Run this DAG

1. Install Astronomer CLISkip if you already have the CLI

2. Initate the project:

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:

#
# 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.
"""
import os
from copy import deepcopy
from typing import Dict, List
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,
AutoMLListDatasetOperator,
AutoMLPredictOperator,
AutoMLTablesListColumnSpecsOperator,
AutoMLTablesListTableSpecsOperator,
AutoMLTablesUpdateDatasetOperator,
AutoMLTrainModelOperator,
)
from airflow.utils.dates import days_ago
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",
schedule_interval=None, # Override to match your needs
start_date=days_ago(1),
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 = "{{ task_instance.xcom_pull('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 = "{{ task_instance.xcom_pull('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,
)
(
create_dataset_task # noqa
>> import_dataset_task # noqa
>> list_tables_spec_task # noqa
>> list_columns_spec_task # noqa
>> update_dataset_task # noqa
>> create_model_task # noqa
>> delete_model_task # noqa
>> delete_datasets_task # noqa
)
# Example DAG for AutoML datasets operations
with models.DAG(
"example_automl_dataset",
schedule_interval=None, # Override to match your needs
start_date=days_ago(1),
user_defined_macros={"extract_object_id": extract_object_id},
) as example_dag:
create_dataset_task = AutoMLCreateDatasetOperator(
task_id="create_dataset_task",
dataset=DATASET,
location=GCP_AUTOML_LOCATION,
project_id=GCP_PROJECT_ID,
)
dataset_id = '{{ task_instance.xcom_pull("create_dataset_task", 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]
(
create_dataset_task # noqa
>> import_dataset_task # noqa
>> list_tables_spec_task # noqa
>> list_columns_spec_task # noqa
>> list_datasets_task # noqa
>> delete_datasets_task # noqa
)
with models.DAG(
"example_gcp_get_deploy",
schedule_interval=None, # Override to match your needs
start_date=days_ago(1),
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",
schedule_interval=None, # Override to match your needs
start_date=days_ago(1),
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]