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"""This module contains Google Vertex AI operators."""
from __future__ import annotations
from functools import cached_property
from typing import TYPE_CHECKING, Any, Sequence
from google.api_core.exceptions import NotFound
from google.api_core.gapic_v1.method import DEFAULT, _MethodDefault
from google.cloud.aiplatform_v1 import types
from airflow.configuration import conf
from airflow.exceptions import AirflowException
from airflow.providers.google.cloud.hooks.vertex_ai.pipeline_job import PipelineJobHook
from airflow.providers.google.cloud.links.vertex_ai import (
VertexAIPipelineJobLink,
VertexAIPipelineJobListLink,
)
from airflow.providers.google.cloud.operators.cloud_base import GoogleCloudBaseOperator
from airflow.providers.google.cloud.triggers.vertex_ai import RunPipelineJobTrigger
if TYPE_CHECKING:
from google.api_core.retry import Retry
from google.cloud.aiplatform import PipelineJob
from google.cloud.aiplatform.metadata import experiment_resources
from airflow.utils.context import Context
[docs]class RunPipelineJobOperator(GoogleCloudBaseOperator):
"""
Create and run a Pipeline job.
: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 display_name: Required. The user-defined name of this Pipeline.
:param template_path: Required. The path of PipelineJob or PipelineSpec JSON or YAML file. It can be
a local path, a Google Cloud Storage URI (e.g. "gs://project.name"), an Artifact Registry URI
(e.g. "https://us-central1-kfp.pkg.dev/proj/repo/pack/latest"), or an HTTPS URI.
:param job_id: Optional. The unique ID of the job run. If not specified, pipeline name + timestamp
will be used.
:param pipeline_root: Optional. The root of the pipeline outputs. If not set, the staging bucket set
in aiplatform.init will be used. If that's not set a pipeline-specific artifacts bucket will be
used.
:param parameter_values: Optional. The mapping from runtime parameter names to its values that
control the pipeline run.
:param input_artifacts: Optional. The mapping from the runtime parameter name for this artifact to
its resource id. For example: "vertex_model":"456". Note: full resource name
("projects/123/locations/us-central1/metadataStores/default/artifacts/456") cannot be used.
:param enable_caching: Optional. Whether to turn on caching for the run.
If this is not set, defaults to the compile time settings, which are True for all tasks by
default, while users may specify different caching options for individual tasks.
If this is set, the setting applies to all tasks in the pipeline. Overrides the compile time
settings.
:param encryption_spec_key_name: 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 PipelineJob will be encrypted with the provided encryption key.
Overrides encryption_spec_key_name set in aiplatform.init.
:param labels: Optional. The user defined metadata to organize PipelineJob.
:param failure_policy: Optional. The failure policy - "slow" or "fast". Currently, the default of a
pipeline is that the pipeline will continue to run until no more tasks can be executed, also
known as PIPELINE_FAILURE_POLICY_FAIL_SLOW (corresponds to "slow"). However, if a pipeline is set
to PIPELINE_FAILURE_POLICY_FAIL_FAST (corresponds to "fast"), it will stop scheduling any new
tasks when a task has failed. Any scheduled tasks will continue to completion.
:param service_account: Optional. Specifies the service account for workload run-as account. Users
submitting jobs must have act-as permission on this run-as account.
:param network: 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.
:param create_request_timeout: Optional. The timeout for the create request in seconds.
:param experiment: Optional. The Vertex AI experiment name or instance to associate to this PipelineJob.
Metrics produced by the PipelineJob as system.Metric Artifacts will be associated as metrics
to the current Experiment Run. Pipeline parameters will be associated as parameters to
the current Experiment Run.
:param gcp_conn_id: The connection ID to use connecting 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).
:param deferrable: If True, run the task in the deferrable mode.
Note that it requires calling the operator with `sync=False` parameter.
:param poll_interval: Time (seconds) to wait between two consecutive calls to check the job.
The default is 300 seconds.
"""
[docs] template_fields = [
"region",
"project_id",
"input_artifacts",
"impersonation_chain",
]
def __init__(
self,
*,
project_id: str,
region: str,
display_name: str,
template_path: str,
job_id: str | None = None,
pipeline_root: str | None = None,
parameter_values: dict[str, Any] | None = None,
input_artifacts: dict[str, str] | None = None,
enable_caching: bool | None = None,
encryption_spec_key_name: str | None = None,
labels: dict[str, str] | None = None,
failure_policy: str | None = None,
service_account: str | None = None,
network: str | None = None,
create_request_timeout: float | None = None,
experiment: str | experiment_resources.Experiment | None = None,
gcp_conn_id: str = "google_cloud_default",
impersonation_chain: str | Sequence[str] | None = None,
deferrable: bool = conf.getboolean("operators", "default_deferrable", fallback=False),
poll_interval: int = 5 * 60,
**kwargs,
) -> None:
super().__init__(**kwargs)
self.region = region
self.project_id = project_id
self.display_name = display_name
self.template_path = template_path
self.job_id = job_id
self.pipeline_root = pipeline_root
self.parameter_values = parameter_values
self.input_artifacts = input_artifacts
self.enable_caching = enable_caching
self.encryption_spec_key_name = encryption_spec_key_name
self.labels = labels
self.failure_policy = failure_policy
self.service_account = service_account
self.network = network
self.create_request_timeout = create_request_timeout
self.experiment = experiment
self.gcp_conn_id = gcp_conn_id
self.impersonation_chain = impersonation_chain
self.deferrable = deferrable
self.poll_interval = poll_interval
[docs] def execute(self, context: Context):
self.log.info("Running Pipeline job")
pipeline_job_obj: PipelineJob = self.hook.submit_pipeline_job(
project_id=self.project_id,
region=self.region,
display_name=self.display_name,
template_path=self.template_path,
job_id=self.job_id,
pipeline_root=self.pipeline_root,
parameter_values=self.parameter_values,
input_artifacts=self.input_artifacts,
enable_caching=self.enable_caching,
encryption_spec_key_name=self.encryption_spec_key_name,
labels=self.labels,
failure_policy=self.failure_policy,
service_account=self.service_account,
network=self.network,
create_request_timeout=self.create_request_timeout,
experiment=self.experiment,
)
pipeline_job_id = pipeline_job_obj.job_id
self.log.info("Pipeline job was created. Job id: %s", pipeline_job_id)
self.xcom_push(context, key="pipeline_job_id", value=pipeline_job_id)
VertexAIPipelineJobLink.persist(context=context, task_instance=self, pipeline_id=pipeline_job_id)
if self.deferrable:
pipeline_job_obj.wait_for_resource_creation()
self.defer(
trigger=RunPipelineJobTrigger(
conn_id=self.gcp_conn_id,
project_id=self.project_id,
location=pipeline_job_obj.location,
job_id=pipeline_job_id,
poll_interval=self.poll_interval,
impersonation_chain=self.impersonation_chain,
),
method_name="execute_complete",
)
pipeline_job_obj.wait()
pipeline_job = pipeline_job_obj.to_dict()
return pipeline_job
[docs] def execute_complete(self, context: Context, event: dict[str, Any]) -> dict[str, Any]:
if event["status"] == "error":
raise AirflowException(event["message"])
return event["job"]
[docs] def on_kill(self) -> None:
"""Act as a callback called when the operator is killed; cancel any running job."""
if self.hook:
self.hook.cancel_pipeline_job()
@cached_property
[docs] def hook(self) -> PipelineJobHook:
return PipelineJobHook(
gcp_conn_id=self.gcp_conn_id,
impersonation_chain=self.impersonation_chain,
)
[docs]class GetPipelineJobOperator(GoogleCloudBaseOperator):
"""
Get a Pipeline job.
: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 pipeline_job_id: Required. The ID of the PipelineJob resource.
: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 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",
"pipeline_job_id",
"project_id",
"impersonation_chain",
]
def __init__(
self,
*,
project_id: str,
region: str,
pipeline_job_id: str,
retry: Retry | _MethodDefault = DEFAULT,
timeout: float | None = None,
metadata: Sequence[tuple[str, str]] = (),
gcp_conn_id: str = "google_cloud_default",
impersonation_chain: str | Sequence[str] | None = None,
**kwargs,
) -> None:
super().__init__(**kwargs)
self.region = region
self.project_id = project_id
self.pipeline_job_id = pipeline_job_id
self.retry = retry
self.timeout = timeout
self.metadata = metadata
self.gcp_conn_id = gcp_conn_id
self.impersonation_chain = impersonation_chain
[docs] def execute(self, context: Context):
hook = PipelineJobHook(
gcp_conn_id=self.gcp_conn_id,
impersonation_chain=self.impersonation_chain,
)
try:
self.log.info("Get Pipeline job: %s", self.pipeline_job_id)
result = hook.get_pipeline_job(
project_id=self.project_id,
region=self.region,
pipeline_job_id=self.pipeline_job_id,
retry=self.retry,
timeout=self.timeout,
metadata=self.metadata,
)
VertexAIPipelineJobLink.persist(
context=context, task_instance=self, pipeline_id=self.pipeline_job_id
)
self.log.info("Pipeline job was gotten.")
return types.PipelineJob.to_dict(result)
except NotFound:
self.log.info("The Pipeline job %s does not exist.", self.pipeline_job_id)
[docs]class ListPipelineJobOperator(GoogleCloudBaseOperator):
"""Lists PipelineJob 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 filter: Optional. Lists the PipelineJobs that match the filter expression. The
following fields are supported:
- ``pipeline_name``: Supports ``=`` and ``!=`` comparisons.
- ``display_name``: Supports ``=``, ``!=`` comparisons, and
``:`` wildcard.
- ``pipeline_job_user_id``: Supports ``=``, ``!=``
comparisons, and ``:`` wildcard. for example, can check
if pipeline's display_name contains *step* by doing
display_name:"*step*"
- ``create_time``: Supports ``=``, ``!=``, ``<``, ``>``,
``<=``, and ``>=`` comparisons. Values must be in RFC
3339 format.
- ``update_time``: Supports ``=``, ``!=``, ``<``, ``>``,
``<=``, and ``>=`` comparisons. Values must be in RFC
3339 format.
- ``end_time``: Supports ``=``, ``!=``, ``<``, ``>``,
``<=``, and ``>=`` comparisons. Values must be in RFC
3339 format.
- ``labels``: Supports key-value equality and key presence.
Filter expressions can be combined together using logical
operators (``AND`` & ``OR``). For example:
``pipeline_name="test" AND create_time>"2020-05-18T13:30:00Z"``.
The syntax to define filter expression is based on
https://google.aip.dev/160.
:param page_size: Optional. The standard list page size.
:param page_token: Optional. The standard list page token. Typically obtained via
[ListPipelineJobsResponse.next_page_token][google.cloud.aiplatform.v1.ListPipelineJobsResponse.next_page_token]
of the previous
[PipelineService.ListPipelineJobs][google.cloud.aiplatform.v1.PipelineService.ListPipelineJobs]
call.
:param order_by: Optional. A comma-separated list of fields to order by. The default
sort order is in ascending order. Use "desc" after a field
name for descending. You can have multiple order_by fields
provided e.g. "create_time desc, end_time", "end_time,
start_time, update_time" For example, using "create_time
desc, end_time" will order results by create time in
descending order, and if there are multiple jobs having the
same create time, order them by the end time in ascending
order. if order_by is not specified, it will order by
default order is create time in descending order. Supported
fields:
- ``create_time``
- ``update_time``
- ``end_time``
- ``start_time``
: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 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,
page_size: int | None = None,
page_token: str | None = None,
filter: 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",
impersonation_chain: str | Sequence[str] | None = None,
**kwargs,
) -> None:
super().__init__(**kwargs)
self.region = region
self.project_id = project_id
self.page_size = page_size
self.page_token = page_token
self.filter = filter
self.order_by = order_by
self.retry = retry
self.timeout = timeout
self.metadata = metadata
self.gcp_conn_id = gcp_conn_id
self.impersonation_chain = impersonation_chain
[docs] def execute(self, context: Context):
hook = PipelineJobHook(
gcp_conn_id=self.gcp_conn_id,
impersonation_chain=self.impersonation_chain,
)
results = hook.list_pipeline_jobs(
region=self.region,
project_id=self.project_id,
page_size=self.page_size,
page_token=self.page_token,
filter=self.filter,
order_by=self.order_by,
retry=self.retry,
timeout=self.timeout,
metadata=self.metadata,
)
VertexAIPipelineJobListLink.persist(context=context, task_instance=self)
return [types.PipelineJob.to_dict(result) for result in results]
[docs]class DeletePipelineJobOperator(GoogleCloudBaseOperator):
"""
Delete a Pipeline job.
: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 pipeline_job_id: Required. The ID of the PipelineJob 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 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",
"pipeline_job_id",
"impersonation_chain",
]
def __init__(
self,
*,
project_id: str,
region: str,
pipeline_job_id: str,
retry: Retry | _MethodDefault = DEFAULT,
timeout: float | None = None,
metadata: Sequence[tuple[str, str]] = (),
gcp_conn_id: str = "google_cloud_default",
impersonation_chain: str | Sequence[str] | None = None,
**kwargs,
) -> None:
super().__init__(**kwargs)
self.region = region
self.project_id = project_id
self.pipeline_job_id = pipeline_job_id
self.retry = retry
self.timeout = timeout
self.metadata = metadata
self.gcp_conn_id = gcp_conn_id
self.impersonation_chain = impersonation_chain
[docs] def execute(self, context: Context):
hook = PipelineJobHook(
gcp_conn_id=self.gcp_conn_id,
impersonation_chain=self.impersonation_chain,
)
try:
self.log.info("Deleting Pipeline job: %s", self.pipeline_job_id)
operation = hook.delete_pipeline_job(
region=self.region,
project_id=self.project_id,
pipeline_job_id=self.pipeline_job_id,
retry=self.retry,
timeout=self.timeout,
metadata=self.metadata,
)
hook.wait_for_operation(timeout=self.timeout, operation=operation)
self.log.info("Pipeline job was deleted.")
except NotFound:
self.log.info("The Pipeline Job ID %s does not exist.", self.pipeline_job_id)