Deferrable mode for Custom Training Job operators

airflow/providers/google/cloud/links/vertex_ai.py

@@ -25,7 +25,8 @@
 
 VERTEX_AI_BASE_LINK = "/vertex-ai"
 VERTEX_AI_MODEL_LINK = (
-    VERTEX_AI_BASE_LINK + "/locations/{region}/models/{model_id}/deploy?project={project_id}"
+    VERTEX_AI_BASE_LINK
+    + "/models/locations/{region}/models/{model_id}/versions/default/properties?project={project_id}"
 )
 VERTEX_AI_MODEL_LIST_LINK = VERTEX_AI_BASE_LINK + "/models?project={project_id}"
 VERTEX_AI_MODEL_EXPORT_LINK = "/storage/browser/{bucket_name}/model-{model_id}?project={project_id}"

airflow/providers/google/cloud/operators/vertex_ai/pipeline_job.py

@@ -102,7 +102,6 @@ class RunPipelineJobOperator(GoogleCloudBaseOperator):
         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.
     """

airflow/providers/google/cloud/triggers/vertex_ai.py

@@ -29,6 +29,7 @@
 
 from airflow.exceptions import AirflowException
 from airflow.providers.google.cloud.hooks.vertex_ai.batch_prediction_job import BatchPredictionJobAsyncHook
+from airflow.providers.google.cloud.hooks.vertex_ai.custom_job import CustomJobAsyncHook
 from airflow.providers.google.cloud.hooks.vertex_ai.hyperparameter_tuning_job import (
     HyperparameterTuningJobAsyncHook,
 )
@@ -189,3 +190,96 @@ async def _wait_job(self) -> types.PipelineJob:
             poll_interval=self.poll_interval,
         )
         return job
+
+
+class CustomTrainingJobTrigger(BaseVertexAIJobTrigger):
+    """
+    Make async calls to Vertex AI to check the state of a running custom training job.
+
+    Return the job when it enters a completed state.
+    """
+
+    job_type_verbose_name = "Custom Training Job"
+    job_serializer_class = types.TrainingPipeline
+    statuses_success = {
+        PipelineState.PIPELINE_STATE_PAUSED,
+        PipelineState.PIPELINE_STATE_SUCCEEDED,
+    }
+
+    @cached_property
+    def async_hook(self) -> CustomJobAsyncHook:
+        return CustomJobAsyncHook(
+            gcp_conn_id=self.conn_id,
+            impersonation_chain=self.impersonation_chain,
+        )
+
+    async def _wait_job(self) -> types.TrainingPipeline:
+        pipeline: types.TrainingPipeline = await self.async_hook.wait_for_training_pipeline(
+            project_id=self.project_id,
+            location=self.location,
+            pipeline_id=self.job_id,
+            poll_interval=self.poll_interval,
+        )
+        return pipeline
+
+
+class CustomContainerTrainingJobTrigger(BaseVertexAIJobTrigger):
+    """
+    Make async calls to Vertex AI to check the state of a running custom container training job.
+
+    Return the job when it enters a completed state.
+    """
+
+    job_type_verbose_name = "Custom Container Training Job"
+    job_serializer_class = types.TrainingPipeline
+    statuses_success = {
+        PipelineState.PIPELINE_STATE_PAUSED,
+        PipelineState.PIPELINE_STATE_SUCCEEDED,
+    }
+
+    @cached_property
+    def async_hook(self) -> CustomJobAsyncHook:
+        return CustomJobAsyncHook(
+            gcp_conn_id=self.conn_id,
+            impersonation_chain=self.impersonation_chain,
+        )
+
+    async def _wait_job(self) -> types.TrainingPipeline:
+        pipeline: types.TrainingPipeline = await self.async_hook.wait_for_training_pipeline(
+            project_id=self.project_id,
+            location=self.location,
+            pipeline_id=self.job_id,
+            poll_interval=self.poll_interval,
+        )
+        return pipeline
+
+
+class CustomPythonPackageTrainingJobTrigger(BaseVertexAIJobTrigger):
+    """
+    Make async calls to Vertex AI to check the state of a running custom python package training job.
+
+    Return the job when it enters a completed state.
+    """
+
+    job_type_verbose_name = "Custom Python Package Training Job"
+    job_serializer_class = types.TrainingPipeline
+    statuses_success = {
+        PipelineState.PIPELINE_STATE_PAUSED,
+        PipelineState.PIPELINE_STATE_SUCCEEDED,
+    }
+
+    @cached_property
+    def async_hook(self) -> CustomJobAsyncHook:
+        return CustomJobAsyncHook(
+            gcp_conn_id=self.conn_id,
+            impersonation_chain=self.impersonation_chain,
+        )
+
+    async def _wait_job(self) -> types.TrainingPipeline:
+        pipeline: types.TrainingPipeline = await self.async_hook.wait_for_training_pipeline(
+            project_id=self.project_id,
+            location=self.location,
+            pipeline_id=self.job_id,
+            poll_interval=self.poll_interval,
+        )
+        return pipeline

docs/apache-airflow-providers-google/operators/cloud/vertex_ai.rst

@@ -107,7 +107,7 @@ Preparation step
 
 For each operator you must prepare and create dataset. Then put dataset id to ``dataset_id`` parameter in operator.
 
-How to run Container Training Job
+How to run a Custom Container Training Job
 :class:`~airflow.providers.google.cloud.operators.vertex_ai.custom_job.CreateCustomContainerTrainingJobOperator`
 
 Before start running this Job you should create a docker image with training script inside. Documentation how to
@@ -121,7 +121,16 @@ for container which will be created from this image in ``command`` parameter.
     :start-after: [START how_to_cloud_vertex_ai_create_custom_container_training_job_operator]
     :end-before: [END how_to_cloud_vertex_ai_create_custom_container_training_job_operator]
 
-How to run Python Package Training Job
+The :class:`~airflow.providers.google.cloud.operators.vertex_ai.custom_job.CreateCustomContainerTrainingJobOperator`
+also provides the deferrable mode:
+
+.. exampleinclude:: /../../tests/system/providers/google/cloud/vertex_ai/example_vertex_ai_custom_container.py
+    :language: python
+    :dedent: 4
+    :start-after: [START how_to_cloud_vertex_ai_create_custom_container_training_job_operator_deferrable]
+    :end-before: [END how_to_cloud_vertex_ai_create_custom_container_training_job_operator_deferrable]
+
+How to run a Python Package Training Job
 :class:`~airflow.providers.google.cloud.operators.vertex_ai.custom_job.CreateCustomPythonPackageTrainingJobOperator`
 
 Before start running this Job you should create a python package with training script inside. Documentation how to
@@ -135,27 +144,52 @@ parameter should has the name of script which will run your training task.
     :start-after: [START how_to_cloud_vertex_ai_create_custom_python_package_training_job_operator]
     :end-before: [END how_to_cloud_vertex_ai_create_custom_python_package_training_job_operator]
 
-How to run Training Job
+The :class:`~airflow.providers.google.cloud.operators.vertex_ai.custom_job.CreateCustomPythonPackageTrainingJobOperator`
+also provides the deferrable mode:
+
+.. exampleinclude:: /../../tests/system/providers/google/cloud/vertex_ai/example_vertex_ai_custom_job_python_package.py
+    :language: python
+    :dedent: 4
+    :start-after: [START how_to_cloud_vertex_ai_create_custom_python_package_training_job_operator_deferrable]
+    :end-before: [END how_to_cloud_vertex_ai_create_custom_python_package_training_job_operator_deferrable]
+
+How to run a Custom Training Job
 :class:`~airflow.providers.google.cloud.operators.vertex_ai.custom_job.CreateCustomTrainingJobOperator`.
 
-For this Job you should put path to your local training script inside ``script_path`` parameter.
+To create and run a Custom Training Job you should put the path to your local training script inside the ``script_path`` parameter.
 
 .. exampleinclude:: /../../tests/system/providers/google/cloud/vertex_ai/example_vertex_ai_custom_job.py
     :language: python
     :dedent: 4
     :start-after: [START how_to_cloud_vertex_ai_create_custom_training_job_operator]
     :end-before: [END how_to_cloud_vertex_ai_create_custom_training_job_operator]
 
-Additionally, you can create new version of existing Training Job instead. In this case, the result will be new
-version of existing Model instead of new Model created in Model Registry. This can be done by specifying
-``parent_model`` parameter when running Training Job.
+The same operation can be performed in the deferrable mode:
+
+.. exampleinclude:: /../../tests/system/providers/google/cloud/vertex_ai/example_vertex_ai_custom_job.py
+    :language: python
+    :dedent: 4
+    :start-after: [START how_to_cloud_vertex_ai_create_custom_training_job_operator_deferrable]
+    :end-before: [END how_to_cloud_vertex_ai_create_custom_training_job_operator_deferrable]
+
+Additionally, you can create a new version of an existing Custom Training Job. It will replace the existing
+Model with another version, instead of creating a new Model in the Model Registry.
+This can be done by specifying the ``parent_model`` parameter when running a Custom Training Job.
 
 .. exampleinclude:: /../../tests/system/providers/google/cloud/vertex_ai/example_vertex_ai_custom_job.py
     :language: python
     :dedent: 4
     :start-after: [START how_to_cloud_vertex_ai_create_custom_training_job_v2_operator]
     :end-before: [END how_to_cloud_vertex_ai_create_custom_training_job_v2_operator]
 
+The same operation can be performed in the deferrable mode:
+
+.. exampleinclude:: /../../tests/system/providers/google/cloud/vertex_ai/example_vertex_ai_custom_job.py
+    :language: python
+    :dedent: 4
+    :start-after: [START how_to_cloud_vertex_ai_create_custom_training_job_v2_operator_deferrable]
+    :end-before: [END how_to_cloud_vertex_ai_create_custom_training_job_v2_operator_deferrable]
+
 
 You can get a list of Training Jobs using
 :class:`~airflow.providers.google.cloud.operators.vertex_ai.custom_job.ListCustomTrainingJobOperator`.