

Updates an existing Metadata resource with new metadata.

• Parameters

  • metadata (Dict) – Optional. metadata contains the updated metadata information.

  • description (str) – Optional. Description describes the resource to be updated.

  • credentials (auth_credentials.Credentials) – Custom credentials to use to update this resource. Overrides credentials set in aiplatform.init.

property update_time(: [datetime.datetime](https://python.readthedocs.io/en/latest/library/datetime.html#datetime.datetime )

Time this resource was last updated.

property uri(: Optional[str )

Uri for this Artifact.

wait()

Helper method that blocks until all futures are complete.

class google.cloud.aiplatform.AutoMLForecastingTrainingJob(display_name: Optional[str] = None, optimization_objective: Optional[str] = None, column_specs: Optional[Dict[str, str]] = None, column_transformations: Optional[List[Dict[str, Dict[str, str]]]] = None, project: Optional[str] = None, location: Optional[str] = None, credentials: Optional[google.auth.credentials.Credentials] = None, labels: Optional[Dict[str, str]] = None, training_encryption_spec_key_name: Optional[str] = None, model_encryption_spec_key_name: Optional[str] = None)

Bases: google.cloud.aiplatform.training_jobs._ForecastingTrainingJob

Class to train AutoML forecasting models.

The AutoMLForecastingTrainingJob class uses the AutoML training method to train and run a forecasting model. The AutoML training method is a good choice for most forecasting use cases. If your use case doesn’t benefit from the Seq2seq or the Temporal fusion transformer training method offered by the SequenceToSequencePlusForecastingTrainingJob and [TemporalFusionTransformerForecastingTrainingJob]https://cloud.google.com/python/docs/reference/aiplatform/latest/google.cloud.aiplatform.TemporalFusionTransformerForecastingTrainingJob) classes respectively, then AutoML is likely the best training method for your forecasting predictions.

For sample code that shows you how to use AutoMLForecastingTrainingJob see the Create a training pipeline forecasting sample on GitHub.

Constructs a Forecasting Training Job.

• Parameters

  • display_name (str) – Optional. The user-defined name of this TrainingPipeline.

  • optimization_objective (str) – Optional. Objective function the model is to be optimized towards. The training process creates a Model that optimizes the value of the objective function over the validation set. The supported optimization objectives: “minimize-rmse” (default) - Minimize root-mean-squared error (RMSE). “minimize-mae” - Minimize mean-absolute error (MAE). “minimize-rmsle” - Minimize root-mean-squared log error (RMSLE). “minimize-rmspe” - Minimize root-mean-squared percentage error (RMSPE). “minimize-wape-mae” - Minimize the combination of weighted absolute percentage error (WAPE)

    and mean-absolute-error (MAE).

    ”minimize-quantile-loss” - Minimize the quantile loss at the defined quantiles.

    (Set this objective to build quantile forecasts.)

  • column_specs (Dict[str, *[str](https://python.readthedocs.io/en/latest/library/stdtypes.html#str)]*) – Optional. Alternative to column_transformations where the keys of the dict are column names and their respective values are one of AutoMLTabularTrainingJob.column_data_types. When creating transformation for BigQuery Struct column, the column should be flattened using “.” as the delimiter. Only columns with no child should have a transformation. If an input column has no transformations on it, such a column is ignored by the training, except for the targetColumn, which should have no transformations defined on. Only one of column_transformations or column_specs should be passed.

  • column_transformations (List[Dict[str, **Dict[str, *[str](https://python.readthedocs.io/en/latest/library/stdtypes.html#str)]]]*) – Optional. Transformations to apply to the input columns (i.e. columns other than the targetColumn). Each transformation may produce multiple result values from the column’s value, and all are used for training. When creating transformation for BigQuery Struct column, the column should be flattened using “.” as the delimiter. Only columns with no child should have a transformation. If an input column has no transformations on it, such a column is ignored by the training, except for the targetColumn, which should have no transformations defined on. Only one of column_transformations or column_specs should be passed. Consider using column_specs as column_transformations will be deprecated eventually.

  • project (str) – Optional. Project to run training in. Overrides project set in aiplatform.init.

  • location (str) – Optional. Location to run training in. Overrides location set in aiplatform.init.

  • credentials (auth_credentials.Credentials) – Optional. Custom credentials to use to run call training service. Overrides credentials set in aiplatform.init.

  • labels (Dict[str, *[str](https://python.readthedocs.io/en/latest/library/stdtypes.html#str)]*) – Optional. The labels with user-defined metadata to organize TrainingPipelines. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels.

  • training_encryption_spec_key_name (Optional[str]) – Optional. The Cloud KMS resource identifier of the customer managed encryption key used to protect the training pipeline. Has the form: projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key. The key needs to be in the same region as where the compute resource is created. If set, this TrainingPipeline will be secured by this key. Note: Model trained by this TrainingPipeline is also secured by this key if model_to_upload is not set separately. Overrides encryption_spec_key_name set in aiplatform.init.

  • model_encryption_spec_key_name (Optional[str]) – Optional. The Cloud KMS resource identifier of the customer managed encryption key used to protect the model. Has the form: projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key. The key needs to be in the same region as where the compute resource is created. If set, the trained Model will be secured by this key. Overrides encryption_spec_key_name set in aiplatform.init.

• Raises

  ValueError – If both column_transformations and column_specs were provided.

cancel()

Asynchronously attempts to cancel a training job.

The server makes a best effort to cancel the job, but the training job can’t always be cancelled. If the training job is canceled, its state transitions to CANCELLED and it’s not deleted.

• Raises

  RuntimeError – If this training job isn’t running, then a runtime error is raised.

client_class()

alias of google.cloud.aiplatform.utils.PipelineClientWithOverride

property create_time(: [datetime.datetime](https://python.readthedocs.io/en/latest/library/datetime.html#datetime.datetime )

Time this resource was created.

delete(sync: bool = True)

Deletes this Vertex AI resource. WARNING: This deletion is permanent.

• Parameters

  sync (bool) – Whether to execute this deletion synchronously. If False, this method will be executed in concurrent Future and any downstream object will be immediately returned and synced when the Future has completed.

property display_name(: [str](https://python.readthedocs.io/en/latest/library/stdtypes.html#str )

Display name of this resource.

done()

Method indicating whether a job has completed.

• Returns

  True if the job has completed.

property encryption_spec(: Optional[[google.cloud.aiplatform_v1.types.encryption_spec.EncryptionSpec](../aiplatform_v1/types.md#google.cloud.aiplatform_v1.types.EncryptionSpec)_ )

Customer-managed encryption key options for this Vertex AI resource.

If this is set, then all resources created by this Vertex AI resource will be encrypted with the provided encryption key.

property end_time(: Optional[datetime.datetime )

Optional. The time when the training job entered the PIPELINE_STATE_SUCCEEDED, PIPELINE_STATE_FAILED, or PIPELINE_STATE_CANCELLED state.

property error(: Optional[google.rpc.status_pb2.Status )

Optional. Detailed error information for this training job resource. Error information is created only when the state of the training job is PIPELINE_STATE_FAILED or PIPELINE_STATE_CANCELLED.

property evaluated_data_items_bigquery_uri(: Optional[str )

BigQuery location of exported evaluated examples from the Training Job :returns:

BigQuery uri for the exported evaluated examples if the export

feature is enabled for training.

None: If the export feature was not enabled for training.

• Return type

  str

property gca_resource(: [proto.message.Message](https://proto-plus-python.readthedocs.io/en/latest/reference/message.html#proto.message.Message )

The underlying resource proto representation.

classmethod get(resource_name: str, project: Optional[str] = None, location: Optional[str] = None, credentials: Optional[google.auth.credentials.Credentials] = None)

Gets a training job using the resource_name that’s passed in.

• Parameters

  • resource_name (str) – Required. A fully-qualified resource name or ID.

  • project (str) – Optional. The name of the Google Cloud project to retrieve the training job from. This overrides the project that was set by aiplatform.init.

  • location (str) – Optional. The Google Cloud region from where the training job is retrieved. This region overrides the region that was set by aiplatform.init.

  • credentials (auth_credentials.Credentials) – Optional. The credentials that are used to upload this model. These credentials override the credentials set by aiplatform.init.

• Raises

  ValueError – A ValueError is raised if the task definition of the retrieved training job doesn’t match the custom training task definition.

• Returns

  A Vertex AI training job.

get_model(sync=True)

Returns the Vertex AI model produced by this training job.

• Parameters

  sync (bool) – If set to true, this method runs synchronously. If false, this method runs asynchronously.

• Returns

  The Vertex AI model that was produced by this training job.

• Raises

  RuntimeError – A runtime error is raised if the training job failed or if a model wasn’t produced by the training job.

property has_failed(: [bool](https://python.readthedocs.io/en/latest/library/functions.html#bool )

Returns true if the training job failed, otherwise false.

property labels(: Dict[str, str )

User-defined labels containing metadata about this resource.

Read more about labels at https://goo.gl/xmQnxf

classmethod list(filter: Optional[str] = None, order_by: Optional[str] = None, project: Optional[str] = None, location: Optional[str] = None, credentials: Optional[google.auth.credentials.Credentials] = None)

Lists all instances of this training job resource.

The following shows an example of how to call CustomTrainingJob.list:

``

`

py aiplatform.CustomTrainingJob.list(

filter=’display_name=”experiment_a27”’, order_by=’create_time desc’

• Parameters

  • filter (str) – Optional. An expression for filtering the results of the request. For field names, snake_case and camelCase are supported.

  • order_by (str) – Optional. A comma-separated list of fields used to sort the returned traing job resources. The defauilt sorting order is ascending. To sort by a field name in descending order, use desc after the field name. The following fields are supported: display_name, create_time, update_time.

  • project (str) – Optional. The name of the Google Cloud project to which to retrieve the list of training job resources. This overrides the project that was set by aiplatform.init.

  • location (str) – Optional. The Google Cloud region from where the training job resources are retrieved. This region overrides the region that was set by aiplatform.init.

  • credentials (auth_credentials.Credentials) – Optional. The credentials that are used to retrieve list. These credentials override the credentials set by aiplatform.init.

• Returns

  A list of training job resources.

• Return type

  List[VertexAiResourceNoun]

property name(: [str](https://python.readthedocs.io/en/latest/library/stdtypes.html#str )

Name of this resource.

property resource_name(: [str](https://python.readthedocs.io/en/latest/library/stdtypes.html#str )

Full qualified resource name.

run(dataset: google.cloud.aiplatform.datasets.time_series_dataset.TimeSeriesDataset, target_column: str, time_column: str, time_series_identifier_column: str, unavailable_at_forecast_columns: List[str], available_at_forecast_columns: List[str], forecast_horizon: int, data_granularity_unit: str, data_granularity_count: int, training_fraction_split: Optional[float] = None, validation_fraction_split: Optional[float] = None, test_fraction_split: Optional[float] = None, predefined_split_column_name: Optional[str] = None, timestamp_split_column_name: Optional[str] = None, weight_column: Optional[str] = None, time_series_attribute_columns: Optional[List[str]] = None, context_window: Optional[int] = None, export_evaluated_data_items: bool = False, export_evaluated_data_items_bigquery_destination_uri: Optional[str] = None, export_evaluated_data_items_override_destination: bool = False, quantiles: Optional[List[float]] = None, validation_options: Optional[str] = None, budget_milli_node_hours: int = 1000, model_display_name: Optional[str] = None, model_labels: Optional[Dict[str, str]] = None, model_id: Optional[str] = None, parent_model: Optional[str] = None, is_default_version: Optional[bool] = True, model_version_aliases: Optional[Sequence[str]] = None, model_version_description: Optional[str] = None, additional_experiments: Optional[List[str]] = None, hierarchy_group_columns: Optional[List[str]] = None, hierarchy_group_total_weight: Optional[float] = None, hierarchy_temporal_total_weight: Optional[float] = None, hierarchy_group_temporal_total_weight: Optional[float] = None, window_column: Optional[str] = None, window_stride_length: Optional[int] = None, window_max_count: Optional[int] = None, holiday_regions: Optional[List[str]] = None, sync: bool = True, create_request_timeout: Optional[float] = None, enable_probabilistic_inference: bool = False)

Runs the training job and returns a model.

If training on a Vertex AI dataset, you can use one of the following split configurations:

Data fraction splits:
Any of `training_fraction_split`, `validation_fraction_split` and
`test_fraction_split` may optionally be provided, they must sum to up to 1. If
the provided ones sum to less than 1, the remainder is assigned to sets as
decided by Vertex AI. If none of the fractions are set, by default roughly 80%
of data will be used for training, 10% for validation, and 10% for test.

Predefined splits:
Assigns input data to training, validation, and test sets based on the value of a provided key.
If using predefined splits, `predefined_split_column_name` must be provided.
Supported only for tabular Datasets.

Timestamp splits:
Assigns input data to training, validation, and test sets
based on a provided timestamps. The youngest data pieces are
assigned to training set, next to validation set, and the oldest
to the test set.
Supported only for tabular Datasets.

• Parameters

  • dataset (datasets.TimeSeriesDataset) – Required. The dataset within the same Project from which data will be used to train the Model. The Dataset must use schema compatible with Model being trained, and what is compatible should be described in the used TrainingPipeline’s [training_task_definition] [google.cloud.aiplatform.v1beta1.TrainingPipeline.training_task_definition]. For time series Datasets, all their data is exported to training, to pick and choose from.

  • target_column (str) – Required. Name of the column that the Model is to predict values for. This column must be unavailable at forecast.

  • time_column (str) – Required. Name of the column that identifies time order in the time series. This column must be available at forecast.

  • time_series_identifier_column (str) – Required. Name of the column that identifies the time series.

  • unavailable_at_forecast_columns (List[str]) – Required. Column names of columns that are unavailable at forecast. Each column contains information for the given entity (identified by the [time_series_identifier_column]) that is unknown before the forecast (e.g. population of a city in a given year, or weather on a given day).

  • available_at_forecast_columns (List[str]) – Required. Column names of columns that are available at forecast. Each column contains information for the given entity (identified by the [time_series_identifier_column]) that is known at forecast.

  • forecast_horizon – (int): Required. The amount of time into the future for which forecasted values for the target are returned. Expressed in number of units defined by the [data_granularity_unit] and [data_granularity_count] field. Inclusive.

  • data_granularity_unit (str) – Required. The data granularity unit. Accepted values are minute, hour, day, week, month, year.

  • data_granularity_count (int) – Required. The number of data granularity units between data points in the training data. If [data_granularity_unit] is minute, can be 1, 5, 10, 15, or 30. For all other values of [data_granularity_unit], must be 1.

  • predefined_split_column_name (str) – Optional. The key is a name of one of the Dataset’s data columns. The value of the key (either the label’s value or value in the column) must be one of {TRAIN, VALIDATE, TEST}, and it defines to which set the given piece of data is assigned. If for a piece of data the key is not present or has an invalid value, that piece is ignored by the pipeline.

    Supported only for tabular and time series Datasets.

  • timestamp_split_column_name (str) – Optional. The key is a name of one of the Dataset’s data columns. The value of the key values of the key (the values in the column) must be in RFC 3339 date-time format, where time-offset = “Z” (e.g. 1985-04-12T23:20:50.52Z). If for a piece of data the key is not present or has an invalid value, that piece is ignored by the pipeline. Supported only for tabular and time series Datasets. This parameter must be used with training_fraction_split, validation_fraction_split, and test_fraction_split.

  • weight_column (str) – Optional. Name of the column that should be used as the weight column. Higher values in this column give more importance to the row during Model training. The column must have numeric values between 0 and 10000 inclusively, and 0 value means that the row is ignored. If the weight column field is not set, then all rows are assumed to have equal weight of 1.

  • time_series_attribute_columns (List[str]) – Optional. Column names that should be used as attribute columns. Each column is constant within a time series.

  • context_window (int) – Optional. The amount of time into the past training and prediction data is used for model training and prediction respectively. Expressed in number of units defined by the [data_granularity_unit] and [data_granularity_count] fields. When not provided uses the default value of 0 which means the model sets each series context window to be 0 (also known as “cold start”). Inclusive.

  • export_evaluated_data_items (bool) – Whether to export the test set predictions to a BigQuery table. If False, then the export is not performed.

  • export_evaluated_data_items_bigquery_destination_uri (string) – Optional. URI of desired destination BigQuery table for exported test set predictions.

    Expected format: bq://<project_id>:<dataset_id>:<table>

    If not specified, then results are exported to the following auto-created BigQuery table: <project_id>:export_evaluated_examples_<model_name>_<yyyy_MM_dd'T'HH_mm_ss_SSS'Z'>.evaluated_examples

    Applies only if [export_evaluated_data_items] is True.

  • export_evaluated_data_items_override_destination (bool) – Whether to override the contents of [export_evaluated_data_items_bigquery_destination_uri], if the table exists, for exported test set predictions. If False, and the table exists, then the training job will fail.

    Applies only if [export_evaluated_data_items] is True and [export_evaluated_data_items_bigquery_destination_uri] is specified.

  • quantiles (List[float]) – Quantiles to use for the minimize-quantile-loss [AutoMLForecastingTrainingJob.optimization_objective]. This argument is required in this case.

    Accepts up to 5 quantiles in the form of a double from 0 to 1, exclusive. Each quantile must be unique.

  • validation_options (str) – Validation options for the data validation component. The available options are: “fail-pipeline” - (default), will validate against the validation and fail the pipeline

    if it fails.

    ”ignore-validation” - ignore the results of the validation and continue the pipeline

  • budget_milli_node_hours (int) – Optional. The train budget of creating this Model, expressed in milli node hours i.e. 1,000 value in this field means 1 node hour. The training cost of the model will not exceed this budget. The final cost will be attempted to be close to the budget, though may end up being (even) noticeably smaller - at the backend’s discretion. This especially may happen when further model training ceases to provide any improvements. If the budget is set to a value known to be insufficient to train a Model for the given training set, the training won’t be attempted and will error. The minimum value is 1000 and the maximum is 72000.

  • model_display_name (str) – Optional. If the script produces a managed Vertex AI Model. The display name of the Model. The name can be up to 128 characters long and can be consist of any UTF-8 characters.

    If not provided upon creation, the job’s display_name is used.

  • model_labels (Dict[str, *[str](https://python.readthedocs.io/en/latest/library/stdtypes.html#str)]*) – Optional. The labels with user-defined metadata to organize your Models. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels.

  • model_id (str) – Optional. The ID to use for the Model produced by this job, which will become the final component of the model resource name. This value may be up to 63 characters, and valid characters are [a-z0-9_-]. The first character cannot be a number or hyphen.

  • parent_model (str) – Optional. The resource name or model ID of an existing model. The new model uploaded by this job will be a version of parent_model.

    Only set this field when training a new version of an existing model.

  • is_default_version (bool) – Optional. When set to True, the newly uploaded model version will automatically have alias “default” included. Subsequent uses of the model produced by this job without a version specified will use this “default” version.

    When set to False, the “default” alias will not be moved. Actions targeting the model version produced by this job will need to specifically reference this version by ID or alias.

    New model uploads, i.e. version 1, will always be “default” aliased.

  • model_version_aliases (Sequence[str]) – Optional. User provided version aliases so that the model version uploaded by this job can be referenced via alias instead of auto-generated version ID. A default version alias will be created for the first version of the model.

    The format is [a-z][a-zA-Z0-9-]{0,126}[a-z0-9]

  • model_version_description (str) – Optional. The description of the model version being uploaded by this job.

  • additional_experiments (List[str]) – Optional. Additional experiment flags for the time series forcasting training.

  • create_request_timeout (float) – Optional. The timeout for the create request in seconds.

  • hierarchy_group_columns (List[str]) – Optional. A list of time series attribute column names that define the time series hierarchy. Only one level of hierarchy is supported, ex. region for a hierarchy of stores or department for a hierarchy of products. If multiple columns are specified, time series will be grouped by their combined values, ex. (blue, large) for color and size, up to 5 columns are accepted. If no group columns are specified, all time series are considered to be part of the same group.

  • hierarchy_group_total_weight (float) – Optional. The weight of the loss for predictions aggregated over time series in the same hierarchy group.

  • hierarchy_temporal_total_weight (float) – Optional. The weight of the loss for predictions aggregated over the horizon for a single time series.

  • hierarchy_group_temporal_total_weight (float) – Optional. The weight of the loss for predictions aggregated over both the horizon and time series in the same hierarchy group.

  • window_column (str) – Optional. Name of the column that should be used to filter input rows. The column should contain either booleans or string booleans; if the value of the row is True, generate a sliding window from that row.

  • window_stride_length (int) – Optional. Step length used to generate input examples. Every window_stride_length rows will be used to generate a sliding window.

  • window_max_count (int) – Optional. Number of rows that should be used to generate input examples. If the total row count is larger than this number, the input data will be randomly sampled to hit the count.

  • holiday_regions (List[str]) – Optional. The geographical regions to use when creating holiday features. This option is only allowed when data_granularity_unit is day. Acceptable values can come from any of the following levels:

    Top level: GLOBAL Second level: continental regions

    NA: North America JAPAC: Japan and Asia Pacific EMEA: Europe, the Middle East and Africa LAC: Latin America and the Caribbean

    Third level: countries from ISO 3166-1 Country codes.

  • sync (bool) – Whether to execute this method synchronously. If False, this method will be executed in concurrent Future and any downstream object will be immediately returned and synced when the Future has completed.

  • enable_probabilistic_inference (bool) – If probabilistic inference is enabled, the model will fit a distribution that captures the uncertainty of a prediction. At inference time, the predictive distribution is used to make a point prediction that minimizes the optimization objective. For example, the mean of a predictive distribution is the point prediction that minimizes RMSE loss. If quantiles are specified, then the quantiles of the distribution are also returned. The optimization objective cannot be minimize-quantile-loss.

• Returns

  The trained Vertex AI Model resource or None if training did not

    produce a Vertex AI Model.
  

• Return type

  model

• Raises

  RuntimeError – If Training job has already been run or is waiting to run.

property start_time(: Optional[datetime.datetime )

Optional. The time when the training job first entered the PIPELINE_STATE_RUNNING state.

property state(: Optional[[google.cloud.aiplatform_v1.types.pipeline_state.PipelineState](../aiplatform_v1/types.md#google.cloud.aiplatform_v1.types.PipelineState)_ )

Current training state.

to_dict()

Returns the resource proto as a dictionary.

property update_time(: [datetime.datetime](https://python.readthedocs.io/en/latest/library/datetime.html#datetime.datetime )

Time this resource was last updated.

wait()

Helper method that blocks until all futures are complete.

wait_for_resource_creation()

Waits until the resource has been created.

class google.cloud.aiplatform.AutoMLImageTrainingJob(display_name: Optional[str] = None, prediction_type: str = 'classification', multi_label: bool = False, model_type: str = 'CLOUD', base_model: Optional[google.cloud.aiplatform.models.Model] = None, incremental_train_base_model: Optional[google.cloud.aiplatform.models.Model] = None, project: Optional[str] = None, location: Optional[str] = None, credentials: Optional[google.auth.credentials.Credentials] = None, labels: Optional[Dict[str, str]] = None, training_encryption_spec_key_name: Optional[str] = None, model_encryption_spec_key_name: Optional[str] = None, checkpoint_name: Optional[str] = None, trainer_config: Optional[Dict[str, str]] = None, metric_spec: Optional[Dict[str, str]] = None, parameter_spec: Optional[Dict[str, Union[google.cloud.aiplatform.hyperparameter_tuning.DoubleParameterSpec, google.cloud.aiplatform.hyperparameter_tuning.IntegerParameterSpec, google.cloud.aiplatform.hyperparameter_tuning.CategoricalParameterSpec, google.cloud.aiplatform.hyperparameter_tuning.DiscreteParameterSpec]]] = None, search_algorithm: Optional[str] = None, measurement_selection: Optional[str] = None)

Bases: google.cloud.aiplatform.training_jobs._TrainingJob

Creates an AutoML image training job.

Use the AutoMLImageTrainingJob class to create, train, and return an image model. For more information about working with image data models in Vertex AI, see Image data.

For an example of how to use the AutoMLImageTrainingJob class, see the tutorial in the AutoML image classification notebook on GitHub.

• Parameters

  • display_name (str) – Optional. The user-defined name of the training pipeline. The name must contain 128 or fewer UTF-8 characters.

  • prediction_type (str) – The type of prediction the model produces. Valid values are: classification to predict one of multiple target values that are selected from each row. object_detection to predict a value based on its relation to other values. object_detection is available only for columns that contain semantically numeric values. For example, columns might contain numeric values stored as an integer, float, or string.

  • multi_label (bool = False) – Required. If false, a single-label (multi-class) model is trained (i.e. assuming that for each image just up to one annotation may be applicable). If true, a multi-label model is trained. If you pass in true, it’s assumed multiple annotations might apply to each image. Multi-label models are supported only when prediction_type is set to classification. If multi_label is true and prediction_type is object_detection, then then multi_label is ignored. The default value is false.

  • model_type (str = "CLOUD") – Required. The type of model to create. The following are the valid values:

    CLOUD - The default for image classification. This model type is deigned to work in Google Cloud and can’t be exported.

    CLOUD_1 - This model type is deigned to work in Google Cloud and can’t be exported. This model type is expected to have higher prediction accuracy than CLOUD.

    CLOUD_HIGH_ACCURACY_1 - The default for image object detection. This model type is deigned to work in Google Cloud and can’t be exported. It’s designed to have a higher latency and a higher prediction quality than other cloud model types.

    CLOUD_LOW_LATENCY_1 - This model type is deigned to work in Google Cloud and can’t be exported. It’s also designed to have low latency, but might might lower prediction quality than other cloud model types.

    MOBILE_TF_LOW_LATENCY_1 - This model type is deigned to work in Google Cloud and can be exported as a TensorFlow or Core ML model for use on a mobile or edge device. It’s also designed to have to have low latency, but might have lower prediction quality than other mobile models types.

    MOBILE_TF_VERSATILE_1 - This model type is deigned to work in Google Cloud and can be exported as a TensorFlow or Core ML model for use on a mobile or edge device.

    MOBILE_TF_HIGH_ACCURACY_1 - This model type is deigned to work in Google Cloud and can be exported as a TensorFlow or Core ML model for use on a mobile or edge device. It’s designed to have a higher latency, but should also have a higher prediction quality than other mobile model types.

    EFFICIENTNET - This model type is available in the Vertex Model Garden image classification training with customizable hyperparameters. It’s deigned to work in Google Cloud and can’t be exported.

    VIT - This model type is available in the Vertex Model Garden image classification training with customizable hyperparameters. Best tailored to be used within Google Cloud, and cannot be exported externally.

    MAXVIT - This model type is available in the Vertex Model Garden image classification training with customizable hyperparameters. It’s deigned to work in Google Cloud and can’t be exported.

    COCA - This model type is available in the Vertex Model Garden image classification training with customizable hyperparameters. It’s deigned to work in Google Cloud and can’t be exported.

    SPINENET - This model type is available in the Vertex Model Garden image object detection training with customizable hyperparameters. It’s deigned to work in Google Cloud and can’t be exported.

    YOLO - This model type is available in the Vertex Model Garden image object detection training with customizable hyperparameters. It’s deigned to work in Google Cloud and can’t be exported.

  • base_model – Optional[models.Model] = Optional. You can specify a base_model for image classification models only. If it is specified, the new model is trained based on the base_model. Otherwise, the new model is trained from scratch. The base model must be in the same Google Project and region as the new model to train, and it must have the same model_type.

  • incremental_train_base_model – Optional[models.Model] = Optional. You can specify an incremental_train_base_model for image classification models and object detection models only. If specified, the new model is incrementally trained using an existing model as the starting point. This can reduce the training time. If not specified, the new model is trained from scratch. The model specified in incremental_train_base_model model must be in the same Google Project and region as the new model to train, and it must have the same prediction_type and model_type.

  • project (str) – Optional. The Google Cloud region where this where the training runs. This region overrides the region that was set by aiplatform.init.

  • location (str) – Optional. Location to run training in. Overrides location set in aiplatform.init.

  • credentials (auth_credentials.Credentials) – Optional. The credentials that are used to train the model. These credentials override the credentials set by aiplatform.init.

  • labels (Dict[str, *[str](https://python.readthedocs.io/en/latest/library/stdtypes.html#str)]*) – Optional. Labels with user-defined metadata to organize your training pipelines. The maximum length of a key and value is 64 unicode characters. Labels and keys can contain only lowercase letters, numeric characters, underscores, and dashes. International characters are allowed. For more information and examples of using labels, see Using labels to organize Google Cloud Platform resources.

  • training_encryption_spec_key_name (Optional[str]) – Optional. The Cloud KMS resource identifier of the customer managed encryption key used to protect the training pipeline. The key has the following format: projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key. The key needs to be in the same region as where the compute resource is created. If set, the key secures the training pipeline and overrides the key set using aiplatform.init. The model trained by this training pipeline is also secured by this key if model_encryption_spec_key_name is not set separately.

  • model_encryption_spec_key_name (Optional[str]) – Optional. The Cloud KMS resource identifier of the customer managed encryption key used to protect the model. The key has the following format: projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key. The key needs to be in the same region as where the compute resource is created. If set, the key secures the trained model and overrides the key set using aiplatform.init.

  • checkpoint_name – Optional[str] = Optional. The field is reserved for Model Garden model training and is based on the provided pre-trained model checkpoint. checkpoint_name is needed only when model_type is set to EFFICIENTNET, VIT, COCA, SPINENET, or YOLO.

  • trainer_config – Optional[Dict[str, str]] = None, Optional. A field that’s used with the Model Garden model training when passing customized configs for the trainer. The following is an example that uses all trainer_config parameters:

```python
``
```



```python
`
```

py
trainer_config = {

> ’global_batch_size’: ‘8’,
> ‘learning_rate’: ‘0.001’,
> ‘optimizer_type’: ‘sgd’,
> ‘optimizer_momentum’: ‘0.9’,
> ‘train_steps’: ‘10000’,
> ‘accelerator_count’: ‘1’,
> ‘anchor_size’: ‘8’,  – IOD only

trainer_config is required for only Model Garden models when
model_type is EFFICIENTNET, VIT, COCA, SPINENET, or
YOLO.



* **metric_spec** – Dict[str, str] Required. A dictionary that represents
metrics use for optimization. The dictionary key is the
metric_id that’s reported by your training job and can be
‘loss’ or ‘accuracy’. The dictionary value is the optimization
goal of the metric, and can be ‘minimize’ or ‘maximize’. The
following is an example of a metric_spec: metric_spec =
{‘loss’: ‘minimize’, ‘accuracy’: ‘maximize’}. metric_spec is
required for only Model Garden models when model_type is
EFFICIENTNET, VIT, COCA, SPINENET, or YOLO.


* **parameter_spec** (*Dict**[*[*str*](https://python.readthedocs.io/en/latest/library/stdtypes.html#str)*, **hpt._ParameterSpec**]*) – Required. A
dictionary representing parameters to use for optimization.
The dictionary key is the metric_id that’s reported by your
training job as a command line keyword argument. The dictionary
value is the parameter specification of the metric. The
following is an example of how to specify a parameter_spec:



```python
``
```



```python
`
```

py
from google.cloud.aiplatform import hpt as hpt

parameter_spec = {

    ‘learning_rate’: hpt.DoubleParameterSpec(min=1e-7, max=1, scale=’linear’),

Supported parameter specifications can be found in
aiplatform.hyperparameter_tuning. The following parameter
specifications are supported: Union[DoubleParameterSpec,
IntegerParameterSpec, CategoricalParameterSpec,
DiscreteParameterSpec]. parameter_spec \`is required for only
Model Garden models when \`model_type is EFFICIENTNET, VIT,
COCA, SPINENET, or YOLO.



* **search_algorithm** ([*str*](https://python.readthedocs.io/en/latest/library/stdtypes.html#str)) – The search algorithm that’s specified for
the study. The search algorithm can be one of the following:

None - If you don’t specify an algorithm, your job uses the
default Vertex AI algorithm. The default algorithm applies
Bayesian optimization to arrive at the optimal solution with a
more effective search over the parameter space.

’grid’ - A simple grid search within the feasible space. This
option is particularly useful if you want to specify a quantity
of trials that is greater than the number of points in the
feasible space. In these cases, if you don’t specify a grid
search, the Vertex AI default algorithm might generate duplicate
suggestions. To use grid search, all parameter specifications
must be IntegerParameterSpec, CategoricalParameterSpec, or
DiscreteParameterSpec.

’random’ - A simple random search within the feasible space.

search_algorithm is required for only Model Garden models when
model_type is EFFICIENTNE\`T, VIT, COCA, SPINENET, or
YOLO.



* **measurement_selection** ([*str*](https://python.readthedocs.io/en/latest/library/stdtypes.html#str)) – Indicates which measurement to use
when the service selects the final measurement from previously
reported intermediate measurements. measurement_selection can
be best or last. If you expect your measurements to
monotonically improve, then choose last. If your system can
over-train and you expect the performance to improve and then
start to decline, then choose best. If your measurements are
significantly noisy or not reproducible, then best is likely
to be over-optimistic, so in this case last is the preferred
option. measurement_selection is required for only Model
Garden models when model_type is EFFICIENTNET, VIT,
COCA, SPINENET, or YOLO.

• Raises

  ValueError – When an invalid prediction_type or model_type is provided.

cancel()

Asynchronously attempts to cancel a training job.

The server makes a best effort to cancel the job, but the training job can’t always be cancelled. If the training job is canceled, its state transitions to CANCELLED and it’s not deleted.

• Raises

  RuntimeError – If this training job isn’t running, then a runtime error is raised.

client_class()

alias of google.cloud.aiplatform.utils.PipelineClientWithOverride

property create_time(: [datetime.datetime](https://python.readthedocs.io/en/latest/library/datetime.html#datetime.datetime )

Time this resource was created.

delete(sync: bool = True)

Deletes this Vertex AI resource. WARNING: This deletion is permanent.

• Parameters

  sync (bool) – Whether to execute this deletion synchronously. If False, this method will be executed in concurrent Future and any downstream object will be immediately returned and synced when the Future has completed.

property display_name(: [str](https://python.readthedocs.io/en/latest/library/stdtypes.html#str )

Display name of this resource.

done()

Method indicating whether a job has completed.

• Returns

  True if the job has completed.

property encryption_spec(: Optional[[google.cloud.aiplatform_v1.types.encryption_spec.EncryptionSpec](../aiplatform_v1/types.md#google.cloud.aiplatform_v1.types.EncryptionSpec)_ )

Customer-managed encryption key options for this Vertex AI resource.

If this is set, then all resources created by this Vertex AI resource will be encrypted with the provided encryption key.

property end_time(: Optional[datetime.datetime )

Optional. The time when the training job entered the PIPELINE_STATE_SUCCEEDED, PIPELINE_STATE_FAILED, or PIPELINE_STATE_CANCELLED state.

property error(: Optional[google.rpc.status_pb2.Status )

Optional. Detailed error information for this training job resource. Error information is created only when the state of the training job is PIPELINE_STATE_FAILED or PIPELINE_STATE_CANCELLED.

property gca_resource(: [proto.message.Message](https://proto-plus-python.readthedocs.io/en/latest/reference/message.html#proto.message.Message )

The underlying resource proto representation.

classmethod get(resource_name: str, project: Optional[str] = None, location: Optional[str] = None, credentials: Optional[google.auth.credentials.Credentials] = None)

Gets a training job using the resource_name that’s passed in.

• Parameters

  • resource_name (str) – Required. A fully-qualified resource name or ID.

  • project (str) – Optional. The name of the Google Cloud project to retrieve the training job from. This overrides the project that was set by aiplatform.init.

  • location (str) – Optional. The Google Cloud region from where the training job is retrieved. This region overrides the region that was set by aiplatform.init.

  • credentials (auth_credentials.Credentials) – Optional. The credentials that are used to upload this model. These credentials override the credentials set by aiplatform.init.

• Raises

  ValueError – A ValueError is raised if the task definition of the retrieved training job doesn’t match the custom training task definition.

• Returns

  A Vertex AI training job.

get_model(sync=True)

Returns the Vertex AI model produced by this training job.

• Parameters

  sync (bool) – If set to true, this method runs synchronously. If false, this method runs asynchronously.

• Returns

  The Vertex AI model that was produced by this training job.

• Raises

  RuntimeError – A runtime error is raised if the training job failed or if a model wasn’t produced by the training job.

property has_failed(: [bool](https://python.readthedocs.io/en/latest/library/functions.html#bool )

Returns true if the training job failed, otherwise false.

property labels(: Dict[str, str )

User-defined labels containing metadata about this resource.

Read more about labels at https://goo.gl/xmQnxf

classmethod list(filter: Optional[str] = None, order_by: Optional[str] = None, project: Optional[str] = None, location: Optional[str] = None, credentials: Optional[google.auth.credentials.Credentials] = None)

Lists all instances of this training job resource.

The following shows an example of how to call CustomTrainingJob.list:

``

`

py aiplatform.CustomTrainingJob.list(

filter=’display_name=”experiment_a27”’, order_by=’create_time desc’

• Parameters

  • filter (str) – Optional. An expression for filtering the results of the request. For field names, snake_case and camelCase are supported.

  • order_by (str) – Optional. A comma-separated list of fields used to sort the returned traing job resources. The defauilt sorting order is ascending. To sort by a field name in descending order, use desc after the field name. The following fields are supported: display_name, create_time, update_time.

  • project (str) – Optional. The name of the Google Cloud project to which to retrieve the list of training job resources. This overrides the project that was set by aiplatform.init.

  • location (str) – Optional. The Google Cloud region from where the training job resources are retrieved. This region overrides the region that was set by aiplatform.init.

  • credentials (auth_credentials.Credentials) – Optional. The credentials that are used to retrieve list. These credentials override the credentials set by aiplatform.init.

• Returns

  A list of training job resources.

• Return type

  List[VertexAiResourceNoun]

property name(: [str](https://python.readthedocs.io/en/latest/library/stdtypes.html#str )

Name of this resource.

property resource_name(: [str](https://python.readthedocs.io/en/latest/library/stdtypes.html#str )

Full qualified resource name.

run(dataset: google.cloud.aiplatform.datasets.image_dataset.ImageDataset, training_fraction_split: Optional[float] = None, validation_fraction_split: Optional[float] = None, test_fraction_split: Optional[float] = None, training_filter_split: Optional[str] = None, validation_filter_split: Optional[str] = None, test_filter_split: Optional[str] = None, budget_milli_node_hours: Optional[int] = None, model_display_name: Optional[str] = None, model_labels: Optional[Dict[str, str]] = None, model_id: Optional[str] = None, parent_model: Optional[str] = None, is_default_version: Optional[bool] = True, model_version_aliases: Optional[Sequence[str]] = None, model_version_description: Optional[str] = None, disable_early_stopping: bool = False, sync: bool = True, create_request_timeout: Optional[float] = None)

Runs the AutoML Image training job and returns a model.

If training on a Vertex AI dataset, you can use one of the following split configurations:

Data fraction splits: Any of training_fraction_split, validation_fraction_split and test_fraction_split may optionally be provided, they must sum to up to 1. If the provided ones sum to less than 1, the remainder is assigned to sets as decided by Vertex AI. If none of the fractions are set, by default roughly 80% of data will be used for training, 10% for validation, and 10% for test.

Data filter splits: Assigns input data to training, validation, and test sets based on the given filters, data pieces not matched by any filter are ignored. Currently only supported for Datasets containing DataItems. If any of the filters in this message are to match nothing, then they can be set as ‘-’ (the minus sign). If using filter splits, all of training_filter_split, validation_filter_split and test_filter_split must be provided. Supported only for unstructured Datasets.

• Parameters

  • dataset (datasets.ImageDataset) – Required. The dataset within the same Project from which data will be used to train the Model. The Dataset must use schema compatible with Model being trained, and what is compatible should be described in the used TrainingPipeline’s [training_task_definition] [google.cloud.aiplatform.v1beta1.TrainingPipeline.training_task_definition]. For tabular Datasets, all their data is exported to training, to pick and choose from.

  • training_fraction_split (float) – Optional. The fraction of the input data that is to be used to train the Model. This is ignored if Dataset is not provided.

  • validation_fraction_split (float) – Optional. The fraction of the input data that is to be used to validate the Model. This is ignored if Dataset is not provided.

  • test_fraction_split (float) – Optional. The fraction of the input data that is to be used to evaluate the Model. This is ignored if Dataset is not provided.

  • training_filter_split (str) – Optional. A filter on DataItems of the Dataset. DataItems that match this filter are used to train the Model. A filter with same syntax as the one used in DatasetService.ListDataItems may be used. If a single DataItem is matched by more than one of the FilterSplit filters, then it is assigned to the first set that applies to it in the training, validation, test order. This is ignored if Dataset is not provided.

  • validation_filter_split (str) – Optional. A filter on DataItems of the Dataset. DataItems that match this filter are used to validate the Model. A filter with same syntax as the one used in DatasetService.ListDataItems may be used. If a single DataItem is matched by more than one of the FilterSplit filters, then it is assigned to the first set that applies to it in the training, validation, test order. This is ignored if Dataset is not provided.

  • test_filter_split (str) – Optional. A filter on DataItems of the Dataset. DataItems that match this filter are used to test the Model. A filter with same syntax as the one used in DatasetService.ListDataItems may be used. If a single DataItem is matched by more than one of the FilterSplit filters, then it is assigned to the first set that applies to it in the training, validation, test order. This is ignored if Dataset is not provided.

  • budget_milli_node_hours (int) – Optional. The train budget of creating this Model, expressed in milli node hours i.e. 1,000 value in this field means 1 node hour. Defaults by prediction_type: classification - For Cloud models the budget must be: 8,000 - 800,000 milli node hours (inclusive). The default value is 192,000 which represents one day in wall time, assuming 8 nodes are used. object_detection - For Cloud models the budget must be: 20,000 - 900,000 milli node hours (inclusive). The default value is 216,000 which represents one day in wall time, assuming 9 nodes are used. The training cost of the model will not exceed this budget. The final cost will be attempted to be close to the budget, though may end up being (even) noticeably smaller - at the backend’s discretion. This especially may happen when further model training ceases to provide any improvements. If the budget is set to a value known to be insufficient to train a Model for the given training set, the training won’t be attempted and will error.

  • model_display_name (str) – Optional. The display name of the managed Vertex AI Model. The name can be up to 128 characters long and can be consist of any UTF-8 characters. If not provided upon creation, the job’s display_name is used.

  • model_labels (Dict[str, *[str](https://python.readthedocs.io/en/latest/library/stdtypes.html#str)]*) – Optional. The labels with user-defined metadata to organize your Models. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels.

  • model_id (str) – Optional. The ID to use for the Model produced by this job, which will become the final component of the model resource name. This value may be up to 63 characters, and valid characters are [a-z0-9_-]. The first character cannot be a number or hyphen.

  • parent_model (str) – Optional. The resource name or model ID of an existing model. The new model uploaded by this job will be a version of parent_model. Only set this field when training a new version of an existing model.

  • is_default_version (bool) – Optional. When set to True, the newly uploaded model version will automatically have alias “default” included. Subsequent uses of the model produced by this job without a version specified will use this “default” version. When set to False, the “default” alias will not be moved. Actions targeting the model version produced by this job will need to specifically reference this version by ID or alias. New model uploads, i.e. version 1, will always be “default” aliased.

  • model_version_aliases (Sequence[str]) – Optional. User provided version aliases so that the model version uploaded by this job can be referenced via alias instead of auto-generated version ID. A default version alias will be created for the first version of the model. The format is [a-z][a-zA-Z0-9-]{0,126}[a-z0-9]

  • model_version_description (str) – Optional. The description of the model version being uploaded by this job.

  • disable_early_stopping – bool = False Required. If true, the entire budget is used. This disables the early stopping feature. By default, the early stopping feature is enabled, which means that training might stop before the entire training budget has been used, if further training does no longer brings significant improvement to the model.

  • sync – bool = True Whether to execute this method synchronously. If False, this method will be executed in concurrent Future and any downstream object will be immediately returned and synced when the Future has completed.

  • create_request_timeout (float) – Optional. The timeout for the create request in seconds.

• Returns

  The trained Vertex AI Model resource or None if training did not

    produce a Vertex AI Model.
  

• Return type

  model

• Raises

  RuntimeError – If Training job has already been run or is waiting to run.

property start_time(: Optional[datetime.datetime )

Optional. The time when the training job first entered the PIPELINE_STATE_RUNNING state.

property state(: Optional[[google.cloud.aiplatform_v1.types.pipeline_state.PipelineState](../aiplatform_v1/types.md#google.cloud.aiplatform_v1.types.PipelineState)_ )

Current training state.

to_dict()

Returns the resource proto as a dictionary.

property update_time(: [datetime.datetime](https://python.readthedocs.io/en/latest/library/datetime.html#datetime.datetime )

Time this resource was last updated.

wait()

Helper method that blocks until all futures are complete.

wait_for_resource_creation()

Waits until the resource has been created.

class google.cloud.aiplatform.AutoMLTabularTrainingJob(display_name: str, optimization_prediction_type: str, optimization_objective: Optional[str] = None, column_specs: Optional[Dict[str, str]] = None, column_transformations: Optional[List[Dict[str, Dict[str, str]]]] = None, optimization_objective_recall_value: Optional[float] = None, optimization_objective_precision_value: Optional[float] = None, project: Optional[str] = None, location: Optional[str] = None, credentials: Optional[google.auth.credentials.Credentials] = None, labels: Optional[Dict[str, str]] = None, training_encryption_spec_key_name: Optional[str] = None, model_encryption_spec_key_name: Optional[str] = None)

Bases: google.cloud.aiplatform.training_jobs._TrainingJob

Constructs a AutoML Tabular Training Job.

Example usage:

job = training_jobs.AutoMLTabularTrainingJob(

display_name=”my_display_name”,
optimization_prediction_type=”classification”,
optimization_objective=”minimize-log-loss”,
column_specs={“column_1”: “auto”, “column_2”: “numeric”},
labels={‘key’: ‘value’},

)

• Parameters

  • display_name (str) – Required. The user-defined name of this TrainingPipeline.

  • optimization_prediction_type (str) – The type of prediction the Model is to produce. “classification” - Predict one out of multiple target values is picked for each row. “regression” - Predict a value based on its relation to other values. This type is available only to columns that contain semantically numeric values, i.e. integers or floating point number, even if stored as e.g. strings.

  • optimization_objective (str) – Optional. Objective function the Model is to be optimized towards. The training task creates a Model that maximizes/minimizes the value of the objective function over the validation set.

    The supported optimization objectives depend on the prediction type, and in the case of classification also the number of distinct values in the target column (two distint values -> binary, 3 or more distinct values -> multi class). If the field is not set, the default objective function is used.

    Classification (binary): “maximize-au-roc” (default) - Maximize the area under the receiver

    operating characteristic (ROC) curve.

    ”minimize-log-loss” - Minimize log loss. “maximize-au-prc” - Maximize the area under the precision-recall curve. “maximize-precision-at-recall” - Maximize precision for a specified

    recall value.

    ”maximize-recall-at-precision” - Maximize recall for a specified

    precision value.

    Classification (multi class): “minimize-log-loss” (default) - Minimize log loss.

    Regression: “minimize-rmse” (default) - Minimize root-mean-squared error (RMSE). “minimize-mae” - Minimize mean-absolute error (MAE). “minimize-rmsle” - Minimize root-mean-squared log error (RMSLE).

  • column_specs (Dict[str, *[str](https://python.readthedocs.io/en/latest/library/stdtypes.html#str)]*) – Optional. Alternative to column_transformations where the keys of the dict are column names and their respective values are one of AutoMLTabularTrainingJob.column_data_types. When creating transformation for BigQuery Struct column, the column should be flattened using “.” as the delimiter. Only columns with no child should have a transformation. If an input column has no transformations on it, such a column is ignored by the training, except for the targetColumn, which should have no transformations defined on. Only one of column_transformations or column_specs should be passed. If none of column_transformations or column_specs is passed, the local credentials being used will try setting column_specs to “auto”. To do this, the local credentials require read access to the GCS or BigQuery training data source.

  • column_transformations (List[Dict[str, **Dict[str, *[str](https://python.readthedocs.io/en/latest/library/stdtypes.html#str)]]]*) – Optional. Transformations to apply to the input columns (i.e. columns other than the targetColumn). Each transformation may produce multiple result values from the column’s value, and all are used for training. When creating transformation for BigQuery Struct column, the column should be flattened using “.” as the delimiter. Only columns with no child should have a transformation. If an input column has no transformations on it, such a column is ignored by the training, except for the targetColumn, which should have no transformations defined on. Only one of column_transformations or column_specs should be passed. Consider using column_specs as column_transformations will be deprecated eventually. If none of column_transformations or column_specs is passed, the local credentials being used will try setting column_transformations to “auto”. To do this, the local credentials require read access to the GCS or BigQuery training data source.

  • optimization_objective_recall_value (float) – Optional. Required when maximize-precision-at-recall optimizationObjective was picked, represents the recall value at which the optimization is done.

    The minimum value is 0 and the maximum is 1.0.

  • optimization_objective_precision_value (float) – Optional. Required when maximize-recall-at-precision optimizationObjective was picked, represents the precision value at which the optimization is done.

    The minimum value is 0 and the maximum is 1.0.

  • project (str) – Optional. Project to run training in. Overrides project set in aiplatform.init.

  • location (str) – Optional. Location to run training in. Overrides location set in aiplatform.init.

  • credentials (auth_credentials.Credentials) – Optional. Custom credentials to use to run call training service. Overrides credentials set in aiplatform.init.

  • labels (Dict[str, *[str](https://python.readthedocs.io/en/latest/library/stdtypes.html#str)]*) – Optional. The labels with user-defined metadata to organize TrainingPipelines. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels.

  • training_encryption_spec_key_name (Optional[str]) – Optional. The Cloud KMS resource identifier of the customer managed encryption key used to protect the training pipeline. Has the form: projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key. The key needs to be in the same region as where the compute resource is created.

    If set, this TrainingPipeline will be secured by this key.

    Note: Model trained by this TrainingPipeline is also secured by this key if model_to_upload is not set separately.

    Overrides encryption_spec_key_name set in aiplatform.init.

  • model_encryption_spec_key_name (Optional[str]) – Optional. The Cloud KMS resource identifier of the customer managed encryption key used to protect the model. Has the form: projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key. The key needs to be in the same region as where the compute resource is created.

    If set, the trained Model will be secured by this key.

    Overrides encryption_spec_key_name set in aiplatform.init.

• Raises

  ValueError – If both column_transformations and column_specs were provided.

cancel()

Asynchronously attempts to cancel a training job.

The server makes a best effort to cancel the job, but the training job can’t always be cancelled. If the training job is canceled, its state transitions to CANCELLED and it’s not deleted.

• Raises

  RuntimeError – If this training job isn’t running, then a runtime error is raised.

client_class()

alias of google.cloud.aiplatform.utils.PipelineClientWithOverride

property create_time(: [datetime.datetime](https://python.readthedocs.io/en/latest/library/datetime.html#datetime.datetime )

Time this resource was created.

delete(sync: bool = True)

Deletes this Vertex AI resource. WARNING: This deletion is permanent.

• Parameters

  sync (bool) – Whether to execute this deletion synchronously. If False, this method will be executed in concurrent Future and any downstream object will be immediately returned and synced when the Future has completed.

property display_name(: [str](https://python.readthedocs.io/en/latest/library/stdtypes.html#str )

Display name of this resource.

done()

Method indicating whether a job has completed.

• Returns

  True if the job has completed.

property encryption_spec(: Optional[[google.cloud.aiplatform_v1.types.encryption_spec.EncryptionSpec](../aiplatform_v1/types.md#google.cloud.aiplatform_v1.types.EncryptionSpec)_ )

Customer-managed encryption key options for this Vertex AI resource.

If this is set, then all resources created by this Vertex AI resource will be encrypted with the provided encryption key.

property end_time(: Optional[datetime.datetime )

Optional. The time when the training job entered the PIPELINE_STATE_SUCCEEDED, PIPELINE_STATE_FAILED, or PIPELINE_STATE_CANCELLED state.

property error(: Optional[google.rpc.status_pb2.Status )

Optional. Detailed error information for this training job resource. Error information is created only when the state of the training job is PIPELINE_STATE_FAILED or PIPELINE_STATE_CANCELLED.

property gca_resource(: [proto.message.Message](https://proto-plus-python.readthedocs.io/en/latest/reference/message.html#proto.message.Message )

The underlying resource proto representation.

classmethod get(resource_name: str, project: Optional[str] = None, location: Optional[str] = None, credentials: Optional[google.auth.credentials.Credentials] = None)

Gets a training job using the resource_name that’s passed in.

• Parameters

  • resource_name (str) – Required. A fully-qualified resource name or ID.

  • project (str) – Optional. The name of the Google Cloud project to retrieve the training job from. This overrides the project that was set by aiplatform.init.

  • location (str) – Optional. The Google Cloud region from where the training job is retrieved. This region overrides the region that was set by aiplatform.init.

  • credentials (auth_credentials.Credentials) – Optional. The credentials that are used to upload this model. These credentials override the credentials set by aiplatform.init.

• Raises

  ValueError – A ValueError is raised if the task definition of the retrieved training job doesn’t match the custom training task definition.

• Returns

  A Vertex AI training job.

static get_auto_column_specs(dataset: google.cloud.aiplatform.datasets.tabular_dataset.TabularDataset, target_column: str)

Returns a dict with all non-target columns as keys and ‘auto’ as values.

Example usage:

column_specs = training_jobs.AutoMLTabularTrainingJob.get_auto_column_specs(

dataset=my_dataset,
target_column=”my_target_column”,

)

• Parameters

  • dataset (datasets.TabularDataset) – Required. Intended dataset.

  • target_column (str) – Required. Intended target column.

• Returns

  Dict[str, str]

    Column names as keys and ‘auto’ as values
  

get_model(sync=True)

Returns the Vertex AI model produced by this training job.

• Parameters

  sync (bool) – If set to true, this method runs synchronously. If false, this method runs asynchronously.

• Returns

  The Vertex AI model that was produced by this training job.

• Raises

  RuntimeError – A runtime error is raised if the training job failed or if a model wasn’t produced by the training job.

property has_failed(: [bool](https://python.readthedocs.io/en/latest/library/functions.html#bool )

Returns true if the training job failed, otherwise false.

property labels(: Dict[str, str )

User-defined labels containing metadata about this resource.

Read more about labels at https://goo.gl/xmQnxf

classmethod list(filter: Optional[str] = None, order_by: Optional[str] = None, project: Optional[str] = None, location: Optional[str] = None, credentials: Optional[google.auth.credentials.Credentials] = None)

Lists all instances of this training job resource.

The following shows an example of how to call CustomTrainingJob.list:

``

`

py aiplatform.CustomTrainingJob.list(

filter=’display_name=”experiment_a27”’, order_by=’create_time desc’

• Parameters

  • filter (str) – Optional. An expression for filtering the results of the request. For field names, snake_case and camelCase are supported.

  • order_by (str) – Optional. A comma-separated list of fields used to sort the returned traing job resources. The defauilt sorting order is ascending. To sort by a field name in descending order, use desc after the field name. The following fields are supported: display_name, create_time, update_time.

  • project (str) – Optional. The name of the Google Cloud project to which to retrieve the list of training job resources. This overrides the project that was set by aiplatform.init.

  • location (str) – Optional. The Google Cloud region from where the training job resources are retrieved. This region overrides the region that was set by aiplatform.init.

  • credentials (auth_credentials.Credentials) – Optional. The credentials that are used to retrieve list. These credentials override the credentials set by aiplatform.init.

• Returns

  A list of training job resources.

• Return type

  List[VertexAiResourceNoun]

property name(: [str](https://python.readthedocs.io/en/latest/library/stdtypes.html#str )

Name of this resource.

property resource_name(: [str](https://python.readthedocs.io/en/latest/library/stdtypes.html#str )

Full qualified resource name.

run(dataset: google.cloud.aiplatform.datasets.tabular_dataset.TabularDataset, target_column: str, training_fraction_split: Optional[float] = None, validation_fraction_split: Optional[float] = None, test_fraction_split: Optional[float] = None, predefined_split_column_name: Optional[str] = None, timestamp_split_column_name: Optional[str] = None, weight_column: Optional[str] = None, budget_milli_node_hours: int = 1000, model_display_name: Optional[str] = None, model_labels: Optional[Dict[str, str]] = None, model_id: Optional[str] = None, parent_model: Optional[str] = None, is_default_version: Optional[bool] = True, model_version_aliases: Optional[Sequence[str]] = None, model_version_description: Optional[str] = None, disable_early_stopping: bool = False, export_evaluated_data_items: bool = False, export_evaluated_data_items_bigquery_destination_uri: Optional[str] = None, export_evaluated_data_items_override_destination: bool = False, additional_experiments: Optional[List[str]] = None, sync: bool = True, create_request_timeout: Optional[float] = None)

Runs the training job and returns a model.

If training on a Vertex AI dataset, you can use one of the following split configurations:

Data fraction splits:
Any of `training_fraction_split`, `validation_fraction_split` and
`test_fraction_split` may optionally be provided, they must sum to up to 1. If
the provided ones sum to less than 1, the remainder is assigned to sets as
decided by Vertex AI. If none of the fractions are set, by default roughly 80%
of data will be used for training, 10% for validation, and 10% for test.

Predefined splits:
Assigns input data to training, validation, and test sets based on the value of a provided key.
If using predefined splits, `predefined_split_column_name` must be provided.
Supported only for tabular Datasets.

Timestamp splits:
Assigns input data to training, validation, and test sets
based on a provided timestamps. The youngest data pieces are
assigned to training set, next to validation set, and the oldest
to the test set.
Supported only for tabular Datasets.

• Parameters

  • dataset (datasets.TabularDataset) – Required. The dataset within the same Project from which data will be used to train the Model. The Dataset must use schema compatible with Model being trained, and what is compatible should be described in the used TrainingPipeline’s [training_task_definition] [google.cloud.aiplatform.v1beta1.TrainingPipeline.training_task_definition]. For tabular Datasets, all their data is exported to training, to pick and choose from.

  • target_column (str) – Required. The name of the column values of which the Model is to predict.

  • training_fraction_split (float) – Optional. The fraction of the input data that is to be used to train the Model. This is ignored if Dataset is not provided.

  • validation_fraction_split (float) – Optional. The fraction of the input data that is to be used to validate the Model. This is ignored if Dataset is not provided.

  • test_fraction_split (float) – Optional. The fraction of the input data that is to be used to evaluate the Model. This is ignored if Dataset is not provided.

  • predefined_split_column_name (str) – Optional. The key is a name of one of the Dataset’s data columns. The value of the key (either the label’s value or value in the column) must be one of {training, validation, test}, and it defines to which set the given piece of data is assigned. If for a piece of data the key is not present or has an invalid value, that piece is ignored by the pipeline.

    Supported only for tabular and time series Datasets.

  • timestamp_split_column_name (str) – Optional. The key is a name of one of the Dataset’s data columns. The value of the key values of the key (the values in the column) must be in RFC 3339 date-time format, where time-offset = “Z” (e.g. 1985-04-12T23:20:50.52Z). If for a piece of data the key is not present or has an invalid value, that piece is ignored by the pipeline. Supported only for tabular and time series Datasets. This parameter must be used with training_fraction_split, validation_fraction_split, and test_fraction_split.

  • weight_column (str) – Optional. Name of the column that should be used as the weight column. Higher values in this column give more importance to the row during Model training. The column must have numeric values between 0 and 10000 inclusively, and 0 value means that the row is ignored. If the weight column field is not set, then all rows are assumed to have equal weight of 1.

  • budget_milli_node_hours (int) – Optional. The train budget of creating this Model, expressed in milli node hours i.e. 1,000 value in this field means 1 node hour. The training cost of the model will not exceed this budget. The final cost will be attempted to be close to the budget, though may end up being (even) noticeably smaller - at the backend’s discretion. This especially may happen when further model training ceases to provide any improvements. If the budget is set to a value known to be insufficient to train a Model for the given training set, the training won’t be attempted and will error. The minimum value is 1000 and the maximum is 72000.

  • model_display_name (str) – Optional. If the script produces a managed Vertex AI Model. The display name of the Model. The name can be up to 128 characters long and can be consist of any UTF-8 characters.

    If not provided upon creation, the job’s display_name is used.

  • model_labels (Dict[str, *[str](https://python.readthedocs.io/en/latest/library/stdtypes.html#str)]*) – Optional. The labels with user-defined metadata to organize your Models. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels.

  • model_id (str) – Optional. The ID to use for the Model produced by this job, which will become the final component of the model resource name. This value may be up to 63 characters, and valid characters are [a-z0-9_-]. The first character cannot be a number or hyphen.

  • parent_model (str) – Optional. The resource name or model ID of an existing model. The new model uploaded by this job will be a version of parent_model.

    Only set this field when training a new version of an existing model.

  • is_default_version (bool) – Optional. When set to True, the newly uploaded model version will automatically have alias “default” included. Subsequent uses of the model produced by this job without a version specified will use this “default” version.

    When set to False, the “default” alias will not be moved. Actions targeting the model version produced by this job will need to specifically reference this version by ID or alias.

    New model uploads, i.e. version 1, will always be “default” aliased.

  • model_version_aliases (Sequence[str]) – Optional. User provided version aliases so that the model version uploaded by this job can be referenced via alias instead of auto-generated version ID. A default version alias will be created for the first version of the model.

    The format is [a-z][a-zA-Z0-9-]{0,126}[a-z0-9]

  • model_version_description (str) – Optional. The description of the model version being uploaded by this job.

  • disable_early_stopping (bool) – Required. If true, the entire budget is used. This disables the early stopping feature. By default, the early stopping feature is enabled, which means that training might stop before the entire training budget has been used, if further training does no longer brings significant improvement to the model.

  • export_evaluated_data_items (bool) – Whether to export the test set predictions to a BigQuery table. If False, then the export is not performed.

  • export_evaluated_data_items_bigquery_destination_uri (string) – Optional. URI of desired destination BigQuery table for exported test set predictions.

    Expected format: bq://<project_id>:<dataset_id>:<table>

    If not specified, then results are exported to the following auto-created BigQuery table: <project_id>:export_evaluated_examples_<model_name>_<yyyy_MM_dd'T'HH_mm_ss_SSS'Z'>.evaluated_examples

    Applies only if [export_evaluated_data_items] is True.

  • export_evaluated_data_items_override_destination (bool) – Whether to override the contents of [export_evaluated_data_items_bigquery_destination_uri], if the table exists, for exported test set predictions. If False, and the table exists, then the training job will fail.

    Applies only if [export_evaluated_data_items] is True and [export_evaluated_data_items_bigquery_destination_uri] is specified.

  • additional_experiments (List[str]) – Optional. Additional experiment flags for the automl tables training.

  • sync (bool) – Whether to execute this method synchronously. If False, this method will be executed in concurrent Future and any downstream object will be immediately returned and synced when the Future has completed.

  • create_request_timeout (float) – Optional. The timeout for the create request in seconds.

• Returns

  The trained Vertex AI Model resource or None if training did not

    produce a Vertex AI Model.
  

• Return type

  model

• Raises

  RuntimeError – If Training job has already been run or is waiting to run.

property start_time(: Optional[datetime.datetime )

Optional. The time when the training job first entered the PIPELINE_STATE_RUNNING state.

property state(: Optional[[google.cloud.aiplatform_v1.types.pipeline_state.PipelineState](../aiplatform_v1/types.md#google.cloud.aiplatform_v1.types.PipelineState)_ )

Current training state.

to_dict()

Returns the resource proto as a dictionary.

property update_time(: [datetime.datetime](https://python.readthedocs.io/en/latest/library/datetime.html#datetime.datetime )

Time this resource was last updated.

wait()

Helper method that blocks until all futures are complete.

wait_for_resource_creation()

Waits until the resource has been created.

class google.cloud.aiplatform.AutoMLTextTrainingJob(display_name: str, prediction_type: str, multi_label: bool = False, sentiment_max: int = 10, project: Optional[str] = None, location: Optional[str] = None, credentials: Optional[google.auth.credentials.Credentials] = None, labels: Optional[Dict[str, str]] = None, training_encryption_spec_key_name: Optional[str] = None, model_encryption_spec_key_name: Optional[str] = None)

Bases: google.cloud.aiplatform.training_jobs._TrainingJob

Constructs a AutoML Text Training Job.

• Parameters

  • display_name (str) – Required. The user-defined name of this TrainingPipeline.

  • prediction_type (str) – The type of prediction the Model is to produce, one of:

    ”classification” - A classification model analyzes text data and

      returns a list of categories that apply to the text found in the data.
      Vertex AI offers both single-label and multi-label text classification models.
    

    ”extraction” - An entity extraction model inspects text data

      for known entities referenced in the data and
      labels those entities in the text.
    

    ”sentiment” - A sentiment analysis model inspects text data and identifies the

      prevailing emotional opinion within it, especially to determine a writer’s attitude
      as positive, negative, or neutral.
    

  • multi_label (bool) – Required and only applicable for text classification task. If false, a single-label (multi-class) Model will be trained (i.e. assuming that for each text snippet just up to one annotation may be applicable). If true, a multi-label Model will be trained (i.e. assuming that for each text snippet multiple annotations may be applicable).

  • sentiment_max (int) – Required and only applicable for sentiment task. A sentiment is expressed as an integer ordinal, where higher value means a more positive sentiment. The range of sentiments that will be used is between 0 and sentimentMax (inclusive on both ends), and all the values in the range must be represented in the dataset before a model can be created. Only the Annotations with this sentimentMax will be used for training. sentimentMax value must be between 1 and 10 (inclusive).

  • project (str) – Optional. Project to run training in. Overrides project set in aiplatform.init.

  • location (str) – Optional. Location to run training in. Overrides location set in aiplatform.init.

  • credentials (auth_credentials.Credentials) – Optional. Custom credentials to use to run call training service. Overrides credentials set in aiplatform.init.

  • labels (Dict[str, *[str](https://python.readthedocs.io/en/latest/library/stdtypes.html#str)]*) – Optional. The labels with user-defined metadata to organize TrainingPipelines. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels.

  • training_encryption_spec_key_name (Optional[str]) – Optional. The Cloud KMS resource identifier of the customer managed encryption key used to protect the training pipeline. Has the form: projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key. The key needs to be in the same region as where the compute resource is created.

    If set, this TrainingPipeline will be secured by this key.

    Note: Model trained by this TrainingPipeline is also secured by this key if model_to_upload is not set separately.

    Overrides encryption_spec_key_name set in aiplatform.init.

  • model_encryption_spec_key_name (Optional[str]) – Optional. The Cloud KMS resource identifier of the customer managed encryption key used to protect the model. Has the form: projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key. The key needs to be in the same region as where the compute resource is created.

    If set, the trained Model will be secured by this key.

    Overrides encryption_spec_key_name set in aiplatform.init.

cancel()

Asynchronously attempts to cancel a training job.

The server makes a best effort to cancel the job, but the training job can’t always be cancelled. If the training job is canceled, its state transitions to CANCELLED and it’s not deleted.

• Raises

  RuntimeError – If this training job isn’t running, then a runtime error is raised.

client_class()

alias of google.cloud.aiplatform.utils.PipelineClientWithOverride

property create_time(: [datetime.datetime](https://python.readthedocs.io/en/latest/library/datetime.html#datetime.datetime )

Time this resource was created.

delete(sync: bool = True)

Deletes this Vertex AI resource. WARNING: This deletion is permanent.

• Parameters

  sync (bool) – Whether to execute this deletion synchronously. If False, this method will be executed in concurrent Future and any downstream object will be immediately returned and synced when the Future has completed.

property display_name(: [str](https://python.readthedocs.io/en/latest/library/stdtypes.html#str )

Display name of this resource.

done()

Method indicating whether a job has completed.

• Returns

  True if the job has completed.

property encryption_spec(: Optional[[google.cloud.aiplatform_v1.types.encryption_spec.EncryptionSpec](../aiplatform_v1/types.md#google.cloud.aiplatform_v1.types.EncryptionSpec)_ )

Customer-managed encryption key options for this Vertex AI resource.

If this is set, then all resources created by this Vertex AI resource will be encrypted with the provided encryption key.

property end_time(: Optional[datetime.datetime )

Optional. The time when the training job entered the PIPELINE_STATE_SUCCEEDED, PIPELINE_STATE_FAILED, or PIPELINE_STATE_CANCELLED state.

property error(: Optional[google.rpc.status_pb2.Status )

Optional. Detailed error information for this training job resource. Error information is created only when the state of the training job is PIPELINE_STATE_FAILED or PIPELINE_STATE_CANCELLED.

property gca_resource(: [proto.message.Message](https://proto-plus-python.readthedocs.io/en/latest/reference/message.html#proto.message.Message )

The underlying resource proto representation.

classmethod get(resource_name: str, project: Optional[str] = None, location: Optional[str] = None, credentials: Optional[google.auth.credentials.Credentials] = None)

Gets a training job using the resource_name that’s passed in.

• Parameters

  • resource_name (str) – Required. A fully-qualified resource name or ID.

  • project (str) – Optional. The name of the Google Cloud project to retrieve the training job from. This overrides the project that was set by aiplatform.init.

  • location (str) – Optional. The Google Cloud region from where the training job is retrieved. This region overrides the region that was set by aiplatform.init.

  • credentials (auth_credentials.Credentials) – Optional. The credentials that are used to upload this model. These credentials override the credentials set by aiplatform.init.

• Raises

  ValueError – A ValueError is raised if the task definition of the retrieved training job doesn’t match the custom training task definition.

• Returns

  A Vertex AI training job.

get_model(sync=True)

Returns the Vertex AI model produced by this training job.

• Parameters

  sync (bool) – If set to true, this method runs synchronously. If false, this method runs asynchronously.

• Returns

  The Vertex AI model that was produced by this training job.

• Raises

  RuntimeError – A runtime error is raised if the training job failed or if a model wasn’t produced by the training job.

property has_failed(: [bool](https://python.readthedocs.io/en/latest/library/functions.html#bool )

Returns true if the training job failed, otherwise false.

property labels(: Dict[str, str )

User-defined labels containing metadata about this resource.

Read more about labels at https://goo.gl/xmQnxf

classmethod list(filter: Optional[str] = None, order_by: Optional[str] = None, project: Optional[str] = None, location: Optional[str] = None, credentials: Optional[google.auth.credentials.Credentials] = None)

Lists all instances of this training job resource.

The following shows an example of how to call CustomTrainingJob.list:

``

`

py aiplatform.CustomTrainingJob.list(

filter=’display_name=”experiment_a27”’, order_by=’create_time desc’

• Parameters

  • filter (str) – Optional. An expression for filtering the results of the request. For field names, snake_case and camelCase are supported.

  • order_by (str) – Optional. A comma-separated list of fields used to sort the returned traing job resources. The defauilt sorting order is ascending. To sort by a field name in descending order, use desc after the field name. The following fields are supported: display_name, create_time, update_time.

  • project (str) – Optional. The name of the Google Cloud project to which to retrieve the list of training job resources. This overrides the project that was set by aiplatform.init.

  • location (str) – Optional. The Google Cloud region from where the training job resources are retrieved. This region overrides the region that was set by aiplatform.init.

  • credentials (auth_credentials.Credentials) – Optional. The credentials that are used to retrieve list. These credentials override the credentials set by aiplatform.init.

• Returns

  A list of training job resources.

• Return type

  List[VertexAiResourceNoun]

property name(: [str](https://python.readthedocs.io/en/latest/library/stdtypes.html#str )

Name of this resource.

property resource_name(: [str](https://python.readthedocs.io/en/latest/library/stdtypes.html#str )

Full qualified resource name.

run(dataset: google.cloud.aiplatform.datasets.text_dataset.TextDataset, training_fraction_split: Optional[float] = None, validation_fraction_split: Optional[float] = None, test_fraction_split: Optional[float] = None, training_filter_split: Optional[str] = None, validation_filter_split: Optional[str] = None, test_filter_split: Optional[str] = None, model_display_name: Optional[str] = None, model_labels: Optional[Dict[str, str]] = None, model_id: Optional[str] = None, parent_model: Optional[str] = None, is_default_version: Optional[bool] = True, model_version_aliases: Optional[Sequence[str]] = None, model_version_description: Optional[str] = None, sync: bool = True, create_request_timeout: Optional[float] = None)

Runs the training job and returns a model.

If training on a Vertex AI dataset, you can use one of the following split configurations:

Data fraction splits:
Any of `training_fraction_split`, `validation_fraction_split` and
`test_fraction_split` may optionally be provided, they must sum to up to 1. If
the provided ones sum to less than 1, the remainder is assigned to sets as
decided by Vertex AI. If none of the fractions are set, by default roughly 80%
of data will be used for training, 10% for validation, and 10% for test.

Data filter splits:
Assigns input data to training, validation, and test sets
based on the given filters, data pieces not matched by any
filter are ignored. Currently only supported for Datasets
containing DataItems.
If any of the filters in this message are to match nothing, then
they can be set as ‘-’ (the minus sign).
If using filter splits, all of `training_filter_split`, `validation_filter_split` and
`test_filter_split` must be provided.
Supported only for unstructured Datasets.

• Parameters

  • dataset (datasets.TextDataset) – Required. The dataset within the same Project from which data will be used to train the Model. The Dataset must use schema compatible with Model being trained, and what is compatible should be described in the used TrainingPipeline’s [training_task_definition] [google.cloud.aiplatform.v1beta1.TrainingPipeline.training_task_definition].

  • training_fraction_split (float) – Optional. The fraction of the input data that is to be used to train the Model. This is ignored if Dataset is not provided.

  • validation_fraction_split (float) – Optional. The fraction of the input data that is to be used to validate the Model. This is ignored if Dataset is not provided.

  • test_fraction_split (float) – Optional. The fraction of the input data that is to be used to evaluate the Model. This is ignored if Dataset is not provided.

  • training_filter_split (str) – Optional. A filter on DataItems of the Dataset. DataItems that match this filter are used to train the Model. A filter with same syntax as the one used in DatasetService.ListDataItems may be used. If a single DataItem is matched by more than one of the FilterSplit filters, then it is assigned to the first set that applies to it in the training, validation, test order. This is ignored if Dataset is not provided.

  • validation_filter_split (str) – Optional. A filter on DataItems of the Dataset. DataItems that match this filter are used to validate the Model. A filter with same syntax as the one used in DatasetService.ListDataItems may be used. If a single DataItem is matched by more than one of the FilterSplit filters, then it is assigned to the first set that applies to it in the training, validation, test order. This is ignored if Dataset is not provided.

  • test_filter_split (str) – Optional. A filter on DataItems of the Dataset. DataItems that match this filter are used to test the Model. A filter with same syntax as the one used in DatasetService.ListDataItems may be used. If a single DataItem is matched by more than one of the FilterSplit filters, then it is assigned to the first set that applies to it in the training, validation, test order. This is ignored if Dataset is not provided.

  • model_display_name (str) – Optional. The display name of the managed Vertex AI Model. The name can be up to 128 characters long and can consist of any UTF-8 characters.

    If not provided upon creation, the job’s display_name is used.

  • model_labels (Dict[str, *[str](https://python.readthedocs.io/en/latest/library/stdtypes.html#str)]*) – Optional. The labels with user-defined metadata to organize your Models. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels..

  • model_id (str) – Optional. The ID to use for the Model produced by this job, which will become the final component of the model resource name. This value may be up to 63 characters, and valid characters are [a-z0-9_-]. The first character cannot be a number or hyphen.

  • parent_model (str) – Optional. The resource name or model ID of an existing model. The new model uploaded by this job will be a version of parent_model.

    Only set this field when training a new version of an existing model.

  • is_default_version (bool) – Optional. When set to True, the newly uploaded model version will automatically have alias “default” included. Subsequent uses of the model produced by this job without a version specified will use this “default” version.

    When set to False, the “default” alias will not be moved. Actions targeting the model version produced by this job will need to specifically reference this version by ID or alias.

    New model uploads, i.e. version 1, will always be “default” aliased.

  • model_version_aliases (Sequence[str]) – Optional. User provided version aliases so that the model version uploaded by this job can be referenced via alias instead of auto-generated version ID. A default version alias will be created for the first version of the model.

    The format is [a-z][a-zA-Z0-9-]{0,126}[a-z0-9]

  • model_version_description (str) – Optional. The description of the model version being uploaded by this job.

  • sync (bool) – Whether to execute this method synchronously. If False, this method will be executed in concurrent Future and any downstream object will be immediately returned and synced when the Future has completed.

  • create_request_timeout (float) – Optional. The timeout for the create request in seconds

• Returns

  The trained Vertex AI Model resource.

• Return type

  model

• Raises

  RuntimeError – If Training job has already been run or is waiting to run.

property start_time(: Optional[datetime.datetime )

Optional. The time when the training job first entered the PIPELINE_STATE_RUNNING state.

property state(: Optional[[google.cloud.aiplatform_v1.types.pipeline_state.PipelineState](../aiplatform_v1/types.md#google.cloud.aiplatform_v1.types.PipelineState)_ )

Current training state.

to_dict()

Returns the resource proto as a dictionary.

property update_time(: [datetime.datetime](https://python.readthedocs.io/en/latest/library/datetime.html#datetime.datetime )

Time this resource was last updated.

wait()

Helper method that blocks until all futures are complete.

wait_for_resource_creation()

Waits until the resource has been created.

class google.cloud.aiplatform.AutoMLVideoTrainingJob(display_name: Optional[str] = None, prediction_type: str = 'classification', model_type: str = 'CLOUD', project: Optional[str] = None, location: Optional[str] = None, credentials: Optional[google.auth.credentials.Credentials] = None, labels: Optional[Dict[str, str]] = None, training_encryption_spec_key_name: Optional[str] = None, model_encryption_spec_key_name: Optional[str] = None)

Bases: google.cloud.aiplatform.training_jobs._TrainingJob

Constructs a AutoML Video Training Job.

• Parameters

  • display_name (str) – Required. The user-defined name of this TrainingPipeline.

  • prediction_type (str) – The type of prediction the Model is to produce, one of:

    ”classification” - A video classification model classifies shots

      and segments in your videos according to your own defined labels.
    

    ”object_tracking” - A video object tracking model detects and tracks

      multiple objects in shots and segments. You can use these
      models to track objects in your videos according to your
      own pre-defined, custom labels.
    

    ”action_recognition” - A video action recognition model pinpoints

      the location of actions with short temporal durations (~1 second).
    

  • model_type – str = “CLOUD” Required. One of the following:

    ”CLOUD” - available for “classification”, “object_tracking” and “action_recognition”

    A Model best tailored to be used within Google Cloud,
    and which cannot be exported.
    

    ”MOBILE_VERSATILE_1” - available for “classification”, “object_tracking” and “action_recognition”

    A model that, in addition to being available within Google
    Cloud, can also be exported (see ModelService.ExportModel)
    as a TensorFlow or TensorFlow Lite model and used on a
    mobile or edge device with afterwards.
    

    ”MOBILE_CORAL_VERSATILE_1” - available only for “object_tracking”

    A versatile model that is meant to be exported (see
    ModelService.ExportModel) and used on a Google Coral device.
    

    ”MOBILE_CORAL_LOW_LATENCY_1” - available only for “object_tracking”

    A model that trades off quality for low latency, to be
    exported (see ModelService.ExportModel) and used on a
    Google Coral device.
    

    ”MOBILE_JETSON_VERSATILE_1” - available only for “object_tracking”

    A versatile model that is meant to be exported (see
    ModelService.ExportModel) and used on an NVIDIA Jetson device.
    

    ”MOBILE_JETSON_LOW_LATENCY_1” - available only for “object_tracking”

    A model that trades off quality for low latency, to be
    exported (see ModelService.ExportModel) and used on an
    NVIDIA Jetson device.
    

  • project (str) – Optional. Project to run training in. Overrides project set in aiplatform.init.

  • location (str) – Optional. Location to run training in. Overrides location set in aiplatform.init.

  • credentials (auth_credentials.Credentials) – Optional. Custom credentials to use to run call training service. Overrides credentials set in aiplatform.init.

  • labels (Dict[str, *[str](https://python.readthedocs.io/en/latest/library/stdtypes.html#str)]*) – Optional. The labels with user-defined metadata to organize TrainingPipelines. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels.

  • training_encryption_spec_key_name (Optional[str]) – Optional. The Cloud KMS resource identifier of the customer managed encryption key used to protect the training pipeline. Has the form: projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key. The key needs to be in the same region as where the compute resource is created.

    If set, this TrainingPipeline will be secured by this key.

    Note: Model trained by this TrainingPipeline is also secured by this key if model_to_upload is not set separately.

    Overrides encryption_spec_key_name set in aiplatform.init.

  • model_encryption_spec_key_name (Optional[str]) – Optional. The Cloud KMS resource identifier of the customer managed encryption key used to protect the model. Has the form: projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key. The key needs to be in the same region as where the compute resource is created.

    If set, the trained Model will be secured by this key.

    Overrides encryption_spec_key_name set in aiplatform.init.

• Raises

  ValueError – When an invalid prediction_type and/or model_type is provided.

cancel()

Asynchronously attempts to cancel a training job.

The server makes a best effort to cancel the job, but the training job can’t always be cancelled. If the training job is canceled, its state transitions to CANCELLED and it’s not deleted.

• Raises

  RuntimeError – If this training job isn’t running, then a runtime error is raised.

client_class()

alias of google.cloud.aiplatform.utils.PipelineClientWithOverride

property create_time(: [datetime.datetime](https://python.readthedocs.io/en/latest/library/datetime.html#datetime.datetime )

Time this resource was created.

delete(sync: bool = True)

Deletes this Vertex AI resource. WARNING: This deletion is permanent.

• Parameters

  sync (bool) – Whether to execute this deletion synchronously. If False, this method will be executed in concurrent Future and any downstream object will be immediately returned and synced when the Future has completed.

property display_name(: [str](https://python.readthedocs.io/en/latest/library/stdtypes.html#str )

Display name of this resource.

done()

Method indicating whether a job has completed.

• Returns

  True if the job has completed.

property encryption_spec(: Optional[[google.cloud.aiplatform_v1.types.encryption_spec.EncryptionSpec](../aiplatform_v1/types.md#google.cloud.aiplatform_v1.types.EncryptionSpec)_ )

Customer-managed encryption key options for this Vertex AI resource.

If this is set, then all resources created by this Vertex AI resource will be encrypted with the provided encryption key.

property end_time(: Optional[datetime.datetime )

Optional. The time when the training job entered the PIPELINE_STATE_SUCCEEDED, PIPELINE_STATE_FAILED, or PIPELINE_STATE_CANCELLED state.

property error(: Optional[google.rpc.status_pb2.Status )

Optional. Detailed error information for this training job resource. Error information is created only when the state of the training job is PIPELINE_STATE_FAILED or PIPELINE_STATE_CANCELLED.

property gca_resource(: [proto.message.Message](https://proto-plus-python.readthedocs.io/en/latest/reference/message.html#proto.message.Message )

The underlying resource proto representation.

classmethod get(resource_name: str, project: Optional[str] = None, location: Optional[str] = None, credentials: Optional[google.auth.credentials.Credentials] = None)

Gets a training job using the resource_name that’s passed in.

• Parameters

  • resource_name (str) – Required. A fully-qualified resource name or ID.

  • project (str) – Optional. The name of the Google Cloud project to retrieve the training job from. This overrides the project that was set by aiplatform.init.

  • location (str) – Optional. The Google Cloud region from where the training job is retrieved. This region overrides the region that was set by aiplatform.init.

  • credentials (auth_credentials.Credentials) – Optional. The credentials that are used to upload this model. These credentials override the credentials set by aiplatform.init.

• Raises

  ValueError – A ValueError is raised if the task definition of the retrieved training job doesn’t match the custom training task definition.

• Returns

  A Vertex AI training job.

get_model(sync=True)

Returns the Vertex AI model produced by this training job.

• Parameters

  sync (bool) – If set to true, this method runs synchronously. If false, this method runs asynchronously.

• Returns

  The Vertex AI model that was produced by this training job.

• Raises

  RuntimeError – A runtime error is raised if the training job failed or if a model wasn’t produced by the training job.

property has_failed(: [bool](https://python.readthedocs.io/en/latest/library/functions.html#bool )

Returns true if the training job failed, otherwise false.

property labels(: Dict[str, str )

User-defined labels containing metadata about this resource.

Read more about labels at https://goo.gl/xmQnxf

classmethod list(filter: Optional[str] = None, order_by: Optional[str] = None, project: Optional[str] = None, location: Optional[str] = None, credentials: Optional[google.auth.credentials.Credentials] = None)

Lists all instances of this training job resource.

The following shows an example of how to call CustomTrainingJob.list:

``

`

py aiplatform.CustomTrainingJob.list(

filter=’display_name=”experiment_a27”’, order_by=’create_time desc’

• Parameters

  • filter (str) – Optional. An expression for filtering the results of the request. For field names, snake_case and camelCase are supported.

  • order_by (str) – Optional. A comma-separated list of fields used to sort the returned traing job resources. The defauilt sorting order is ascending. To sort by a field name in descending order, use desc after the field name. The following fields are supported: display_name, create_time, update_time.

  • project (str) – Optional. The name of the Google Cloud project to which to retrieve the list of training job resources. This overrides the project that was set by aiplatform.init.

  • location (str) – Optional. The Google Cloud region from where the training job resources are retrieved. This region overrides the region that was set by aiplatform.init.

  • credentials (auth_credentials.Credentials) – Optional. The credentials that are used to retrieve list. These credentials override the credentials set by aiplatform.init.

• Returns

  A list of training job resources.

• Return type

  List[VertexAiResourceNoun]

property name(: [str](https://python.readthedocs.io/en/latest/library/stdtypes.html#str )

Name of this resource.

property resource_name(: [str](https://python.readthedocs.io/en/latest/library/stdtypes.html#str )

Full qualified resource name.

run(dataset: google.cloud.aiplatform.datasets.video_dataset.VideoDataset, training_fraction_split: Optional[float] = None, test_fraction_split: Optional[float] = None, training_filter_split: Optional[str] = None, test_filter_split: Optional[str] = None, model_display_name: Optional[str] = None, model_labels: Optional[Dict[str, str]] = None, model_id: Optional[str] = None, parent_model: Optional[str] = None, is_default_version: Optional[bool] = True, model_version_aliases: Optional[Sequence[str]] = None, model_version_description: Optional[str] = None, sync: bool = True, create_request_timeout: Optional[float] = None)

Runs the AutoML Video training job and returns a model.

If training on a Vertex AI dataset, you can use one of the following split configurations:

Data fraction splits:
`training_fraction_split`, and `test_fraction_split` may optionally
be provided, they must sum to up to 1. If none of the fractions are set,
by default roughly 80% of data will be used for training, and 20% for test.

Data filter splits:
Assigns input data to training, validation, and test sets
based on the given filters, data pieces not matched by any
filter are ignored. Currently only supported for Datasets
containing DataItems.
If any of the filters in this message are to match nothing, then
they can be set as ‘-’ (the minus sign).
If using filter splits, all of `training_filter_split`, `validation_filter_split` and
`test_filter_split` must be provided.
Supported only for unstructured Datasets.

• Parameters

  • dataset (datasets.VideoDataset) – Required. The dataset within the same Project from which data will be used to train the Model. The Dataset must use schema compatible with Model being trained, and what is compatible should be described in the used TrainingPipeline’s [training_task_definition] [google.cloud.aiplatform.v1beta1.TrainingPipeline.training_task_definition]. For tabular Datasets, all their data is exported to training, to pick and choose from.

  • training_fraction_split (float) – Optional. The fraction of the input data that is to be used to train the Model. This is ignored if Dataset is not provided.

  • test_fraction_split (float) – Optional. The fraction of the input data that is to be used to evaluate the Model. This is ignored if Dataset is not provided.

  • training_filter_split (str) – Optional. A filter on DataItems of the Dataset. DataItems that match this filter are used to train the Model. A filter with same syntax as the one used in DatasetService.ListDataItems may be used. If a single DataItem is matched by more than one of the FilterSplit filters, then it is assigned to the first set that applies to it in the training, validation, test order. This is ignored if Dataset is not provided.

  • test_filter_split (str) – Optional. A filter on DataItems of the Dataset. DataItems that match this filter are used to test the Model. A filter with same syntax as the one used in DatasetService.ListDataItems may be used. If a single DataItem is matched by more than one of the FilterSplit filters, then it is assigned to the first set that applies to it in the training, validation, test order. This is ignored if Dataset is not provided.

  • model_display_name (str) – Optional. The display name of the managed Vertex AI Model. The name can be up to 128 characters long and can be consist of any UTF-8 characters. If not provided upon creation, the job’s display_name is used.

  • model_labels (Dict[str, *[str](https://python.readthedocs.io/en/latest/library/stdtypes.html#str)]*) – Optional. The labels with user-defined metadata to organize your Models. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels.

  • model_id (str) – Optional. The ID to use for the Model produced by this job, which will become the final component of the model resource name. This value may be up to 63 characters, and valid characters are [a-z0-9_-]. The first character cannot be a number or hyphen.

  • parent_model (str) – Optional. The resource name or model ID of an existing model. The new model uploaded by this job will be a version of parent_model.

    Only set this field when training a new version of an existing model.

  • is_default_version (bool) – Optional. When set to True, the newly uploaded model version will automatically have alias “default” included. Subsequent uses of the model produced by this job without a version specified will use this “default” version.

    When set to False, the “default” alias will not be moved. Actions targeting the model version produced by this job will need to specifically reference this version by ID or alias.

    New model uploads, i.e. version 1, will always be “default” aliased.

  • model_version_aliases (Sequence[str]) – Optional. User provided version aliases so that the model version uploaded by this job can be referenced via alias instead of auto-generated version ID. A default version alias will be created for the first version of the model.

    The format is [a-z][a-zA-Z0-9-]{0,126}[a-z0-9]

  • model_version_description (str) – Optional. The description of the model version being uploaded by this job.

  • sync – bool = True Whether to execute this method synchronously. If False, this method will be executed in concurrent Future and any downstream object will be immediately returned and synced when the Future has completed.

  • create_request_timeout (float) – Optional. The timeout for the create request in seconds.

• Returns

  The trained Vertex AI Model resource or None if training did not

    produce a Vertex AI Model.
  

• Return type

  model

• Raises

  RuntimeError – If Training job has already been run or is waiting to run.

property start_time(: Optional[datetime.datetime )

Optional. The time when the training job first entered the PIPELINE_STATE_RUNNING state.

property state(: Optional[[google.cloud.aiplatform_v1.types.pipeline_state.PipelineState](../aiplatform_v1/types.md#google.cloud.aiplatform_v1.types.PipelineState)_ )

Current training state.

to_dict()

Returns the resource proto as a dictionary.

property update_time(: [datetime.datetime](https://python.readthedocs.io/en/latest/library/datetime.html#datetime.datetime )

Time this resource was last updated.

wait()

Helper method that blocks until all futures are complete.

wait_for_resource_creation()

Waits until the resource has been created.

class google.cloud.aiplatform.BatchPredictionJob(batch_prediction_job_name: str, project: Optional[str] = None, location: Optional[str] = None, credentials: Optional[google.auth.credentials.Credentials] = None)

Bases: google.cloud.aiplatform.jobs._Job

Retrieves a BatchPredictionJob resource and instantiates its representation.

• Parameters

  • batch_prediction_job_name (str) – Required. A fully-qualified BatchPredictionJob resource name or ID. Example: “projects/…/locations/…/batchPredictionJobs/456” or “456” when project and location are initialized or passed.

  • project – Optional[str] = None, Optional. project to retrieve BatchPredictionJob from. If not set, project set in aiplatform.init will be used.

  • location – Optional[str] = None, Optional. location to retrieve BatchPredictionJob from. If not set, location set in aiplatform.init will be used.

  • credentials – Optional[auth_credentials.Credentials] = None, Custom credentials to use. If not set, credentials set in aiplatform.init will be used.

cancel()

Cancels this Job.

Success of cancellation is not guaranteed. Use Job.state property to verify if cancellation was successful.

client_class()

alias of google.cloud.aiplatform.utils.JobClientWithOverride

property completion_stats(: Optional[[google.cloud.aiplatform_v1.types.completion_stats.CompletionStats](../aiplatform_v1/types.md#google.cloud.aiplatform_v1.types.CompletionStats)_ )

Statistics on completed and failed prediction instances.

classmethod create(job_display_name: str, model_name: Union[str, google.cloud.aiplatform.models.Model], instances_format: str = 'jsonl', predictions_format: str = 'jsonl', gcs_source: Optional[Union[str, Sequence[str]]] = None, bigquery_source: Optional[str] = None, gcs_destination_prefix: Optional[str] = None, bigquery_destination_prefix: Optional[str] = None, model_parameters: Optional[Dict] = None, machine_type: Optional[str] = None, accelerator_type: Optional[str] = None, accelerator_count: Optional[int] = None, starting_replica_count: Optional[int] = None, max_replica_count: Optional[int] = None, generate_explanation: Optional[bool] = False, explanation_metadata: Optional[google.cloud.aiplatform_v1.types.explanation_metadata.ExplanationMetadata] = None, explanation_parameters: Optional[google.cloud.aiplatform_v1.types.explanation.ExplanationParameters] = None, labels: Optional[Dict[str, str]] = None, project: Optional[str] = None, location: Optional[str] = None, credentials: Optional[google.auth.credentials.Credentials] = None, encryption_spec_key_name: Optional[str] = None, sync: bool = True, create_request_timeout: Optional[float] = None, batch_size: Optional[int] = None, model_monitoring_objective_config: Optional[google.cloud.aiplatform.model_monitoring.objective.ObjectiveConfig] = None, model_monitoring_alert_config: Optional[google.cloud.aiplatform.model_monitoring.alert.AlertConfig] = None, analysis_instance_schema_uri: Optional[str] = None, service_account: Optional[str] = None)

Create a batch prediction job.

• Parameters

  • job_display_name (str) – Required. The user-defined name of the BatchPredictionJob. The name can be up to 128 characters long and can be consist of any UTF-8 characters.

  • model_name (Union[str, **aiplatform.Model]) – Required. A fully-qualified model resource name or model ID. Example: “projects/123/locations/us-central1/models/456” or “456” when project and location are initialized or passed. May optionally contain a version ID or alias in {model_name}@{version} form.

    Or an instance of aiplatform.Model.

  • instances_format (str) – Required. The format in which instances are provided. Must be one of the formats listed in Model.supported_input_storage_formats. Default is “jsonl” when using gcs_source. If a bigquery_source is provided, this is overridden to “bigquery”.

  • predictions_format (str) – Required. The format in which Vertex AI outputs the predictions, must be one of the formats specified in Model.supported_output_storage_formats. Default is “jsonl” when using gcs_destination_prefix. If a bigquery_destination_prefix is provided, this is overridden to “bigquery”.

  • gcs_source (Optional[Sequence[str]]) – Google Cloud Storage URI(-s) to your instances to run batch prediction on. They must match instances_format.

  • bigquery_source (Optional[str]) – BigQuery URI to a table, up to 2000 characters long. For example: bq://projectId.bqDatasetId.bqTableId

  • gcs_destination_prefix (Optional[str]) – The Google Cloud Storage location of the directory where the output is to be written to. In the given directory a new directory is created. Its name is prediction-<model-display-name>-<job-create-time>, where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format. Inside of it files predictions_0001.<extension>, predictions_0002.<extension>, …, predictions_N.<extension> are created where <extension> depends on chosen predictions_format, and N may equal 0001 and depends on the total number of successfully predicted instances. If the Model has both instance and prediction schemata defined then each such file contains predictions as per the predictions_format. If prediction for any instance failed (partially or completely), then an additional errors_0001.<extension>, errors_0002.<extension>,…, errors_N.<extension> files are created (N depends on total number of failed predictions). These files contain the failed instances, as per their schema, followed by an additional error field which as value has \google.rpc.Status<Status>\__ containing only code and message fields.

  • bigquery_destination_prefix (Optional[str]) – The BigQuery project or dataset location where the output is to be written to. If project is provided, a new dataset is created with name prediction_<model-display-name>_<job-create-time> where is made BigQuery-dataset-name compatible (for example, most special characters become underscores), and timestamp is in YYYY_MM_DDThh_mm_ss_sssZ “based on ISO-8601” format. In the dataset two tables will be created, predictions, and errors. If the Model has both [instance][google.cloud.aiplatform.v1.PredictSchemata.instance_schema_uri] and [prediction][google.cloud.aiplatform.v1.PredictSchemata.parameters_schema_uri] schemata defined then the tables have columns as follows: The predictions table contains instances for which the prediction succeeded, it has columns as per a concatenation of the Model’s instance and prediction schemata. The errors table contains rows for which the prediction has failed, it has instance columns, as per the instance schema, followed by a single “errors” column, which as values has [google.rpc.Status][google.rpc.Status] represented as a STRUCT, and containing only code and message.

  • model_parameters (Optional[Dict]) – The parameters that govern the predictions. The schema of the parameters may be specified via the Model’s parameters_schema_uri.

  • machine_type (Optional[str]) – The type of machine for running batch prediction on dedicated resources. Not specifying machine type will result in batch prediction job being run with automatic resources.

  • accelerator_type (Optional[str]) – The type of accelerator(s) that may be attached to the machine as per accelerator_count. Only used if machine_type is set.

  • accelerator_count (Optional[int]) – The number of accelerators to attach to the machine_type. Only used if machine_type is set.

  • starting_replica_count (Optional[int]) – The number of machine replicas used at the start of the batch operation. If not set, Vertex AI decides starting number, not greater than max_replica_count. Only used if machine_type is set.

  • max_replica_count (Optional[int]) – The maximum number of machine replicas the batch operation may be scaled to. Only used if machine_type is set. Default is 10.

  • generate_explanation (bool) – Optional. Generate explanation along with the batch prediction results. This will cause the batch prediction output to include explanations based on the prediction_format:

    * bigquery: output includes a column named explanation. The value
    
    is a struct that conforms to the [aiplatform.gapic.Explanation] object.
    

>     * jsonl: The JSON objects on each line include an additional entry

>     keyed explanation. The value of the entry is a JSON object that
>     conforms to the [aiplatform.gapic.Explanation] object.


>     * csv: Generating explanations for CSV format is not supported.



* **explanation_metadata** (*aiplatform.explain.ExplanationMetadata*) – Optional. Explanation metadata configuration for this BatchPredictionJob.
Can be specified only if generate_explanation is set to True.

This value overrides the value of Model.explanation_metadata.
All fields of explanation_metadata are optional in the request. If
a field of the explanation_metadata object is not populated, the
corresponding field of the Model.explanation_metadata object is inherited.
For more details, see Ref docs <http://tinyurl.com/1igh60kt>



* **explanation_parameters** (*aiplatform.explain.ExplanationParameters*) – Optional. Parameters to configure explaining for Model’s predictions.
Can be specified only if generate_explanation is set to True.

This value overrides the value of Model.explanation_parameters.
All fields of explanation_parameters are optional in the request. If
a field of the explanation_parameters object is not populated, the
corresponding field of the Model.explanation_parameters object is inherited.
For more details, see Ref docs <http://tinyurl.com/1an4zake>



* **labels** (*Dict**[*[*str*](https://python.readthedocs.io/en/latest/library/stdtypes.html#str)*, *[*str*](https://python.readthedocs.io/en/latest/library/stdtypes.html#str)*]*) – Optional. The labels with user-defined metadata to organize your
BatchPredictionJobs. Label keys and values can be no longer than
64 characters (Unicode codepoints), can only contain lowercase
letters, numeric characters, underscores and dashes.
International characters are allowed. See [https://goo.gl/xmQnxf](https://goo.gl/xmQnxf)
for more information and examples of labels.


* **credentials** (*Optional**[**auth_credentials.Credentials**]*) – Custom credentials to use to create this batch prediction
job. Overrides credentials set in aiplatform.init.


* **encryption_spec_key_name** (*Optional**[*[*str*](https://python.readthedocs.io/en/latest/library/stdtypes.html#str)*]*) – Optional. The Cloud KMS resource identifier of the customer
managed encryption key used to protect the job. Has the
form:
`projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key`.
The key needs to be in the same region as where the compute
resource is created.

If this is set, then all
resources created by the BatchPredictionJob will
be encrypted with the provided encryption key.

Overrides encryption_spec_key_name set in aiplatform.init.



* **sync** ([*bool*](https://python.readthedocs.io/en/latest/library/functions.html#bool)) – Whether to execute this method synchronously. If False, this method
will be executed in concurrent Future and any downstream object will
be immediately returned and synced when the Future has completed.


* **create_request_timeout** ([*float*](https://python.readthedocs.io/en/latest/library/functions.html#float)) – Optional. The timeout for the create request in seconds.


* **batch_size** ([*int*](https://python.readthedocs.io/en/latest/library/functions.html#int)) – Optional. The number of the records (e.g. instances) of the operation given in each batch
to a machine replica. Machine type, and size of a single record should be considered
when setting this parameter, higher value speeds up the batch operation’s execution,
but too high value will result in a whole batch not fitting in a machine’s memory,
and the whole operation will fail.
The default value is 64.


* **model_monitoring_objective_config** (*aiplatform.model_monitoring.ObjectiveConfig*) – Optional. The objective config for model monitoring. Passing this parameter enables
monitoring on the model associated with this batch prediction job.


* **model_monitoring_alert_config** (*aiplatform.model_monitoring.EmailAlertConfig*) – Optional. Configures how model monitoring alerts are sent to the user. Right now
only email alert is supported.


* **analysis_instance_schema_uri** ([*str*](https://python.readthedocs.io/en/latest/library/stdtypes.html#str)) – Optional. Only applicable if model_monitoring_objective_config is also passed.
This parameter specifies the YAML schema file uri describing the format of a single
instance that you want Tensorflow Data Validation (TFDV) to
analyze. If this field is empty, all the feature data types are
inferred from predict_instance_schema_uri, meaning that TFDV
will use the data in the exact format as prediction request/response.
If there are any data type differences between predict instance
and TFDV instance, this field can be used to override the schema.
For models trained with Vertex AI, this field must be set as all the
fields in predict instance formatted as string.


* **service_account** ([*str*](https://python.readthedocs.io/en/latest/library/stdtypes.html#str)) – Optional. Specifies the service account for workload run-as account.
Users submitting jobs must have act-as permission on this run-as account.

• Returns

  Instantiated representation of the created batch prediction job.

• Return type

  (jobs.BatchPredictionJob)

property create_time(: [datetime.datetime](https://python.readthedocs.io/en/latest/library/datetime.html#datetime.datetime )

Time this resource was created.

delete(sync: bool = True)

Deletes this Vertex AI resource. WARNING: This deletion is permanent.

• Parameters

  sync (bool) – Whether to execute this deletion synchronously. If False, this method will be executed in concurrent Future and any downstream object will be immediately returned and synced when the Future has completed.

property display_name(: [str](https://python.readthedocs.io/en/latest/library/stdtypes.html#str )

Display name of this resource.

done()

Method indicating whether a job has completed.

• Returns

  True if the job has completed.

property encryption_spec(: Optional[[google.cloud.aiplatform_v1.types.encryption_spec.EncryptionSpec](../aiplatform_v1/types.md#google.cloud.aiplatform_v1.types.EncryptionSpec)_ )

Customer-managed encryption key options for this Vertex AI resource.

If this is set, then all resources created by this Vertex AI resource will be encrypted with the provided encryption key.

property end_time(: Optional[datetime.datetime )

Time when the Job resource entered the JOB_STATE_SUCCEEDED, JOB_STATE_FAILED, or JOB_STATE_CANCELLED state.

property error(: Optional[google.rpc.status_pb2.Status )

Detailed error info for this Job resource. Only populated when the Job’s state is JOB_STATE_FAILED or JOB_STATE_CANCELLED.

property gca_resource(: [proto.message.Message](https://proto-plus-python.readthedocs.io/en/latest/reference/message.html#proto.message.Message )

The underlying resource proto representation.

iter_outputs(bq_max_results: Optional[int] = 100)

Returns an Iterable object to traverse the output files, either a list of GCS Blobs or a BigQuery RowIterator depending on the output config set when the BatchPredictionJob was created.

• Parameters

  bq_max_results – Optional[int] = 100 Limit on rows to retrieve from prediction table in BigQuery dataset. Only used when retrieving predictions from a bigquery_destination_prefix. Default is 100.

• Returns

  Either a list of GCS Blob objects within the prediction output directory or an iterable BigQuery RowIterator with predictions.

• Return type

  Union[Iterable[storage.Blob], Iterable[bigquery.table.RowIterator]]

• Raises

  • RuntimeError – If BatchPredictionJob is in a JobState other than SUCCEEDED, since outputs cannot be retrieved until the Job has finished.

  • NotImplementedError – If BatchPredictionJob succeeded and output_info does not have a GCS or BQ output provided.

property labels(: Dict[str, str )

User-defined labels containing metadata about this resource.

Read more about labels at https://goo.gl/xmQnxf

classmethod list(filter: Optional[str] = None, order_by: Optional[str] = None, project: Optional[str] = None, location: Optional[str] = None, credentials: Optional[google.auth.credentials.Credentials] = None)

List all instances of this Job Resource.

Example Usage:

aiplatform.BatchPredictionJobs.list(

filter=’state=”JOB_STATE_SUCCEEDED” AND display_name=”my_job”’,

)

• Parameters

  • filter (str) – Optional. An expression for filtering the results of the request. For field names both snake_case and camelCase are supported.

  • order_by (str) – Optional. A comma-separated list of fields to order by, sorted in ascending order. Use “desc” after a field name for descending. Supported fields: display_name, create_time, update_time

  • project (str) – Optional. Project to retrieve list from. If not set, project set in aiplatform.init will be used.

  • location (str) – Optional. Location to retrieve list from. If not set, location set in aiplatform.init will be used.

  • credentials (auth_credentials.Credentials) – Optional. Custom credentials to use to retrieve list. Overrides credentials set in aiplatform.init.

• Returns

  List[VertexAiResourceNoun] - A list of Job resource objects.

property name(: [str](https://python.readthedocs.io/en/latest/library/stdtypes.html#str )

Name of this resource.

property output_info(: Optional[[google.cloud.aiplatform_v1.types.batch_prediction_job.BatchPredictionJob.OutputInfo](../aiplatform_v1/types.md#google.cloud.aiplatform_v1.types.BatchPredictionJob.OutputInfo)_ )

Information describing the output of this job, including output location into which prediction output is written.

This is only available for batch prediction jobs that have run successfully.

property partial_failures(: Optional[Sequence[google.rpc.status_pb2.Status] )

Partial failures encountered. For example, single files that can’t be read. This field never exceeds 20 entries. Status details fields contain standard GCP error details.

property resource_name(: [str](https://python.readthedocs.io/en/latest/library/stdtypes.html#str )

Full qualified resource name.

property start_time(: Optional[datetime.datetime )

Time when the Job resource entered the JOB_STATE_RUNNING for the first time.

property state(: [google.cloud.aiplatform_v1.types.job_state.JobState](../aiplatform_v1/types.md#google.cloud.aiplatform_v1.types.JobState_ )

Fetch Job again and return the current JobState.

• Returns

  Enum that describes the state of a Vertex AI job.

• Return type

  state (job_state.JobState)

classmethod submit(*, job_display_name: Optional[str] = None, model_name: Union[str, google.cloud.aiplatform.models.Model], instances_format: str = 'jsonl', predictions_format: str = 'jsonl', gcs_source: Optional[Union[str, Sequence[str]]] = None, bigquery_source: Optional[str] = None, gcs_destination_prefix: Optional[str] = None, bigquery_destination_prefix: Optional[str] = None, model_parameters: Optional[Dict] = None, machine_type: Optional[str] = None, accelerator_type: Optional[str] = None, accelerator_count: Optional[int] = None, starting_replica_count: Optional[int] = None, max_replica_count: Optional[int] = None, generate_explanation: Optional[bool] = False, explanation_metadata: Optional[google.cloud.aiplatform_v1.types.explanation_metadata.ExplanationMetadata] = None, explanation_parameters: Optional[google.cloud.aiplatform_v1.types.explanation.ExplanationParameters] = None, labels: Optional[Dict[str, str]] = None, project: Optional[str] = None, location: Optional[str] = None, credentials: Optional[google.auth.credentials.Credentials] = None, encryption_spec_key_name: Optional[str] = None, create_request_timeout: Optional[float] = None, batch_size: Optional[int] = None, model_monitoring_objective_config: Optional[google.cloud.aiplatform.model_monitoring.objective.ObjectiveConfig] = None, model_monitoring_alert_config: Optional[google.cloud.aiplatform.model_monitoring.alert.AlertConfig] = None, analysis_instance_schema_uri: Optional[str] = None, service_account: Optional[str] = None)

Sumbit a batch prediction job (not waiting for completion).

• Parameters

  • job_display_name (str) – Required. The user-defined name of the BatchPredictionJob. The name can be up to 128 characters long and can be consist of any UTF-8 characters.

  • model_name (Union[str, **aiplatform.Model]) – Required. A fully-qualified model resource name or model ID. Example: “projects/123/locations/us-central1/models/456” or “456” when project and location are initialized or passed. May optionally contain a version ID or alias in {model_name}@{version} form.

    Or an instance of aiplatform.Model.

  • instances_format (str) – Required. The format in which instances are provided. Must be one of the formats listed in Model.supported_input_storage_formats. Default is “jsonl” when using gcs_source. If a bigquery_source is provided, this is overridden to “bigquery”.

  • predictions_format (str) – Required. The format in which Vertex AI outputs the predictions, must be one of the formats specified in Model.supported_output_storage_formats. Default is “jsonl” when using gcs_destination_prefix. If a bigquery_destination_prefix is provided, this is overridden to “bigquery”.

  • gcs_source (Optional[Sequence[str]]) – Google Cloud Storage URI(-s) to your instances to run batch prediction on. They must match instances_format.

  • bigquery_source (Optional[str]) – BigQuery URI to a table, up to 2000 characters long. For example: bq://projectId.bqDatasetId.bqTableId

  • gcs_destination_prefix (Optional[str]) – The Google Cloud Storage location of the directory where the output is to be written to. In the given directory a new directory is created. Its name is prediction-<model-display-name>-<job-create-time>, where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format. Inside of it files predictions_0001.<extension>, predictions_0002.<extension>, …, predictions_N.<extension> are created where <extension> depends on chosen predictions_format, and N may equal 0001 and depends on the total number of successfully predicted instances. If the Model has both instance and prediction schemata defined then each such file contains predictions as per the predictions_format. If prediction for any instance failed (partially or completely), then an additional errors_0001.<extension>, errors_0002.<extension>,…, errors_N.<extension> files are created (N depends on total number of failed predictions). These files contain the failed instances, as per their schema, followed by an additional error field which as value has \google.rpc.Status<Status>\__ containing only code and message fields.

  • bigquery_destination_prefix (Optional[str]) – The BigQuery project or dataset location where the output is to be written to. If project is provided, a new dataset is created with name prediction_<model-display-name>_<job-create-time> where is made BigQuery-dataset-name compatible (for example, most special characters become underscores), and timestamp is in YYYY_MM_DDThh_mm_ss_sssZ “based on ISO-8601” format. In the dataset two tables will be created, predictions, and errors. If the Model has both [instance][google.cloud.aiplatform.v1.PredictSchemata.instance_schema_uri] and [prediction][google.cloud.aiplatform.v1.PredictSchemata.parameters_schema_uri] schemata defined then the tables have columns as follows: The predictions table contains instances for which the prediction succeeded, it has columns as per a concatenation of the Model’s instance and prediction schemata. The errors table contains rows for which the prediction has failed, it has instance columns, as per the instance schema, followed by a single “errors” column, which as values has [google.rpc.Status][google.rpc.Status] represented as a STRUCT, and containing only code and message.

  • model_parameters (Optional[Dict]) – The parameters that govern the predictions. The schema of the parameters may be specified via the Model’s parameters_schema_uri.

  • machine_type (Optional[str]) – The type of machine for running batch prediction on dedicated resources. Not specifying machine type will result in batch prediction job being run with automatic resources.

  • accelerator_type (Optional[str]) – The type of accelerator(s) that may be attached to the machine as per accelerator_count. Only used if machine_type is set.

  • accelerator_count (Optional[int]) – The number of accelerators to attach to the machine_type. Only used if machine_type is set.

  • starting_replica_count (Optional[int]) – The number of machine replicas used at the start of the batch operation. If not set, Vertex AI decides starting number, not greater than max_replica_count. Only used if machine_type is set.

  • max_replica_count (Optional[int]) – The maximum number of machine replicas the batch operation may be scaled to. Only used if machine_type is set. Default is 10.

  • generate_explanation (bool) – Optional. Generate explanation along with the batch prediction results. This will cause the batch prediction output to include explanations based on the prediction_format:

    * bigquery: output includes a column named explanation. The value
    
    is a struct that conforms to the [aiplatform.gapic.Explanation] object.
    

>     * jsonl: The JSON objects on each line include an additional entry

>     keyed explanation. The value of the entry is a JSON object that
>     conforms to the [aiplatform.gapic.Explanation] object.


>     * csv: Generating explanations for CSV format is not supported.



* **explanation_metadata** (*aiplatform.explain.ExplanationMetadata*) – Optional. Explanation metadata configuration for this BatchPredictionJob.
Can be specified only if generate_explanation is set to True.

This value overrides the value of Model.explanation_metadata.
All fields of explanation_metadata are optional in the request. If
a field of the explanation_metadata object is not populated, the
corresponding field of the Model.explanation_metadata object is inherited.
For more details, see Ref docs <http://tinyurl.com/1igh60kt>



* **explanation_parameters** (*aiplatform.explain.ExplanationParameters*) – Optional. Parameters to configure explaining for Model’s predictions.
Can be specified only if generate_explanation is set to True.

This value overrides the value of Model.explanation_parameters.
All fields of explanation_parameters are optional in the request. If
a field of the explanation_parameters object is not populated, the
corresponding field of the Model.explanation_parameters object is inherited.
For more details, see Ref docs <http://tinyurl.com/1an4zake>



* **labels** (*Dict**[*[*str*](https://python.readthedocs.io/en/latest/library/stdtypes.html#str)*, *[*str*](https://python.readthedocs.io/en/latest/library/stdtypes.html#str)*]*) – Optional. The labels with user-defined metadata to organize your
BatchPredictionJobs. Label keys and values can be no longer than
64 characters (Unicode codepoints), can only contain lowercase
letters, numeric characters, underscores and dashes.
International characters are allowed. See [https://goo.gl/xmQnxf](https://goo.gl/xmQnxf)
for more information and examples of labels.


* **credentials** (*Optional**[**auth_credentials.Credentials**]*) – Custom credentials to use to create this batch prediction
job. Overrides credentials set in aiplatform.init.


* **encryption_spec_key_name** (*Optional**[*[*str*](https://python.readthedocs.io/en/latest/library/stdtypes.html#str)*]*) – Optional. The Cloud KMS resource identifier of the customer
managed encryption key used to protect the job. Has the
form:
`projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key`.
The key needs to be in the same region as where the compute
resource is created.

If this is set, then all
resources created by the BatchPredictionJob will
be encrypted with the provided encryption key.

Overrides encryption_spec_key_name set in aiplatform.init.



* **create_request_timeout** ([*float*](https://python.readthedocs.io/en/latest/library/functions.html#float)) – Optional. The timeout for the create request in seconds.


* **batch_size** ([*int*](https://python.readthedocs.io/en/latest/library/functions.html#int)) – Optional. The number of the records (e.g. instances) of the operation given in each batch
to a machine replica. Machine type, and size of a single record should be considered
when setting this parameter, higher value speeds up the batch operation’s execution,
but too high value will result in a whole batch not fitting in a machine’s memory,
and the whole operation will fail.
The default value is 64.


* **model_monitoring_objective_config** (*aiplatform.model_monitoring.ObjectiveConfig*) – Optional. The objective config for model monitoring. Passing this parameter enables
monitoring on the model associated with this batch prediction job.


* **model_monitoring_alert_config** (*aiplatform.model_monitoring.EmailAlertConfig*) – Optional. Configures how model monitoring alerts are sent to the user. Right now
only email alert is supported.


* **analysis_instance_schema_uri** ([*str*](https://python.readthedocs.io/en/latest/library/stdtypes.html#str)) – Optional. Only applicable if model_monitoring_objective_config is also passed.
This parameter specifies the YAML schema file uri describing the format of a single
instance that you want Tensorflow Data Validation (TFDV) to
analyze. If this field is empty, all the feature data types are
inferred from predict_instance_schema_uri, meaning that TFDV
will use the data in the exact format as prediction request/response.
If there are any data type differences between predict instance
and TFDV instance, this field can be used to override the schema.
For models trained with Vertex AI, this field must be set as all the
fields in predict instance formatted as string.


* **service_account** ([*str*](https://python.readthedocs.io/en/latest/library/stdtypes.html#str)) – Optional. Specifies the service account for workload run-as account.
Users submitting jobs must have act-as permission on this run-as account.

• Returns

  Instantiated representation of the created batch prediction job.

• Return type

  (jobs.BatchPredictionJob)

to_dict()

Returns the resource proto as a dictionary.

property update_time(: [datetime.datetime](https://python.readthedocs.io/en/latest/library/datetime.html#datetime.datetime )

Time this resource was last updated.

wait()

Helper method that blocks until all futures are complete.

wait_for_completion()

Waits for job to complete.

• Raises

  RuntimeError – If job failed or cancelled.

wait_for_resource_creation()

Waits until resource has been created.

class google.cloud.aiplatform.CustomContainerTrainingJob(display_name: str, container_uri: str, command: Optional[Sequence[str]] = None, model_serving_container_image_uri: Optional[str] = None, model_serving_container_predict_route: Optional[str] = None, model_serving_container_health_route: Optional[str] = None, model_serving_container_command: Optional[Sequence[str]] = None, model_serving_container_args: Optional[Sequence[str]] = None, model_serving_container_environment_variables: Optional[Dict[str, str]] = None, model_serving_container_ports: Optional[Sequence[int]] = None, model_description: Optional[str] = None, model_instance_schema_uri: Optional[str] = None, model_parameters_schema_uri: Optional[str] = None, model_prediction_schema_uri: Optional[str] = None, explanation_metadata: Optional[google.cloud.aiplatform_v1.types.explanation_metadata.ExplanationMetadata] = None, explanation_parameters: Optional[google.cloud.aiplatform_v1.types.explanation.ExplanationParameters] = None, project: Optional[str] = None, location: Optional[str] = None, credentials: Optional[google.auth.credentials.Credentials] = None, labels: Optional[Dict[str, str]] = None, training_encryption_spec_key_name: Optional[str] = None, model_encryption_spec_key_name: Optional[str] = None, staging_bucket: Optional[str] = None)

Bases: google.cloud.aiplatform.training_jobs._CustomTrainingJob

Class to launch a Custom Training Job in Vertex AI using a Container.

Constructs a Custom Container Training Job.

job = aiplatform.CustomContainerTrainingJob(

display_name=’test-train’,
container_uri=’gcr.io/my_project_id/my_image_name:tag’,
command=[‘python3’, ‘run_script.py’]
model_serving_container_image_uri=’gcr.io/my-trainer/serving:1’,
model_serving_container_predict_route=’predict’,
model_serving_container_health_route=’metadata,
labels={‘key’: ‘value’},

)

Usage with Dataset:

ds = aiplatform.TabularDataset(

‘projects/my-project/locations/us-central1/datasets/12345’)

job.run(

ds,
replica_count=1,
model_display_name=’my-trained-model’,
model_labels={‘key’: ‘value’},

)

Usage without Dataset:

job.run(replica_count=1, model_display_name=’my-trained-model)

To ensure your model gets saved in Vertex AI, write your saved model to os.environ[“AIP_MODEL_DIR”] in your provided training script.

• Parameters

  • display_name (str) – Required. The user-defined name of this TrainingPipeline.

  • container_uri (str) – Required: Uri of the training container image in the GCR.

  • command (Sequence[str]) – The command to be invoked when the container is started. It overrides the entrypoint instruction in Dockerfile when provided

  • model_serving_container_image_uri (str) – If the training produces a managed Vertex AI Model, the URI of the Model serving container suitable for serving the model produced by the training script.

  • model_serving_container_predict_route (str) – If the training produces a managed Vertex AI Model, An HTTP path to send prediction requests to the container, and which must be supported by it. If not specified a default HTTP path will be used by Vertex AI.

  • model_serving_container_health_route (str) – If the training produces a managed Vertex AI Model, an HTTP path to send health check requests to the container, and which must be supported by it. If not specified a standard HTTP path will be used by AI Platform.

  • model_serving_container_command (Sequence[str]) – The command with which the container is run. Not executed within a shell. The Docker image’s ENTRYPOINT is used if this is not provided. Variable references $(VAR_NAME) are expanded using the container’s environment. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not.

  • model_serving_container_args (Sequence[str]) – The arguments to the command. The Docker image’s CMD is used if this is not provided. Variable references $(VAR_NAME) are expanded using the container’s environment. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not.

  • model_serving_container_environment_variables (Dict[str, *[str](https://python.readthedocs.io/en/latest/library/stdtypes.html#str)]*) – The environment variables that are to be present in the container. Should be a dictionary where keys are environment variable names and values are environment variable values for those names.

  • model_serving_container_ports (Sequence[int]) – Declaration of ports that are exposed by the container. This field is primarily informational, it gives Vertex AI information about the network connections the container uses. Listing or not a port here has no impact on whether the port is actually exposed, any port listening on the default “0.0.0.0” address inside a container will be accessible from the network.

  • model_description (str) – The description of the Model.

  • model_instance_schema_uri (str) – Optional. Points to a YAML file stored on Google Cloud Storage describing the format of a single instance, which are used in PredictRequest.instances, ExplainRequest.instances and BatchPredictionJob.input_config. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by AI Platform. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.

  • model_parameters_schema_uri (str) – Optional. Points to a YAML file stored on Google Cloud Storage describing the parameters of prediction and explanation via PredictRequest.parameters, ExplainRequest.parameters and BatchPredictionJob.model_parameters. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by AI Platform, if no parameters are supported it is set to an empty string. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.

  • model_prediction_schema_uri (str) – Optional. Points to a YAML file stored on Google Cloud Storage describing the format of a single prediction produced by this Model, which are returned via PredictResponse.predictions, ExplainResponse.explanations, and BatchPredictionJob.output_config. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by AI Platform. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.

  • explanation_metadata (explain.ExplanationMetadata) – Optional. Metadata describing the Model’s input and output for explanation. explanation_metadata is optional while explanation_parameters must be specified when used. For more details, see Ref docs http://tinyurl.com/1igh60kt

  • explanation_parameters (explain.ExplanationParameters) – Optional. Parameters to configure explaining for Model’s predictions. For more details, see Ref docs http://tinyurl.com/1an4zake

  • project (str) – Project to run training in. Overrides project set in aiplatform.init.

  • location (str) – Location to run training in. Overrides location set in aiplatform.init.

  • credentials (auth_credentials.Credentials) – Custom credentials to use to run call training service. Overrides credentials set in aiplatform.init.

  • labels (Dict[str, *[str](https://python.readthedocs.io/en/latest/library/stdtypes.html#str)]*) – Optional. The labels with user-defined metadata to organize TrainingPipelines. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels.

  • training_encryption_spec_key_name (Optional[str]) – Optional. The Cloud KMS resource identifier of the customer managed encryption key used to protect the training pipeline. Has the form: projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key. The key needs to be in the same region as where the compute resource is created.

    If set, this TrainingPipeline will be secured by this key.

    Note: Model trained by this TrainingPipeline is also secured by this key if model_to_upload is not set separately.

    Overrides encryption_spec_key_name set in aiplatform.init.

  • model_encryption_spec_key_name (Optional[str]) – Optional. The Cloud KMS resource identifier of the customer managed encryption key used to protect the model. Has the form: projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key. The key needs to be in the same region as where the compute resource is created.

    If set, the trained Model will be secured by this key.

    Overrides encryption_spec_key_name set in aiplatform.init.

  • staging_bucket (str) – Bucket used to stage source and training artifacts. Overrides staging_bucket set in aiplatform.init.

cancel()

Asynchronously attempts to cancel a training job.

The server makes a best effort to cancel the job, but the training job can’t always be cancelled. If the training job is canceled, its state transitions to CANCELLED and it’s not deleted.

• Raises

  RuntimeError – If this training job isn’t running, then a runtime error is raised.

client_class()

alias of google.cloud.aiplatform.utils.PipelineClientWithOverride

property create_time(: [datetime.datetime](https://python.readthedocs.io/en/latest/library/datetime.html#datetime.datetime )

Time this resource was created.

delete(sync: bool = True)

Deletes this Vertex AI resource. WARNING: This deletion is permanent.

• Parameters

  sync (bool) – Whether to execute this deletion synchronously. If False, this method will be executed in concurrent Future and any downstream object will be immediately returned and synced when the Future has completed.

property display_name(: [str](https://python.readthedocs.io/en/latest/library/stdtypes.html#str )

Display name of this resource.

done()

Method indicating whether a job has completed.

• Returns

  True if the job has completed.

property encryption_spec(: Optional[[google.cloud.aiplatform_v1.types.encryption_spec.EncryptionSpec](../aiplatform_v1/types.md#google.cloud.aiplatform_v1.types.EncryptionSpec)_ )

Customer-managed encryption key options for this Vertex AI resource.

If this is set, then all resources created by this Vertex AI resource will be encrypted with the provided encryption key.

property end_time(: Optional[datetime.datetime )

Optional. The time when the training job entered the PIPELINE_STATE_SUCCEEDED, PIPELINE_STATE_FAILED, or PIPELINE_STATE_CANCELLED state.

property error(: Optional[google.rpc.status_pb2.Status )

Optional. Detailed error information for this training job resource. Error information is created only when the state of the training job is PIPELINE_STATE_FAILED or PIPELINE_STATE_CANCELLED.

property gca_resource(: [proto.message.Message](https://proto-plus-python.readthedocs.io/en/latest/reference/message.html#proto.message.Message )

The underlying resource proto representation.

classmethod get(resource_name: str, project: Optional[str] = None, location: Optional[str] = None, credentials: Optional[google.auth.credentials.Credentials] = None)

Gets a training job using the resource_name that’s passed in.

• Parameters

  • resource_name (str) – Required. A fully-qualified resource name or ID.

  • project (str) – Optional. The name of the Google Cloud project to retrieve the training job from. This overrides the project that was set by aiplatform.init.

  • location (str) – Optional. The Google Cloud region from where the training job is retrieved. This region overrides the region that was set by aiplatform.init.

  • credentials (auth_credentials.Credentials) – Optional. The credentials that are used to upload this model. These credentials override the credentials set by aiplatform.init.

• Raises

  ValueError – A ValueError is raised if the task definition of the retrieved training job doesn’t match the custom training task definition.

• Returns

  A Vertex AI training job.

get_model(sync=True)

Returns the Vertex AI model produced by this training job.

• Parameters

  sync (bool) – If set to true, this method runs synchronously. If false, this method runs asynchronously.

• Returns

  The Vertex AI model that was produced by this training job.

• Raises

  RuntimeError – A runtime error is raised if the training job failed or if a model wasn’t produced by the training job.

property has_failed(: [bool](https://python.readthedocs.io/en/latest/library/functions.html#bool )

Returns true if the training job failed, otherwise false.

property labels(: Dict[str, str )

User-defined labels containing metadata about this resource.

Read more about labels at https://goo.gl/xmQnxf

classmethod list(filter: Optional[str] = None, order_by: Optional[str] = None, project: Optional[str] = None, location: Optional[str] = None, credentials: Optional[google.auth.credentials.Credentials] = None)

Lists all instances of this training job resource.

The following shows an example of how to call CustomTrainingJob.list:

``

`

py aiplatform.CustomTrainingJob.list(

filter=’display_name=”experiment_a27”’, order_by=’create_time desc’

• Parameters

  • filter (str) – Optional. An expression for filtering the results of the request. For field names, snake_case and camelCase are supported.

  • order_by (str) – Optional. A comma-separated list of fields used to sort the returned traing job resources. The defauilt sorting order is ascending. To sort by a field name in descending order, use desc after the field name. The following fields are supported: display_name, create_time, update_time.

  • project (str) – Optional. The name of the Google Cloud project to which to retrieve the list of training job resources. This overrides the project that was set by aiplatform.init.

  • location (str) – Optional. The Google Cloud region from where the training job resources are retrieved. This region overrides the region that was set by aiplatform.init.

  • credentials (auth_credentials.Credentials) – Optional. The credentials that are used to retrieve list. These credentials override the credentials set by aiplatform.init.

• Returns

  A list of training job resources.

• Return type

  List[VertexAiResourceNoun]

property name(: [str](https://python.readthedocs.io/en/latest/library/stdtypes.html#str )

Name of this resource.

property network(: Optional[str )

The full name of the Google Compute Engine network to which this CustomTrainingJob should be peered.

Specify the name of the network using the format projects/{project}/global/networks/{network}. Replace {project} with the project number, such as 12345, and {network} with a network name.

Before specifying a network, private services access must be configured for the network. If private services access isn’t configured, then the custom training job can’t be peered with a network.

property resource_name(: [str](https://python.readthedocs.io/en/latest/library/stdtypes.html#str )

Full qualified resource name.

run(dataset: Optional[Union[google.cloud.aiplatform.datasets.image_dataset.ImageDataset, google.cloud.aiplatform.datasets.tabular_dataset.TabularDataset, google.cloud.aiplatform.datasets.text_dataset.TextDataset, google.cloud.aiplatform.datasets.video_dataset.VideoDataset]] = None, annotation_schema_uri: Optional[str] = None, model_display_name: Optional[str] = None, model_labels: Optional[Dict[str, str]] = None, model_id: Optional[str] = None, parent_model: Optional[str] = None, is_default_version: Optional[bool] = True, model_version_aliases: Optional[Sequence[str]] = None, model_version_description: Optional[str] = None, base_output_dir: Optional[str] = None, service_account: Optional[str] = None, network: Optional[str] = None, bigquery_destination: Optional[str] = None, args: Optional[List[Union[str, float, int]]] = None, environment_variables: Optional[Dict[str, str]] = None, replica_count: int = 1, machine_type: str = 'n1-standard-4', accelerator_type: str = 'ACCELERATOR_TYPE_UNSPECIFIED', accelerator_count: int = 0, boot_disk_type: str = 'pd-ssd', boot_disk_size_gb: int = 100, reduction_server_replica_count: int = 0, reduction_server_machine_type: Optional[str] = None, reduction_server_container_uri: Optional[str] = None, training_fraction_split: Optional[float] = None, validation_fraction_split: Optional[float] = None, test_fraction_split: Optional[float] = None, training_filter_split: Optional[str] = None, validation_filter_split: Optional[str] = None, test_filter_split: Optional[str] = None, predefined_split_column_name: Optional[str] = None, timestamp_split_column_name: Optional[str] = None, timeout: Optional[int] = None, restart_job_on_worker_restart: bool = False, enable_web_access: bool = False, enable_dashboard_access: bool = False, tensorboard: Optional[str] = None, sync=True, create_request_timeout: Optional[float] = None, disable_retries: bool = False, persistent_resource_id: Optional[str] = None, tpu_topology: Optional[str] = None, scheduling_strategy: Optional[google.cloud.aiplatform_v1.types.custom_job.Scheduling.Strategy] = None, reservation_affinity_type: Optional[Literal['NO_RESERVATION', 'ANY_RESERVATION', 'SPECIFIC_RESERVATION']] = None, reservation_affinity_key: Optional[str] = None, reservation_affinity_values: Optional[List[str]] = None, max_wait_duration: Optional[int] = None)

Runs the custom training job.

Distributed Training Support: If replica count = 1 then one chief replica will be provisioned. If replica_count > 1 the remainder will be provisioned as a worker replica pool. ie: replica_count = 10 will result in 1 chief and 9 workers All replicas have same machine_type, accelerator_type, and accelerator_count

If training on a Vertex AI dataset, you can use one of the following split configurations:

Data fraction splits:
Any of `training_fraction_split`, `validation_fraction_split` and
`test_fraction_split` may optionally be provided, they must sum to up to 1. If
the provided ones sum to less than 1, the remainder is assigned to sets as
decided by Vertex AI. If none of the fractions are set, by default roughly 80%
of data will be used for training, 10% for validation, and 10% for test.

Data filter splits:
Assigns input data to training, validation, and test sets
based on the given filters, data pieces not matched by any
filter are ignored. Currently only supported for Datasets
containing DataItems.
If any of the filters in this message are to match nothing, then
they can be set as ‘-’ (the minus sign).
If using filter splits, all of `training_filter_split`, `validation_filter_split` and
`test_filter_split` must be provided.
Supported only for unstructured Datasets.

Predefined splits:
Assigns input data to training, validation, and test sets based on the value of a provided key.
If using predefined splits, `predefined_split_column_name` must be provided.
Supported only for tabular Datasets.

Timestamp splits:
Assigns input data to training, validation, and test sets
based on a provided timestamps. The youngest data pieces are
assigned to training set, next to validation set, and the oldest
to the test set.
Supported only for tabular Datasets.

• Parameters

  • dataset (Union[datasets.ImageDataset,datasets.TabularDataset,datasets.TextDataset,datasets.VideoDataset]) – Vertex AI to fit this training against. Custom training script should retrieve datasets through passed in environment variables uris:

    os.environ[“AIP_TRAINING_DATA_URI”] os.environ[“AIP_VALIDATION_DATA_URI”] os.environ[“AIP_TEST_DATA_URI”]

    Additionally the dataset format is passed in as:

    os.environ[“AIP_DATA_FORMAT”]

  • annotation_schema_uri (str) – Google Cloud Storage URI points to a YAML file describing annotation schema. The schema is defined as an OpenAPI 3.0.2 Schema Object The schema files that can be used here are found in gs://google-cloud-aiplatform/schema/dataset/annotation/, note that the chosen schema must be consistent with metadata of the Dataset specified by dataset_id.

    Only Annotations that both match this schema and belong to DataItems not ignored by the split method are used in respectively training, validation or test role, depending on the role of the DataItem they are on.

    When used in conjunction with annotations_filter, the Annotations used for training are filtered by both annotations_filter and annotation_schema_uri.

  • model_display_name (str) – If the script produces a managed Vertex AI Model. The display name of the Model. The name can be up to 128 characters long and can be consist of any UTF-8 characters.

    If not provided upon creation, the job’s display_name is used.

  • model_labels (Dict[str, *[str](https://python.readthedocs.io/en/latest/library/stdtypes.html#str)]*) – Optional. The labels with user-defined metadata to organize your Models. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels.

  • model_id (str) – Optional. The ID to use for the Model produced by this job, which will become the final component of the model resource name. This value may be up to 63 characters, and valid characters are [a-z0-9_-]. The first character cannot be a number or hyphen.

  • parent_model (str) – Optional. The resource name or model ID of an existing model. The new model uploaded by this job will be a version of parent_model.

    Only set this field when training a new version of an existing model.

  • is_default_version (bool) – Optional. When set to True, the newly uploaded model version will automatically have alias “default” included. Subsequent uses of the model produced by this job without a version specified will use this “default” version.

    When set to False, the “default” alias will not be moved. Actions targeting the model version produced by this job will need to specifically reference this version by ID or alias.

    New model uploads, i.e. version 1, will always be “default” aliased.

  • model_version_aliases (Sequence[str]) – Optional. User provided version aliases so that the model version uploaded by this job can be referenced via alias instead of auto-generated version ID. A default version alias will be created for the first version of the model.

    The format is [a-z][a-zA-Z0-9-]{0,126}[a-z0-9]

  • model_version_description (str) – Optional. The description of the model version being uploaded by this job.

  • base_output_dir (str) – GCS output directory of job. If not provided a timestamped directory in the staging directory will be used.

    Vertex AI sets the following environment variables when it runs your training code:

    • AIP_MODEL_DIR: a Cloud Storage URI of a directory intended for saving model artifacts, i.e. <base_output_dir>/model/

    • AIP_CHECKPOINT_DIR: a Cloud Storage URI of a directory intended for saving checkpoints, i.e. <base_output_dir>/checkpoints/

    • AIP_TENSORBOARD_LOG_DIR: a Cloud Storage URI of a directory intended for saving TensorBoard logs, i.e. <base_output_dir>/logs/

  • service_account (str) – Specifies the service account for workload run-as account. Users submitting jobs must have act-as permission on this run-as account.

  • network (str) – The full name of the Compute Engine network to which the job should be peered. For example, projects/12345/global/networks/myVPC. Private services access must already be configured for the network. If left unspecified, the network set in aiplatform.init will be used. Otherwise, the job is not peered with any network.

  • bigquery_destination (str) – Provide this field if dataset is a BigQuery dataset. The BigQuery project location where the training data is to be written to. In the given project a new dataset is created with name dataset_<dataset-id>_<annotation-type>_<timestamp-of-training-call> where timestamp is in YYYY_MM_DDThh_mm_ss_sssZ format. All training input data will be written into that dataset. In the dataset three tables will be created, training, validation and test.

    • AIP_DATA_FORMAT = “bigquery”.

    • AIP_TRAINING_DATA_URI =”bigquery_destination.dataset_*.training”

    • AIP_VALIDATION_DATA_URI = “bigquery_destination.dataset_*.validation”

    • AIP_TEST_DATA_URI = “bigquery_destination.dataset_*.test”

  • args (List[Unions[str, *[int](https://python.readthedocs.io/en/latest/library/functions.html#int), [float](https://python.readthedocs.io/en/latest/library/functions.html#float)]*]) – Command line arguments to be passed to the Python script.

  • environment_variables (Dict[str, *[str](https://python.readthedocs.io/en/latest/library/stdtypes.html#str)]*) – Environment variables to be passed to the container. Should be a dictionary where keys are environment variable names and values are environment variable values for those names. At most 10 environment variables can be specified. The Name of the environment variable must be unique.

    environment_variables = {

    ‘MY_KEY’: ‘MY_VALUE’

    }

  • replica_count (int) – The number of worker replicas. If replica count = 1 then one chief replica will be provisioned. If replica_count > 1 the remainder will be provisioned as a worker replica pool.

  • machine_type (str) – The type of machine to use for training.

  • accelerator_type (str) – Hardware accelerator type. One of ACCELERATOR_TYPE_UNSPECIFIED, NVIDIA_TESLA_K80, NVIDIA_TESLA_P100, NVIDIA_TESLA_V100, NVIDIA_TESLA_P4, NVIDIA_TESLA_T4

  • accelerator_count (int) – The number of accelerators to attach to a worker replica.

  • boot_disk_type (str) – Type of the boot disk, default is pd-ssd. Valid values: pd-ssd (Persistent Disk Solid State Drive) or pd-standard (Persistent Disk Hard Disk Drive).

  • boot_disk_size_gb (int) – Size in GB of the boot disk, default is 100GB. boot disk size must be within the range of [100, 64000].

  • reduction_server_replica_count (int) – The number of reduction server replicas, default is 0.

  • reduction_server_machine_type (str) – Optional. The type of machine to use for reduction server.

  • reduction_server_container_uri (str) – Optional. The Uri of the reduction server container image. See details: https://cloud.google.com/vertex-ai/docs/training/distributed-training#reduce_training_time_with_reduction_server

  • training_fraction_split (float) – Optional. The fraction of the input data that is to be used to train the Model. This is ignored if Dataset is not provided.

  • validation_fraction_split (float) – Optional. The fraction of the input data that is to be used to validate the Model. This is ignored if Dataset is not provided.

  • test_fraction_split (float) – Optional. The fraction of the input data that is to be used to evaluate the Model. This is ignored if Dataset is not provided.

  • training_filter_split (str) – Optional. A filter on DataItems of the Dataset. DataItems that match this filter are used to train the Model. A filter with same syntax as the one used in DatasetService.ListDataItems may be used. If a single DataItem is matched by more than one of the FilterSplit filters, then it is assigned to the first set that applies to it in the training, validation, test order. This is ignored if Dataset is not provided.

  • validation_filter_split (str) – Optional. A filter on DataItems of the Dataset. DataItems that match this filter are used to validate the Model. A filter with same syntax as the one used in DatasetService.ListDataItems may be used. If a single DataItem is matched by more than one of the FilterSplit filters, then it is assigned to the first set that applies to it in the training, validation, test order. This is ignored if Dataset is not provided.

  • test_filter_split (str) – Optional. A filter on DataItems of the Dataset. DataItems that match this filter are used to test the Model. A filter with same syntax as the one used in DatasetService.ListDataItems may be used. If a single DataItem is matched by more than one of the FilterSplit filters, then it is assigned to the first set that applies to it in the training, validation, test order. This is ignored if Dataset is not provided.

  • predefined_split_column_name (str) – Optional. The key is a name of one of the Dataset’s data columns. The value of the key (either the label’s value or value in the column) must be one of {training, validation, test}, and it defines to which set the given piece of data is assigned. If for a piece of data the key is not present or has an invalid value, that piece is ignored by the pipeline.

    Supported only for tabular and time series Datasets.

  • timestamp_split_column_name (str) – Optional. The key is a name of one of the Dataset’s data columns. The value of the key values of the key (the values in the column) must be in RFC 3339 date-time format, where time-offset = “Z” (e.g. 1985-04-12T23:20:50.52Z). If for a piece of data the key is not present or has an invalid value, that piece is ignored by the pipeline.

    Supported only for tabular and time series Datasets.

  • timeout (int) – The maximum job running time in seconds. The default is 7 days.

  • restart_job_on_worker_restart (bool) – Restarts the entire CustomJob if a worker gets restarted. This feature can be used by distributed training jobs that are not resilient to workers leaving and joining a job.

  • enable_web_access (bool) – Whether you want Vertex AI to enable interactive shell access to training containers. https://cloud.google.com/vertex-ai/docs/training/monitor-debug-interactive-shell

  • enable_dashboard_access (bool) – Whether you want Vertex AI to enable access to the customized dashboard to training containers.

  • tensorboard (str) – Optional. The name of a Vertex AI [Tensorboard][google.cloud.aiplatform.v1beta1.Tensorboard] resource to which this CustomJob will upload Tensorboard logs. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}

    The training script should write Tensorboard to following Vertex AI environment variable:

    AIP_TENSORBOARD_LOG_DIR

    service_account is required with provided tensorboard. For more information on configuring your service account please visit: https://cloud.google.com/vertex-ai/docs/experiments/tensorboard-training

  • sync (bool) – Whether to execute this method synchronously. If False, this method will be executed in concurrent Future and any downstream object will be immediately returned and synced when the Future has completed.

  • create_request_timeout (float) – Optional. The timeout for the create request in seconds.

  • disable_retries (bool) – Indicates if the job should retry for internal errors after the job starts running. If True, overrides restart_job_on_worker_restart to False.

  • persistent_resource_id (str) – Optional. The ID of the PersistentResource in the same Project and Location. If this is specified, the job will be run on existing machines held by the PersistentResource instead of on-demand short-live machines. The network, CMEK, and node pool configs on the job should be consistent with those on the PersistentResource, otherwise, the job will be rejected.

  • tpu_topology (str) – Optional. Specifies the tpu topology to be used for TPU training job. This field is required for TPU v5 versions. For details on the TPU topology, refer to https://cloud.google.com/tpu/docs/v5e#tpu-v5e-config. The topology must be a supported value for the TPU machine type.

  • scheduling_strategy (gca_custom_job_compat.Scheduling.Strategy) – Optional. Indicates the job scheduling strategy.

  • reservation_affinity_type (str) – Optional. The type of reservation affinity. One of: * “NO_RESERVATION” : No reservation is used. * “ANY_RESERVATION” : Any reservation that matches machine spec can be used. * “SPECIFIC_RESERVATION” : A specific reservation must be use used. See reservation_affinity_key and reservation_affinity_values for how to specify the reservation.

  • reservation_affinity_key (str) – Optional. Corresponds to the label key of a reservation resource. To target a SPECIFIC_RESERVATION by name, use compute.googleapis.com/reservation-name as the key and specify the name of your reservation as its value.

  • reservation_affinity_values (List[str]) – Optional. Corresponds to the label values of a reservation resource. This must be the full resource name of the reservation. Format: ‘projects/{project_id_or_number}/zones/{zone}/reservations/{reservation_name}’

  • max_wait_duration (int) – This is the maximum duration that a job will wait for the requested resources to be provisioned in seconds. If set to 0, the job will wait indefinitely. The default is 30 minutes.

• Returns

  The trained Vertex AI Model resource or None if training did not

    produce a Vertex AI Model.
  

• Return type

  model

• Raises

  RuntimeError – If Training job has already been run, staging_bucket has not been set, or model_display_name was provided but required arguments were not provided in constructor.

property start_time(: Optional[datetime.datetime )

Optional. The time when the training job first entered the PIPELINE_STATE_RUNNING state.

property state(: Optional[[google.cloud.aiplatform_v1.types.pipeline_state.PipelineState](../aiplatform_v1/types.md#google.cloud.aiplatform_v1.types.PipelineState)_ )

Current training state.

submit(dataset: Optional[Union[google.cloud.aiplatform.datasets.image_dataset.ImageDataset, google.cloud.aiplatform.datasets.tabular_dataset.TabularDataset, google.cloud.aiplatform.datasets.text_dataset.TextDataset, google.cloud.aiplatform.datasets.video_dataset.VideoDataset]] = None, annotation_schema_uri: Optional[str] = None, model_display_name: Optional[str] = None, model_labels: Optional[Dict[str, str]] = None, model_id: Optional[str] = None, parent_model: Optional[str] = None, is_default_version: Optional[bool] = True, model_version_aliases: Optional[Sequence[str]] = None, model_version_description: Optional[str] = None, base_output_dir: Optional[str] = None, service_account: Optional[str] = None, network: Optional[str] = None, bigquery_destination: Optional[str] = None, args: Optional[List[Union[str, float, int]]] = None, environment_variables: Optional[Dict[str, str]] = None, replica_count: int = 1, machine_type: str = 'n1-standard-4', accelerator_type: str = 'ACCELERATOR_TYPE_UNSPECIFIED', accelerator_count: int = 0, boot_disk_type: str = 'pd-ssd', boot_disk_size_gb: int = 100, reduction_server_replica_count: int = 0, reduction_server_machine_type: Optional[str] = None, reduction_server_container_uri: Optional[str] = None, training_fraction_split: Optional[float] = None, validation_fraction_split: Optional[float] = None, test_fraction_split: Optional[float] = None, training_filter_split: Optional[str] = None, validation_filter_split: Optional[str] = None, test_filter_split: Optional[str] = None, predefined_split_column_name: Optional[str] = None, timestamp_split_column_name: Optional[str] = None, timeout: Optional[int] = None, restart_job_on_worker_restart: bool = False, enable_web_access: bool = False, enable_dashboard_access: bool = False, tensorboard: Optional[str] = None, sync=True, create_request_timeout: Optional[float] = None, disable_retries: bool = False, persistent_resource_id: Optional[str] = None, tpu_topology: Optional[str] = None, scheduling_strategy: Optional[google.cloud.aiplatform_v1.types.custom_job.Scheduling.Strategy] = None, reservation_affinity_type: Optional[Literal['NO_RESERVATION', 'ANY_RESERVATION', 'SPECIFIC_RESERVATION']] = None, reservation_affinity_key: Optional[str] = None, reservation_affinity_values: Optional[List[str]] = None, max_wait_duration: Optional[int] = None)

Submits the custom training job without blocking until completion.

Distributed Training Support: If replica count = 1 then one chief replica will be provisioned. If replica_count > 1 the remainder will be provisioned as a worker replica pool. ie: replica_count = 10 will result in 1 chief and 9 workers All replicas have same machine_type, accelerator_type, and accelerator_count

If training on a Vertex AI dataset, you can use one of the following split configurations:

Data fraction splits:
Any of `training_fraction_split`, `validation_fraction_split` and
`test_fraction_split` may optionally be provided, they must sum to up to 1. If
the provided ones sum to less than 1, the remainder is assigned to sets as
decided by Vertex AI. If none of the fractions are set, by default roughly 80%
of data will be used for training, 10% for validation, and 10% for test.

Data filter splits:
Assigns input data to training, validation, and test sets
based on the given filters, data pieces not matched by any
filter are ignored. Currently only supported for Datasets
containing DataItems.
If any of the filters in this message are to match nothing, then
they can be set as ‘-’ (the minus sign).
If using filter splits, all of `training_filter_split`, `validation_filter_split` and
`test_filter_split` must be provided.
Supported only for unstructured Datasets.

Predefined splits:
Assigns input data to training, validation, and test sets based on the value of a provided key.
If using predefined splits, `predefined_split_column_name` must be provided.
Supported only for tabular Datasets.

Timestamp splits:
Assigns input data to training, validation, and test sets
based on a provided timestamps. The youngest data pieces are
assigned to training set, next to validation set, and the oldest
to the test set.
Supported only for tabular Datasets.

• Parameters

  • dataset (Union[datasets.ImageDataset,datasets.TabularDataset,datasets.TextDataset,datasets.VideoDataset]) – Vertex AI to fit this training against. Custom training script should retrieve datasets through passed in environment variables uris:

    os.environ[“AIP_TRAINING_DATA_URI”] os.environ[“AIP_VALIDATION_DATA_URI”] os.environ[“AIP_TEST_DATA_URI”]

    Additionally the dataset format is passed in as:

    os.environ[“AIP_DATA_FORMAT”]

  • annotation_schema_uri (str) – Google Cloud Storage URI points to a YAML file describing annotation schema. The schema is defined as an OpenAPI 3.0.2 Schema Object The schema files that can be used here are found in gs://google-cloud-aiplatform/schema/dataset/annotation/, note that the chosen schema must be consistent with metadata of the Dataset specified by dataset_id.

    Only Annotations that both match this schema and belong to DataItems not ignored by the split method are used in respectively training, validation or test role, depending on the role of the DataItem they are on.

    When used in conjunction with annotations_filter, the Annotations used for training are filtered by both annotations_filter and annotation_schema_uri.

  • model_display_name (str) – If the script produces a managed Vertex AI Model. The display name of the Model. The name can be up to 128 characters long and can be consist of any UTF-8 characters.

    If not provided upon creation, the job’s display_name is used.

  • model_labels (Dict[str, *[str](https://python.readthedocs.io/en/latest/library/stdtypes.html#str)]*) – Optional. The labels with user-defined metadata to organize your Models. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels.

  • model_id (str) – Optional. The ID to use for the Model produced by this job, which will become the final component of the model resource name. This value may be up to 63 characters, and valid characters are [a-z0-9_-]. The first character cannot be a number or hyphen.

  • parent_model (str) – Optional. The resource name or model ID of an existing model. The new model uploaded by this job will be a version of parent_model.

    Only set this field when training a new version of an existing model.

  • is_default_version (bool) – Optional. When set to True, the newly uploaded model version will automatically have alias “default” included. Subsequent uses of the model produced by this job without a version specified will use this “default” version.

    When set to False, the “default” alias will not be moved. Actions targeting the model version produced by this job will need to specifically reference this version by ID or alias.

    New model uploads, i.e. version 1, will always be “default” aliased.

  • model_version_aliases (Sequence[str]) – Optional. User provided version aliases so that the model version uploaded by this job can be referenced via alias instead of auto-generated version ID. A default version alias will be created for the first version of the model.

    The format is [a-z][a-zA-Z0-9-]{0,126}[a-z0-9]

  • model_version_description (str) – Optional. The description of the model version being uploaded by this job.

  • base_output_dir (str) – GCS output directory of job. If not provided a timestamped directory in the staging directory will be used.

    Vertex AI sets the following environment variables when it runs your training code:

    • AIP_MODEL_DIR: a Cloud Storage URI of a directory intended for saving model artifacts, i.e. <base_output_dir>/model/

    • AIP_CHECKPOINT_DIR: a Cloud Storage URI of a directory intended for saving checkpoints, i.e. <base_output_dir>/checkpoints/

    • AIP_TENSORBOARD_LOG_DIR: a Cloud Storage URI of a directory intended for saving TensorBoard logs, i.e. <base_output_dir>/logs/

  • service_account (str) – Specifies the service account for workload run-as account. Users submitting jobs must have act-as permission on this run-as account.

  • network (str) – The full name of the Compute Engine network to which the job should be peered. For example, projects/12345/global/networks/myVPC. Private services access must already be configured for the network. If left unspecified, the network set in aiplatform.init will be used. Otherwise, the job is not peered with any network.

  • bigquery_destination (str) – Provide this field if dataset is a BigQuery dataset. The BigQuery project location where the training data is to be written to. In the given project a new dataset is created with name dataset_<dataset-id>_<annotation-type>_<timestamp-of-training-call> where timestamp is in YYYY_MM_DDThh_mm_ss_sssZ format. All training input data will be written into that dataset. In the dataset three tables will be created, training, validation and test.

    • AIP_DATA_FORMAT = “bigquery”.

    • AIP_TRAINING_DATA_URI =”bigquery_destination.dataset_*.training”

    • AIP_VALIDATION_DATA_URI = “bigquery_destination.dataset_*.validation”

    • AIP_TEST_DATA_URI = “bigquery_destination.dataset_*.test”

  • args (List[Unions[str, *[int](https://python.readthedocs.io/en/latest/library/functions.html#int), [float](https://python.readthedocs.io/en/latest/library/functions.html#float)]*]) – Command line arguments to be passed to the Python script.

  • environment_variables (Dict[str, *[str](https://python.readthedocs.io/en/latest/library/stdtypes.html#str)]*) – Environment variables to be passed to the container. Should be a dictionary where keys are environment variable names and values are environment variable values for those names. At most 10 environment variables can be specified. The Name of the environment variable must be unique.

    environment_variables = {

    ‘MY_KEY’: ‘MY_VALUE’

    }

  • replica_count (int) – The number of worker replicas. If replica count = 1 then one chief replica will be provisioned. If replica_count > 1 the remainder will be provisioned as a worker replica pool.

  • machine_type (str) – The type of machine to use for training.

  • accelerator_type (str) – Hardware accelerator type. One of ACCELERATOR_TYPE_UNSPECIFIED, NVIDIA_TESLA_K80, NVIDIA_TESLA_P100, NVIDIA_TESLA_V100, NVIDIA_TESLA_P4, NVIDIA_TESLA_T4

  • accelerator_count (int) – The number of accelerators to attach to a worker replica.

  • boot_disk_type (str) – Type of the boot disk, default is pd-ssd. Valid values: pd-ssd (Persistent Disk Solid State Drive) or pd-standard (Persistent Disk Hard Disk Drive).

  • boot_disk_size_gb (int) – Size in GB of the boot disk, default is 100GB. boot disk size must be within the range of [100, 64000].

  • reduction_server_replica_count (int) – The number of reduction server replicas, default is 0.

  • reduction_server_machine_type (str) – Optional. The type of machine to use for reduction server.

  • reduction_server_container_uri (str) – Optional. The Uri of the reduction server container image. See details: https://cloud.google.com/vertex-ai/docs/training/distributed-training#reduce_training_time_with_reduction_server

  • training_fraction_split (float) – Optional. The fraction of the input data that is to be used to train the Model. This is ignored if Dataset is not provided.

  • validation_fraction_split (float) – Optional. The fraction of the input data that is to be used to validate the Model. This is ignored if Dataset is not provided.

  • test_fraction_split (float) – Optional. The fraction of the input data that is to be used to evaluate the Model. This is ignored if Dataset is not provided.

  • training_filter_split (str) – Optional. A filter on DataItems of the Dataset. DataItems that match this filter are used to train the Model. A filter with same syntax as the one used in DatasetService.ListDataItems may be used. If a single DataItem is matched by more than one of the FilterSplit filters, then it is assigned to the first set that applies to it in the training, validation, test order. This is ignored if Dataset is not provided.

  • validation_filter_split (str) – Optional. A filter on DataItems of the Dataset. DataItems that match this filter are used to validate the Model. A filter with same syntax as the one used in DatasetService.ListDataItems may be used. If a single DataItem is matched by more than one of the FilterSplit filters, then it is assigned to the first set that applies to it in the training, validation, test order. This is ignored if Dataset is not provided.

  • test_filter_split (str) – Optional. A filter on DataItems of the Dataset. DataItems that match this filter are used to test the Model. A filter with same syntax as the one used in DatasetService.ListDataItems may be used. If a single DataItem is matched by more than one of the FilterSplit filters, then it is assigned to the first set that applies to it in the training, validation, test order. This is ignored if Dataset is not provided.

  • predefined_split_column_name (str) – Optional. The key is a name of one of the Dataset’s data columns. The value of the key (either the label’s value or value in the column) must be one of {training, validation, test}, and it defines to which set the given piece of data is assigned. If for a piece of data the key is not present or has an invalid value, that piece is ignored by the pipeline.

    Supported only for tabular and time series Datasets.

  • timestamp_split_column_name (str) – Optional. The key is a name of one of the Dataset’s data columns. The value of the key values of the key (the values in the column) must be in RFC 3339 date-time format, where time-offset = “Z” (e.g. 1985-04-12T23:20:50.52Z). If for a piece of data the key is not present or has an invalid value, that piece is ignored by the pipeline.

    Supported only for tabular and time series Datasets.

  • timeout (int) – The maximum job running time in seconds. The default is 7 days.

  • restart_job_on_worker_restart (bool) – Restarts the entire CustomJob if a worker gets restarted. This feature can be used by distributed training jobs that are not resilient to workers leaving and joining a job.

  • enable_web_access (bool) – Whether you want Vertex AI to enable interactive shell access to training containers. https://cloud.google.com/vertex-ai/docs/training/monitor-debug-interactive-shell

  • enable_dashboard_access (bool) – Whether you want Vertex AI to enable access to the customized dashboard to training containers.

  • tensorboard (str) – Optional. The name of a Vertex AI [Tensorboard][google.cloud.aiplatform.v1beta1.Tensorboard] resource to which this CustomJob will upload Tensorboard logs. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}

    The training script should write Tensorboard to following Vertex AI environment variable:

    AIP_TENSORBOARD_LOG_DIR

    service_account is required with provided tensorboard. For more information on configuring your service account please visit: https://cloud.google.com/vertex-ai/docs/experiments/tensorboard-training

  • sync (bool) – Whether to execute this method synchronously. If False, this method will be executed in concurrent Future and any downstream object will be immediately returned and synced when the Future has completed.

  • create_request_timeout (float) – Optional. The timeout for the create request in seconds.

  • disable_retries (bool) – Indicates if the job should retry for internal errors after the job starts running. If True, overrides restart_job_on_worker_restart to False.

  • persistent_resource_id (str) – Optional. The ID of the PersistentResource in the same Project and Location. If this is specified, the job will be run on existing machines held by the PersistentResource instead of on-demand short-live machines. The network, CMEK, and node pool configs on the job should be consistent with those on the PersistentResource, otherwise, the job will be rejected.

  • tpu_topology (str) – Optional. Specifies the tpu topology to be used for TPU training job. This field is required for TPU v5 versions. For details on the TPU topology, refer to https://cloud.google.com/tpu/docs/v5e#tpu-v5e-config. The topology must be a supported value for the TPU machine type.

  • scheduling_strategy (gca_custom_job_compat.Scheduling.Strategy) – Optional. Indicates the job scheduling strategy.

  • reservation_affinity_type (str) – Optional. The type of reservation affinity. One of: * “NO_RESERVATION” : No reservation is used. * “ANY_RESERVATION” : Any reservation that matches machine spec can be used. * “SPECIFIC_RESERVATION” : A specific reservation must be use used. See reservation_affinity_key and reservation_affinity_values for how to specify the reservation.

  • reservation_affinity_key (str) – Optional. Corresponds to the label key of a reservation resource. To target a SPECIFIC_RESERVATION by name, use compute.googleapis.com/reservation-name as the key and specify the name of your reservation as its value.

  • reservation_affinity_values (List[str]) – Optional. Corresponds to the label values of a reservation resource. This must be the full resource name of the reservation. Format: ‘projects/{project_id_or_number}/zones/{zone}/reservations/{reservation_name}’

  • max_wait_duration (int) – This is the maximum duration that a job will wait for the requested resources to be provisioned in seconds. If set to 0, the job will wait indefinitely. The default is 30 minutes.

• Returns

  The trained Vertex AI Model resource or None if training did not

    produce a Vertex AI Model.
  

• Return type

  model

• Raises

  RuntimeError – If Training job has already been run, staging_bucket has not been set, or model_display_name was provided but required arguments were not provided in constructor.

to_dict()

Returns the resource proto as a dictionary.

property update_time(: [datetime.datetime](https://python.readthedocs.io/en/latest/library/datetime.html#datetime.datetime )

Time this resource was last updated.

wait()

Helper method that blocks until all futures are complete.

wait_for_resource_creation()

Waits until the resource has been created.

property web_access_uris(: Dict[str, str )

Returns the URIs used to access the custom training job.

class google.cloud.aiplatform.CustomJob(display_name: str, worker_pool_specs: Union[List[Dict], List[google.cloud.aiplatform_v1.types.custom_job.WorkerPoolSpec]], base_output_dir: Optional[str] = None, project: Optional[str] = None, location: Optional[str] = None, credentials: Optional[google.auth.credentials.Credentials] = None, labels: Optional[Dict[str, str]] = None, encryption_spec_key_name: Optional[str] = None, staging_bucket: Optional[str] = None, persistent_resource_id: Optional[str] = None)

Bases: google.cloud.aiplatform.jobs._RunnableJob, google.cloud.aiplatform.base.PreviewMixin

Vertex AI Custom Job.

Constructs a Custom Job with Worker Pool Specs.

``

` Example usage: worker_pool_specs = [

{

“machine_spec”: {

    “machine_type”: “n1-standard-4”,
    “accelerator_type”: “NVIDIA_TESLA_K80”,
    “accelerator_count”: 1,

},
“replica_count”: 1,
“container_spec”: {

> “image_uri”: container_image_uri,
> “command”: [],
> “args”: [],

},

}

]

my_job = aiplatform.CustomJob(

display_name=’my_job’,
worker_pool_specs=worker_pool_specs,
labels={‘my_key’: ‘my_value’},

)

my_job.run()

``

`

For more information on configuring worker pool specs please visit: https://cloud.google.com/ai-platform-unified/docs/training/create-custom-job

• Parameters

  • display_name (str) – Required. The user-defined name of the HyperparameterTuningJob. The name can be up to 128 characters long and can be consist of any UTF-8 characters.

  • worker_pool_specs (Union[List[Dict], **List[aiplatform.gapic.WorkerPoolSpec]]) – Required. The spec of the worker pools including machine type and Docker image. Can provided as a list of dictionaries or list of WorkerPoolSpec proto messages.

  • base_output_dir (str) – Optional. GCS output directory of job. If not provided a timestamped directory in the staging directory will be used.

  • project (str) – Optional.Project to run the custom job in. Overrides project set in aiplatform.init.

  • location (str) – Optional.Location to run the custom job in. Overrides location set in aiplatform.init.

  • credentials (auth_credentials.Credentials) – Optional.Custom credentials to use to run call custom job service. Overrides credentials set in aiplatform.init.

  • labels (Dict[str, *[str](https://python.readthedocs.io/en/latest/library/stdtypes.html#str)]*) – Optional. The labels with user-defined metadata to organize CustomJobs. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels.

  • encryption_spec_key_name (str) – Optional.Customer-managed encryption key name for a CustomJob. If this is set, then all resources created by the CustomJob will be encrypted with the provided encryption key.

  • staging_bucket (str) – Optional. Bucket for produced custom job artifacts. Overrides staging_bucket set in aiplatform.init.

  • persistent_resource_id (str) – Optional. The ID of the PersistentResource in the same Project and Location. If this is specified, the job will be run on existing machines held by the PersistentResource instead of on-demand short-live machines. The network and CMEK configs on the job should be consistent with those on the PersistentResource, otherwise, the job will be rejected.

• Raises

  RuntimeError – If staging bucket was not set using aiplatform.init and a staging bucket was not passed in.

cancel()

Cancels this Job.

Success of cancellation is not guaranteed. Use Job.state property to verify if cancellation was successful.

client_class()

alias of google.cloud.aiplatform.utils.JobClientWithOverride

property create_time(: [datetime.datetime](https://python.readthedocs.io/en/latest/library/datetime.html#datetime.datetime )

Time this resource was created.

delete(sync: bool = True)

Deletes this Vertex AI resource. WARNING: This deletion is permanent.

• Parameters

  sync (bool) – Whether to execute this deletion synchronously. If False, this method will be executed in concurrent Future and any downstream object will be immediately returned and synced when the Future has completed.

property display_name(: [str](https://python.readthedocs.io/en/latest/library/stdtypes.html#str )

Display name of this resource.

done()

Method indicating whether a job has completed.

• Returns

  True if the job has completed.

property encryption_spec(: Optional[[google.cloud.aiplatform_v1.types.encryption_spec.EncryptionSpec](../aiplatform_v1/types.md#google.cloud.aiplatform_v1.types.EncryptionSpec)_ )

Customer-managed encryption key options for this Vertex AI resource.

If this is set, then all resources created by this Vertex AI resource will be encrypted with the provided encryption key.

property end_time(: Optional[datetime.datetime )

Time when the Job resource entered the JOB_STATE_SUCCEEDED, JOB_STATE_FAILED, or JOB_STATE_CANCELLED state.

property error(: Optional[google.rpc.status_pb2.Status )

Detailed error info for this Job resource. Only populated when the Job’s state is JOB_STATE_FAILED or JOB_STATE_CANCELLED.

classmethod from_local_script(display_name: str, script_path: str, container_uri: str, enable_autolog: bool = False, args: Optional[Sequence[str]] = None, requirements: Optional[Sequence[str]] = None, environment_variables: Optional[Dict[str, str]] = None, replica_count: int = 1, machine_type: str = 'n1-standard-4', accelerator_type: str = 'ACCELERATOR_TYPE_UNSPECIFIED', accelerator_count: int = 0, boot_disk_type: str = 'pd-ssd', boot_disk_size_gb: int = 100, reduction_server_replica_count: int = 0, reduction_server_machine_type: Optional[str] = None, reduction_server_container_uri: Optional[str] = None, base_output_dir: Optional[str] = None, project: Optional[str] = None, location: Optional[str] = None, credentials: Optional[google.auth.credentials.Credentials] = None, labels: Optional[Dict[str, str]] = None, encryption_spec_key_name: Optional[str] = None, staging_bucket: Optional[str] = None, persistent_resource_id: Optional[str] = None, tpu_topology: Optional[str] = None)

Configures a custom job from a local script.

Example usage:

``

` job = aiplatform.CustomJob.from_local_script(

display_name=”my-custom-job”, script_path=”training_script.py”, container_uri=”gcr.io/cloud-aiplatform/training/tf-cpu.2-2:latest”, requirements=[“gcsfs==0.7.1”], replica_count=1, args=[’–dataset’, ‘gs://my-bucket/my-dataset’, ‘–model_output_uri’, ‘gs://my-bucket/model’] labels={‘my_key’: ‘my_value’},

)

job.run()

``

`

• Parameters

  • display_name (str) – Required. The user-defined name of this CustomJob.

  • script_path (str) – Required. Local path to training script.

  • container_uri (str) – Required. Uri of the training container image to use for custom job. Support images in Artifact Registry, Container Registry, or Docker Hub. Vertex AI provides a wide range of executor images with pre-installed packages to meet users’ various use cases. See the list of pre-built containers for training https://cloud.google.com/vertex-ai/docs/training/pre-built-containers. If not using image from this list, please make sure python3 and pip3 are installed in your container.

  • enable_autolog (bool) – Optional. If True, the Vertex Experiments autologging feature will be enabled in the CustomJob. Note that this will wrap your training script with some autologging-related code.

  • args (Optional[Sequence[str]]) – Optional. Command line arguments to be passed to the Python task.

  • requirements (Sequence[str]) – Optional. List of python packages dependencies of script.

  • environment_variables (Dict[str, *[str](https://python.readthedocs.io/en/latest/library/stdtypes.html#str)]*) – Optional. Environment variables to be passed to the container. Should be a dictionary where keys are environment variable names and values are environment variable values for those names. At most 10 environment variables can be specified. The Name of the environment variable must be unique.

    environment_variables = {

    ‘MY_KEY’: ‘MY_VALUE’

    }

  • replica_count (int) – Optional. The number of worker replicas. If replica count = 1 then one chief replica will be provisioned. If replica_count > 1 the remainder will be provisioned as a worker replica pool.

  • machine_type (str) – Optional. The type of machine to use for training.

  • accelerator_type (str) – Optional. Hardware accelerator type. One of ACCELERATOR_TYPE_UNSPECIFIED, NVIDIA_TESLA_K80, NVIDIA_TESLA_P100, NVIDIA_TESLA_V100, NVIDIA_TESLA_P4, NVIDIA_TESLA_T4

  • accelerator_count (int) – Optional. The number of accelerators to attach to a worker replica.

  • boot_disk_type (str) – Optional. Type of the boot disk, default is pd-ssd. Valid values: pd-ssd (Persistent Disk Solid State Drive) or pd-standard (Persistent Disk Hard Disk Drive).

  • boot_disk_size_gb (int) – Optional. Size in GB of the boot disk, default is 100GB. boot disk size must be within the range of [100, 64000].

  • reduction_server_replica_count (int) – The number of reduction server replicas, default is 0.

  • reduction_server_machine_type (str) – Optional. The type of machine to use for reduction server.

  • reduction_server_container_uri (str) – Optional. The Uri of the reduction server container image. See details: https://cloud.google.com/vertex-ai/docs/training/distributed-training#reduce_training_time_with_reduction_server

  • base_output_dir (str) – Optional. GCS output directory of job. If not provided a timestamped directory in the staging directory will be used.

  • project (str) – Optional. Project to run the custom job in. Overrides project set in aiplatform.init.

  • location (str) – Optional. Location to run the custom job in. Overrides location set in aiplatform.init.

  • credentials (auth_credentials.Credentials) – Optional. Custom credentials to use to run call custom job service. Overrides credentials set in aiplatform.init.

  • labels (Dict[str, *[str](https://python.readthedocs.io/en/latest/library/stdtypes.html#str)]*) – Optional. The labels with user-defined metadata to organize CustomJobs. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels.

  • encryption_spec_key_name (str) – Optional. Customer-managed encryption key name for a CustomJob. If this is set, then all resources created by the CustomJob will be encrypted with the provided encryption key.

  • staging_bucket (str) – Optional. Bucket for produced custom job artifacts. Overrides staging_bucket set in aiplatform.init.

  • persistent_resource_id (str) – Optional. The ID of the PersistentResource in the same Project and Location. If this is specified, the job will be run on existing machines held by the PersistentResource instead of on-demand short-live machines. The network, CMEK, and node pool configs on the job should be consistent with those on the PersistentResource, otherwise, the job will be rejected.

  • tpu_topology (str) – Optional. Specifies the tpu topology to be used for TPU training job. This field is required for TPU v5 versions. For details on the TPU topology, refer to https://cloud.google.com/tpu/docs/v5e#tpu-v5e-config. The topology must be a supported value for the TPU machine type.

• Raises

  RuntimeError – If staging bucket was not set using aiplatform.init and a staging bucket was not passed in.

property gca_resource(: [proto.message.Message](https://proto-plus-python.readthedocs.io/en/latest/reference/message.html#proto.message.Message )

The underlying resource proto representation.

classmethod get(resource_name: str, project: Optional[str] = None, location: Optional[str] = None, credentials: Optional[google.auth.credentials.Credentials] = None)

Get a Vertex AI Job for the given resource_name.

• Parameters

  • resource_name (str) – Required. A fully-qualified resource name or ID.

  • project (str) – Optional. project to retrieve dataset from. If not set, project set in aiplatform.init will be used.

  • location (str) – Optional. location to retrieve dataset from. If not set, location set in aiplatform.init will be used.

  • credentials (auth_credentials.Credentials) – Custom credentials to use to upload this model. Overrides credentials set in aiplatform.init.

• Returns

  A Vertex AI Job.

property labels(: Dict[str, str )

User-defined labels containing metadata about this resource.

Read more about labels at https://goo.gl/xmQnxf

classmethod list(filter: Optional[str] = None, order_by: Optional[str] = None, project: Optional[str] = None, location: Optional[str] = None, credentials: Optional[google.auth.credentials.Credentials] = None)

List all instances of this Job Resource.

Example Usage:

aiplatform.BatchPredictionJobs.list(

filter=’state=”JOB_STATE_SUCCEEDED” AND display_name=”my_job”’,

)

• Parameters

  • filter (str) – Optional. An expression for filtering the results of the request. For field names both snake_case and camelCase are supported.

  • order_by (str) – Optional. A comma-separated list of fields to order by, sorted in ascending order. Use “desc” after a field name for descending. Supported fields: display_name, create_time, update_time

  • project (str) – Optional. Project to retrieve list from. If not set, project set in aiplatform.init will be used.

  • location (str) – Optional. Location to retrieve list from. If not set, location set in aiplatform.init will be used.

  • credentials (auth_credentials.Credentials) – Optional. Custom credentials to use to retrieve list. Overrides credentials set in aiplatform.init.

• Returns

  List[VertexAiResourceNoun] - A list of Job resource objects.

property name(: [str](https://python.readthedocs.io/en/latest/library/stdtypes.html#str )

Name of this resource.

property network(: Optional[str )

The full name of the Google Compute Engine network to which this CustomJob should be peered.

Takes the format projects/{project}/global/networks/{network}. Where {project} is a project number, as in 12345, and {network} is a network name.

Private services access must already be configured for the network. If left unspecified, the CustomJob is not peered with any network.

property preview(: google.cloud.aiplatform.base.PreviewClas )

Exposes features available in preview for this class.

property resource_name(: [str](https://python.readthedocs.io/en/latest/library/stdtypes.html#str )

Full qualified resource name.

run(service_account: Optional[str] = None, network: Optional[str] = None, timeout: Optional[int] = None, restart_job_on_worker_restart: bool = False, enable_web_access: bool = False, experiment: Optional[Union[google.cloud.aiplatform.metadata.experiment_resources.Experiment, str]] = None, experiment_run: Optional[Union[google.cloud.aiplatform.metadata.experiment_run_resource.ExperimentRun, str]] = None, tensorboard: Optional[str] = None, sync: bool = True, create_request_timeout: Optional[float] = None, disable_retries: bool = False, persistent_resource_id: Optional[str] = None, scheduling_strategy: Optional[google.cloud.aiplatform_v1.types.custom_job.Scheduling.Strategy] = None, max_wait_duration: Optional[int] = None)

Run this configured CustomJob.

• Parameters

  • service_account (str) – Optional. Specifies the service account for workload run-as account. Users submitting jobs must have act-as permission on this run-as account.

  • network (str) – Optional. The full name of the Compute Engine network to which the job should be peered. For example, projects/12345/global/networks/myVPC. Private services access must already be configured for the network. If left unspecified, the network set in aiplatform.init will be used. Otherwise, the job is not peered with any network.

  • timeout (int) – The maximum job running time in seconds. The default is 7 days.

  • restart_job_on_worker_restart (bool) – Restarts the entire CustomJob if a worker gets restarted. This feature can be used by distributed training jobs that are not resilient to workers leaving and joining a job.

  • enable_web_access (bool) – Whether you want Vertex AI to enable interactive shell access to training containers. https://cloud.google.com/vertex-ai/docs/training/monitor-debug-interactive-shell

  • experiment (Union[aiplatform.Experiment, *[str](https://python.readthedocs.io/en/latest/library/stdtypes.html#str)]*) – Optional. The instance or name of an Experiment resource to which this CustomJob will upload training parameters and metrics.

    service_account is required with provided experiment. For more information on configuring your service account please visit: https://cloud.google.com/vertex-ai/docs/experiments/tensorboard-training

  • experiment_run (Union[aiplatform.ExperimentRun, *[str](https://python.readthedocs.io/en/latest/library/stdtypes.html#str)]*) – Optional. The instance or name of an ExperimentRun resource to which this CustomJob will upload training parameters and metrics. This arg can only be set when experiment is set. If ‘experiment’ is set but ‘experiment_run` is not, an ExperimentRun resource will still be auto-generated.

  • tensorboard (str) – Optional. The name of a Vertex AI [Tensorboard][google.cloud.aiplatform.v1beta1.Tensorboard] resource to which this CustomJob will upload Tensorboard logs. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}

    The training script should write Tensorboard to following Vertex AI environment variable:

    AIP_TENSORBOARD_LOG_DIR

    service_account is required with provided tensorboard. For more information on configuring your service account please visit: https://cloud.google.com/vertex-ai/docs/experiments/tensorboard-training

  • sync (bool) – Whether to execute this method synchronously. If False, this method will unblock and it will be executed in a concurrent Future.

  • create_request_timeout (float) – Optional. The timeout for the create request in seconds.

  • disable_retries (bool) – Indicates if the job should retry for internal errors after the job starts running. If True, overrides restart_job_on_worker_restart to False.

  • persistent_resource_id (str) – Optional. The ID of the PersistentResource in the same Project and Location. If this is specified, the job will be run on existing machines held by the PersistentResource instead of on-demand short-live machines. The network, CMEK, and node pool configs on the job should be consistent with those on the PersistentResource, otherwise, the job will be rejected.

  • scheduling_strategy (gca_custom_job_compat.Scheduling.Strategy) – Optional. Indicates the job scheduling strategy.

  • max_wait_duration (int) – This is the maximum duration that a job will wait for the requested resources to be provisioned in seconds. If set to 0, the job will wait indefinitely. The default is 1 day.

property start_time(: Optional[datetime.datetime )

Time when the Job resource entered the JOB_STATE_RUNNING for the first time.

property state(: [google.cloud.aiplatform_v1.types.job_state.JobState](../aiplatform_v1/types.md#google.cloud.aiplatform_v1.types.JobState_ )

Fetch Job again and return the current JobState.

• Returns

  Enum that describes the state of a Vertex AI job.

• Return type

  state (job_state.JobState)

submit(*, service_account: Optional[str] = None, network: Optional[str] = None, timeout: Optional[int] = None, restart_job_on_worker_restart: bool = False, enable_web_access: bool = False, experiment: Optional[Union[google.cloud.aiplatform.metadata.experiment_resources.Experiment, str]] = None, experiment_run: Optional[Union[google.cloud.aiplatform.metadata.experiment_run_resource.ExperimentRun, str]] = None, tensorboard: Optional[str] = None, create_request_timeout: Optional[float] = None, disable_retries: bool = False, persistent_resource_id: Optional[str] = None, scheduling_strategy: Optional[google.cloud.aiplatform_v1.types.custom_job.Scheduling.Strategy] = None, max_wait_duration: Optional[int] = None)

Submit the configured CustomJob.

• Parameters

  • service_account (str) – Optional. Specifies the service account for workload run-as account. Users submitting jobs must have act-as permission on this run-as account.

  • network (str) – Optional. The full name of the Compute Engine network to which the job should be peered. For example, projects/12345/global/networks/myVPC. Private services access must already be configured for the network.

  • timeout (int) – The maximum job running time in seconds. The default is 7 days.

  • restart_job_on_worker_restart (bool) – Restarts the entire CustomJob if a worker gets restarted. This feature can be used by distributed training jobs that are not resilient to workers leaving and joining a job.

  • enable_web_access (bool) – Whether you want Vertex AI to enable interactive shell access to training containers. https://cloud.google.com/vertex-ai/docs/training/monitor-debug-interactive-shell

  • experiment (Union[aiplatform.Experiment, *[str](https://python.readthedocs.io/en/latest/library/stdtypes.html#str)]*) – Optional. The instance or name of an Experiment resource to which this CustomJob will upload training parameters and metrics.

    service_account is required with provided experiment. For more information on configuring your service account please visit: https://cloud.google.com/vertex-ai/docs/experiments/tensorboard-training

  • experiment_run (Union[aiplatform.ExperimentRun, *[str](https://python.readthedocs.io/en/latest/library/stdtypes.html#str)]*) – Optional. The instance or name of an ExperimentRun resource to which this CustomJob will upload training parameters and metrics. This arg can only be set when experiment is set. If ‘experiment’ is set but ‘experiment_run` is not, an ExperimentRun resource will still be auto-generated.

  • tensorboard (str) – Optional. The name of a Vertex AI [Tensorboard][google.cloud.aiplatform.v1beta1.Tensorboard] resource to which this CustomJob will upload Tensorboard logs. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}

    The training script should write Tensorboard to following Vertex AI environment variable:

    AIP_TENSORBOARD_LOG_DIR

    service_account is required with provided tensorboard. For more information on configuring your service account please visit: https://cloud.google.com/vertex-ai/docs/experiments/tensorboard-training

  • create_request_timeout (float) – Optional. The timeout for the create request in seconds.

  • disable_retries (bool) – Indicates if the job should retry for internal errors after the job starts running. If True, overrides restart_job_on_worker_restart to False.

  • persistent_resource_id (str) – Optional. The ID of the PersistentResource in the same Project and Location. If this is specified, the job will be run on existing machines held by the PersistentResource instead of on-demand short-live machines. The network, CMEK, and node pool configs on the job should be consistent with those on the PersistentResource, otherwise, the job will be rejected.

  • scheduling_strategy (gca_custom_job_compat.Scheduling.Strategy) – Optional. Indicates the job scheduling strategy.

  • max_wait_duration (int) – This is the maximum duration that a job will wait for the requested resources to be provisioned in seconds. If set to 0, the job will wait indefinitely. The default is 1 day.

• Raises

  ValueError – If both experiment and tensorboard are specified or if enable_autolog is True in CustomJob.from_local_script but experiment is not specified or the specified experiment doesn’t have a backing tensorboard.

to_dict()

Returns the resource proto as a dictionary.

property update_time(: [datetime.datetime](https://python.readthedocs.io/en/latest/library/datetime.html#datetime.datetime )

Time this resource was last updated.

wait()

Helper method that blocks until all futures are complete.

wait_for_completion()

Waits for job to complete.

• Raises

  RuntimeError – If job failed or cancelled.

wait_for_resource_creation()

Waits until resource has been created.

property web_access_uris(: Dict[str, Union[str, Dict[str, str]] )

Fetch the runnable job again and return the latest web access uris.

• Returns

  Web access uris of the runnable job.

• Return type

  (Dict[str, Union[str, Dict[str, str]]])

class google.cloud.aiplatform.CustomPythonPackageTrainingJob(display_name: str, python_package_gcs_uri: Union[str, List[str]], python_module_name: str, container_uri: str, model_serving_container_image_uri: Optional[str] = None, model_serving_container_predict_route: Optional[str] = None, model_serving_container_health_route: Optional[str] = None, model_serving_container_command: Optional[Sequence[str]] = None, model_serving_container_args: Optional[Sequence[str]] = None, model_serving_container_environment_variables: Optional[Dict[str, str]] = None, model_serving_container_ports: Optional[Sequence[int]] = None, model_description: Optional[str] = None, model_instance_schema_uri: Optional[str] = None, model_parameters_schema_uri: Optional[str] = None, model_prediction_schema_uri: Optional[str] = None, explanation_metadata: Optional[google.cloud.aiplatform_v1.types.explanation_metadata.ExplanationMetadata] = None, explanation_parameters: Optional[google.cloud.aiplatform_v1.types.explanation.ExplanationParameters] = None, project: Optional[str] = None, location: Optional[str] = None, credentials: Optional[google.auth.credentials.Credentials] = None, labels: Optional[Dict[str, str]] = None, training_encryption_spec_key_name: Optional[str] = None, model_encryption_spec_key_name: Optional[str] = None, staging_bucket: Optional[str] = None)

Bases: google.cloud.aiplatform.training_jobs._CustomTrainingJob

Class to launch a Custom Training Job in Vertex AI using a Python Package.

Use the CustomPythonPackageTrainingJob class to use a Python package to launch a custom training pipeline in Vertex AI. For an example of how to use the CustomPythonPackageTrainingJob class, see the tutorial in the Custom training using Python package, managed text dataset, and TensorFlow serving container notebook.

Constructs a Custom Training Job from a Python Package.

A class to launch a custom training job in Vertex AI using a Python Package.

Use the CustomPythonPackageTrainingJob class to use a Python package to launch a custom training pipeline in Vertex AI. For an example of how to use the CustomPythonPackageTrainingJob class, see the tutorial in the Custom training using Python package, managed text dataset, and TensorFlow serving container notebook.

job = aiplatform.CustomPythonPackageTrainingJob(

display_name=’test-train’,
python_package_gcs_uri=’gs://my-bucket/my-python-package.tar.gz’,
python_module_name=’my-training-python-package.task’,
container_uri=’gcr.io/cloud-aiplatform/training/tf-cpu.2-2:latest’,
model_serving_container_image_uri=’gcr.io/my-trainer/serving:1’,
model_serving_container_predict_route=’predict’,
model_serving_container_health_route=’metadata,
labels={‘key’: ‘value’},

)

Usage with Dataset:

ds = aiplatform.TabularDataset(

‘projects/my-project/locations/us-central1/datasets/12345’

)

job.run(

ds,
replica_count=1,
model_display_name=’my-trained-model’,
model_labels={‘key’: ‘value’},

)

Usage without Dataset:

job.run(

replica_count=1,
model_display_name=’my-trained-model’,
model_labels={‘key’: ‘value’},

)

To ensure your model gets saved in Vertex AI, write your saved model to os.environ[“AIP_MODEL_DIR”] in your provided training script.

• Parameters

  • display_name (str) – Required. The user-defined name of this TrainingPipeline.

  • python_package_gcs_uri (Union[str, **List[str]]) – Required. GCS location of the training python package. Could be a string for single package or a list of string for multiple packages.

  • python_module_name (str) – Required. The module name of the training python package.

  • container_uri (str) – Required. Uri of the training container image in the GCR.

  • model_serving_container_image_uri (str) – Optional. If the training produces a managed Vertex AI Model, the URI of the model serving container suitable for serving the model produced by the training script.

  • model_serving_container_predict_route (str) – Optional. If the training produces a managed Vertex AI Model, an HTTP path to send prediction requests to the container, and which must be supported by it. If not specified a default HTTP path will be used by Vertex AI.

  • model_serving_container_health_route (str) – Optional. If the training produces a managed Vertex AI Model, an HTTP path to send health check requests to the container, and which must be supported by it. If not specified a standard HTTP path will be used by AI Platform.

  • model_serving_container_command (Sequence[str]) – Optional. The command with which the container is run. Not executed within a shell. The Docker image’s ENTRYPOINT is used if this is not provided. Variable references $(VAR_NAME) are expanded using the container’s environment. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not.

  • model_serving_container_args (Sequence[str]) – Optional. The arguments to the command. The Docker image’s CMD is used if this is not provided. Variable references $(VAR_NAME) are expanded using the container’s environment. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not.

  • model_serving_container_environment_variables (Dict[str, *[str](https://python.readthedocs.io/en/latest/library/stdtypes.html#str)]*) – Optional. The environment variables that are to be present in the container. Should be a dictionary where keys are environment variable names and values are environment variable values for those names.

  • model_serving_container_ports (Sequence[int]) – Optional. Declaration of ports that are exposed by the container. This field is primarily informational, it gives Vertex AI information about the network connections the container uses. Listing or not a port here has no impact on whether the port is actually exposed, any port listening on the default “0.0.0.0” address inside a container will be accessible from the network.

  • model_description (str) – Optional. The description of the Model.

  • model_instance_schema_uri (str) – Optional. Points to a YAML file stored on Google Cloud Storage describing the format of a single instance, which are used in PredictRequest.instances, ExplainRequest.instances and BatchPredictionJob.input_config. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by AI Platform. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.

  • model_parameters_schema_uri (str) – Optional. Points to a YAML file stored on Google Cloud Storage describing the parameters of prediction and explanation via PredictRequest.parameters, ExplainRequest.parameters and BatchPredictionJob.model_parameters. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by AI Platform, if no parameters are supported it is set to an empty string. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.

  • model_prediction_schema_uri (str) – Optional. Points to a YAML file stored on Google Cloud Storage describing the format of a single prediction produced by this Model, which are returned via PredictResponse.predictions, ExplainResponse.explanations, and BatchPredictionJob.output_config. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by AI Platform. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.

  • explanation_metadata (explain.ExplanationMetadata) – Optional. Metadata describing the Model’s input and output for explanation. explanation_metadata is optional while explanation_parameters must be specified when used. For more details, see Ref docs http://tinyurl.com/1igh60kt

  • explanation_parameters (explain.ExplanationParameters) – Optional. Parameters to configure explaining for Model’s predictions. For more details, see Ref docs http://tinyurl.com/1an4zake

  • project (str) – Project to run training in. Overrides project set in aiplatform.init.

  • location (str) – Location to run training in. Overrides location set in aiplatform.init.

  • credentials (auth_credentials.Credentials) – Custom credentials to use to run call training service. Overrides credentials set in aiplatform.init.

  • labels (Dict[str, *[str](https://python.readthedocs.io/en/latest/library/stdtypes.html#str)]*) – Optional. The labels with user-defined metadata to organize TrainingPipelines. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels.

  • training_encryption_spec_key_name (Optional[str]) – Optional. The Cloud KMS resource identifier of the customer managed encryption key used to protect the training pipeline. Has the form: projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key. The key needs to be in the same region as where the compute resource is created.

    If set, this TrainingPipeline will be secured by this key.

    Note: Model trained by this TrainingPipeline is also secured by this key if model_to_upload is not set separately.

    Overrides encryption_spec_key_name set in aiplatform.init.

  • model_encryption_spec_key_name (Optional[str]) – Optional. The Cloud KMS resource identifier of the customer managed encryption key used to protect the model. Has the form: projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key. The key needs to be in the same region as where the compute resource is created.

    If set, the trained Model will be secured by this key.

    Overrides encryption_spec_key_name set in aiplatform.init.

  • staging_bucket (str) – Optional. Bucket used to stage source and training artifacts. Overrides staging_bucket set in aiplatform.init.

cancel()

Asynchronously attempts to cancel a training job.

The server makes a best effort to cancel the job, but the training job can’t always be cancelled. If the training job is canceled, its state transitions to CANCELLED and it’s not deleted.

• Raises

  RuntimeError – If this training job isn’t running, then a runtime error is raised.

client_class()

alias of google.cloud.aiplatform.utils.PipelineClientWithOverride

property create_time(: [datetime.datetime](https://python.readthedocs.io/en/latest/library/datetime.html#datetime.datetime )

Time this resource was created.

delete(sync: bool = True)

Deletes this Vertex AI resource. WARNING: This deletion is permanent.

• Parameters

  sync (bool) – Whether to execute this deletion synchronously. If False, this method will be executed in concurrent Future and any downstream object will be immediately returned and synced when the Future has completed.

property display_name(: [str](https://python.readthedocs.io/en/latest/library/stdtypes.html#str )

Display name of this resource.

done()

Method indicating whether a job has completed.

• Returns

  True if the job has completed.

property encryption_spec(: Optional[[google.cloud.aiplatform_v1.types.encryption_spec.EncryptionSpec](../aiplatform_v1/types.md#google.cloud.aiplatform_v1.types.EncryptionSpec)_ )

Customer-managed encryption key options for this Vertex AI resource.

If this is set, then all resources created by this Vertex AI resource will be encrypted with the provided encryption key.

property end_time(: Optional[datetime.datetime )

Optional. The time when the training job entered the PIPELINE_STATE_SUCCEEDED, PIPELINE_STATE_FAILED, or PIPELINE_STATE_CANCELLED state.

property error(: Optional[google.rpc.status_pb2.Status )

Optional. Detailed error information for this training job resource. Error information is created only when the state of the training job is PIPELINE_STATE_FAILED or PIPELINE_STATE_CANCELLED.

property gca_resource(: [proto.message.Message](https://proto-plus-python.readthedocs.io/en/latest/reference/message.html#proto.message.Message )

The underlying resource proto representation.

classmethod get(resource_name: str, project: Optional[str] = None, location: Optional[str] = None, credentials: Optional[google.auth.credentials.Credentials] = None)

Gets a training job using the resource_name that’s passed in.

• Parameters

  • resource_name (str) – Required. A fully-qualified resource name or ID.

  • project (str) – Optional. The name of the Google Cloud project to retrieve the training job from. This overrides the project that was set by aiplatform.init.

  • location (str) – Optional. The Google Cloud region from where the training job is retrieved. This region overrides the region that was set by aiplatform.init.

  • credentials (auth_credentials.Credentials) – Optional. The credentials that are used to upload this model. These credentials override the credentials set by aiplatform.init.

• Raises

  ValueError – A ValueError is raised if the task definition of the retrieved training job doesn’t match the custom training task definition.

• Returns

  A Vertex AI training job.

get_model(sync=True)

Returns the Vertex AI model produced by this training job.

• Parameters

  sync (bool) – If set to true, this method runs synchronously. If false, this method runs asynchronously.

• Returns

  The Vertex AI model that was produced by this training job.

• Raises

  RuntimeError – A runtime error is raised if the training job failed or if a model wasn’t produced by the training job.

property has_failed(: [bool](https://python.readthedocs.io/en/latest/library/functions.html#bool )

Returns true if the training job failed, otherwise false.

property labels(: Dict[str, str )

User-defined labels containing metadata about this resource.

Read more about labels at https://goo.gl/xmQnxf

classmethod list(filter: Optional[str] = None, order_by: Optional[str] = None, project: Optional[str] = None, location: Optional[str] = None, credentials: Optional[google.auth.credentials.Credentials] = None)

Lists all instances of this training job resource.

The following shows an example of how to call CustomTrainingJob.list:

``

`

py aiplatform.CustomTrainingJob.list(

filter=’display_name=”experiment_a27”’, order_by=’create_time desc’

• Parameters

  • filter (str) – Optional. An expression for filtering the results of the request. For field names, snake_case and camelCase are supported.

  • order_by (str) – Optional. A comma-separated list of fields used to sort the returned traing job resources. The defauilt sorting order is ascending. To sort by a field name in descending order, use desc after the field name. The following fields are supported: display_name, create_time, update_time.

  • project (str) – Optional. The name of the Google Cloud project to which to retrieve the list of training job resources. This overrides the project that was set by aiplatform.init.

  • location (str) – Optional. The Google Cloud region from where the training job resources are retrieved. This region overrides the region that was set by aiplatform.init.

  • credentials (auth_credentials.Credentials) – Optional. The credentials that are used to retrieve list. These credentials override the credentials set by aiplatform.init.

• Returns

  A list of training job resources.

• Return type

  List[VertexAiResourceNoun]

property name(: [str](https://python.readthedocs.io/en/latest/library/stdtypes.html#str )

Name of this resource.

property network(: Optional[str )

The full name of the Google Compute Engine network to which this CustomTrainingJob should be peered.

Specify the name of the network using the format projects/{project}/global/networks/{network}. Replace {project} with the project number, such as 12345, and {network} with a network name.

Before specifying a network, private services access must be configured for the network. If private services access isn’t configured, then the custom training job can’t be peered with a network.

property resource_name(: [str](https://python.readthedocs.io/en/latest/library/stdtypes.html#str )

Full qualified resource name.

run(dataset: Optional[Union[google.cloud.aiplatform.datasets.image_dataset.ImageDataset, google.cloud.aiplatform.datasets.tabular_dataset.TabularDataset, google.cloud.aiplatform.datasets.text_dataset.TextDataset, google.cloud.aiplatform.datasets.video_dataset.VideoDataset]] = None, annotation_schema_uri: Optional[str] = None, model_display_name: Optional[str] = None, model_labels: Optional[Dict[str, str]] = None, model_id: Optional[str] = None, parent_model: Optional[str] = None, is_default_version: Optional[bool] = True, model_version_aliases: Optional[Sequence[str]] = None, model_version_description: Optional[str] = None, base_output_dir: Optional[str] = None, service_account: Optional[str] = None, network: Optional[str] = None, bigquery_destination: Optional[str] = None, args: Optional[List[Union[str, float, int]]] = None, environment_variables: Optional[Dict[str, str]] = None, replica_count: int = 1, machine_type: str = 'n1-standard-4', accelerator_type: str = 'ACCELERATOR_TYPE_UNSPECIFIED', accelerator_count: int = 0, boot_disk_type: str = 'pd-ssd', boot_disk_size_gb: int = 100, reduction_server_replica_count: int = 0, reduction_server_machine_type: Optional[str] = None, reduction_server_container_uri: Optional[str] = None, training_fraction_split: Optional[float] = None, validation_fraction_split: Optional[float] = None, test_fraction_split: Optional[float] = None, training_filter_split: Optional[str] = None, validation_filter_split: Optional[str] = None, test_filter_split: Optional[str] = None, predefined_split_column_name: Optional[str] = None, timestamp_split_column_name: Optional[str] = None, timeout: Optional[int] = None, restart_job_on_worker_restart: bool = False, enable_web_access: bool = False, enable_dashboard_access: bool = False, tensorboard: Optional[str] = None, sync=True, create_request_timeout: Optional[float] = None, disable_retries: bool = False, persistent_resource_id: Optional[str] = None, tpu_topology: Optional[str] = None, scheduling_strategy: Optional[google.cloud.aiplatform_v1.types.custom_job.Scheduling.Strategy] = None, reservation_affinity_type: Optional[Literal['NO_RESERVATION', 'ANY_RESERVATION', 'SPECIFIC_RESERVATION']] = None, reservation_affinity_key: Optional[str] = None, reservation_affinity_values: Optional[List[str]] = None, max_wait_duration: Optional[int] = None)

Runs the custom training job.

Distributed Training Support: If replica count = 1 then one chief replica will be provisioned. If replica_count > 1 the remainder will be provisioned as a worker replica pool. ie: replica_count = 10 will result in 1 chief and 9 workers All replicas have same machine_type, accelerator_type, and accelerator_count

If training on a Vertex AI dataset, you can use one of the following split configurations:

Data fraction splits:
Any of `training_fraction_split`, `validation_fraction_split` and
`test_fraction_split` may optionally be provided, they must sum to up to 1. If
the provided ones sum to less than 1, the remainder is assigned to sets as
decided by Vertex AI. If none of the fractions are set, by default roughly 80%
of data will be used for training, 10% for validation, and 10% for test.

Data filter splits:
Assigns input data to training, validation, and test sets
based on the given filters, data pieces not matched by any
filter are ignored. Currently only supported for Datasets
containing DataItems.
If any of the filters in this message are to match nothing, then
they can be set as ‘-’ (the minus sign).
If using filter splits, all of `training_filter_split`, `validation_filter_split` and
`test_filter_split` must be provided.
Supported only for unstructured Datasets.

Predefined splits:
Assigns input data to training, validation, and test sets based on the value of a provided key.
If using predefined splits, `predefined_split_column_name` must be provided.
Supported only for tabular Datasets.

Timestamp splits:
Assigns input data to training, validation, and test sets
based on a provided timestamps. The youngest data pieces are
assigned to training set, next to validation set, and the oldest
to the test set.
Supported only for tabular Datasets.

• Parameters

  • dataset (Union[datasets.ImageDataset,datasets.TabularDataset,datasets.TextDataset,datasets.VideoDataset,]) – Vertex AI to fit this training against. Custom training script should retrieve datasets through passed in environment variables uris:

    os.environ[“AIP_TRAINING_DATA_URI”] os.environ[“AIP_VALIDATION_DATA_URI”] os.environ[“AIP_TEST_DATA_URI”]

    Additionally the dataset format is passed in as:

    os.environ[“AIP_DATA_FORMAT”]

  • annotation_schema_uri (str) – Google Cloud Storage URI points to a YAML file describing annotation schema. The schema is defined as an OpenAPI 3.0.2 Schema Object The schema files that can be used here are found in gs://google-cloud-aiplatform/schema/dataset/annotation/, note that the chosen schema must be consistent with metadata of the Dataset specified by dataset_id.

    Only Annotations that both match this schema and belong to DataItems not ignored by the split method are used in respectively training, validation or test role, depending on the role of the DataItem they are on.

    When used in conjunction with annotations_filter, the Annotations used for training are filtered by both annotations_filter and annotation_schema_uri.

  • model_display_name (str) – If the script produces a managed Vertex AI Model. The display name of the Model. The name can be up to 128 characters long and can be consist of any UTF-8 characters.

    If not provided upon creation, the job’s display_name is used.

  • model_labels (Dict[str, *[str](https://python.readthedocs.io/en/latest/library/stdtypes.html#str)]*) – Optional. The labels with user-defined metadata to organize your Models. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels.

  • model_id (str) – Optional. The ID to use for the Model produced by this job, which will become the final component of the model resource name. This value may be up to 63 characters, and valid characters are [a-z0-9_-]. The first character cannot be a number or hyphen.

  • parent_model (str) – Optional. The resource name or model ID of an existing model. The new model uploaded by this job will be a version of parent_model.

    Only set this field when training a new version of an existing model.

  • is_default_version (bool) – Optional. When set to True, the newly uploaded model version will automatically have alias “default” included. Subsequent uses of the model produced by this job without a version specified will use this “default” version.

    When set to False, the “default” alias will not be moved. Actions targeting the model version produced by this job will need to specifically reference this version by ID or alias.

    New model uploads, i.e. version 1, will always be “default” aliased.

  • model_version_aliases (Sequence[str]) – Optional. User provided version aliases so that the model version uploaded by this job can be referenced via alias instead of auto-generated version ID. A default version alias will be created for the first version of the model.

    The format is [a-z][a-zA-Z0-9-]{0,126}[a-z0-9]

  • model_version_description (str) – Optional. The description of the model version being uploaded by this job.

  • base_output_dir (str) – GCS output directory of job. If not provided a timestamped directory in the staging directory will be used.

    Vertex AI sets the following environment variables when it runs your training code:

    • AIP_MODEL_DIR: a Cloud Storage URI of a directory intended for saving model artifacts, i.e. <base_output_dir>/model/

    • AIP_CHECKPOINT_DIR: a Cloud Storage URI of a directory intended for saving checkpoints, i.e. <base_output_dir>/checkpoints/

    • AIP_TENSORBOARD_LOG_DIR: a Cloud Storage URI of a directory intended for saving TensorBoard logs, i.e. <base_output_dir>/logs/

  • service_account (str) – Specifies the service account for workload run-as account. Users submitting jobs must have act-as permission on this run-as account. If not specified, uses the service account set in aiplatform.init.

  • network (str) – The full name of the Compute Engine network to which the job should be peered. For example, projects/12345/global/networks/myVPC. Private services access must already be configured for the network. If left unspecified, the network set in aiplatform.init will be used. Otherwise, the job is not peered with any network.

  • bigquery_destination (str) – Provide this field if dataset is a BigQuery dataset. The BigQuery project location where the training data is to be written to. In the given project a new dataset is created with name dataset_<dataset-id>_<annotation-type>_<timestamp-of-training-call> where timestamp is in YYYY_MM_DDThh_mm_ss_sssZ format. All training input data will be written into that dataset. In the dataset three tables will be created, training, validation and test.

    • AIP_DATA_FORMAT = “bigquery”.

    • AIP_TRAINING_DATA_URI =”bigquery_destination.dataset_*.training”

    • AIP_VALIDATION_DATA_URI = “bigquery_destination.dataset_*.validation”

    • AIP_TEST_DATA_URI = “bigquery_destination.dataset_*.test”

  • args (List[Unions[str, *[int](https://python.readthedocs.io/en/latest/library/functions.html#int), [float](https://python.readthedocs.io/en/latest/library/functions.html#float)]*]) – Command line arguments to be passed to the Python script.

  • environment_variables (Dict[str, *[str](https://python.readthedocs.io/en/latest/library/stdtypes.html#str)]*) – Environment variables to be passed to the container. Should be a dictionary where keys are environment variable names and values are environment variable values for those names. At most 10 environment variables can be specified. The Name of the environment variable must be unique.

    environment_variables = {

    ‘MY_KEY’: ‘MY_VALUE’

    }

  • replica_count (int) – The number of worker replicas. If replica count = 1 then one chief replica will be provisioned. If replica_count > 1 the remainder will be provisioned as a worker replica pool.

  • machine_type (str) – The type of machine to use for training.

  • accelerator_type (str) – Hardware accelerator type. One of ACCELERATOR_TYPE_UNSPECIFIED, NVIDIA_TESLA_K80, NVIDIA_TESLA_P100, NVIDIA_TESLA_V100, NVIDIA_TESLA_P4, NVIDIA_TESLA_T4

  • accelerator_count (int) – The number of accelerators to attach to a worker replica.

  • boot_disk_type (str) – Type of the boot disk, default is pd-ssd. Valid values: pd-ssd (Persistent Disk Solid State Drive) or pd-standard (Persistent Disk Hard Disk Drive).

  • boot_disk_size_gb (int) – Size in GB of the boot disk, default is 100GB. boot disk size must be within the range of [100, 64000].

  • reduction_server_replica_count (int) – The number of reduction server replicas, default is 0.

  • reduction_server_machine_type (str) – Optional. The type of machine to use for reduction server.

  • reduction_server_container_uri (str) – Optional. The Uri of the reduction server container image. See details: https://cloud.google.com/vertex-ai/docs/training/distributed-training#reduce_training_time_with_reduction_server

  • training_fraction_split (float) – Optional. The fraction of the input data that is to be used to train the Model. This is ignored if Dataset is not provided.

  • validation_fraction_split (float) – Optional. The fraction of the input data that is to be used to validate the Model. This is ignored if Dataset is not provided.

  • test_fraction_split (float) – Optional. The fraction of the input data that is to be used to evaluate the Model. This is ignored if Dataset is not provided.

  • training_filter_split (str) – Optional. A filter on DataItems of the Dataset. DataItems that match this filter are used to train the Model. A filter with same syntax as the one used in DatasetService.ListDataItems may be used. If a single DataItem is matched by more than one of the FilterSplit filters, then it is assigned to the first set that applies to it in the training, validation, test order. This is ignored if Dataset is not provided.

  • validation_filter_split (str) – Optional. A filter on DataItems of the Dataset. DataItems that match this filter are used to validate the Model. A filter with same syntax as the one used in DatasetService.ListDataItems may be used. If a single DataItem is matched by more than one of the FilterSplit filters, then it is assigned to the first set that applies to it in the training, validation, test order. This is ignored if Dataset is not provided.

  • test_filter_split (str) – Optional. A filter on DataItems of the Dataset. DataItems that match this filter are used to test the Model. A filter with same syntax as the one used in DatasetService.ListDataItems may be used. If a single DataItem is matched by more than one of the FilterSplit filters, then it is assigned to the first set that applies to it in the training, validation, test order. This is ignored if Dataset is not provided.

  • predefined_split_column_name (str) – Optional. The key is a name of one of the Dataset’s data columns. The value of the key (either the label’s value or value in the column) must be one of {training, validation, test}, and it defines to which set the given piece of data is assigned. If for a piece of data the key is not present or has an invalid value, that piece is ignored by the pipeline.

    Supported only for tabular and time series Datasets.

  • timestamp_split_column_name (str) – Optional. The key is a name of one of the Dataset’s data columns. The value of the key values of the key (the values in the column) must be in RFC 3339 date-time format, where time-offset = “Z” (e.g. 1985-04-12T23:20:50.52Z). If for a piece of data the key is not present or has an invalid value, that piece is ignored by the pipeline.

    Supported only for tabular and time series Datasets.

  • timeout (int) – The maximum job running time in seconds. The default is 7 days.

  • restart_job_on_worker_restart (bool) – Restarts the entire CustomJob if a worker gets restarted. This feature can be used by distributed training jobs that are not resilient to workers leaving and joining a job.

  • enable_web_access (bool) – Whether you want Vertex AI to enable interactive shell access to training containers. https://cloud.google.com/vertex-ai/docs/training/monitor-debug-interactive-shell

  • enable_dashboard_access (bool) – Whether you want Vertex AI to enable access to the customized dashboard to training containers.

  • tensorboard (str) – Optional. The name of a Vertex AI [Tensorboard][google.cloud.aiplatform.v1beta1.Tensorboard] resource to which this CustomJob will upload Tensorboard logs. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}

    The training script should write Tensorboard to following Vertex AI environment variable:

    AIP_TENSORBOARD_LOG_DIR

    service_account is required with provided tensorboard. For more information on configuring your service account please visit: https://cloud.google.com/vertex-ai/docs/experiments/tensorboard-training

  • sync (bool) – Whether to execute this method synchronously. If False, this method will be executed in concurrent Future and any downstream object will be immediately returned and synced when the Future has completed.

  • create_request_timeout (float) – Optional. The timeout for the create request in seconds.

  • disable_retries (bool) – Indicates if the job should retry for internal errors after the job starts running. If True, overrides restart_job_on_worker_restart to False.

  • persistent_resource_id (str) – Optional. The ID of the PersistentResource in the same Project and Location. If this is specified, the job will be run on existing machines held by the PersistentResource instead of on-demand short-live machines. The network, CMEK, and node pool configs on the job should be consistent with those on the PersistentResource, otherwise, the job will be rejected.

  • tpu_topology (str) – Optional. Specifies the tpu topology to be used for TPU training job. This field is required for TPU v5 versions. For details on the TPU topology, refer to https://cloud.google.com/tpu/docs/v5e#tpu-v5e-config. The topology must be a supported value for the TPU machine type.

  • scheduling_strategy (gca_custom_job_compat.Scheduling.Strategy) – Optional. Indicates the job scheduling strategy.

  • reservation_affinity_type (str) – Optional. The type of reservation affinity. One of: * “NO_RESERVATION” : No reservation is used. * “ANY_RESERVATION” : Any reservation that matches machine spec can be used. * “SPECIFIC_RESERVATION” : A specific reservation must be use used. See reservation_affinity_key and reservation_affinity_values for how to specify the reservation.

  • reservation_affinity_key (str) – Optional. Corresponds to the label key of a reservation resource. To target a SPECIFIC_RESERVATION by name, use compute.googleapis.com/reservation-name as the key and specify the name of your reservation as its value.

  • reservation_affinity_values (List[str]) – Optional. Corresponds to the label values of a reservation resource. This must be the full resource name of the reservation. Format: ‘projects/{project_id_or_number}/zones/{zone}/reservations/{reservation_name}’

  • max_wait_duration (int) – This is the maximum duration that a job will wait for the requested resources to be provisioned in seconds. If set to 0, the job will wait indefinitely. The default is 30 minutes.

• Returns

  The trained Vertex AI Model resource or None if training did not

    produce a Vertex AI Model.
  

• Return type

  model

property start_time(: Optional[datetime.datetime )

Optional. The time when the training job first entered the PIPELINE_STATE_RUNNING state.

property state(: Optional[[google.cloud.aiplatform_v1.types.pipeline_state.PipelineState](../aiplatform_v1/types.md#google.cloud.aiplatform_v1.types.PipelineState)_ )

Current training state.

to_dict()

Returns the resource proto as a dictionary.

property update_time(: [datetime.datetime](https://python.readthedocs.io/en/latest/library/datetime.html#datetime.datetime )

Time this resource was last updated.

wait()

Helper method that blocks until all futures are complete.

wait_for_resource_creation()

Waits until the resource has been created.

property web_access_uris(: Dict[str, str )

Returns the URIs used to access the custom training job.

class google.cloud.aiplatform.CustomTrainingJob(display_name: str, script_path: str, container_uri: str, requirements: Optional[Sequence[str]] = None, model_serving_container_image_uri: Optional[str] = None, model_serving_container_predict_route: Optional[str] = None, model_serving_container_health_route: Optional[str] = None, model_serving_container_command: Optional[Sequence[str]] = None, model_serving_container_args: Optional[Sequence[str]] = None, model_serving_container_environment_variables: Optional[Dict[str, str]] = None, model_serving_container_ports: Optional[Sequence[int]] = None, model_description: Optional[str] = None, model_instance_schema_uri: Optional[str] = None, model_parameters_schema_uri: Optional[str] = None, model_prediction_schema_uri: Optional[str] = None, explanation_metadata: Optional[google.cloud.aiplatform_v1.types.explanation_metadata.ExplanationMetadata] = None, explanation_parameters: Optional[google.cloud.aiplatform_v1.types.explanation.ExplanationParameters] = None, project: Optional[str] = None, location: Optional[str] = None, credentials: Optional[google.auth.credentials.Credentials] = None, labels: Optional[Dict[str, str]] = None, training_encryption_spec_key_name: Optional[str] = None, model_encryption_spec_key_name: Optional[str] = None, staging_bucket: Optional[str] = None)

Bases: google.cloud.aiplatform.training_jobs._CustomTrainingJob

Class to launch a Custom Training Job in Vertex AI using a script.

Takes a training implementation as a python script and executes that script in Cloud Vertex AI Training.

Constructs a Custom Training Job from a Python script.

job = aiplatform.CustomTrainingJob(

display_name=’test-train’,
script_path=’test_script.py’,
requirements=[‘pandas’, ‘numpy’],
container_uri=’gcr.io/cloud-aiplatform/training/tf-cpu.2-2:latest’,
model_serving_container_image_uri=’gcr.io/my-trainer/serving:1’,
model_serving_container_predict_route=’predict’,
model_serving_container_health_route=’metadata,
labels={‘key’: ‘value’},

)

Usage with Dataset:

ds = aiplatform.TabularDataset(

‘projects/my-project/locations/us-central1/datasets/12345’)

job.run(

ds,
replica_count=1,
model_display_name=’my-trained-model’,
model_labels={‘key’: ‘value’},

)

Usage without Dataset:

job.run(replica_count=1, model_display_name=’my-trained-model)

To ensure your model gets saved in Vertex AI, write your saved model to os.environ[“AIP_MODEL_DIR”] in your provided training script.

• Parameters

  • display_name (str) – Required. The user-defined name of this TrainingPipeline.

  • script_path (str) – Required. Local path to training script.

  • container_uri (str) – Required: Uri of the training container image in the GCR.

  • requirements (Sequence[str]) – List of python packages dependencies of script.

  • model_serving_container_image_uri (str) – If the training produces a managed Vertex AI Model, the URI of the Model serving container suitable for serving the model produced by the training script.

  • model_serving_container_predict_route (str) – If the training produces a managed Vertex AI Model, An HTTP path to send prediction requests to the container, and which must be supported by it. If not specified a default HTTP path will be used by Vertex AI.

  • model_serving_container_health_route (str) – If the training produces a managed Vertex AI Model, an HTTP path to send health check requests to the container, and which must be supported by it. If not specified a standard HTTP path will be used by AI Platform.

  • model_serving_container_command (Sequence[str]) – The command with which the container is run. Not executed within a shell. The Docker image’s ENTRYPOINT is used if this is not provided. Variable references $(VAR_NAME) are expanded using the container’s environment. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not.

  • model_serving_container_args (Sequence[str]) – The arguments to the command. The Docker image’s CMD is used if this is not provided. Variable references $(VAR_NAME) are expanded using the container’s environment. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not.

  • model_serving_container_environment_variables (Dict[str, *[str](https://python.readthedocs.io/en/latest/library/stdtypes.html#str)]*) – The environment variables that are to be present in the container. Should be a dictionary where keys are environment variable names and values are environment variable values for those names.

  • model_serving_container_ports (Sequence[int]) – Declaration of ports that are exposed by the container. This field is primarily informational, it gives Vertex AI information about the network connections the container uses. Listing or not a port here has no impact on whether the port is actually exposed, any port listening on the default “0.0.0.0” address inside a container will be accessible from the network.

  • model_description (str) – The description of the Model.

  • model_instance_schema_uri (str) – Optional. Points to a YAML file stored on Google Cloud Storage describing the format of a single instance, which are used in PredictRequest.instances, ExplainRequest.instances and BatchPredictionJob.input_config. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by AI Platform. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.

  • model_parameters_schema_uri (str) – Optional. Points to a YAML file stored on Google Cloud Storage describing the parameters of prediction and explanation via PredictRequest.parameters, ExplainRequest.parameters and BatchPredictionJob.model_parameters. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by AI Platform, if no parameters are supported it is set to an empty string. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.

  • model_prediction_schema_uri (str) – Optional. Points to a YAML file stored on Google Cloud Storage describing the format of a single prediction produced by this Model, which are returned via PredictResponse.predictions, ExplainResponse.explanations, and BatchPredictionJob.output_config. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by AI Platform. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.

  • explanation_metadata (explain.ExplanationMetadata) – Optional. Metadata describing the Model’s input and output for explanation. explanation_metadata is optional while explanation_parameters must be specified when used. For more details, see Ref docs http://tinyurl.com/1igh60kt

  • explanation_parameters (explain.ExplanationParameters) – Optional. Parameters to configure explaining for Model’s predictions. For more details, see Ref docs http://tinyurl.com/1an4zake

  • project (str) – Project to run training in. Overrides project set in aiplatform.init.

  • location (str) – Location to run training in. Overrides location set in aiplatform.init.

  • credentials (auth_credentials.Credentials) – Custom credentials to use to run call training service. Overrides credentials set in aiplatform.init.

  • labels (Dict[str, *[str](https://python.readthedocs.io/en/latest/library/stdtypes.html#str)]*) – Optional. The labels with user-defined metadata to organize TrainingPipelines. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels.

  • training_encryption_spec_key_name (Optional[str]) – Optional. The Cloud KMS resource identifier of the customer managed encryption key used to protect the training pipeline. Has the form: projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key. The key needs to be in the same region as where the compute resource is created.

    If set, this TrainingPipeline will be secured by this key.

    Note: Model trained by this TrainingPipeline is also secured by this key if model_to_upload is not set separately.

    Overrides encryption_spec_key_name set in aiplatform.init.

  • model_encryption_spec_key_name (Optional[str]) – Optional. The Cloud KMS resource identifier of the customer managed encryption key used to protect the model. Has the form: projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key. The key needs to be in the same region as where the compute resource is created.

    If set, the trained Model will be secured by this key.

    Overrides encryption_spec_key_name set in aiplatform.init.

  • staging_bucket (str) – Bucket used to stage source and training artifacts. Overrides staging_bucket set in aiplatform.init.