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"""
Example Airflow DAG for Google Vertex AI service testing Hyperparameter Tuning Job operations.
"""
from __future__ import annotations
import os
from datetime import datetime
from google.cloud import aiplatform
from airflow.models.dag import DAG
from airflow.providers.google.cloud.operators.gcs import GCSCreateBucketOperator, GCSDeleteBucketOperator
from airflow.providers.google.cloud.operators.vertex_ai.hyperparameter_tuning_job import (
CreateHyperparameterTuningJobOperator,
DeleteHyperparameterTuningJobOperator,
GetHyperparameterTuningJobOperator,
ListHyperparameterTuningJobOperator,
)
from airflow.utils.trigger_rule import TriggerRule
[docs]ENV_ID = os.environ.get("SYSTEM_TESTS_ENV_ID", "default")
[docs]PROJECT_ID = os.environ.get("SYSTEM_TESTS_GCP_PROJECT", "default")
[docs]DAG_ID = "example_vertex_ai_hyperparameter_tuning_job_operations"
[docs]DISPLAY_NAME = f"hyperparameter-tuning-job-{ENV_ID}"
[docs]DATA_SAMPLE_GCS_BUCKET_NAME = f"bucket_hyperparameter_tuning_job_{ENV_ID}"
[docs]STAGING_BUCKET = f"gs://{DATA_SAMPLE_GCS_BUCKET_NAME}"
[docs]MACHINE_TYPE = "n1-standard-4"
[docs]ACCELERATOR_TYPE = "ACCELERATOR_TYPE_UNSPECIFIED"
[docs]WORKER_POOL_SPECS = [
{
"machine_spec": {
"machine_type": MACHINE_TYPE,
"accelerator_type": ACCELERATOR_TYPE,
"accelerator_count": ACCELERATOR_COUNT,
},
"replica_count": REPLICA_COUNT,
"container_spec": {
"image_uri": "us-docker.pkg.dev/composer-256318/horse-human/horse-human-image:latest",
},
}
]
[docs]PARAM_SPECS = {
"learning_rate": aiplatform.hyperparameter_tuning.DoubleParameterSpec(min=0.01, max=1, scale="log"),
"momentum": aiplatform.hyperparameter_tuning.DoubleParameterSpec(min=0, max=1, scale="linear"),
"num_neurons": aiplatform.hyperparameter_tuning.DiscreteParameterSpec(
values=[64, 128, 512], scale="linear"
),
}
[docs]METRIC_SPEC = {
"accuracy": "maximize",
}
with DAG(
DAG_ID,
schedule="@once",
start_date=datetime(2021, 1, 1),
catchup=False,
tags=["example", "vertex_ai", "hyperparameter_tuning_job"],
) as dag:
[docs] 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_hyperparameter_tuning_job_operator]
create_hyperparameter_tuning_job = CreateHyperparameterTuningJobOperator(
task_id="create_hyperparameter_tuning_job",
staging_bucket=STAGING_BUCKET,
display_name=DISPLAY_NAME,
worker_pool_specs=WORKER_POOL_SPECS,
sync=False,
region=REGION,
project_id=PROJECT_ID,
parameter_spec=PARAM_SPECS,
metric_spec=METRIC_SPEC,
max_trial_count=15,
parallel_trial_count=3,
)
# [END how_to_cloud_vertex_ai_create_hyperparameter_tuning_job_operator]
# [START how_to_cloud_vertex_ai_create_hyperparameter_tuning_job_operator_deferrable]
create_hyperparameter_tuning_job_def = CreateHyperparameterTuningJobOperator(
task_id="create_hyperparameter_tuning_job_def",
staging_bucket=STAGING_BUCKET,
display_name=DISPLAY_NAME,
worker_pool_specs=WORKER_POOL_SPECS,
sync=False,
region=REGION,
project_id=PROJECT_ID,
parameter_spec=PARAM_SPECS,
metric_spec=METRIC_SPEC,
max_trial_count=15,
parallel_trial_count=3,
deferrable=True,
)
# [END how_to_cloud_vertex_ai_create_hyperparameter_tuning_job_operator_deferrable]
# [START how_to_cloud_vertex_ai_get_hyperparameter_tuning_job_operator]
get_hyperparameter_tuning_job = GetHyperparameterTuningJobOperator(
task_id="get_hyperparameter_tuning_job",
project_id=PROJECT_ID,
region=REGION,
hyperparameter_tuning_job_id="{{ task_instance.xcom_pull("
"task_ids='create_hyperparameter_tuning_job', key='hyperparameter_tuning_job_id') }}",
)
# [END how_to_cloud_vertex_ai_get_hyperparameter_tuning_job_operator]
# [START how_to_cloud_vertex_ai_delete_hyperparameter_tuning_job_operator]
delete_hyperparameter_tuning_job = DeleteHyperparameterTuningJobOperator(
task_id="delete_hyperparameter_tuning_job",
project_id=PROJECT_ID,
region=REGION,
hyperparameter_tuning_job_id="{{ task_instance.xcom_pull("
"task_ids='create_hyperparameter_tuning_job', key='hyperparameter_tuning_job_id') }}",
trigger_rule=TriggerRule.ALL_DONE,
)
# [END how_to_cloud_vertex_ai_delete_hyperparameter_tuning_job_operator]
delete_hyperparameter_tuning_job_def = DeleteHyperparameterTuningJobOperator(
task_id="delete_hyperparameter_tuning_job_def",
project_id=PROJECT_ID,
region=REGION,
hyperparameter_tuning_job_id="{{ task_instance.xcom_pull("
"task_ids='create_hyperparameter_tuning_job_def', "
"key='hyperparameter_tuning_job_id') }}",
trigger_rule=TriggerRule.ALL_DONE,
)
# [START how_to_cloud_vertex_ai_list_hyperparameter_tuning_job_operator]
list_hyperparameter_tuning_job = ListHyperparameterTuningJobOperator(
task_id="list_hyperparameter_tuning_job",
region=REGION,
project_id=PROJECT_ID,
)
# [END how_to_cloud_vertex_ai_list_hyperparameter_tuning_job_operator]
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_hyperparameter_tuning_job, create_hyperparameter_tuning_job_def]
>> get_hyperparameter_tuning_job
>> [delete_hyperparameter_tuning_job, delete_hyperparameter_tuning_job_def]
>> list_hyperparameter_tuning_job
# TEST TEARDOWN
>> delete_bucket
)
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)