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"""
Example Airflow DAG for Google AutoML service testing model operations.
"""
from __future__ import annotations
import os
from copy import deepcopy
from datetime import datetime
from google.protobuf.struct_pb2 import Value
from airflow import models
from airflow.providers.google.cloud.hooks.automl import CloudAutoMLHook
from airflow.providers.google.cloud.operators.automl import (
AutoMLBatchPredictOperator,
AutoMLCreateDatasetOperator,
AutoMLDeleteDatasetOperator,
AutoMLDeleteModelOperator,
AutoMLDeployModelOperator,
AutoMLGetModelOperator,
AutoMLImportDataOperator,
AutoMLPredictOperator,
AutoMLTablesListColumnSpecsOperator,
AutoMLTablesListTableSpecsOperator,
AutoMLTablesUpdateDatasetOperator,
AutoMLTrainModelOperator,
)
from airflow.providers.google.cloud.operators.gcs import (
GCSCreateBucketOperator,
GCSDeleteBucketOperator,
GCSSynchronizeBucketsOperator,
)
from airflow.utils.trigger_rule import TriggerRule
[docs]ENV_ID = os.environ.get("SYSTEM_TESTS_ENV_ID", "default")
[docs]DAG_ID = "example_automl_model"
[docs]GCP_PROJECT_ID = os.environ.get("SYSTEM_TESTS_GCP_PROJECT", "default")
[docs]GCP_AUTOML_LOCATION = "us-central1"
[docs]DATA_SAMPLE_GCS_BUCKET_NAME = f"bucket_{DAG_ID}_{ENV_ID}".replace("_", "-")
[docs]RESOURCE_DATA_BUCKET = "airflow-system-tests-resources"
[docs]DATASET_NAME = f"md_tabular_{ENV_ID}".replace("-", "_")
[docs]DATASET = {
"display_name": DATASET_NAME,
"tables_dataset_metadata": {"target_column_spec_id": ""},
}
[docs]AUTOML_DATASET_BUCKET = f"gs://{DATA_SAMPLE_GCS_BUCKET_NAME}/automl/bank-marketing-split.csv"
[docs]IMPORT_OUTPUT_CONFIG = {
"gcs_destination": {"output_uri_prefix": f"gs://{DATA_SAMPLE_GCS_BUCKET_NAME}/automl"}
}
# change the name here
[docs]MODEL_NAME = f"md_tabular_{ENV_ID}".replace("-", "_")
[docs]MODEL = {
"display_name": MODEL_NAME,
"tables_model_metadata": {"train_budget_milli_node_hours": 1000},
}
[docs]PREDICT_VALUES = [
Value(string_value="TRAINING"),
Value(string_value="51"),
Value(string_value="blue-collar"),
Value(string_value="married"),
Value(string_value="primary"),
Value(string_value="no"),
Value(string_value="620"),
Value(string_value="yes"),
Value(string_value="yes"),
Value(string_value="cellular"),
Value(string_value="29"),
Value(string_value="jul"),
Value(string_value="88"),
Value(string_value="10"),
Value(string_value="-1"),
Value(string_value="0"),
Value(string_value="unknown"),
]
[docs]def get_target_column_spec(columns_specs: list[dict], column_name: str) -> str:
"""
Using column name returns spec of the column.
"""
for column in columns_specs:
if column["display_name"] == column_name:
return extract_object_id(column)
raise Exception(f"Unknown target column: {column_name}")
with models.DAG(
dag_id=DAG_ID,
schedule="@once",
start_date=datetime(2021, 1, 1),
catchup=False,
user_defined_macros={
"get_target_column_spec": get_target_column_spec,
"target": "Deposit",
"extract_object_id": extract_object_id,
},
tags=["example", "automl", "model"],
) as dag:
[docs] create_bucket = GCSCreateBucketOperator(
task_id="create_bucket",
bucket_name=DATA_SAMPLE_GCS_BUCKET_NAME,
storage_class="REGIONAL",
location=GCP_AUTOML_LOCATION,
)
move_dataset_file = GCSSynchronizeBucketsOperator(
task_id="move_data_to_bucket",
source_bucket=RESOURCE_DATA_BUCKET,
source_object="automl/datasets/model",
destination_bucket=DATA_SAMPLE_GCS_BUCKET_NAME,
destination_object="automl",
recursive=True,
)
create_dataset = AutoMLCreateDatasetOperator(
task_id="create_dataset",
dataset=DATASET,
location=GCP_AUTOML_LOCATION,
project_id=GCP_PROJECT_ID,
)
dataset_id = create_dataset.output["dataset_id"]
MODEL["dataset_id"] = dataset_id
import_dataset = AutoMLImportDataOperator(
task_id="import_dataset",
dataset_id=dataset_id,
location=GCP_AUTOML_LOCATION,
input_config=IMPORT_INPUT_CONFIG,
)
list_tables_spec = AutoMLTablesListTableSpecsOperator(
task_id="list_tables_spec",
dataset_id=dataset_id,
location=GCP_AUTOML_LOCATION,
project_id=GCP_PROJECT_ID,
)
list_columns_spec = AutoMLTablesListColumnSpecsOperator(
task_id="list_columns_spec",
dataset_id=dataset_id,
table_spec_id="{{ extract_object_id(task_instance.xcom_pull('list_tables_spec')[0]) }}",
location=GCP_AUTOML_LOCATION,
project_id=GCP_PROJECT_ID,
)
update = deepcopy(DATASET)
update["name"] = '{{ task_instance.xcom_pull("create_dataset")["name"] }}'
update["tables_dataset_metadata"][ # type: ignore
"target_column_spec_id"
] = "{{ get_target_column_spec(task_instance.xcom_pull('list_columns_spec'), target) }}"
update_dataset = AutoMLTablesUpdateDatasetOperator(
task_id="update_dataset",
dataset=update,
location=GCP_AUTOML_LOCATION,
)
# [START howto_operator_automl_create_model]
create_model = AutoMLTrainModelOperator(
task_id="create_model",
model=MODEL,
location=GCP_AUTOML_LOCATION,
project_id=GCP_PROJECT_ID,
)
model_id = create_model.output["model_id"]
# [END howto_operator_automl_create_model]
# [START howto_operator_get_model]
get_model = AutoMLGetModelOperator(
task_id="get_model",
model_id=model_id,
location=GCP_AUTOML_LOCATION,
project_id=GCP_PROJECT_ID,
)
# [END howto_operator_get_model]
# [START howto_operator_deploy_model]
deploy_model = AutoMLDeployModelOperator(
task_id="deploy_model",
model_id=model_id,
location=GCP_AUTOML_LOCATION,
project_id=GCP_PROJECT_ID,
)
# [END howto_operator_deploy_model]
# [START howto_operator_prediction]
predict_task = AutoMLPredictOperator(
task_id="predict_task",
model_id=model_id,
payload={
"row": {
"values": PREDICT_VALUES,
}
},
location=GCP_AUTOML_LOCATION,
project_id=GCP_PROJECT_ID,
)
# [END howto_operator_prediction]
# [START howto_operator_batch_prediction]
batch_predict_task = AutoMLBatchPredictOperator(
task_id="batch_predict_task",
model_id=model_id,
input_config=IMPORT_INPUT_CONFIG,
output_config=IMPORT_OUTPUT_CONFIG,
location=GCP_AUTOML_LOCATION,
project_id=GCP_PROJECT_ID,
)
# [END howto_operator_batch_prediction]
# [START howto_operator_automl_delete_model]
delete_model = AutoMLDeleteModelOperator(
task_id="delete_model",
model_id=model_id,
location=GCP_AUTOML_LOCATION,
project_id=GCP_PROJECT_ID,
)
# [END howto_operator_automl_delete_model]
delete_dataset = AutoMLDeleteDatasetOperator(
task_id="delete_dataset",
dataset_id=dataset_id,
location=GCP_AUTOML_LOCATION,
project_id=GCP_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 >> move_dataset_file, create_dataset]
>> import_dataset
>> list_tables_spec
>> list_columns_spec
>> update_dataset
# TEST BODY
>> create_model
>> get_model
>> deploy_model
>> predict_task
>> batch_predict_task
# TEST TEARDOWN
>> delete_model
>> delete_dataset
>> delete_bucket
)
from tests.system.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.system.utils import get_test_run # noqa: E402
# Needed to run the example DAG with pytest (see: tests/system/README.md#run_via_pytest)
[docs]test_run = get_test_run(dag)
