Run this DAG

1. Install the Astronomer CLI:Skip if you already have the CLI

2. Initate the project in a local directory:

3. Copy and paste the code below into a file in the

dags
directory.

4. Add the following to your

requirements.txt
file:

5. Run the DAG from the local directory where the project was initiated:

#
# 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 uses Google AutoML services.
"""
from __future__ import annotations
import os
from copy import deepcopy
from datetime import datetime
from typing import cast
from airflow import models
from airflow.models.xcom_arg import XComArg
from airflow.providers.google.cloud.hooks.automl import CloudAutoMLHook
from airflow.providers.google.cloud.operators.automl import (
AutoMLBatchPredictOperator,
AutoMLCreateDatasetOperator,
AutoMLDeleteDatasetOperator,
AutoMLDeleteModelOperator,
AutoMLDeployModelOperator,
AutoMLGetModelOperator,
AutoMLImportDataOperator,
AutoMLListDatasetOperator,
AutoMLPredictOperator,
AutoMLTablesListColumnSpecsOperator,
AutoMLTablesListTableSpecsOperator,
AutoMLTablesUpdateDatasetOperator,
AutoMLTrainModelOperator,
)
START_DATE = datetime(2021, 1, 1)
GCP_PROJECT_ID = os.environ.get("GCP_PROJECT_ID", "your-project-id")
GCP_AUTOML_LOCATION = os.environ.get("GCP_AUTOML_LOCATION", "us-central1")
GCP_AUTOML_DATASET_BUCKET = os.environ.get(
"GCP_AUTOML_DATASET_BUCKET", "gs://INVALID BUCKET NAME/bank-marketing.csv"
)
TARGET = os.environ.get("GCP_AUTOML_TARGET", "Deposit")
# Example values
MODEL_ID = "TBL123456"
DATASET_ID = "TBL123456"
# Example model
MODEL = {
"display_name": "auto_model_1",
"dataset_id": DATASET_ID,
"tables_model_metadata": {"train_budget_milli_node_hours": 1000},
}
# Example dataset
DATASET = {
"display_name": "test_set",
"tables_dataset_metadata": {"target_column_spec_id": ""},
}
IMPORT_INPUT_CONFIG = {"gcs_source": {"input_uris": [GCP_AUTOML_DATASET_BUCKET]}}
extract_object_id = CloudAutoMLHook.extract_object_id
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}")
# Example DAG to create dataset, train model_id and deploy it.
with models.DAG(
"example_create_and_deploy",
start_date=START_DATE,
catchup=False,
user_defined_macros={
"get_target_column_spec": get_target_column_spec,
"target": TARGET,
"extract_object_id": extract_object_id,
},
tags=['example'],
) as create_deploy_dag:
# [START howto_operator_automl_create_dataset]
create_dataset_task = AutoMLCreateDatasetOperator(
task_id="create_dataset_task",
dataset=DATASET,
location=GCP_AUTOML_LOCATION,
project_id=GCP_PROJECT_ID,
)
dataset_id = cast(str, XComArg(create_dataset_task, key='dataset_id'))
# [END howto_operator_automl_create_dataset]
MODEL["dataset_id"] = dataset_id
# [START howto_operator_automl_import_data]
import_dataset_task = AutoMLImportDataOperator(
task_id="import_dataset_task",
dataset_id=dataset_id,
location=GCP_AUTOML_LOCATION,
input_config=IMPORT_INPUT_CONFIG,
)
# [END howto_operator_automl_import_data]
# [START howto_operator_automl_specs]
list_tables_spec_task = AutoMLTablesListTableSpecsOperator(
task_id="list_tables_spec_task",
dataset_id=dataset_id,
location=GCP_AUTOML_LOCATION,
project_id=GCP_PROJECT_ID,
)
# [END howto_operator_automl_specs]
# [START howto_operator_automl_column_specs]
list_columns_spec_task = AutoMLTablesListColumnSpecsOperator(
task_id="list_columns_spec_task",
dataset_id=dataset_id,
table_spec_id="{{ extract_object_id(task_instance.xcom_pull('list_tables_spec_task')[0]) }}",
location=GCP_AUTOML_LOCATION,
project_id=GCP_PROJECT_ID,
)
# [END howto_operator_automl_column_specs]
# [START howto_operator_automl_update_dataset]
update = deepcopy(DATASET)
update["name"] = '{{ task_instance.xcom_pull("create_dataset_task")["name"] }}'
update["tables_dataset_metadata"][ # type: ignore
"target_column_spec_id"
] = "{{ get_target_column_spec(task_instance.xcom_pull('list_columns_spec_task'), target) }}"
update_dataset_task = AutoMLTablesUpdateDatasetOperator(
task_id="update_dataset_task",
dataset=update,
location=GCP_AUTOML_LOCATION,
)
# [END howto_operator_automl_update_dataset]
# [START howto_operator_automl_create_model]
create_model_task = AutoMLTrainModelOperator(
task_id="create_model_task",
model=MODEL,
location=GCP_AUTOML_LOCATION,
project_id=GCP_PROJECT_ID,
)
model_id = cast(str, XComArg(create_model_task, key='model_id'))
# [END howto_operator_automl_create_model]
# [START howto_operator_automl_delete_model]
delete_model_task = AutoMLDeleteModelOperator(
task_id="delete_model_task",
model_id=model_id,
location=GCP_AUTOML_LOCATION,
project_id=GCP_PROJECT_ID,
)
# [END howto_operator_automl_delete_model]
delete_datasets_task = AutoMLDeleteDatasetOperator(
task_id="delete_datasets_task",
dataset_id=dataset_id,
location=GCP_AUTOML_LOCATION,
project_id=GCP_PROJECT_ID,
)
(
import_dataset_task
>> list_tables_spec_task
>> list_columns_spec_task
>> update_dataset_task
>> create_model_task
)
delete_model_task >> delete_datasets_task
# Task dependencies created via `XComArgs`:
# create_dataset_task >> import_dataset_task
# create_dataset_task >> list_tables_spec_task
# create_dataset_task >> list_columns_spec_task
# create_dataset_task >> create_model_task
# create_model_task >> delete_model_task
# create_dataset_task >> delete_datasets_task
# Example DAG for AutoML datasets operations
with models.DAG(
"example_automl_dataset",
start_date=START_DATE,
catchup=False,
user_defined_macros={"extract_object_id": extract_object_id},
) as example_dag:
create_dataset_task2 = AutoMLCreateDatasetOperator(
task_id="create_dataset_task",
dataset=DATASET,
location=GCP_AUTOML_LOCATION,
project_id=GCP_PROJECT_ID,
)
dataset_id = cast(str, XComArg(create_dataset_task2, key='dataset_id'))
import_dataset_task = AutoMLImportDataOperator(
task_id="import_dataset_task",
dataset_id=dataset_id,
location=GCP_AUTOML_LOCATION,
input_config=IMPORT_INPUT_CONFIG,
)
list_tables_spec_task = AutoMLTablesListTableSpecsOperator(
task_id="list_tables_spec_task",
dataset_id=dataset_id,
location=GCP_AUTOML_LOCATION,
project_id=GCP_PROJECT_ID,
)
list_columns_spec_task = AutoMLTablesListColumnSpecsOperator(
task_id="list_columns_spec_task",
dataset_id=dataset_id,
table_spec_id="{{ extract_object_id(task_instance.xcom_pull('list_tables_spec_task')[0]) }}",
location=GCP_AUTOML_LOCATION,
project_id=GCP_PROJECT_ID,
)
# [START howto_operator_list_dataset]
list_datasets_task = AutoMLListDatasetOperator(
task_id="list_datasets_task",
location=GCP_AUTOML_LOCATION,
project_id=GCP_PROJECT_ID,
)
# [END howto_operator_list_dataset]
# [START howto_operator_delete_dataset]
delete_datasets_task = AutoMLDeleteDatasetOperator(
task_id="delete_datasets_task",
dataset_id="{{ task_instance.xcom_pull('list_datasets_task', key='dataset_id_list') | list }}",
location=GCP_AUTOML_LOCATION,
project_id=GCP_PROJECT_ID,
)
# [END howto_operator_delete_dataset]
(
import_dataset_task
>> list_tables_spec_task
>> list_columns_spec_task
>> list_datasets_task
>> delete_datasets_task
)
# Task dependencies created via `XComArgs`:
# create_dataset_task >> import_dataset_task
# create_dataset_task >> list_tables_spec_task
# create_dataset_task >> list_columns_spec_task
with models.DAG(
"example_gcp_get_deploy",
start_date=START_DATE,
catchup=False,
tags=["example"],
) as get_deploy_dag:
# [START howto_operator_get_model]
get_model_task = AutoMLGetModelOperator(
task_id="get_model_task",
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_task = AutoMLDeployModelOperator(
task_id="deploy_model_task",
model_id=MODEL_ID,
location=GCP_AUTOML_LOCATION,
project_id=GCP_PROJECT_ID,
)
# [END howto_operator_deploy_model]
with models.DAG(
"example_gcp_predict",
start_date=START_DATE,
catchup=False,
tags=["example"],
) as predict_dag:
# [START howto_operator_prediction]
predict_task = AutoMLPredictOperator(
task_id="predict_task",
model_id=MODEL_ID,
payload={}, # Add your own payload, the used model_id must be deployed
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={}, # Add your config
output_config={}, # Add your config
location=GCP_AUTOML_LOCATION,
project_id=GCP_PROJECT_ID,
)
# [END howto_operator_batch_prediction]