#
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# 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
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# KIND, either express or implied. See the License for the
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# mypy ignore arg types (for templated fields)
# type: ignore[arg-type]
"""
Example Airflow DAG that demonstrates operators for the Google Vertex AI service in the Google
Cloud Platform.
This DAG relies on the following OS environment variables:
* GCP_VERTEX_AI_BUCKET - Google Cloud Storage bucket where the model will be saved
after training process was finished.
* CUSTOM_CONTAINER_URI - path to container with model.
* PYTHON_PACKAGE_GSC_URI - path to test model in archive.
* LOCAL_TRAINING_SCRIPT_PATH - path to local training script.
* DATASET_ID - ID of dataset which will be used in training process.
* MODEL_ID - ID of model which will be used in predict process.
* MODEL_ARTIFACT_URI - The artifact_uri should be the path to a GCS directory containing saved model
artifacts.
"""
import os
from datetime import datetime
from uuid import uuid4
from google.cloud import aiplatform
from google.protobuf import json_format
from google.protobuf.struct_pb2 import Value
from airflow import models
from airflow.providers.google.cloud.operators.vertex_ai.auto_ml import (
CreateAutoMLForecastingTrainingJobOperator,
CreateAutoMLImageTrainingJobOperator,
CreateAutoMLTabularTrainingJobOperator,
CreateAutoMLTextTrainingJobOperator,
CreateAutoMLVideoTrainingJobOperator,
DeleteAutoMLTrainingJobOperator,
ListAutoMLTrainingJobOperator,
)
from airflow.providers.google.cloud.operators.vertex_ai.batch_prediction_job import (
CreateBatchPredictionJobOperator,
DeleteBatchPredictionJobOperator,
ListBatchPredictionJobsOperator,
)
from airflow.providers.google.cloud.operators.vertex_ai.custom_job import (
CreateCustomContainerTrainingJobOperator,
CreateCustomPythonPackageTrainingJobOperator,
CreateCustomTrainingJobOperator,
DeleteCustomTrainingJobOperator,
ListCustomTrainingJobOperator,
)
from airflow.providers.google.cloud.operators.vertex_ai.dataset import (
CreateDatasetOperator,
DeleteDatasetOperator,
ExportDataOperator,
GetDatasetOperator,
ImportDataOperator,
ListDatasetsOperator,
UpdateDatasetOperator,
)
from airflow.providers.google.cloud.operators.vertex_ai.endpoint_service import (
CreateEndpointOperator,
DeleteEndpointOperator,
DeployModelOperator,
ListEndpointsOperator,
UndeployModelOperator,
)
from airflow.providers.google.cloud.operators.vertex_ai.hyperparameter_tuning_job import (
CreateHyperparameterTuningJobOperator,
DeleteHyperparameterTuningJobOperator,
GetHyperparameterTuningJobOperator,
ListHyperparameterTuningJobOperator,
)
from airflow.providers.google.cloud.operators.vertex_ai.model_service import (
DeleteModelOperator,
ExportModelOperator,
ListModelsOperator,
UploadModelOperator,
)
[docs]PROJECT_ID = os.environ.get("GCP_PROJECT_ID", "an-id")
[docs]REGION = os.environ.get("GCP_LOCATION", "us-central1")
[docs]BUCKET = os.environ.get("GCP_VERTEX_AI_BUCKET", "vertex-ai-system-tests")
[docs]STAGING_BUCKET = f"gs://{BUCKET}"
[docs]DISPLAY_NAME = str(uuid4()) # Create random display name
[docs]CONTAINER_URI = "gcr.io/cloud-aiplatform/training/tf-cpu.2-2:latest"
[docs]CUSTOM_CONTAINER_URI = os.environ.get("CUSTOM_CONTAINER_URI", "path_to_container_with_model")
[docs]MODEL_SERVING_CONTAINER_URI = "gcr.io/cloud-aiplatform/prediction/tf2-cpu.2-2:latest"
[docs]MACHINE_TYPE = "n1-standard-4"
[docs]ACCELERATOR_TYPE = "ACCELERATOR_TYPE_UNSPECIFIED"
[docs]TRAINING_FRACTION_SPLIT = 0.7
[docs]TEST_FRACTION_SPLIT = 0.15
[docs]VALIDATION_FRACTION_SPLIT = 0.15
[docs]PYTHON_PACKAGE_GCS_URI = os.environ.get("PYTHON_PACKAGE_GSC_URI", "path_to_test_model_in_arch")
[docs]PYTHON_MODULE_NAME = "aiplatform_custom_trainer_script.task"
[docs]LOCAL_TRAINING_SCRIPT_PATH = os.environ.get("LOCAL_TRAINING_SCRIPT_PATH", "path_to_training_script")
[docs]TRAINING_PIPELINE_ID = "test-training-pipeline-id"
[docs]CUSTOM_JOB_ID = "test-custom-job-id"
[docs]IMAGE_DATASET = {
"display_name": str(uuid4()),
"metadata_schema_uri": "gs://google-cloud-aiplatform/schema/dataset/metadata/image_1.0.0.yaml",
"metadata": Value(string_value="test-image-dataset"),
}
[docs]TABULAR_DATASET = {
"display_name": str(uuid4()),
"metadata_schema_uri": "gs://google-cloud-aiplatform/schema/dataset/metadata/tabular_1.0.0.yaml",
"metadata": Value(string_value="test-tabular-dataset"),
}
[docs]TEXT_DATASET = {
"display_name": str(uuid4()),
"metadata_schema_uri": "gs://google-cloud-aiplatform/schema/dataset/metadata/text_1.0.0.yaml",
"metadata": Value(string_value="test-text-dataset"),
}
[docs]VIDEO_DATASET = {
"display_name": str(uuid4()),
"metadata_schema_uri": "gs://google-cloud-aiplatform/schema/dataset/metadata/video_1.0.0.yaml",
"metadata": Value(string_value="test-video-dataset"),
}
[docs]TIME_SERIES_DATASET = {
"display_name": str(uuid4()),
"metadata_schema_uri": "gs://google-cloud-aiplatform/schema/dataset/metadata/time_series_1.0.0.yaml",
"metadata": Value(string_value="test-video-dataset"),
}
[docs]DATASET_ID = os.environ.get("DATASET_ID", "test-dataset-id")
[docs]TEST_EXPORT_CONFIG = {"gcs_destination": {"output_uri_prefix": "gs://test-vertex-ai-bucket/exports"}}
[docs]TEST_IMPORT_CONFIG = [
{
"data_item_labels": {
"test-labels-name": "test-labels-value",
},
"import_schema_uri": (
"gs://google-cloud-aiplatform/schema/dataset/ioformat/image_bounding_box_io_format_1.0.0.yaml"
),
"gcs_source": {
"uris": ["gs://ucaip-test-us-central1/dataset/salads_oid_ml_use_public_unassigned.jsonl"]
},
},
]
[docs]DATASET_TO_UPDATE = {"display_name": "test-name"}
[docs]TEST_UPDATE_MASK = {"paths": ["displayName"]}
[docs]TEST_TIME_COLUMN = "date"
[docs]TEST_TIME_SERIES_IDENTIFIER_COLUMN = "store_name"
[docs]TEST_TARGET_COLUMN = "sale_dollars"
[docs]COLUMN_SPECS = {
TEST_TIME_COLUMN: "timestamp",
TEST_TARGET_COLUMN: "numeric",
"city": "categorical",
"zip_code": "categorical",
"county": "categorical",
}
]
[docs]MODEL_ID = os.environ.get("MODEL_ID", "test-model-id")
[docs]MODEL_ARTIFACT_URI = os.environ.get("MODEL_ARTIFACT_URI", "path_to_folder_with_model_artifacts")
[docs]MODEL_NAME = f"projects/{PROJECT_ID}/locations/{REGION}/models/{MODEL_ID}"
[docs]JOB_DISPLAY_NAME = f"temp_create_batch_prediction_job_test_{uuid4()}"
[docs]BIGQUERY_SOURCE = f"bq://{PROJECT_ID}.test_iowa_liquor_sales_forecasting_us.2021_sales_predict"
[docs]GCS_DESTINATION_PREFIX = "gs://test-vertex-ai-bucket-us/output"
[docs]MODEL_PARAMETERS = json_format.ParseDict({}, Value())
[docs]ENDPOINT_CONF = {
"display_name": f"endpoint_test_{uuid4()}",
}
[docs]DEPLOYED_MODEL = {
# format: 'projects/{project}/locations/{location}/models/{model}'
'model': f"projects/{PROJECT_ID}/locations/{REGION}/models/{MODEL_ID}",
'display_name': f"temp_endpoint_test_{uuid4()}",
"dedicated_resources": {
"machine_spec": {
"machine_type": "n1-standard-2",
"accelerator_type": aiplatform.gapic.AcceleratorType.NVIDIA_TESLA_K80,
"accelerator_count": 1,
},
'min_replica_count': 1,
"max_replica_count": 1,
},
}
[docs]MODEL_OUTPUT_CONFIG = {
"artifact_destination": {
"output_uri_prefix": STAGING_BUCKET,
},
"export_format_id": "custom-trained",
}
[docs]MODEL_OBJ = {
"display_name": f"model-{str(uuid4())}",
"artifact_uri": MODEL_ARTIFACT_URI,
"container_spec": {
"image_uri": MODEL_SERVING_CONTAINER_URI,
"command": [],
"args": [],
"env": [],
"ports": [],
"predict_route": "",
"health_route": "",
},
}
with models.DAG(
"example_gcp_vertex_ai_custom_jobs",
schedule_interval="@once",
start_date=datetime(2021, 1, 1),
catchup=False,
) as custom_jobs_dag:
# [START how_to_cloud_vertex_ai_create_custom_container_training_job_operator]
[docs] create_custom_container_training_job = CreateCustomContainerTrainingJobOperator(
task_id="custom_container_task",
staging_bucket=STAGING_BUCKET,
display_name=f"train-housing-container-{DISPLAY_NAME}",
container_uri=CUSTOM_CONTAINER_URI,
model_serving_container_image_uri=MODEL_SERVING_CONTAINER_URI,
# run params
dataset_id=DATASET_ID,
command=["python3", "task.py"],
model_display_name=f"container-housing-model-{DISPLAY_NAME}",
replica_count=REPLICA_COUNT,
machine_type=MACHINE_TYPE,
accelerator_type=ACCELERATOR_TYPE,
accelerator_count=ACCELERATOR_COUNT,
training_fraction_split=TRAINING_FRACTION_SPLIT,
validation_fraction_split=VALIDATION_FRACTION_SPLIT,
test_fraction_split=TEST_FRACTION_SPLIT,
region=REGION,
project_id=PROJECT_ID,
)
# [END how_to_cloud_vertex_ai_create_custom_container_training_job_operator]
# [START how_to_cloud_vertex_ai_create_custom_python_package_training_job_operator]
create_custom_python_package_training_job = CreateCustomPythonPackageTrainingJobOperator(
task_id="python_package_task",
staging_bucket=STAGING_BUCKET,
display_name=f"train-housing-py-package-{DISPLAY_NAME}",
python_package_gcs_uri=PYTHON_PACKAGE_GCS_URI,
python_module_name=PYTHON_MODULE_NAME,
container_uri=CONTAINER_URI,
model_serving_container_image_uri=MODEL_SERVING_CONTAINER_URI,
# run params
dataset_id=DATASET_ID,
model_display_name=f"py-package-housing-model-{DISPLAY_NAME}",
replica_count=REPLICA_COUNT,
machine_type=MACHINE_TYPE,
accelerator_type=ACCELERATOR_TYPE,
accelerator_count=ACCELERATOR_COUNT,
training_fraction_split=TRAINING_FRACTION_SPLIT,
validation_fraction_split=VALIDATION_FRACTION_SPLIT,
test_fraction_split=TEST_FRACTION_SPLIT,
region=REGION,
project_id=PROJECT_ID,
)
# [END how_to_cloud_vertex_ai_create_custom_python_package_training_job_operator]
# [START how_to_cloud_vertex_ai_create_custom_training_job_operator]
create_custom_training_job = CreateCustomTrainingJobOperator(
task_id="custom_task",
staging_bucket=STAGING_BUCKET,
display_name=f"train-housing-custom-{DISPLAY_NAME}",
script_path=LOCAL_TRAINING_SCRIPT_PATH,
container_uri=CONTAINER_URI,
requirements=["gcsfs==0.7.1"],
model_serving_container_image_uri=MODEL_SERVING_CONTAINER_URI,
# run params
dataset_id=DATASET_ID,
replica_count=1,
model_display_name=f"custom-housing-model-{DISPLAY_NAME}",
sync=False,
region=REGION,
project_id=PROJECT_ID,
)
# [END how_to_cloud_vertex_ai_create_custom_training_job_operator]
# [START how_to_cloud_vertex_ai_delete_custom_training_job_operator]
delete_custom_training_job = DeleteCustomTrainingJobOperator(
task_id="delete_custom_training_job",
training_pipeline_id=TRAINING_PIPELINE_ID,
custom_job_id=CUSTOM_JOB_ID,
region=REGION,
project_id=PROJECT_ID,
)
# [END how_to_cloud_vertex_ai_delete_custom_training_job_operator]
# [START how_to_cloud_vertex_ai_list_custom_training_job_operator]
list_custom_training_job = ListCustomTrainingJobOperator(
task_id="list_custom_training_job",
region=REGION,
project_id=PROJECT_ID,
)
# [END how_to_cloud_vertex_ai_list_custom_training_job_operator]
with models.DAG(
"example_gcp_vertex_ai_dataset",
schedule_interval="@once",
start_date=datetime(2021, 1, 1),
catchup=False,
) as dataset_dag:
# [START how_to_cloud_vertex_ai_create_dataset_operator]
[docs] 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_text_dataset_job = CreateDatasetOperator(
task_id="text_dataset",
dataset=TEXT_DATASET,
region=REGION,
project_id=PROJECT_ID,
)
create_video_dataset_job = CreateDatasetOperator(
task_id="video_dataset",
dataset=VIDEO_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_delete_dataset_operator]
delete_dataset_job = DeleteDatasetOperator(
task_id="delete_dataset",
dataset_id=create_text_dataset_job.output['dataset_id'],
region=REGION,
project_id=PROJECT_ID,
)
# [END how_to_cloud_vertex_ai_delete_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_video_dataset_job.output['dataset_id'],
dataset=DATASET_TO_UPDATE,
update_mask=TEST_UPDATE_MASK,
)
# [END how_to_cloud_vertex_ai_update_dataset_operator]
create_time_series_dataset_job
create_text_dataset_job >> delete_dataset_job
create_tabular_dataset_job >> get_dataset
create_image_dataset_job >> import_data_job >> export_data_job
create_video_dataset_job >> update_dataset_job
list_dataset_job
with models.DAG(
"example_gcp_vertex_ai_auto_ml",
schedule_interval="@once",
start_date=datetime(2021, 1, 1),
catchup=False,
) as auto_ml_dag:
# [START how_to_cloud_vertex_ai_create_auto_ml_forecasting_training_job_operator]
[docs] create_auto_ml_forecasting_training_job = CreateAutoMLForecastingTrainingJobOperator(
task_id="auto_ml_forecasting_task",
display_name=f"auto-ml-forecasting-{DISPLAY_NAME}",
optimization_objective="minimize-rmse",
column_specs=COLUMN_SPECS,
# run params
dataset_id=DATASET_ID,
target_column=TEST_TARGET_COLUMN,
time_column=TEST_TIME_COLUMN,
time_series_identifier_column=TEST_TIME_SERIES_IDENTIFIER_COLUMN,
available_at_forecast_columns=[TEST_TIME_COLUMN],
unavailable_at_forecast_columns=[TEST_TARGET_COLUMN],
time_series_attribute_columns=["city", "zip_code", "county"],
forecast_horizon=30,
context_window=30,
data_granularity_unit="day",
data_granularity_count=1,
weight_column=None,
budget_milli_node_hours=1000,
model_display_name=f"auto-ml-forecasting-model-{DISPLAY_NAME}",
predefined_split_column_name=None,
region=REGION,
project_id=PROJECT_ID,
)
# [END how_to_cloud_vertex_ai_create_auto_ml_forecasting_training_job_operator]
# [START how_to_cloud_vertex_ai_create_auto_ml_image_training_job_operator]
create_auto_ml_image_training_job = CreateAutoMLImageTrainingJobOperator(
task_id="auto_ml_image_task",
display_name=f"auto-ml-image-{DISPLAY_NAME}",
dataset_id=DATASET_ID,
prediction_type="classification",
multi_label=False,
model_type="CLOUD",
training_fraction_split=0.6,
validation_fraction_split=0.2,
test_fraction_split=0.2,
budget_milli_node_hours=8000,
model_display_name=f"auto-ml-image-model-{DISPLAY_NAME}",
disable_early_stopping=False,
region=REGION,
project_id=PROJECT_ID,
)
# [END how_to_cloud_vertex_ai_create_auto_ml_image_training_job_operator]
# [START how_to_cloud_vertex_ai_create_auto_ml_tabular_training_job_operator]
create_auto_ml_tabular_training_job = CreateAutoMLTabularTrainingJobOperator(
task_id="auto_ml_tabular_task",
display_name=f"auto-ml-tabular-{DISPLAY_NAME}",
optimization_prediction_type="classification",
column_transformations=COLUMN_TRANSFORMATIONS,
dataset_id=DATASET_ID,
target_column="Adopted",
training_fraction_split=0.8,
validation_fraction_split=0.1,
test_fraction_split=0.1,
model_display_name="adopted-prediction-model",
disable_early_stopping=False,
region=REGION,
project_id=PROJECT_ID,
)
# [END how_to_cloud_vertex_ai_create_auto_ml_tabular_training_job_operator]
# [START how_to_cloud_vertex_ai_create_auto_ml_text_training_job_operator]
create_auto_ml_text_training_job = CreateAutoMLTextTrainingJobOperator(
task_id="auto_ml_text_task",
display_name=f"auto-ml-text-{DISPLAY_NAME}",
prediction_type="classification",
multi_label=False,
dataset_id=DATASET_ID,
model_display_name=f"auto-ml-text-model-{DISPLAY_NAME}",
training_fraction_split=0.7,
validation_fraction_split=0.2,
test_fraction_split=0.1,
sync=True,
region=REGION,
project_id=PROJECT_ID,
)
# [END how_to_cloud_vertex_ai_create_auto_ml_text_training_job_operator]
# [START how_to_cloud_vertex_ai_create_auto_ml_video_training_job_operator]
create_auto_ml_video_training_job = CreateAutoMLVideoTrainingJobOperator(
task_id="auto_ml_video_task",
display_name=f"auto-ml-video-{DISPLAY_NAME}",
prediction_type="classification",
model_type="CLOUD",
dataset_id=DATASET_ID,
model_display_name=f"auto-ml-video-model-{DISPLAY_NAME}",
region=REGION,
project_id=PROJECT_ID,
)
# [END how_to_cloud_vertex_ai_create_auto_ml_video_training_job_operator]
# [START how_to_cloud_vertex_ai_delete_auto_ml_training_job_operator]
delete_auto_ml_training_job = DeleteAutoMLTrainingJobOperator(
task_id="delete_auto_ml_training_job",
training_pipeline_id=TRAINING_PIPELINE_ID,
region=REGION,
project_id=PROJECT_ID,
)
# [END how_to_cloud_vertex_ai_delete_auto_ml_training_job_operator]
# [START how_to_cloud_vertex_ai_list_auto_ml_training_job_operator]
list_auto_ml_training_job = ListAutoMLTrainingJobOperator(
task_id="list_auto_ml_training_job",
region=REGION,
project_id=PROJECT_ID,
)
# [END how_to_cloud_vertex_ai_list_auto_ml_training_job_operator]
with models.DAG(
"example_gcp_vertex_ai_batch_prediction_job",
schedule_interval="@once",
start_date=datetime(2021, 1, 1),
catchup=False,
) as batch_prediction_job_dag:
# [START how_to_cloud_vertex_ai_create_batch_prediction_job_operator]
[docs] create_batch_prediction_job = CreateBatchPredictionJobOperator(
task_id="create_batch_prediction_job",
job_display_name=JOB_DISPLAY_NAME,
model_name=MODEL_NAME,
predictions_format="csv",
bigquery_source=BIGQUERY_SOURCE,
gcs_destination_prefix=GCS_DESTINATION_PREFIX,
model_parameters=MODEL_PARAMETERS,
region=REGION,
project_id=PROJECT_ID,
)
# [END how_to_cloud_vertex_ai_create_batch_prediction_job_operator]
# [START how_to_cloud_vertex_ai_list_batch_prediction_job_operator]
list_batch_prediction_job = ListBatchPredictionJobsOperator(
task_id="list_batch_prediction_jobs",
region=REGION,
project_id=PROJECT_ID,
)
# [END how_to_cloud_vertex_ai_list_batch_prediction_job_operator]
# [START how_to_cloud_vertex_ai_delete_batch_prediction_job_operator]
delete_batch_prediction_job = DeleteBatchPredictionJobOperator(
task_id="delete_batch_prediction_job",
batch_prediction_job_id=create_batch_prediction_job.output['batch_prediction_job_id'],
region=REGION,
project_id=PROJECT_ID,
)
# [END how_to_cloud_vertex_ai_delete_batch_prediction_job_operator]
create_batch_prediction_job >> delete_batch_prediction_job
list_batch_prediction_job
with models.DAG(
"example_gcp_vertex_ai_endpoint",
schedule_interval="@once",
start_date=datetime(2021, 1, 1),
catchup=False,
) as endpoint_dag:
# [START how_to_cloud_vertex_ai_create_endpoint_operator]
[docs] create_endpoint = CreateEndpointOperator(
task_id="create_endpoint",
endpoint=ENDPOINT_CONF,
region=REGION,
project_id=PROJECT_ID,
)
# [END how_to_cloud_vertex_ai_create_endpoint_operator]
# [START how_to_cloud_vertex_ai_delete_endpoint_operator]
delete_endpoint = DeleteEndpointOperator(
task_id="delete_endpoint",
endpoint_id=create_endpoint.output['endpoint_id'],
region=REGION,
project_id=PROJECT_ID,
)
# [END how_to_cloud_vertex_ai_delete_endpoint_operator]
# [START how_to_cloud_vertex_ai_list_endpoints_operator]
list_endpoints = ListEndpointsOperator(
task_id="list_endpoints",
region=REGION,
project_id=PROJECT_ID,
)
# [END how_to_cloud_vertex_ai_list_endpoints_operator]
# [START how_to_cloud_vertex_ai_deploy_model_operator]
deploy_model = DeployModelOperator(
task_id="deploy_model",
endpoint_id=create_endpoint.output['endpoint_id'],
deployed_model=DEPLOYED_MODEL,
traffic_split={'0': 100},
region=REGION,
project_id=PROJECT_ID,
)
# [END how_to_cloud_vertex_ai_deploy_model_operator]
# [START how_to_cloud_vertex_ai_undeploy_model_operator]
undeploy_model = UndeployModelOperator(
task_id="undeploy_model",
endpoint_id=create_endpoint.output['endpoint_id'],
deployed_model_id=deploy_model.output['deployed_model_id'],
region=REGION,
project_id=PROJECT_ID,
)
# [END how_to_cloud_vertex_ai_undeploy_model_operator]
create_endpoint >> deploy_model >> undeploy_model >> delete_endpoint
list_endpoints
with models.DAG(
"example_gcp_vertex_ai_hyperparameter_tuning_job",
schedule_interval="@once",
start_date=datetime(2021, 1, 1),
catchup=False,
) as hyperparameter_tuning_job_dag:
# [START how_to_cloud_vertex_ai_create_hyperparameter_tuning_job_operator]
[docs] create_hyperparameter_tuning_job = CreateHyperparameterTuningJobOperator(
task_id="create_hyperparameter_tuning_job",
staging_bucket=STAGING_BUCKET,
display_name=f"horses-humans-hyptertune-{DISPLAY_NAME}",
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": f"gcr.io/{PROJECT_ID}/horse-human:hypertune",
},
}
],
sync=False,
region=REGION,
project_id=PROJECT_ID,
parameter_spec={
'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'
),
},
metric_spec={
'accuracy': 'maximize',
},
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_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=create_hyperparameter_tuning_job.output["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=create_hyperparameter_tuning_job.output["hyperparameter_tuning_job_id"],
)
# [END how_to_cloud_vertex_ai_delete_hyperparameter_tuning_job_operator]
# [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]
create_hyperparameter_tuning_job >> get_hyperparameter_tuning_job >> delete_hyperparameter_tuning_job
list_hyperparameter_tuning_job
with models.DAG(
"example_gcp_vertex_ai_model_service",
schedule_interval="@once",
start_date=datetime(2021, 1, 1),
catchup=False,
) as model_service_dag:
# [START how_to_cloud_vertex_ai_upload_model_operator]
[docs] upload_model = UploadModelOperator(
task_id="upload_model",
region=REGION,
project_id=PROJECT_ID,
model=MODEL_OBJ,
)
# [END how_to_cloud_vertex_ai_upload_model_operator]
# [START how_to_cloud_vertex_ai_export_model_operator]
export_model = ExportModelOperator(
task_id="export_model",
project_id=PROJECT_ID,
region=REGION,
model_id=upload_model.output["model_id"],
output_config=MODEL_OUTPUT_CONFIG,
)
# [END how_to_cloud_vertex_ai_export_model_operator]
# [START how_to_cloud_vertex_ai_delete_model_operator]
delete_model = DeleteModelOperator(
task_id="delete_model",
project_id=PROJECT_ID,
region=REGION,
model_id=upload_model.output["model_id"],
)
# [END how_to_cloud_vertex_ai_delete_model_operator]
# [START how_to_cloud_vertex_ai_list_models_operator]
list_models = ListModelsOperator(
task_id="list_models",
region=REGION,
project_id=PROJECT_ID,
)
# [END how_to_cloud_vertex_ai_list_models_operator]
upload_model >> export_model >> delete_model
list_models
