def execute(self, context: 'Context'):
hook = EndpointServiceHook(
gcp_conn_id=self.gcp_conn_id,
delegate_to=self.delegate_to,
impersonation_chain=self.impersonation_chain,
)
try:
self.log.info("Get endpoint: %s", self.endpoint_id)
endpoint_obj = hook.get_endpoint(
project_id=self.project_id,
region=self.region,
endpoint=self.endpoint_id,
retry=self.retry,
timeout=self.timeout,
metadata=self.metadata,
)
VertexAIEndpointLink.persist(context=context,
task_instance=self,
endpoint_id=self.endpoint_id)
self.log.info("Endpoint was gotten.")
return Endpoint.to_dict(endpoint_obj)
except NotFound:
self.log.info("The Endpoint ID %s does not exist.",
self.endpoint_id)
def execute(self, context: 'Context'):
hook = EndpointServiceHook(
gcp_conn_id=self.gcp_conn_id,
delegate_to=self.delegate_to,
impersonation_chain=self.impersonation_chain,
)
self.log.info("Creating endpoint")
operation = hook.create_endpoint(
project_id=self.project_id,
region=self.region,
endpoint=self.endpoint,
endpoint_id=self.endpoint_id,
retry=self.retry,
timeout=self.timeout,
metadata=self.metadata,
)
result = hook.wait_for_operation(timeout=self.timeout,
operation=operation)
endpoint = Endpoint.to_dict(result)
endpoint_id = hook.extract_endpoint_id(endpoint)
self.log.info("Endpoint was created. Endpoint ID: %s", endpoint_id)
self.xcom_push(context, key="endpoint_id", value=endpoint_id)
VertexAIEndpointLink.persist(context=context,
task_instance=self,
endpoint_id=endpoint_id)
return endpoint
def execute(self, context: 'Context'):
hook = EndpointServiceHook(
gcp_conn_id=self.gcp_conn_id,
delegate_to=self.delegate_to,
impersonation_chain=self.impersonation_chain,
)
self.log.info("Updating endpoint: %s", self.endpoint_id)
result = hook.update_endpoint(
project_id=self.project_id,
region=self.region,
endpoint_id=self.endpoint_id,
endpoint=self.endpoint,
update_mask=self.update_mask,
retry=self.retry,
timeout=self.timeout,
metadata=self.metadata,
)
self.log.info("Endpoint was updated")
VertexAIEndpointLink.persist(context=context,
task_instance=self,
endpoint_id=self.endpoint_id)
return Endpoint.to_dict(result)
class UpdateEndpointOperator(BaseOperator):
"""
Updates an Endpoint.
: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 endpoint_id: Required. The ID of the Endpoint to update.
:param endpoint: Required. The Endpoint which replaces the resource on the server.
:param update_mask: Required. The update mask applies to the resource. See
[google.protobuf.FieldMask][google.protobuf.FieldMask].
: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).
"""
template_fields = ("region", "endpoint_id", "project_id",
"impersonation_chain")
operator_extra_links = (VertexAIEndpointLink(), )
def __init__(
self,
*,
project_id: str,
region: str,
endpoint_id: str,
endpoint: Union[Endpoint, Dict],
update_mask: Union[FieldMask, Dict],
retry: Union[Retry, _MethodDefault] = DEFAULT,
timeout: Optional[float] = None,
metadata: Sequence[Tuple[str, str]] = (),
gcp_conn_id: str = "google_cloud_default",
delegate_to: Optional[str] = None,
impersonation_chain: Optional[Union[str, Sequence[str]]] = None,
**kwargs,
) -> None:
super().__init__(**kwargs)
self.project_id = project_id
self.region = region
self.endpoint_id = endpoint_id
self.endpoint = endpoint
self.update_mask = update_mask
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
def execute(self, context: 'Context'):
hook = EndpointServiceHook(
gcp_conn_id=self.gcp_conn_id,
delegate_to=self.delegate_to,
impersonation_chain=self.impersonation_chain,
)
self.log.info("Updating endpoint: %s", self.endpoint_id)
result = hook.update_endpoint(
project_id=self.project_id,
region=self.region,
endpoint_id=self.endpoint_id,
endpoint=self.endpoint,
update_mask=self.update_mask,
retry=self.retry,
timeout=self.timeout,
metadata=self.metadata,
)
self.log.info("Endpoint was updated")
VertexAIEndpointLink.persist(context=context,
task_instance=self,
endpoint_id=self.endpoint_id)
return Endpoint.to_dict(result)
class CreateEndpointOperator(BaseOperator):
"""
Creates an Endpoint.
: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 endpoint: Required. The Endpoint to create.
:param endpoint_id: The ID of Endpoint. This value should be 1-10 characters, and valid characters
are /[0-9]/. If not provided, Vertex AI will generate a value for this ID.
: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).
"""
template_fields = ("region", "project_id", "impersonation_chain")
operator_extra_links = (VertexAIEndpointLink(), )
def __init__(
self,
*,
region: str,
project_id: str,
endpoint: Union[Endpoint, Dict],
endpoint_id: Optional[str] = None,
retry: Union[Retry, _MethodDefault] = DEFAULT,
timeout: Optional[float] = None,
metadata: Sequence[Tuple[str, str]] = (),
gcp_conn_id: str = "google_cloud_default",
delegate_to: Optional[str] = None,
impersonation_chain: Optional[Union[str, Sequence[str]]] = None,
**kwargs,
) -> None:
super().__init__(**kwargs)
self.region = region
self.project_id = project_id
self.endpoint = endpoint
self.endpoint_id = endpoint_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
def execute(self, context: 'Context'):
hook = EndpointServiceHook(
gcp_conn_id=self.gcp_conn_id,
delegate_to=self.delegate_to,
impersonation_chain=self.impersonation_chain,
)
self.log.info("Creating endpoint")
operation = hook.create_endpoint(
project_id=self.project_id,
region=self.region,
endpoint=self.endpoint,
endpoint_id=self.endpoint_id,
retry=self.retry,
timeout=self.timeout,
metadata=self.metadata,
)
result = hook.wait_for_operation(timeout=self.timeout,
operation=operation)
endpoint = Endpoint.to_dict(result)
endpoint_id = hook.extract_endpoint_id(endpoint)
self.log.info("Endpoint was created. Endpoint ID: %s", endpoint_id)
self.xcom_push(context, key="endpoint_id", value=endpoint_id)
VertexAIEndpointLink.persist(context=context,
task_instance=self,
endpoint_id=endpoint_id)
return endpoint