Source code for airflow.providers.google.cloud.operators.vertex_ai.model_service

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"""This module contains Google Vertex AI operators.

.. spelling::

    aiplatform
    camelCase
"""
from __future__ import annotations

from typing import TYPE_CHECKING, Sequence

from google.api_core.exceptions import NotFound
from google.api_core.gapic_v1.method import DEFAULT, _MethodDefault
from google.api_core.retry import Retry
from google.cloud.aiplatform_v1.types import Model, model_service

from airflow.models import BaseOperator
from airflow.providers.google.cloud.hooks.vertex_ai.model_service import ModelServiceHook
from airflow.providers.google.cloud.links.vertex_ai import (
    VertexAIModelExportLink,
    VertexAIModelLink,
    VertexAIModelListLink,
)

if TYPE_CHECKING:
    from airflow.utils.context import Context


[docs]class DeleteModelOperator(BaseOperator): """ Deletes a Model. :param project_id: Required. The ID of the Google Cloud project that the service belongs to. :param region: Required. The ID of the Google Cloud region that the service belongs to. :param model_id: Required. The name of the Model resource to be deleted. :param retry: Designation of what errors, if any, should be retried. :param timeout: The timeout for this request. :param metadata: Strings which should be sent along with the request as metadata. :param gcp_conn_id: The connection ID to use connecting to Google Cloud. :param delegate_to: The account to impersonate using domain-wide delegation of authority, if any. For this to work, the service account making the request must have domain-wide delegation enabled. :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 = ("region", "model_id", "project_id", "impersonation_chain")
def __init__( self, *, region: str, project_id: str, model_id: str, retry: Retry | _MethodDefault = DEFAULT, timeout: float | None = None, metadata: Sequence[tuple[str, str]] = (), gcp_conn_id: str = "google_cloud_default", delegate_to: str | None = None, impersonation_chain: str | Sequence[str] | None = None, **kwargs, ) -> None: super().__init__(**kwargs) self.region = region self.project_id = project_id self.model_id = model_id self.retry = retry self.timeout = timeout self.metadata = metadata self.gcp_conn_id = gcp_conn_id self.delegate_to = delegate_to self.impersonation_chain = impersonation_chain
[docs] def execute(self, context: Context): hook = ModelServiceHook( gcp_conn_id=self.gcp_conn_id, delegate_to=self.delegate_to, impersonation_chain=self.impersonation_chain, ) try: self.log.info("Deleting model: %s", self.model_id) operation = hook.delete_model( project_id=self.project_id, region=self.region, model=self.model_id, retry=self.retry, timeout=self.timeout, metadata=self.metadata, ) hook.wait_for_operation(timeout=self.timeout, operation=operation) self.log.info("Model was deleted.") except NotFound: self.log.info("The Model ID %s does not exist.", self.model_id)
[docs]class ExportModelOperator(BaseOperator): """ Exports a trained, exportable Model to a location specified by the user. :param project_id: Required. The ID of the Google Cloud project that the service belongs to. :param region: Required. The ID of the Google Cloud region that the service belongs to. :param model_id: Required. The resource name of the Model to export. :param output_config: Required. The desired output location and configuration. :param retry: Designation of what errors, if any, should be retried. :param timeout: The timeout for this request. :param metadata: Strings which should be sent along with the request as metadata. :param gcp_conn_id: The connection ID to use connecting to Google Cloud. :param delegate_to: The account to impersonate using domain-wide delegation of authority, if any. For this to work, the service account making the request must have domain-wide delegation enabled. :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 = ("region", "model_id", "project_id", "impersonation_chain")
def __init__( self, *, project_id: str, region: str, model_id: str, output_config: model_service.ExportModelRequest.OutputConfig | dict, retry: Retry | _MethodDefault = DEFAULT, timeout: float | None = None, metadata: Sequence[tuple[str, str]] = (), gcp_conn_id: str = "google_cloud_default", delegate_to: str | None = None, impersonation_chain: str | Sequence[str] | None = None, **kwargs, ) -> None: super().__init__(**kwargs) self.region = region self.project_id = project_id self.model_id = model_id self.output_config = output_config self.retry = retry self.timeout = timeout self.metadata = metadata self.gcp_conn_id = gcp_conn_id self.delegate_to = delegate_to self.impersonation_chain = impersonation_chain
[docs] def execute(self, context: Context): hook = ModelServiceHook( gcp_conn_id=self.gcp_conn_id, delegate_to=self.delegate_to, impersonation_chain=self.impersonation_chain, ) try: self.log.info("Exporting model: %s", self.model_id) operation = hook.export_model( project_id=self.project_id, region=self.region, model=self.model_id, output_config=self.output_config, retry=self.retry, timeout=self.timeout, metadata=self.metadata, ) hook.wait_for_operation(timeout=self.timeout, operation=operation) VertexAIModelExportLink.persist(context=context, task_instance=self) self.log.info("Model was exported.") except NotFound: self.log.info("The Model ID %s does not exist.", self.model_id)
[docs]class ListModelsOperator(BaseOperator): r""" Lists Models in a Location. :param project_id: Required. The ID of the Google Cloud project that the service belongs to. :param region: Required. The ID of the Google Cloud region that the service belongs to. :param retry: Designation of what errors, if any, should be retried. :param filter: An expression for filtering the results of the request. For field names both snake_case and camelCase are supported. - ``model`` supports = and !=. ``model`` represents the Model ID, i.e. the last segment of the Model's [resource name][google.cloud.aiplatform.v1.Model.name]. - ``display_name`` supports = and != - ``labels`` supports general map functions that is: -- ``labels.key=value`` - key:value equality -- \`labels.key:\* or labels:key - key existence -- A key including a space must be quoted. ``labels."a key"``. :param page_size: The standard list page size. :param page_token: The standard list page token. Typically obtained via [ListModelsResponse.next_page_token][google.cloud.aiplatform.v1.ListModelsResponse.next_page_token] of the previous [ModelService.ListModels][google.cloud.aiplatform.v1.ModelService.ListModels] call. :param read_mask: Mask specifying which fields to read. :param order_by: A comma-separated list of fields to order by, sorted in ascending order. Use "desc" after a field name for descending. :param timeout: The timeout for this request. :param metadata: Strings which should be sent along with the request as metadata. :param gcp_conn_id: The connection ID to use connecting to Google Cloud. :param delegate_to: The account to impersonate using domain-wide delegation of authority, if any. For this to work, the service account making the request must have domain-wide delegation enabled. :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 = ("region", "project_id", "impersonation_chain")
def __init__( self, *, region: str, project_id: str, filter: str | None = None, page_size: int | None = None, page_token: str | None = None, read_mask: str | None = None, order_by: str | None = None, retry: Retry | _MethodDefault = DEFAULT, timeout: float | None = None, metadata: Sequence[tuple[str, str]] = (), gcp_conn_id: str = "google_cloud_default", delegate_to: str | None = None, impersonation_chain: str | Sequence[str] | None = None, **kwargs, ) -> None: super().__init__(**kwargs) self.region = region self.project_id = project_id self.filter = filter self.page_size = page_size self.page_token = page_token self.read_mask = read_mask self.order_by = order_by self.retry = retry self.timeout = timeout self.metadata = metadata self.gcp_conn_id = gcp_conn_id self.delegate_to = delegate_to self.impersonation_chain = impersonation_chain
[docs] def execute(self, context: Context): hook = ModelServiceHook( gcp_conn_id=self.gcp_conn_id, delegate_to=self.delegate_to, impersonation_chain=self.impersonation_chain, ) results = hook.list_models( project_id=self.project_id, region=self.region, filter=self.filter, page_size=self.page_size, page_token=self.page_token, read_mask=self.read_mask, order_by=self.order_by, retry=self.retry, timeout=self.timeout, metadata=self.metadata, ) VertexAIModelListLink.persist(context=context, task_instance=self) return [Model.to_dict(result) for result in results]
[docs]class UploadModelOperator(BaseOperator): """ Uploads a Model artifact into Vertex AI. :param project_id: Required. The ID of the Google Cloud project that the service belongs to. :param region: Required. The ID of the Google Cloud region that the service belongs to. :param model: Required. The Model to create. :param retry: Designation of what errors, if any, should be retried. :param timeout: The timeout for this request. :param metadata: Strings which should be sent along with the request as metadata. :param gcp_conn_id: The connection ID to use connecting to Google Cloud. :param delegate_to: The account to impersonate using domain-wide delegation of authority, if any. For this to work, the service account making the request must have domain-wide delegation enabled. :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 = ("region", "project_id", "model", "impersonation_chain")
def __init__( self, *, project_id: str, region: str, model: Model | dict, retry: Retry | _MethodDefault = DEFAULT, timeout: float | None = None, metadata: Sequence[tuple[str, str]] = (), gcp_conn_id: str = "google_cloud_default", delegate_to: str | None = None, impersonation_chain: str | Sequence[str] | None = None, **kwargs, ) -> None: super().__init__(**kwargs) self.project_id = project_id self.region = region self.model = model self.retry = retry self.timeout = timeout self.metadata = metadata self.gcp_conn_id = gcp_conn_id self.delegate_to = delegate_to self.impersonation_chain = impersonation_chain
[docs] def execute(self, context: Context): hook = ModelServiceHook( gcp_conn_id=self.gcp_conn_id, delegate_to=self.delegate_to, impersonation_chain=self.impersonation_chain, ) self.log.info("Upload model") operation = hook.upload_model( project_id=self.project_id, region=self.region, model=self.model, retry=self.retry, timeout=self.timeout, metadata=self.metadata, ) result = hook.wait_for_operation(timeout=self.timeout, operation=operation) model_resp = model_service.UploadModelResponse.to_dict(result) model_id = hook.extract_model_id(model_resp) self.log.info("Model was uploaded. Model ID: %s", model_id) self.xcom_push(context, key="model_id", value=model_id) VertexAIModelLink.persist(context=context, task_instance=self, model_id=model_id) return model_resp

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