[GitHub] [airflow] MaksYermak commented on a change in pull request #20077: Create CustomJob and Datasets operators for Vertex AI service

[GitBox <gi...@apache.org>]

MaksYermak commented on a change in pull request #20077:
URL: https://github.com/apache/airflow/pull/20077#discussion_r785735655



##########
File path: airflow/providers/google/cloud/example_dags/example_vertex_ai.py
##########
@@ -0,0 +1,313 @@
+#
+# 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 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.
+"""
+import os
+from datetime import datetime
+from uuid import uuid4
+
+from airflow import models
+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,
+)
+
+PROJECT_ID = os.environ.get("GCP_PROJECT_ID", "an-id")
+REGION = os.environ.get("GCP_LOCATION", "us-central1")
+BUCKET = os.environ.get("GCP_VERTEX_AI_BUCKET", "vertex-ai-system-tests")
+
+STAGING_BUCKET = f"gs://{BUCKET}"
+DISPLAY_NAME = str(uuid4())  # Create random display name
+CONTAINER_URI = "gcr.io/cloud-aiplatform/training/tf-cpu.2-2:latest"
+CUSTOM_CONTAINER_URI = os.environ.get("CUSTOM_CONTAINER_URI", "path_to_container_with_model")
+MODEL_SERVING_CONTAINER_URI = "gcr.io/cloud-aiplatform/prediction/tf2-cpu.2-2:latest"
+REPLICA_COUNT = 1
+MACHINE_TYPE = "n1-standard-4"
+ACCELERATOR_TYPE = "ACCELERATOR_TYPE_UNSPECIFIED"
+ACCELERATOR_COUNT = 0
+TRAINING_FRACTION_SPLIT = 0.7
+TEST_FRACTION_SPLIT = 0.15
+VALIDATION_FRACTION_SPLIT = 0.15
+
+PYTHON_PACKAGE_GCS_URI = os.environ.get("PYTHON_PACKAGE_GSC_URI", "path_to_test_model_in_arch")
+PYTHON_MODULE_NAME = "aiplatform_custom_trainer_script.task"
+
+LOCAL_TRAINING_SCRIPT_PATH = os.environ.get("LOCAL_TRAINING_SCRIPT_PATH", "path_to_training_script")
+
+TRAINING_PIPELINE_ID = "test-training-pipeline-id"
+CUSTOM_JOB_ID = "test-custom-job-id"
+
+IMAGE_DATASET = {
+    "display_name": str(uuid4()),
+    "metadata_schema_uri": "gs://google-cloud-aiplatform/schema/dataset/metadata/image_1.0.0.yaml",
+    "metadata": "test-image-dataset",
+}
+TABULAR_DATASET = {
+    "display_name": str(uuid4()),
+    "metadata_schema_uri": "gs://google-cloud-aiplatform/schema/dataset/metadata/tabular_1.0.0.yaml",
+    "metadata": "test-tabular-dataset",
+}
+TEXT_DATASET = {
+    "display_name": str(uuid4()),
+    "metadata_schema_uri": "gs://google-cloud-aiplatform/schema/dataset/metadata/text_1.0.0.yaml",
+    "metadata": "test-text-dataset",
+}
+VIDEO_DATASET = {
+    "display_name": str(uuid4()),
+    "metadata_schema_uri": "gs://google-cloud-aiplatform/schema/dataset/metadata/video_1.0.0.yaml",
+    "metadata": "test-video-dataset",
+}
+TIME_SERIES_DATASET = {
+    "display_name": str(uuid4()),
+    "metadata_schema_uri": "gs://google-cloud-aiplatform/schema/dataset/metadata/time_series_1.0.0.yaml",
+    "metadata": "test-video-dataset",
+}
+DATASET_ID = os.environ.get("DATASET_ID", "test-dataset-id")
+TEST_EXPORT_CONFIG = {"gcs_destination": {"output_uri_prefix": "gs://test-vertex-ai-bucket/exports"}}
+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"]
+        },
+    },
+]
+DATASET_TO_UPDATE = {"display_name": "test-name"}
+TEST_UPDATE_MASK = {"paths": ["displayName"]}
+
+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]
+    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,

Review comment:
       This `sync` relates to aiplatfrom job not to our operator. Our hook runs this job with specific parameters and than this job create CustomJob inside Google Cloud after that our operator waits for CustomJob finished. Our operator works as usual.




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