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"""
Example Airflow DAG for Google Vertex AI service testing Dataset operations.
"""
from __future__ import annotations
import os
from datetime import datetime
from google.cloud.aiplatform import schema
from google.protobuf.json_format import ParseDict
from google.protobuf.struct_pb2 import Value
from airflow.models.dag import DAG
from airflow.providers.google.cloud.operators.gcs import (
GCSCreateBucketOperator,
GCSDeleteBucketOperator,
)
from airflow.providers.google.cloud.operators.vertex_ai.dataset import (
CreateDatasetOperator,
DeleteDatasetOperator,
ExportDataOperator,
GetDatasetOperator,
ImportDataOperator,
ListDatasetsOperator,
UpdateDatasetOperator,
)
try:
from airflow.sdk import TriggerRule
except ImportError:
# Compatibility for Airflow < 3.1
from airflow.utils.trigger_rule import TriggerRule # type: ignore[no-redef,attr-defined]
ENV_ID = os.environ.get("SYSTEM_TESTS_ENV_ID", "default")
PROJECT_ID = os.environ.get("SYSTEM_TESTS_GCP_PROJECT", "default")
DAG_ID = "vertex_ai_dataset_operations"
REGION = "us-central1"
RESOURCE_DATA_BUCKET = "airflow-system-tests-resources"
DATA_SAMPLE_GCS_BUCKET_NAME = f"bucket_{DAG_ID}_{ENV_ID}".replace("_", "-")
TIME_SERIES_DATASET = {
"display_name": f"time-series-dataset-{ENV_ID}",
"metadata_schema_uri": schema.dataset.metadata.time_series,
"metadata": ParseDict(
{
"input_config": {
"gcs_source": {
"uri": [f"gs://{RESOURCE_DATA_BUCKET}/vertex-ai/datasets/forecast-dataset.csv"]
}
}
},
Value(),
),
}
IMAGE_DATASET = {
"display_name": f"image-dataset-{ENV_ID}",
"metadata_schema_uri": schema.dataset.metadata.image,
"metadata": Value(string_value="image-dataset"),
}
TABULAR_DATASET = {
"display_name": f"tabular-dataset-{ENV_ID}",
"metadata_schema_uri": schema.dataset.metadata.tabular,
"metadata": ParseDict(
{
"input_config": {
"gcs_source": {"uri": [f"gs://{RESOURCE_DATA_BUCKET}/vertex-ai/datasets/tabular-dataset.csv"]}
}
},
Value(),
),
}
TEST_EXPORT_CONFIG = {"gcs_destination": {"output_uri_prefix": f"gs://{DATA_SAMPLE_GCS_BUCKET_NAME}/exports"}}
TEST_IMPORT_CONFIG = [
{
"data_item_labels": {
"test-labels-name": "test-labels-value",
},
"import_schema_uri": schema.dataset.ioformat.image.single_label_classification,
"gcs_source": {"uris": [f"gs://{RESOURCE_DATA_BUCKET}/vertex-ai/datasets/image-dataset-flowers.csv"]},
},
]
DATASET_TO_UPDATE = {"display_name": "test-name"}
TEST_UPDATE_MASK = {"paths": ["displayName"]}
with DAG(
DAG_ID,
schedule="@once",
start_date=datetime(2021, 1, 1),
catchup=False,
tags=["example", "vertex_ai", "dataset"],
) as dag:
create_bucket = GCSCreateBucketOperator(
task_id="create_bucket",
bucket_name=DATA_SAMPLE_GCS_BUCKET_NAME,
storage_class="REGIONAL",
location=REGION,
)
# [START how_to_cloud_vertex_ai_create_dataset_operator]
create_image_dataset_job = CreateDatasetOperator(
task_id="image_dataset",
dataset=IMAGE_DATASET,
region=REGION,
project_id=PROJECT_ID,
)
create_tabular_dataset_job = CreateDatasetOperator(
task_id="tabular_dataset",
dataset=TABULAR_DATASET,
region=REGION,
project_id=PROJECT_ID,
)
create_time_series_dataset_job = CreateDatasetOperator(
task_id="time_series_dataset",
dataset=TIME_SERIES_DATASET,
region=REGION,
project_id=PROJECT_ID,
)
# [END how_to_cloud_vertex_ai_create_dataset_operator]
# [START how_to_cloud_vertex_ai_get_dataset_operator]
get_dataset = GetDatasetOperator(
task_id="get_dataset",
project_id=PROJECT_ID,
region=REGION,
dataset_id=create_tabular_dataset_job.output["dataset_id"],
)
# [END how_to_cloud_vertex_ai_get_dataset_operator]
# [START how_to_cloud_vertex_ai_export_data_operator]
export_data_job = ExportDataOperator(
task_id="export_data",
dataset_id=create_image_dataset_job.output["dataset_id"],
region=REGION,
project_id=PROJECT_ID,
export_config=TEST_EXPORT_CONFIG,
)
# [END how_to_cloud_vertex_ai_export_data_operator]
# [START how_to_cloud_vertex_ai_import_data_operator]
import_data_job = ImportDataOperator(
task_id="import_data",
dataset_id=create_image_dataset_job.output["dataset_id"],
region=REGION,
project_id=PROJECT_ID,
import_configs=TEST_IMPORT_CONFIG,
)
# [END how_to_cloud_vertex_ai_import_data_operator]
# [START how_to_cloud_vertex_ai_list_dataset_operator]
list_dataset_job = ListDatasetsOperator(
task_id="list_dataset",
region=REGION,
project_id=PROJECT_ID,
)
# [END how_to_cloud_vertex_ai_list_dataset_operator]
# [START how_to_cloud_vertex_ai_update_dataset_operator]
update_dataset_job = UpdateDatasetOperator(
task_id="update_dataset",
project_id=PROJECT_ID,
region=REGION,
dataset_id=create_time_series_dataset_job.output["dataset_id"],
dataset=DATASET_TO_UPDATE,
update_mask=TEST_UPDATE_MASK,
)
# [END how_to_cloud_vertex_ai_update_dataset_operator]
# [START how_to_cloud_vertex_ai_delete_dataset_operator]
delete_dataset_job = DeleteDatasetOperator(
task_id="delete_dataset",
dataset_id=create_time_series_dataset_job.output["dataset_id"],
region=REGION,
project_id=PROJECT_ID,
trigger_rule=TriggerRule.ALL_DONE,
)
# [END how_to_cloud_vertex_ai_delete_dataset_operator]
delete_tabular_dataset_job = DeleteDatasetOperator(
task_id="delete_tabular_dataset",
dataset_id=create_tabular_dataset_job.output["dataset_id"],
region=REGION,
project_id=PROJECT_ID,
trigger_rule=TriggerRule.ALL_DONE,
)
delete_image_dataset_job = DeleteDatasetOperator(
task_id="delete_image_dataset",
dataset_id=create_image_dataset_job.output["dataset_id"],
region=REGION,
project_id=PROJECT_ID,
trigger_rule=TriggerRule.ALL_DONE,
)
delete_bucket = GCSDeleteBucketOperator(
task_id="delete_bucket",
bucket_name=DATA_SAMPLE_GCS_BUCKET_NAME,
trigger_rule=TriggerRule.ALL_DONE,
)
(
# TEST SETUP
create_bucket
# TEST BODY
>> [
create_time_series_dataset_job >> update_dataset_job >> delete_dataset_job,
create_tabular_dataset_job >> get_dataset >> delete_tabular_dataset_job,
create_image_dataset_job >> import_data_job >> export_data_job >> delete_image_dataset_job,
list_dataset_job,
]
# TEST TEARDOWN
>> delete_bucket
)
# ### Everything below this line is not part of example ###
# ### Just for system tests purpose ###
from tests_common.test_utils.watcher import watcher
# This test needs watcher in order to properly mark success/failure
# when "tearDown" task with trigger rule is part of the DAG
list(dag.tasks) >> watcher()
from tests_common.test_utils.system_tests import get_test_run # noqa: E402
# Needed to run the example DAG with pytest (see: contributing-docs/testing/system_tests.rst)
test_run = get_test_run(dag)