Source code for airflow.providers.google.cloud.operators.automl

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"""This module contains Google AutoML operators."""
from __future__ import annotations

import ast
import warnings
from typing import TYPE_CHECKING, Sequence, Tuple

from google.api_core.gapic_v1.method import DEFAULT, _MethodDefault
from google.cloud.automl_v1beta1 import (
    BatchPredictResult,
    ColumnSpec,
    Dataset,
    Model,
    PredictResponse,
    TableSpec,
)

from airflow.exceptions import AirflowProviderDeprecationWarning
from airflow.providers.google.cloud.hooks.automl import CloudAutoMLHook
from airflow.providers.google.cloud.links.automl import (
    AutoMLDatasetLink,
    AutoMLDatasetListLink,
    AutoMLModelLink,
    AutoMLModelPredictLink,
    AutoMLModelTrainLink,
)
from airflow.providers.google.cloud.operators.cloud_base import GoogleCloudBaseOperator

if TYPE_CHECKING:
    from google.api_core.retry import Retry

    from airflow.utils.context import Context

[docs]MetaData = Sequence[Tuple[str, str]]
[docs]class AutoMLTrainModelOperator(GoogleCloudBaseOperator): """ Creates Google Cloud AutoML model. AutoMLTrainModelOperator for text prediction is deprecated. Please use :class:`airflow.providers.google.cloud.operators.vertex_ai.auto_ml.CreateAutoMLTextTrainingJobOperator` instead. .. seealso:: For more information on how to use this operator, take a look at the guide: :ref:`howto/operator:AutoMLTrainModelOperator` :param model: Model definition. :param project_id: ID of the Google Cloud project where model will be created if None then default project_id is used. :param location: The location of the project. :param retry: A retry object used to retry requests. If `None` is specified, requests will not be retried. :param timeout: The amount of time, in seconds, to wait for the request to complete. Note that if `retry` is specified, the timeout applies to each individual attempt. :param metadata: Additional metadata that is provided to the method. :param gcp_conn_id: The connection ID to use to connect to Google Cloud. :param impersonation_chain: Optional service account to impersonate using short-term credentials, or chained list of accounts required to get the access_token of the last account in the list, which will be impersonated in the request. If set as a string, the account must grant the originating account the Service Account Token Creator IAM role. If set as a sequence, the identities from the list must grant Service Account Token Creator IAM role to the directly preceding identity, with first account from the list granting this role to the originating account (templated). """
[docs] template_fields: Sequence[str] = ( "model", "location", "project_id", "impersonation_chain", )
def __init__( self, *, model: dict, location: str, project_id: str | None = None, metadata: MetaData = (), timeout: float | None = None, retry: Retry | _MethodDefault = DEFAULT, gcp_conn_id: str = "google_cloud_default", impersonation_chain: str | Sequence[str] | None = None, **kwargs, ) -> None: super().__init__(**kwargs) self.model = model self.location = location self.project_id = project_id self.metadata = metadata self.timeout = timeout self.retry = retry self.gcp_conn_id = gcp_conn_id self.impersonation_chain = impersonation_chain
[docs] def execute(self, context: Context): # Output warning if running not AutoML Translation prediction job if "translation_model_metadata" not in self.model: warnings.warn( "AutoMLTrainModelOperator for text, image and video prediction is deprecated. " "All the functionality of legacy " "AutoML Natural Language, Vision and Video Intelligence and new features are available " "on the Vertex AI platform. " "Please use `CreateAutoMLTextTrainingJobOperator`, `CreateAutoMLImageTrainingJobOperator` or" " `CreateAutoMLVideoTrainingJobOperator` from VertexAI.", AirflowProviderDeprecationWarning, stacklevel=3, ) hook = CloudAutoMLHook( gcp_conn_id=self.gcp_conn_id, impersonation_chain=self.impersonation_chain, ) self.log.info("Creating model %s...", self.model["display_name"]) operation = hook.create_model( model=self.model, location=self.location, project_id=self.project_id, retry=self.retry, timeout=self.timeout, metadata=self.metadata, ) project_id = self.project_id or hook.project_id if project_id: AutoMLModelTrainLink.persist(context=context, task_instance=self, project_id=project_id) operation_result = hook.wait_for_operation(timeout=self.timeout, operation=operation) result = Model.to_dict(operation_result) model_id = hook.extract_object_id(result) self.log.info("Model is created, model_id: %s", model_id) self.xcom_push(context, key="model_id", value=model_id) if project_id: AutoMLModelLink.persist( context=context, task_instance=self, dataset_id=self.model["dataset_id"] or "-", model_id=model_id, project_id=project_id, ) return result
[docs]class AutoMLPredictOperator(GoogleCloudBaseOperator): """ Runs prediction operation on Google Cloud AutoML. .. seealso:: For more information on how to use this operator, take a look at the guide: :ref:`howto/operator:AutoMLPredictOperator` :param model_id: Name of the model requested to serve the batch prediction. :param payload: Name od the model used for the prediction. :param project_id: ID of the Google Cloud project where model is located if None then default project_id is used. :param location: The location of the project. :param operation_params: Additional domain-specific parameters for the predictions. :param retry: A retry object used to retry requests. If `None` is specified, requests will not be retried. :param timeout: The amount of time, in seconds, to wait for the request to complete. Note that if `retry` is specified, the timeout applies to each individual attempt. :param metadata: Additional metadata that is provided to the method. :param gcp_conn_id: The connection ID to use to connect to Google Cloud. :param impersonation_chain: Optional service account to impersonate using short-term credentials, or chained list of accounts required to get the access_token of the last account in the list, which will be impersonated in the request. If set as a string, the account must grant the originating account the Service Account Token Creator IAM role. If set as a sequence, the identities from the list must grant Service Account Token Creator IAM role to the directly preceding identity, with first account from the list granting this role to the originating account (templated). """
[docs] template_fields: Sequence[str] = ( "model_id", "location", "project_id", "impersonation_chain", )
def __init__( self, *, model_id: str, location: str, payload: dict, operation_params: dict[str, str] | None = None, project_id: str | None = None, metadata: MetaData = (), timeout: float | None = None, retry: Retry | _MethodDefault = DEFAULT, gcp_conn_id: str = "google_cloud_default", impersonation_chain: str | Sequence[str] | None = None, **kwargs, ) -> None: super().__init__(**kwargs) self.model_id = model_id self.operation_params = operation_params # type: ignore self.location = location self.project_id = project_id self.metadata = metadata self.timeout = timeout self.retry = retry self.payload = payload self.gcp_conn_id = gcp_conn_id self.impersonation_chain = impersonation_chain
[docs] def execute(self, context: Context): hook = CloudAutoMLHook( gcp_conn_id=self.gcp_conn_id, impersonation_chain=self.impersonation_chain, ) result = hook.predict( model_id=self.model_id, payload=self.payload, location=self.location, project_id=self.project_id, params=self.operation_params, retry=self.retry, timeout=self.timeout, metadata=self.metadata, ) project_id = self.project_id or hook.project_id if project_id: AutoMLModelPredictLink.persist( context=context, task_instance=self, model_id=self.model_id, project_id=project_id, ) return PredictResponse.to_dict(result)
[docs]class AutoMLBatchPredictOperator(GoogleCloudBaseOperator): """ Perform a batch prediction on Google Cloud AutoML. .. seealso:: For more information on how to use this operator, take a look at the guide: :ref:`howto/operator:AutoMLBatchPredictOperator` :param project_id: ID of the Google Cloud project where model will be created if None then default project_id is used. :param location: The location of the project. :param model_id: Name of the model_id requested to serve the batch prediction. :param input_config: Required. The input configuration for batch prediction. If a dict is provided, it must be of the same form as the protobuf message `google.cloud.automl_v1beta1.types.BatchPredictInputConfig` :param output_config: Required. The Configuration specifying where output predictions should be written. If a dict is provided, it must be of the same form as the protobuf message `google.cloud.automl_v1beta1.types.BatchPredictOutputConfig` :param prediction_params: Additional domain-specific parameters for the predictions, any string must be up to 25000 characters long. :param project_id: ID of the Google Cloud project where model is located if None then default project_id is used. :param location: The location of the project. :param retry: A retry object used to retry requests. If `None` is specified, requests will not be retried. :param timeout: The amount of time, in seconds, to wait for the request to complete. Note that if `retry` is specified, the timeout applies to each individual attempt. :param metadata: Additional metadata that is provided to the method. :param gcp_conn_id: The connection ID to use to connect to Google Cloud. :param impersonation_chain: Optional service account to impersonate using short-term credentials, or chained list of accounts required to get the access_token of the last account in the list, which will be impersonated in the request. If set as a string, the account must grant the originating account the Service Account Token Creator IAM role. If set as a sequence, the identities from the list must grant Service Account Token Creator IAM role to the directly preceding identity, with first account from the list granting this role to the originating account (templated). """
[docs] template_fields: Sequence[str] = ( "model_id", "input_config", "output_config", "location", "project_id", "impersonation_chain", )
def __init__( self, *, model_id: str, input_config: dict, output_config: dict, location: str, project_id: str | None = None, prediction_params: dict[str, str] | None = None, metadata: MetaData = (), timeout: float | None = None, retry: Retry | _MethodDefault = DEFAULT, gcp_conn_id: str = "google_cloud_default", impersonation_chain: str | Sequence[str] | None = None, **kwargs, ) -> None: super().__init__(**kwargs) self.model_id = model_id self.location = location self.project_id = project_id self.prediction_params = prediction_params self.metadata = metadata self.timeout = timeout self.retry = retry self.gcp_conn_id = gcp_conn_id self.impersonation_chain = impersonation_chain self.input_config = input_config self.output_config = output_config
[docs] def execute(self, context: Context): hook = CloudAutoMLHook( gcp_conn_id=self.gcp_conn_id, impersonation_chain=self.impersonation_chain, ) self.log.info("Fetch batch prediction.") operation = hook.batch_predict( model_id=self.model_id, input_config=self.input_config, output_config=self.output_config, project_id=self.project_id, location=self.location, params=self.prediction_params, retry=self.retry, timeout=self.timeout, metadata=self.metadata, ) operation_result = hook.wait_for_operation(timeout=self.timeout, operation=operation) result = BatchPredictResult.to_dict(operation_result) self.log.info("Batch prediction is ready.") project_id = self.project_id or hook.project_id if project_id: AutoMLModelPredictLink.persist( context=context, task_instance=self, model_id=self.model_id, project_id=project_id, ) return result
[docs]class AutoMLCreateDatasetOperator(GoogleCloudBaseOperator): """ Creates a Google Cloud AutoML dataset. .. seealso:: For more information on how to use this operator, take a look at the guide: :ref:`howto/operator:AutoMLCreateDatasetOperator` :param dataset: The dataset to create. If a dict is provided, it must be of the same form as the protobuf message Dataset. :param project_id: ID of the Google Cloud project where dataset is located if None then default project_id is used. :param location: The location of the project. :param params: Additional domain-specific parameters for the predictions. :param retry: A retry object used to retry requests. If `None` is specified, requests will not be retried. :param timeout: The amount of time, in seconds, to wait for the request to complete. Note that if `retry` is specified, the timeout applies to each individual attempt. :param metadata: Additional metadata that is provided to the method. :param gcp_conn_id: The connection ID to use to connect to Google Cloud. :param impersonation_chain: Optional service account to impersonate using short-term credentials, or chained list of accounts required to get the access_token of the last account in the list, which will be impersonated in the request. If set as a string, the account must grant the originating account the Service Account Token Creator IAM role. If set as a sequence, the identities from the list must grant Service Account Token Creator IAM role to the directly preceding identity, with first account from the list granting this role to the originating account (templated). """
[docs] template_fields: Sequence[str] = ( "dataset", "location", "project_id", "impersonation_chain", )
def __init__( self, *, dataset: dict, location: str, project_id: str | None = None, metadata: MetaData = (), timeout: float | None = None, retry: Retry | _MethodDefault = DEFAULT, gcp_conn_id: str = "google_cloud_default", impersonation_chain: str | Sequence[str] | None = None, **kwargs, ) -> None: super().__init__(**kwargs) self.dataset = dataset self.location = location self.project_id = project_id self.metadata = metadata self.timeout = timeout self.retry = retry self.gcp_conn_id = gcp_conn_id self.impersonation_chain = impersonation_chain
[docs] def execute(self, context: Context): hook = CloudAutoMLHook( gcp_conn_id=self.gcp_conn_id, impersonation_chain=self.impersonation_chain, ) self.log.info("Creating dataset %s...", self.dataset) result = hook.create_dataset( dataset=self.dataset, location=self.location, project_id=self.project_id, retry=self.retry, timeout=self.timeout, metadata=self.metadata, ) result = Dataset.to_dict(result) dataset_id = hook.extract_object_id(result) self.log.info("Creating completed. Dataset id: %s", dataset_id) self.xcom_push(context, key="dataset_id", value=dataset_id) project_id = self.project_id or hook.project_id if project_id: AutoMLDatasetLink.persist( context=context, task_instance=self, dataset_id=dataset_id, project_id=project_id, ) return result
[docs]class AutoMLImportDataOperator(GoogleCloudBaseOperator): """ Imports data to a Google Cloud AutoML dataset. .. seealso:: For more information on how to use this operator, take a look at the guide: :ref:`howto/operator:AutoMLImportDataOperator` :param dataset_id: ID of dataset to be updated. :param input_config: The desired input location and its domain specific semantics, if any. If a dict is provided, it must be of the same form as the protobuf message InputConfig. :param project_id: ID of the Google Cloud project where dataset is located if None then default project_id is used. :param location: The location of the project. :param params: Additional domain-specific parameters for the predictions. :param retry: A retry object used to retry requests. If `None` is specified, requests will not be retried. :param timeout: The amount of time, in seconds, to wait for the request to complete. Note that if `retry` is specified, the timeout applies to each individual attempt. :param metadata: Additional metadata that is provided to the method. :param gcp_conn_id: The connection ID to use to connect to Google Cloud. :param impersonation_chain: Optional service account to impersonate using short-term credentials, or chained list of accounts required to get the access_token of the last account in the list, which will be impersonated in the request. If set as a string, the account must grant the originating account the Service Account Token Creator IAM role. If set as a sequence, the identities from the list must grant Service Account Token Creator IAM role to the directly preceding identity, with first account from the list granting this role to the originating account (templated). """
[docs] template_fields: Sequence[str] = ( "dataset_id", "input_config", "location", "project_id", "impersonation_chain", )
def __init__( self, *, dataset_id: str, location: str, input_config: dict, project_id: str | None = None, metadata: MetaData = (), timeout: float | None = None, retry: Retry | _MethodDefault = DEFAULT, gcp_conn_id: str = "google_cloud_default", impersonation_chain: str | Sequence[str] | None = None, **kwargs, ) -> None: super().__init__(**kwargs) self.dataset_id = dataset_id self.input_config = input_config self.location = location self.project_id = project_id self.metadata = metadata self.timeout = timeout self.retry = retry self.gcp_conn_id = gcp_conn_id self.impersonation_chain = impersonation_chain
[docs] def execute(self, context: Context): hook = CloudAutoMLHook( gcp_conn_id=self.gcp_conn_id, impersonation_chain=self.impersonation_chain, ) self.log.info("Importing data to dataset...") operation = hook.import_data( dataset_id=self.dataset_id, input_config=self.input_config, location=self.location, project_id=self.project_id, retry=self.retry, timeout=self.timeout, metadata=self.metadata, ) hook.wait_for_operation(timeout=self.timeout, operation=operation) self.log.info("Import is completed") project_id = self.project_id or hook.project_id if project_id: AutoMLDatasetLink.persist( context=context, task_instance=self, dataset_id=self.dataset_id, project_id=project_id, )
[docs]class AutoMLTablesListColumnSpecsOperator(GoogleCloudBaseOperator): """ Lists column specs in a table. .. seealso:: For more information on how to use this operator, take a look at the guide: :ref:`howto/operator:AutoMLTablesListColumnSpecsOperator` :param dataset_id: Name of the dataset. :param table_spec_id: table_spec_id for path builder. :param field_mask: Mask specifying which fields to read. If a dict is provided, it must be of the same form as the protobuf message `google.cloud.automl_v1beta1.types.FieldMask` :param filter_: Filter expression, see go/filtering. :param page_size: The maximum number of resources contained in the underlying API response. If page streaming is performed per resource, this parameter does not affect the return value. If page streaming is performed per page, this determines the maximum number of resources in a page. :param project_id: ID of the Google Cloud project where dataset is located if None then default project_id is used. :param location: The location of the project. :param retry: A retry object used to retry requests. If `None` is specified, requests will not be retried. :param timeout: The amount of time, in seconds, to wait for the request to complete. Note that if `retry` is specified, the timeout applies to each individual attempt. :param metadata: Additional metadata that is provided to the method. :param gcp_conn_id: The connection ID to use to connect to Google Cloud. :param impersonation_chain: Optional service account to impersonate using short-term credentials, or chained list of accounts required to get the access_token of the last account in the list, which will be impersonated in the request. If set as a string, the account must grant the originating account the Service Account Token Creator IAM role. If set as a sequence, the identities from the list must grant Service Account Token Creator IAM role to the directly preceding identity, with first account from the list granting this role to the originating account (templated). """
[docs] template_fields: Sequence[str] = ( "dataset_id", "table_spec_id", "field_mask", "filter_", "location", "project_id", "impersonation_chain", )
def __init__( self, *, dataset_id: str, table_spec_id: str, location: str, field_mask: dict | None = None, filter_: str | None = None, page_size: int | None = None, project_id: str | None = None, metadata: MetaData = (), timeout: float | None = None, retry: Retry | _MethodDefault = DEFAULT, gcp_conn_id: str = "google_cloud_default", impersonation_chain: str | Sequence[str] | None = None, **kwargs, ) -> None: super().__init__(**kwargs) self.dataset_id = dataset_id self.table_spec_id = table_spec_id self.field_mask = field_mask self.filter_ = filter_ self.page_size = page_size self.location = location self.project_id = project_id self.metadata = metadata self.timeout = timeout self.retry = retry self.gcp_conn_id = gcp_conn_id self.impersonation_chain = impersonation_chain
[docs] def execute(self, context: Context): hook = CloudAutoMLHook( gcp_conn_id=self.gcp_conn_id, impersonation_chain=self.impersonation_chain, ) self.log.info("Requesting column specs.") page_iterator = hook.list_column_specs( dataset_id=self.dataset_id, table_spec_id=self.table_spec_id, field_mask=self.field_mask, filter_=self.filter_, page_size=self.page_size, location=self.location, project_id=self.project_id, retry=self.retry, timeout=self.timeout, metadata=self.metadata, ) result = [ColumnSpec.to_dict(spec) for spec in page_iterator] self.log.info("Columns specs obtained.") project_id = self.project_id or hook.project_id if project_id: AutoMLDatasetLink.persist( context=context, task_instance=self, dataset_id=self.dataset_id, project_id=project_id, ) return result
[docs]class AutoMLTablesUpdateDatasetOperator(GoogleCloudBaseOperator): """ Updates a dataset. .. seealso:: For more information on how to use this operator, take a look at the guide: :ref:`howto/operator:AutoMLTablesUpdateDatasetOperator` :param dataset: The dataset which replaces the resource on the server. If a dict is provided, it must be of the same form as the protobuf message Dataset. :param update_mask: The update mask applies to the resource. If a dict is provided, it must be of the same form as the protobuf message FieldMask. :param location: The location of the project. :param params: Additional domain-specific parameters for the predictions. :param retry: A retry object used to retry requests. If `None` is specified, requests will not be retried. :param timeout: The amount of time, in seconds, to wait for the request to complete. Note that if `retry` is specified, the timeout applies to each individual attempt. :param metadata: Additional metadata that is provided to the method. :param gcp_conn_id: The connection ID to use to connect to Google Cloud. :param impersonation_chain: Optional service account to impersonate using short-term credentials, or chained list of accounts required to get the access_token of the last account in the list, which will be impersonated in the request. If set as a string, the account must grant the originating account the Service Account Token Creator IAM role. If set as a sequence, the identities from the list must grant Service Account Token Creator IAM role to the directly preceding identity, with first account from the list granting this role to the originating account (templated). """
[docs] template_fields: Sequence[str] = ( "dataset", "update_mask", "location", "impersonation_chain", )
def __init__( self, *, dataset: dict, location: str, update_mask: dict | None = None, metadata: MetaData = (), timeout: float | None = None, retry: Retry | _MethodDefault = DEFAULT, gcp_conn_id: str = "google_cloud_default", impersonation_chain: str | Sequence[str] | None = None, **kwargs, ) -> None: super().__init__(**kwargs) self.dataset = dataset self.update_mask = update_mask self.location = location self.metadata = metadata self.timeout = timeout self.retry = retry self.gcp_conn_id = gcp_conn_id self.impersonation_chain = impersonation_chain
[docs] def execute(self, context: Context): hook = CloudAutoMLHook( gcp_conn_id=self.gcp_conn_id, impersonation_chain=self.impersonation_chain, ) self.log.info("Updating AutoML dataset %s.", self.dataset["name"]) result = hook.update_dataset( dataset=self.dataset, update_mask=self.update_mask, retry=self.retry, timeout=self.timeout, metadata=self.metadata, ) self.log.info("Dataset updated.") project_id = hook.project_id if project_id: AutoMLDatasetLink.persist( context=context, task_instance=self, dataset_id=hook.extract_object_id(self.dataset), project_id=project_id, ) return Dataset.to_dict(result)
[docs]class AutoMLGetModelOperator(GoogleCloudBaseOperator): """ Get Google Cloud AutoML model. .. seealso:: For more information on how to use this operator, take a look at the guide: :ref:`howto/operator:AutoMLGetModelOperator` :param model_id: Name of the model requested to serve the prediction. :param project_id: ID of the Google Cloud project where model is located if None then default project_id is used. :param location: The location of the project. :param params: Additional domain-specific parameters for the predictions. :param retry: A retry object used to retry requests. If `None` is specified, requests will not be retried. :param timeout: The amount of time, in seconds, to wait for the request to complete. Note that if `retry` is specified, the timeout applies to each individual attempt. :param metadata: Additional metadata that is provided to the method. :param gcp_conn_id: The connection ID to use to connect to Google Cloud. :param impersonation_chain: Optional service account to impersonate using short-term credentials, or chained list of accounts required to get the access_token of the last account in the list, which will be impersonated in the request. If set as a string, the account must grant the originating account the Service Account Token Creator IAM role. If set as a sequence, the identities from the list must grant Service Account Token Creator IAM role to the directly preceding identity, with first account from the list granting this role to the originating account (templated). """
[docs] template_fields: Sequence[str] = ( "model_id", "location", "project_id", "impersonation_chain", )
def __init__( self, *, model_id: str, location: str, project_id: str | None = None, metadata: MetaData = (), timeout: float | None = None, retry: Retry | _MethodDefault = DEFAULT, gcp_conn_id: str = "google_cloud_default", impersonation_chain: str | Sequence[str] | None = None, **kwargs, ) -> None: super().__init__(**kwargs) self.model_id = model_id self.location = location self.project_id = project_id self.metadata = metadata self.timeout = timeout self.retry = retry self.gcp_conn_id = gcp_conn_id self.impersonation_chain = impersonation_chain
[docs] def execute(self, context: Context): hook = CloudAutoMLHook( gcp_conn_id=self.gcp_conn_id, impersonation_chain=self.impersonation_chain, ) result = hook.get_model( model_id=self.model_id, location=self.location, project_id=self.project_id, retry=self.retry, timeout=self.timeout, metadata=self.metadata, ) model = Model.to_dict(result) project_id = self.project_id or hook.project_id if project_id: AutoMLModelLink.persist( context=context, task_instance=self, dataset_id=model["dataset_id"], model_id=self.model_id, project_id=project_id, ) return model
[docs]class AutoMLDeleteModelOperator(GoogleCloudBaseOperator): """ Delete Google Cloud AutoML model. .. seealso:: For more information on how to use this operator, take a look at the guide: :ref:`howto/operator:AutoMLDeleteModelOperator` :param model_id: Name of the model requested to serve the prediction. :param project_id: ID of the Google Cloud project where model is located if None then default project_id is used. :param location: The location of the project. :param params: Additional domain-specific parameters for the predictions. :param retry: A retry object used to retry requests. If `None` is specified, requests will not be retried. :param timeout: The amount of time, in seconds, to wait for the request to complete. Note that if `retry` is specified, the timeout applies to each individual attempt. :param metadata: Additional metadata that is provided to the method. :param gcp_conn_id: The connection ID to use to connect to Google Cloud. :param impersonation_chain: Optional service account to impersonate using short-term credentials, or chained list of accounts required to get the access_token of the last account in the list, which will be impersonated in the request. If set as a string, the account must grant the originating account the Service Account Token Creator IAM role. If set as a sequence, the identities from the list must grant Service Account Token Creator IAM role to the directly preceding identity, with first account from the list granting this role to the originating account (templated). """
[docs] template_fields: Sequence[str] = ( "model_id", "location", "project_id", "impersonation_chain", )
def __init__( self, *, model_id: str, location: str, project_id: str | None = None, metadata: MetaData = (), timeout: float | None = None, retry: Retry | _MethodDefault = DEFAULT, gcp_conn_id: str = "google_cloud_default", impersonation_chain: str | Sequence[str] | None = None, **kwargs, ) -> None: super().__init__(**kwargs) self.model_id = model_id self.location = location self.project_id = project_id self.metadata = metadata self.timeout = timeout self.retry = retry self.gcp_conn_id = gcp_conn_id self.impersonation_chain = impersonation_chain
[docs] def execute(self, context: Context): hook = CloudAutoMLHook( gcp_conn_id=self.gcp_conn_id, impersonation_chain=self.impersonation_chain, ) operation = hook.delete_model( model_id=self.model_id, location=self.location, project_id=self.project_id, retry=self.retry, timeout=self.timeout, metadata=self.metadata, ) hook.wait_for_operation(timeout=self.timeout, operation=operation) self.log.info("Deletion is completed")
[docs]class AutoMLDeployModelOperator(GoogleCloudBaseOperator): """ Deploys a model; if a model is already deployed, deploying it with the same parameters has no effect. Deploying with different parameters (as e.g. changing node_number) will reset the deployment state without pausing the model_id's availability. Only applicable for Text Classification, Image Object Detection and Tables; all other domains manage deployment automatically. .. seealso:: For more information on how to use this operator, take a look at the guide: :ref:`howto/operator:AutoMLDeployModelOperator` :param model_id: Name of the model to be deployed. :param image_detection_metadata: Model deployment metadata specific to Image Object Detection. If a dict is provided, it must be of the same form as the protobuf message ImageObjectDetectionModelDeploymentMetadata :param project_id: ID of the Google Cloud project where model is located if None then default project_id is used. :param location: The location of the project. :param params: Additional domain-specific parameters for the predictions. :param retry: A retry object used to retry requests. If `None` is specified, requests will not be retried. :param timeout: The amount of time, in seconds, to wait for the request to complete. Note that if `retry` is specified, the timeout applies to each individual attempt. :param metadata: Additional metadata that is provided to the method. :param gcp_conn_id: The connection ID to use to connect to Google Cloud. :param impersonation_chain: Optional service account to impersonate using short-term credentials, or chained list of accounts required to get the access_token of the last account in the list, which will be impersonated in the request. If set as a string, the account must grant the originating account the Service Account Token Creator IAM role. If set as a sequence, the identities from the list must grant Service Account Token Creator IAM role to the directly preceding identity, with first account from the list granting this role to the originating account (templated). """
[docs] template_fields: Sequence[str] = ( "model_id", "location", "project_id", "impersonation_chain", )
def __init__( self, *, model_id: str, location: str, project_id: str | None = None, image_detection_metadata: dict | None = None, metadata: Sequence[tuple[str, str]] = (), timeout: float | None = None, retry: Retry | _MethodDefault = DEFAULT, gcp_conn_id: str = "google_cloud_default", impersonation_chain: str | Sequence[str] | None = None, **kwargs, ) -> None: super().__init__(**kwargs) self.model_id = model_id self.image_detection_metadata = image_detection_metadata self.location = location self.project_id = project_id self.metadata = metadata self.timeout = timeout self.retry = retry self.gcp_conn_id = gcp_conn_id self.impersonation_chain = impersonation_chain
[docs] def execute(self, context: Context): hook = CloudAutoMLHook( gcp_conn_id=self.gcp_conn_id, impersonation_chain=self.impersonation_chain, ) self.log.info("Deploying model_id %s", self.model_id) operation = hook.deploy_model( model_id=self.model_id, location=self.location, project_id=self.project_id, image_detection_metadata=self.image_detection_metadata, retry=self.retry, timeout=self.timeout, metadata=self.metadata, ) hook.wait_for_operation(timeout=self.timeout, operation=operation) self.log.info("Model was deployed successfully.")
[docs]class AutoMLTablesListTableSpecsOperator(GoogleCloudBaseOperator): """ Lists table specs in a dataset. .. seealso:: For more information on how to use this operator, take a look at the guide: :ref:`howto/operator:AutoMLTablesListTableSpecsOperator` :param dataset_id: Name of the dataset. :param filter_: Filter expression, see go/filtering. :param page_size: The maximum number of resources contained in the underlying API response. If page streaming is performed per resource, this parameter does not affect the return value. If page streaming is performed per-page, this determines the maximum number of resources in a page. :param project_id: ID of the Google Cloud project if None then default project_id is used. :param location: The location of the project. :param retry: A retry object used to retry requests. If `None` is specified, requests will not be retried. :param timeout: The amount of time, in seconds, to wait for the request to complete. Note that if `retry` is specified, the timeout applies to each individual attempt. :param metadata: Additional metadata that is provided to the method. :param gcp_conn_id: The connection ID to use to connect to Google Cloud. :param impersonation_chain: Optional service account to impersonate using short-term credentials, or chained list of accounts required to get the access_token of the last account in the list, which will be impersonated in the request. If set as a string, the account must grant the originating account the Service Account Token Creator IAM role. If set as a sequence, the identities from the list must grant Service Account Token Creator IAM role to the directly preceding identity, with first account from the list granting this role to the originating account (templated). """
[docs] template_fields: Sequence[str] = ( "dataset_id", "filter_", "location", "project_id", "impersonation_chain", )
def __init__( self, *, dataset_id: str, location: str, page_size: int | None = None, filter_: str | None = None, project_id: str | None = None, metadata: MetaData = (), timeout: float | None = None, retry: Retry | _MethodDefault = DEFAULT, gcp_conn_id: str = "google_cloud_default", impersonation_chain: str | Sequence[str] | None = None, **kwargs, ) -> None: super().__init__(**kwargs) self.dataset_id = dataset_id self.filter_ = filter_ self.page_size = page_size self.location = location self.project_id = project_id self.metadata = metadata self.timeout = timeout self.retry = retry self.gcp_conn_id = gcp_conn_id self.impersonation_chain = impersonation_chain
[docs] def execute(self, context: Context): hook = CloudAutoMLHook( gcp_conn_id=self.gcp_conn_id, impersonation_chain=self.impersonation_chain, ) self.log.info("Requesting table specs for %s.", self.dataset_id) page_iterator = hook.list_table_specs( dataset_id=self.dataset_id, filter_=self.filter_, page_size=self.page_size, location=self.location, project_id=self.project_id, retry=self.retry, timeout=self.timeout, metadata=self.metadata, ) result = [TableSpec.to_dict(spec) for spec in page_iterator] self.log.info(result) self.log.info("Table specs obtained.") project_id = self.project_id or hook.project_id if project_id: AutoMLDatasetLink.persist( context=context, task_instance=self, dataset_id=self.dataset_id, project_id=project_id, ) return result
[docs]class AutoMLListDatasetOperator(GoogleCloudBaseOperator): """ Lists AutoML Datasets in project. .. seealso:: For more information on how to use this operator, take a look at the guide: :ref:`howto/operator:AutoMLListDatasetOperator` :param project_id: ID of the Google Cloud project where datasets are located if None then default project_id is used. :param location: The location of the project. :param retry: A retry object used to retry requests. If `None` is specified, requests will not be retried. :param timeout: The amount of time, in seconds, to wait for the request to complete. Note that if `retry` is specified, the timeout applies to each individual attempt. :param metadata: Additional metadata that is provided to the method. :param gcp_conn_id: The connection ID to use to connect to Google Cloud. :param impersonation_chain: Optional service account to impersonate using short-term credentials, or chained list of accounts required to get the access_token of the last account in the list, which will be impersonated in the request. If set as a string, the account must grant the originating account the Service Account Token Creator IAM role. If set as a sequence, the identities from the list must grant Service Account Token Creator IAM role to the directly preceding identity, with first account from the list granting this role to the originating account (templated). """
[docs] template_fields: Sequence[str] = ( "location", "project_id", "impersonation_chain", )
def __init__( self, *, location: str, project_id: str | None = None, metadata: MetaData = (), timeout: float | None = None, retry: Retry | _MethodDefault = DEFAULT, gcp_conn_id: str = "google_cloud_default", impersonation_chain: str | Sequence[str] | None = None, **kwargs, ) -> None: super().__init__(**kwargs) self.location = location self.project_id = project_id self.metadata = metadata self.timeout = timeout self.retry = retry self.gcp_conn_id = gcp_conn_id self.impersonation_chain = impersonation_chain
[docs] def execute(self, context: Context): hook = CloudAutoMLHook( gcp_conn_id=self.gcp_conn_id, impersonation_chain=self.impersonation_chain, ) self.log.info("Requesting datasets") page_iterator = hook.list_datasets( location=self.location, project_id=self.project_id, retry=self.retry, timeout=self.timeout, metadata=self.metadata, ) result = [Dataset.to_dict(dataset) for dataset in page_iterator] self.log.info("Datasets obtained.") self.xcom_push( context, key="dataset_id_list", value=[hook.extract_object_id(d) for d in result], ) project_id = self.project_id or hook.project_id if project_id: AutoMLDatasetListLink.persist(context=context, task_instance=self, project_id=project_id) return result
[docs]class AutoMLDeleteDatasetOperator(GoogleCloudBaseOperator): """ Deletes a dataset and all of its contents. .. seealso:: For more information on how to use this operator, take a look at the guide: :ref:`howto/operator:AutoMLDeleteDatasetOperator` :param dataset_id: Name of the dataset_id, list of dataset_id or string of dataset_id coma separated to be deleted. :param project_id: ID of the Google Cloud project where dataset is located if None then default project_id is used. :param location: The location of the project. :param retry: A retry object used to retry requests. If `None` is specified, requests will not be retried. :param timeout: The amount of time, in seconds, to wait for the request to complete. Note that if `retry` is specified, the timeout applies to each individual attempt. :param metadata: Additional metadata that is provided to the method. :param gcp_conn_id: The connection ID to use to connect to Google Cloud. :param impersonation_chain: Optional service account to impersonate using short-term credentials, or chained list of accounts required to get the access_token of the last account in the list, which will be impersonated in the request. If set as a string, the account must grant the originating account the Service Account Token Creator IAM role. If set as a sequence, the identities from the list must grant Service Account Token Creator IAM role to the directly preceding identity, with first account from the list granting this role to the originating account (templated). """
[docs] template_fields: Sequence[str] = ( "dataset_id", "location", "project_id", "impersonation_chain", )
def __init__( self, *, dataset_id: str | list[str], location: str, project_id: str | None = None, metadata: MetaData = (), timeout: float | None = None, retry: Retry | _MethodDefault = DEFAULT, gcp_conn_id: str = "google_cloud_default", impersonation_chain: str | Sequence[str] | None = None, **kwargs, ) -> None: super().__init__(**kwargs) self.dataset_id = dataset_id self.location = location self.project_id = project_id self.metadata = metadata self.timeout = timeout self.retry = retry self.gcp_conn_id = gcp_conn_id self.impersonation_chain = impersonation_chain @staticmethod def _parse_dataset_id(dataset_id: str | list[str]) -> list[str]: if not isinstance(dataset_id, str): return dataset_id try: return ast.literal_eval(dataset_id) except (SyntaxError, ValueError): return dataset_id.split(",")
[docs] def execute(self, context: Context): hook = CloudAutoMLHook( gcp_conn_id=self.gcp_conn_id, impersonation_chain=self.impersonation_chain, ) dataset_id_list = self._parse_dataset_id(self.dataset_id) for dataset_id in dataset_id_list: self.log.info("Deleting dataset %s", dataset_id) hook.delete_dataset( dataset_id=dataset_id, location=self.location, project_id=self.project_id, retry=self.retry, timeout=self.timeout, metadata=self.metadata, ) self.log.info("Dataset deleted.")

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