docs-archive/apache-airflow-providers-google/8.4.0/_sources/_api/airflow/providers/google/cloud/operators/vertex_ai/model_service/index.rst.txt

:py:mod:`airflow.providers.google.cloud.operators.vertex_ai.model_service`
==========================================================================

.. py:module:: airflow.providers.google.cloud.operators.vertex_ai.model_service

.. autoapi-nested-parse::

   This module contains Google Vertex AI operators.

   .. spelling::

       aiplatform
       camelCase



Module Contents
---------------

Classes
~~~~~~~

.. autoapisummary::

   airflow.providers.google.cloud.operators.vertex_ai.model_service.DeleteModelOperator
   airflow.providers.google.cloud.operators.vertex_ai.model_service.ExportModelOperator
   airflow.providers.google.cloud.operators.vertex_ai.model_service.ListModelsOperator
   airflow.providers.google.cloud.operators.vertex_ai.model_service.UploadModelOperator




.. py:class:: DeleteModelOperator(*, region, project_id, model_id, retry = DEFAULT, timeout = None, metadata = (), gcp_conn_id = 'google_cloud_default', delegate_to = None, impersonation_chain = None, **kwargs)

   Bases: :py:obj:`airflow.models.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).

   .. py:attribute:: template_fields
      :annotation: = ['region', 'model_id', 'project_id', 'impersonation_chain']

      

   .. py:method:: execute(context)

      This is the main method to derive when creating an operator.
      Context is the same dictionary used as when rendering jinja templates.

      Refer to get_template_context for more context.



.. py:class:: ExportModelOperator(*, project_id, region, model_id, output_config, retry = DEFAULT, timeout = None, metadata = (), gcp_conn_id = 'google_cloud_default', delegate_to = None, impersonation_chain = None, **kwargs)

   Bases: :py:obj:`airflow.models.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).

   .. py:attribute:: template_fields
      :annotation: = ['region', 'model_id', 'project_id', 'impersonation_chain']

      

   .. py:attribute:: operator_extra_links
      

      

   .. py:method:: execute(context)

      This is the main method to derive when creating an operator.
      Context is the same dictionary used as when rendering jinja templates.

      Refer to get_template_context for more context.



.. py:class:: ListModelsOperator(*, region, project_id, filter = None, page_size = None, page_token = None, read_mask = None, order_by = None, retry = DEFAULT, timeout = None, metadata = (), gcp_conn_id = 'google_cloud_default', delegate_to = None, impersonation_chain = None, **kwargs)

   Bases: :py:obj:`airflow.models.BaseOperator`

   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).

   .. py:attribute:: template_fields
      :annotation: = ['region', 'project_id', 'impersonation_chain']

      

   .. py:attribute:: operator_extra_links
      

      

   .. py:method:: execute(context)

      This is the main method to derive when creating an operator.
      Context is the same dictionary used as when rendering jinja templates.

      Refer to get_template_context for more context.



.. py:class:: UploadModelOperator(*, project_id, region, model, retry = DEFAULT, timeout = None, metadata = (), gcp_conn_id = 'google_cloud_default', delegate_to = None, impersonation_chain = None, **kwargs)

   Bases: :py:obj:`airflow.models.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).

   .. py:attribute:: template_fields
      :annotation: = ['region', 'project_id', 'impersonation_chain']

      

   .. py:attribute:: operator_extra_links
      

      

   .. py:method:: execute(context)

      This is the main method to derive when creating an operator.
      Context is the same dictionary used as when rendering jinja templates.

      Refer to get_template_context for more context.