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Posted to commits@airflow.apache.org by GitBox <gi...@apache.org> on 2022/01/13 16:47:56 UTC
[GitHub] [airflow] josh-fell commented on a change in pull request #20077: Create CustomJob and Datasets operators for Vertex AI service
josh-fell commented on a change in pull request #20077:
URL: https://github.com/apache/airflow/pull/20077#discussion_r784126348
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File path: airflow/providers/google/cloud/example_dags/example_vertex_ai.py
##########
@@ -0,0 +1,313 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+"""
+Example Airflow DAG that demonstrates operators for the Google Vertex AI service in the Google
+Cloud Platform.
+
+This DAG relies on the following OS environment variables:
+
+* GCP_VERTEX_AI_BUCKET - Google Cloud Storage bucket where the model will be saved
+after training process was finished.
+* CUSTOM_CONTAINER_URI - path to container with model.
+* PYTHON_PACKAGE_GSC_URI - path to test model in archive.
+* LOCAL_TRAINING_SCRIPT_PATH - path to local training script.
+* DATASET_ID - ID of dataset which will be used in training process.
+"""
+import os
+from datetime import datetime
+from uuid import uuid4
+
+from airflow import models
+from airflow.providers.google.cloud.operators.vertex_ai.custom_job import (
+ CreateCustomContainerTrainingJobOperator,
+ CreateCustomPythonPackageTrainingJobOperator,
+ CreateCustomTrainingJobOperator,
+ DeleteCustomTrainingJobOperator,
+ ListCustomTrainingJobOperator,
+)
+from airflow.providers.google.cloud.operators.vertex_ai.dataset import (
+ CreateDatasetOperator,
+ DeleteDatasetOperator,
+ ExportDataOperator,
+ GetDatasetOperator,
+ ImportDataOperator,
+ ListDatasetsOperator,
+ UpdateDatasetOperator,
+)
+
+PROJECT_ID = os.environ.get("GCP_PROJECT_ID", "an-id")
+REGION = os.environ.get("GCP_LOCATION", "us-central1")
+BUCKET = os.environ.get("GCP_VERTEX_AI_BUCKET", "vertex-ai-system-tests")
+
+STAGING_BUCKET = f"gs://{BUCKET}"
+DISPLAY_NAME = str(uuid4()) # Create random display name
+CONTAINER_URI = "gcr.io/cloud-aiplatform/training/tf-cpu.2-2:latest"
+CUSTOM_CONTAINER_URI = os.environ.get("CUSTOM_CONTAINER_URI", "path_to_container_with_model")
+MODEL_SERVING_CONTAINER_URI = "gcr.io/cloud-aiplatform/prediction/tf2-cpu.2-2:latest"
+REPLICA_COUNT = 1
+MACHINE_TYPE = "n1-standard-4"
+ACCELERATOR_TYPE = "ACCELERATOR_TYPE_UNSPECIFIED"
+ACCELERATOR_COUNT = 0
+TRAINING_FRACTION_SPLIT = 0.7
+TEST_FRACTION_SPLIT = 0.15
+VALIDATION_FRACTION_SPLIT = 0.15
+
+PYTHON_PACKAGE_GCS_URI = os.environ.get("PYTHON_PACKAGE_GSC_URI", "path_to_test_model_in_arch")
+PYTHON_MODULE_NAME = "aiplatform_custom_trainer_script.task"
+
+LOCAL_TRAINING_SCRIPT_PATH = os.environ.get("LOCAL_TRAINING_SCRIPT_PATH", "path_to_training_script")
+
+TRAINING_PIPELINE_ID = "test-training-pipeline-id"
+CUSTOM_JOB_ID = "test-custom-job-id"
+
+IMAGE_DATASET = {
+ "display_name": str(uuid4()),
+ "metadata_schema_uri": "gs://google-cloud-aiplatform/schema/dataset/metadata/image_1.0.0.yaml",
+ "metadata": "test-image-dataset",
+}
+TABULAR_DATASET = {
+ "display_name": str(uuid4()),
+ "metadata_schema_uri": "gs://google-cloud-aiplatform/schema/dataset/metadata/tabular_1.0.0.yaml",
+ "metadata": "test-tabular-dataset",
+}
+TEXT_DATASET = {
+ "display_name": str(uuid4()),
+ "metadata_schema_uri": "gs://google-cloud-aiplatform/schema/dataset/metadata/text_1.0.0.yaml",
+ "metadata": "test-text-dataset",
+}
+VIDEO_DATASET = {
+ "display_name": str(uuid4()),
+ "metadata_schema_uri": "gs://google-cloud-aiplatform/schema/dataset/metadata/video_1.0.0.yaml",
+ "metadata": "test-video-dataset",
+}
+TIME_SERIES_DATASET = {
+ "display_name": str(uuid4()),
+ "metadata_schema_uri": "gs://google-cloud-aiplatform/schema/dataset/metadata/time_series_1.0.0.yaml",
+ "metadata": "test-video-dataset",
+}
+DATASET_ID = os.environ.get("DATASET_ID", "test-dataset-id")
+TEST_EXPORT_CONFIG = {"gcs_destination": {"output_uri_prefix": "gs://test-vertex-ai-bucket/exports"}}
+TEST_IMPORT_CONFIG = [
+ {
+ "data_item_labels": {
+ "test-labels-name": "test-labels-value",
+ },
+ "import_schema_uri": (
+ "gs://google-cloud-aiplatform/schema/dataset/ioformat/image_bounding_box_io_format_1.0.0.yaml"
+ ),
+ "gcs_source": {
+ "uris": ["gs://ucaip-test-us-central1/dataset/salads_oid_ml_use_public_unassigned.jsonl"]
+ },
+ },
+]
+DATASET_TO_UPDATE = {"display_name": "test-name"}
+TEST_UPDATE_MASK = {"paths": ["displayName"]}
+
+with models.DAG(
+ "example_gcp_vertex_ai_custom_jobs",
+ schedule_interval="@once",
+ start_date=datetime(2021, 1, 1),
+ catchup=False,
+) as custom_jobs_dag:
+ # [START how_to_cloud_vertex_ai_create_custom_container_training_job_operator]
+ create_custom_container_training_job = CreateCustomContainerTrainingJobOperator(
+ task_id="custom_container_task",
+ staging_bucket=STAGING_BUCKET,
+ display_name=f"train-housing-container-{DISPLAY_NAME}",
+ container_uri=CUSTOM_CONTAINER_URI,
+ model_serving_container_image_uri=MODEL_SERVING_CONTAINER_URI,
+ # run params
+ dataset_id=DATASET_ID,
+ command=["python3", "task.py"],
+ model_display_name=f"container-housing-model-{DISPLAY_NAME}",
+ replica_count=REPLICA_COUNT,
+ machine_type=MACHINE_TYPE,
+ accelerator_type=ACCELERATOR_TYPE,
+ accelerator_count=ACCELERATOR_COUNT,
+ training_fraction_split=TRAINING_FRACTION_SPLIT,
+ validation_fraction_split=VALIDATION_FRACTION_SPLIT,
+ test_fraction_split=TEST_FRACTION_SPLIT,
+ region=REGION,
+ project_id=PROJECT_ID,
+ )
+ # [END how_to_cloud_vertex_ai_create_custom_container_training_job_operator]
+
+ # [START how_to_cloud_vertex_ai_create_custom_python_package_training_job_operator]
+ create_custom_python_package_training_job = CreateCustomPythonPackageTrainingJobOperator(
+ task_id="python_package_task",
+ staging_bucket=STAGING_BUCKET,
+ display_name=f"train-housing-py-package-{DISPLAY_NAME}",
+ python_package_gcs_uri=PYTHON_PACKAGE_GCS_URI,
+ python_module_name=PYTHON_MODULE_NAME,
+ container_uri=CONTAINER_URI,
+ model_serving_container_image_uri=MODEL_SERVING_CONTAINER_URI,
+ # run params
+ dataset_id=DATASET_ID,
+ model_display_name=f"py-package-housing-model-{DISPLAY_NAME}",
+ replica_count=REPLICA_COUNT,
+ machine_type=MACHINE_TYPE,
+ accelerator_type=ACCELERATOR_TYPE,
+ accelerator_count=ACCELERATOR_COUNT,
+ training_fraction_split=TRAINING_FRACTION_SPLIT,
+ validation_fraction_split=VALIDATION_FRACTION_SPLIT,
+ test_fraction_split=TEST_FRACTION_SPLIT,
+ region=REGION,
+ project_id=PROJECT_ID,
+ )
+ # [END how_to_cloud_vertex_ai_create_custom_python_package_training_job_operator]
+
+ # [START how_to_cloud_vertex_ai_create_custom_training_job_operator]
+ create_custom_training_job = CreateCustomTrainingJobOperator(
+ task_id="custom_task",
+ staging_bucket=STAGING_BUCKET,
+ display_name=f"train-housing-custom-{DISPLAY_NAME}",
+ script_path=LOCAL_TRAINING_SCRIPT_PATH,
+ container_uri=CONTAINER_URI,
+ requirements=["gcsfs==0.7.1"],
+ model_serving_container_image_uri=MODEL_SERVING_CONTAINER_URI,
+ # run params
+ dataset_id=DATASET_ID,
+ replica_count=1,
+ model_display_name=f"custom-housing-model-{DISPLAY_NAME}",
+ sync=False,
Review comment:
In the operator doc, this operator and a couple others will "...wait for the operation to complete" but the example shows this operator running asynchronously. Should this operator and the others mentioned in the doc _always_ execute synchronously (it looks like `sync` is forced to `True` when calling the underlying hook method in the operator) or is configurability allowed for running the job in an async manner?
##########
File path: airflow/providers/google/cloud/operators/vertex_ai/custom_job.py
##########
@@ -0,0 +1,594 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+#
+"""This module contains Google Vertex AI operators."""
+
+from typing import Dict, List, Optional, Sequence, Tuple, Union
+
+from google.api_core.exceptions import NotFound
+from google.api_core.retry import Retry
+from google.cloud.aiplatform.models import Model
+from google.cloud.aiplatform_v1.types.dataset import Dataset
+from google.cloud.aiplatform_v1.types.training_pipeline import TrainingPipeline
+
+from airflow.models import BaseOperator, BaseOperatorLink
+from airflow.models.taskinstance import TaskInstance
+from airflow.providers.google.cloud.hooks.vertex_ai.custom_job import CustomJobHook
+
+VERTEX_AI_BASE_LINK = "https://console.cloud.google.com/vertex-ai"
+VERTEX_AI_MODEL_LINK = (
+ VERTEX_AI_BASE_LINK + "/locations/{region}/models/{model_id}/deploy?project={project_id}"
+)
+VERTEX_AI_TRAINING_PIPELINES_LINK = VERTEX_AI_BASE_LINK + "/training/training-pipelines?project={project_id}"
+
+
+class VertexAIModelLink(BaseOperatorLink):
+ """Helper class for constructing Vertex AI Model link"""
+
+ name = "Vertex AI Model"
+
+ def get_link(self, operator, dttm):
+ ti = TaskInstance(task=operator, execution_date=dttm)
+ model_conf = ti.xcom_pull(task_ids=operator.task_id, key="model_conf")
+ return (
+ VERTEX_AI_MODEL_LINK.format(
+ region=model_conf["region"],
+ model_id=model_conf["model_id"],
+ project_id=model_conf["project_id"],
+ )
+ if model_conf
+ else ""
+ )
+
+
+class VertexAITrainingPipelinesLink(BaseOperatorLink):
+ """Helper class for constructing Vertex AI Training Pipelines link"""
+
+ name = "Vertex AI Training Pipelines"
+
+ def get_link(self, operator, dttm):
+ ti = TaskInstance(task=operator, execution_date=dttm)
+ project_id = ti.xcom_pull(task_ids=operator.task_id, key="project_id")
+ return (
+ VERTEX_AI_TRAINING_PIPELINES_LINK.format(
+ project_id=project_id,
+ )
+ if project_id
+ else ""
+ )
+
+
+class CustomTrainingJobBaseOperator(BaseOperator):
+ """The base class for operators that launch Custom jobs on VertexAI."""
+
+ def __init__(
+ self,
+ *,
+ project_id: str,
+ region: str,
+ display_name: str,
+ container_uri: str,
+ model_serving_container_image_uri: Optional[str] = None,
+ model_serving_container_predict_route: Optional[str] = None,
+ model_serving_container_health_route: Optional[str] = None,
+ model_serving_container_command: Optional[Sequence[str]] = None,
+ model_serving_container_args: Optional[Sequence[str]] = None,
+ model_serving_container_environment_variables: Optional[Dict[str, str]] = None,
+ model_serving_container_ports: Optional[Sequence[int]] = None,
+ model_description: Optional[str] = None,
+ model_instance_schema_uri: Optional[str] = None,
+ model_parameters_schema_uri: Optional[str] = None,
+ model_prediction_schema_uri: Optional[str] = None,
+ labels: Optional[Dict[str, str]] = None,
+ training_encryption_spec_key_name: Optional[str] = None,
+ model_encryption_spec_key_name: Optional[str] = None,
+ staging_bucket: Optional[str] = None,
+ # RUN
+ dataset_id: Optional[str] = None,
+ annotation_schema_uri: Optional[str] = None,
+ model_display_name: Optional[str] = None,
+ model_labels: Optional[Dict[str, str]] = None,
+ base_output_dir: Optional[str] = None,
+ service_account: Optional[str] = None,
+ network: Optional[str] = None,
+ bigquery_destination: Optional[str] = None,
+ args: Optional[List[Union[str, float, int]]] = None,
+ environment_variables: Optional[Dict[str, str]] = None,
+ replica_count: int = 1,
+ machine_type: str = "n1-standard-4",
+ accelerator_type: str = "ACCELERATOR_TYPE_UNSPECIFIED",
+ accelerator_count: int = 0,
+ boot_disk_type: str = "pd-ssd",
+ boot_disk_size_gb: int = 100,
+ training_fraction_split: Optional[float] = None,
+ validation_fraction_split: Optional[float] = None,
+ test_fraction_split: Optional[float] = None,
+ training_filter_split: Optional[str] = None,
+ validation_filter_split: Optional[str] = None,
+ test_filter_split: Optional[str] = None,
+ predefined_split_column_name: Optional[str] = None,
+ timestamp_split_column_name: Optional[str] = None,
+ tensorboard: Optional[str] = None,
+ sync=True,
+ 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.display_name = display_name
+ # START Custom
+ self.container_uri = container_uri
+ self.model_serving_container_image_uri = model_serving_container_image_uri
+ self.model_serving_container_predict_route = model_serving_container_predict_route
+ self.model_serving_container_health_route = model_serving_container_health_route
+ self.model_serving_container_command = model_serving_container_command
+ self.model_serving_container_args = model_serving_container_args
+ self.model_serving_container_environment_variables = model_serving_container_environment_variables
+ self.model_serving_container_ports = model_serving_container_ports
+ self.model_description = model_description
+ self.model_instance_schema_uri = model_instance_schema_uri
+ self.model_parameters_schema_uri = model_parameters_schema_uri
+ self.model_prediction_schema_uri = model_prediction_schema_uri
+ self.labels = labels
+ self.training_encryption_spec_key_name = training_encryption_spec_key_name
+ self.model_encryption_spec_key_name = model_encryption_spec_key_name
+ self.staging_bucket = staging_bucket
+ # END Custom
+ # START Run param
+ self.dataset = Dataset(name=dataset_id) if dataset_id else None
+ self.annotation_schema_uri = annotation_schema_uri
+ self.model_display_name = model_display_name
+ self.model_labels = model_labels
+ self.base_output_dir = base_output_dir
+ self.service_account = service_account
+ self.network = network
+ self.bigquery_destination = bigquery_destination
+ self.args = args
+ self.environment_variables = environment_variables
+ self.replica_count = replica_count
+ self.machine_type = machine_type
+ self.accelerator_type = accelerator_type
+ self.accelerator_count = accelerator_count
+ self.boot_disk_type = boot_disk_type
+ self.boot_disk_size_gb = boot_disk_size_gb
+ self.training_fraction_split = training_fraction_split
+ self.validation_fraction_split = validation_fraction_split
+ self.test_fraction_split = test_fraction_split
+ self.training_filter_split = training_filter_split
+ self.validation_filter_split = validation_filter_split
+ self.test_filter_split = test_filter_split
+ self.predefined_split_column_name = predefined_split_column_name
+ self.timestamp_split_column_name = timestamp_split_column_name
+ self.tensorboard = tensorboard
+ self.sync = sync
+ # END Run param
+ self.gcp_conn_id = gcp_conn_id
+ self.delegate_to = delegate_to
+ self.impersonation_chain = impersonation_chain
+ self.hook: Optional[CustomJobHook] = None
+
+ def execute(self, context):
+ self.hook = CustomJobHook(
+ gcp_conn_id=self.gcp_conn_id,
+ delegate_to=self.delegate_to,
+ impersonation_chain=self.impersonation_chain,
+ )
+
+ def on_kill(self) -> None:
+ """
+ Callback called when the operator is killed.
+ Cancel any running job.
+ """
+ if self.hook:
+ self.hook.cancel_job()
+
+
+class CreateCustomContainerTrainingJobOperator(CustomTrainingJobBaseOperator):
+ """Create Custom Container Training job"""
+
+ template_fields = [
+ 'region',
+ 'command',
+ 'impersonation_chain',
+ ]
+ operator_extra_links = (VertexAIModelLink(),)
+
+ def __init__(
+ self,
+ *,
+ command: Sequence[str] = None,
+ **kwargs,
+ ) -> None:
+ super().__init__(**kwargs)
+ self.command = command
+
+ def execute(self, context):
+ super().execute(context)
+ model = self.hook.create_custom_container_training_job(
+ project_id=self.project_id,
+ region=self.region,
+ display_name=self.display_name,
+ container_uri=self.container_uri,
+ command=self.command,
+ model_serving_container_image_uri=self.model_serving_container_image_uri,
+ model_serving_container_predict_route=self.model_serving_container_predict_route,
+ model_serving_container_health_route=self.model_serving_container_health_route,
+ model_serving_container_command=self.model_serving_container_command,
+ model_serving_container_args=self.model_serving_container_args,
+ model_serving_container_environment_variables=self.model_serving_container_environment_variables,
+ model_serving_container_ports=self.model_serving_container_ports,
+ model_description=self.model_description,
+ model_instance_schema_uri=self.model_instance_schema_uri,
+ model_parameters_schema_uri=self.model_parameters_schema_uri,
+ model_prediction_schema_uri=self.model_prediction_schema_uri,
+ labels=self.labels,
+ training_encryption_spec_key_name=self.training_encryption_spec_key_name,
+ model_encryption_spec_key_name=self.model_encryption_spec_key_name,
+ staging_bucket=self.staging_bucket,
+ # RUN
+ dataset=self.dataset,
+ annotation_schema_uri=self.annotation_schema_uri,
+ model_display_name=self.model_display_name,
+ model_labels=self.model_labels,
+ base_output_dir=self.base_output_dir,
+ service_account=self.service_account,
+ network=self.network,
+ bigquery_destination=self.bigquery_destination,
+ args=self.args,
+ environment_variables=self.environment_variables,
+ replica_count=self.replica_count,
+ machine_type=self.machine_type,
+ accelerator_type=self.accelerator_type,
+ accelerator_count=self.accelerator_count,
+ boot_disk_type=self.boot_disk_type,
+ boot_disk_size_gb=self.boot_disk_size_gb,
+ training_fraction_split=self.training_fraction_split,
+ validation_fraction_split=self.validation_fraction_split,
+ test_fraction_split=self.test_fraction_split,
+ training_filter_split=self.training_filter_split,
+ validation_filter_split=self.validation_filter_split,
+ test_filter_split=self.test_filter_split,
+ predefined_split_column_name=self.predefined_split_column_name,
+ timestamp_split_column_name=self.timestamp_split_column_name,
+ tensorboard=self.tensorboard,
+ sync=True,
+ )
+
+ result = Model.to_dict(model)
+ model_id = self.hook.extract_model_id(result)
+ self.xcom_push(
+ context,
+ key="model_conf",
+ value={
+ "model_id": model_id,
+ "region": self.region,
+ "project_id": self.project_id,
+ },
+ )
+ return result
+
+
+class CreateCustomPythonPackageTrainingJobOperator(CustomTrainingJobBaseOperator):
+ """Create Custom Python Package Training job"""
+
+ template_fields = [
+ 'region',
+ 'impersonation_chain',
+ ]
+ operator_extra_links = (VertexAIModelLink(),)
+
+ def __init__(
+ self,
+ *,
+ python_package_gcs_uri: str,
+ python_module_name: str,
+ **kwargs,
+ ) -> None:
+ super().__init__(**kwargs)
+ self.python_package_gcs_uri = python_package_gcs_uri
+ self.python_module_name = python_module_name
+
+ def execute(self, context):
+ super().execute(context)
+ model = self.hook.create_custom_python_package_training_job(
+ project_id=self.project_id,
+ region=self.region,
+ display_name=self.display_name,
+ python_package_gcs_uri=self.python_package_gcs_uri,
+ python_module_name=self.python_module_name,
+ container_uri=self.container_uri,
+ model_serving_container_image_uri=self.model_serving_container_image_uri,
+ model_serving_container_predict_route=self.model_serving_container_predict_route,
+ model_serving_container_health_route=self.model_serving_container_health_route,
+ model_serving_container_command=self.model_serving_container_command,
+ model_serving_container_args=self.model_serving_container_args,
+ model_serving_container_environment_variables=self.model_serving_container_environment_variables,
+ model_serving_container_ports=self.model_serving_container_ports,
+ model_description=self.model_description,
+ model_instance_schema_uri=self.model_instance_schema_uri,
+ model_parameters_schema_uri=self.model_parameters_schema_uri,
+ model_prediction_schema_uri=self.model_prediction_schema_uri,
+ labels=self.labels,
+ training_encryption_spec_key_name=self.training_encryption_spec_key_name,
+ model_encryption_spec_key_name=self.model_encryption_spec_key_name,
+ staging_bucket=self.staging_bucket,
+ # RUN
+ dataset=self.dataset,
+ annotation_schema_uri=self.annotation_schema_uri,
+ model_display_name=self.model_display_name,
+ model_labels=self.model_labels,
+ base_output_dir=self.base_output_dir,
+ service_account=self.service_account,
+ network=self.network,
+ bigquery_destination=self.bigquery_destination,
+ args=self.args,
+ environment_variables=self.environment_variables,
+ replica_count=self.replica_count,
+ machine_type=self.machine_type,
+ accelerator_type=self.accelerator_type,
+ accelerator_count=self.accelerator_count,
+ boot_disk_type=self.boot_disk_type,
+ boot_disk_size_gb=self.boot_disk_size_gb,
+ training_fraction_split=self.training_fraction_split,
+ validation_fraction_split=self.validation_fraction_split,
+ test_fraction_split=self.test_fraction_split,
+ training_filter_split=self.training_filter_split,
+ validation_filter_split=self.validation_filter_split,
+ test_filter_split=self.test_filter_split,
+ predefined_split_column_name=self.predefined_split_column_name,
+ timestamp_split_column_name=self.timestamp_split_column_name,
+ tensorboard=self.tensorboard,
+ sync=True,
+ )
+
+ result = Model.to_dict(model)
+ model_id = self.hook.extract_model_id(result)
+ self.xcom_push(
+ context,
+ key="model_conf",
+ value={
+ "model_id": model_id,
+ "region": self.region,
+ "project_id": self.project_id,
+ },
+ )
+ return result
+
+
+class CreateCustomTrainingJobOperator(CustomTrainingJobBaseOperator):
+ """Create Custom Training job"""
+
+ template_fields = [
+ 'region',
+ 'script_path',
+ 'requirements',
+ 'impersonation_chain',
+ ]
+ operator_extra_links = (VertexAIModelLink(),)
+
+ def __init__(
+ self,
+ *,
+ script_path: str,
+ requirements: Optional[Sequence[str]] = None,
+ **kwargs,
+ ) -> None:
+ super().__init__(**kwargs)
+ self.requirements = requirements
+ self.script_path = script_path
+
+ def execute(self, context):
+ super().execute(context)
+ model = self.hook.create_custom_training_job(
+ project_id=self.project_id,
+ region=self.region,
+ display_name=self.display_name,
+ script_path=self.script_path,
+ container_uri=self.container_uri,
+ requirements=self.requirements,
+ model_serving_container_image_uri=self.model_serving_container_image_uri,
+ model_serving_container_predict_route=self.model_serving_container_predict_route,
+ model_serving_container_health_route=self.model_serving_container_health_route,
+ model_serving_container_command=self.model_serving_container_command,
+ model_serving_container_args=self.model_serving_container_args,
+ model_serving_container_environment_variables=self.model_serving_container_environment_variables,
+ model_serving_container_ports=self.model_serving_container_ports,
+ model_description=self.model_description,
+ model_instance_schema_uri=self.model_instance_schema_uri,
+ model_parameters_schema_uri=self.model_parameters_schema_uri,
+ model_prediction_schema_uri=self.model_prediction_schema_uri,
+ labels=self.labels,
+ training_encryption_spec_key_name=self.training_encryption_spec_key_name,
+ model_encryption_spec_key_name=self.model_encryption_spec_key_name,
+ staging_bucket=self.staging_bucket,
+ # RUN
+ dataset=self.dataset,
+ annotation_schema_uri=self.annotation_schema_uri,
+ model_display_name=self.model_display_name,
+ model_labels=self.model_labels,
+ base_output_dir=self.base_output_dir,
+ service_account=self.service_account,
+ network=self.network,
+ bigquery_destination=self.bigquery_destination,
+ args=self.args,
+ environment_variables=self.environment_variables,
+ replica_count=self.replica_count,
+ machine_type=self.machine_type,
+ accelerator_type=self.accelerator_type,
+ accelerator_count=self.accelerator_count,
+ boot_disk_type=self.boot_disk_type,
+ boot_disk_size_gb=self.boot_disk_size_gb,
+ training_fraction_split=self.training_fraction_split,
+ validation_fraction_split=self.validation_fraction_split,
+ test_fraction_split=self.test_fraction_split,
+ training_filter_split=self.training_filter_split,
+ validation_filter_split=self.validation_filter_split,
+ test_filter_split=self.test_filter_split,
+ predefined_split_column_name=self.predefined_split_column_name,
+ timestamp_split_column_name=self.timestamp_split_column_name,
+ tensorboard=self.tensorboard,
+ sync=True,
+ )
+
+ result = Model.to_dict(model)
+ model_id = self.hook.extract_model_id(result)
+ self.xcom_push(
+ context,
+ key="model_conf",
+ value={
+ "model_id": model_id,
+ "region": self.region,
+ "project_id": self.project_id,
+ },
+ )
+ return result
+
+
+class DeleteCustomTrainingJobOperator(BaseOperator):
+ """Deletes a CustomTrainingJob, CustomPythonTrainingJob, or CustomContainerTrainingJob."""
+
+ template_fields = ("region", "project_id", "impersonation_chain")
+
+ def __init__(
+ self,
+ *,
+ training_pipeline_id: str,
+ custom_job_id: str,
+ region: str,
+ project_id: str,
+ retry: Optional[Retry] = None,
+ timeout: Optional[float] = None,
+ metadata: Optional[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.training_pipeline = training_pipeline_id
+ self.custom_job = custom_job_id
+ self.region = region
+ self.project_id = project_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: Dict):
Review comment:
FYI - As of Airflow 2.2.3, there is a specific `Context` object available to use for typing but to make sure the operator doesn't have an implicit Airflow-version dependency you'll need to use `TYPE_CHECKING`.
Something like:
```python
if TYPE_CHECKING:
from airflow.utils.context import Context
...
def execute(self, context: "Context"): ...
```
This can be done for `ListCustomTrainingJobOperator` and the Dataset operators as well.
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