Source code for tests.system.google.cloud.gen_ai.example_gen_ai_generative_model_tuning
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"""
Example Airflow DAG for Google Vertex AI Generative Model Tuning Tasks.
"""
from __future__ import annotations
import os
from datetime import datetime
from pathlib import Path
import requests
from airflow.providers.google.cloud.operators.gcs import GCSCreateBucketOperator, GCSDeleteBucketOperator
from airflow.providers.google.cloud.transfers.local_to_gcs import LocalFilesystemToGCSOperator
try:
from airflow.sdk import task
except ImportError:
# Airflow 2 path
from airflow.decorators import task # type: ignore[attr-defined,no-redef]
try:
from airflow.sdk import TriggerRule
except ImportError:
# Compatibility for Airflow < 3.1
from airflow.utils.trigger_rule import TriggerRule # type: ignore[no-redef,attr-defined]
from google.genai.types import TuningDataset
from airflow.models.dag import DAG
from airflow.providers.google.cloud.operators.gen_ai import (
GenAISupervisedFineTuningTrainOperator,
)
from airflow.providers.google.common.utils.get_secret import get_secret
def _get_actual_model(key) -> str:
source_model: str | None = None
try:
response = requests.get("https://generativelanguage.googleapis.com/v1/models", {"key": key})
response.raise_for_status()
available_models = response.json()
except requests.exceptions.RequestException as e:
print(f"Error fetching models from API: {e}")
return ""
for model in available_models.get("models", []):
try:
model_name = model["name"].split("/")[-1]
splited_model_name = model_name.split("-")
if not source_model and "flash" in model_name:
source_model = model_name
elif (
source_model
and "flash" in model_name
and float(source_model.split("-")[1]) < float(splited_model_name[1])
):
source_model = model_name
elif (
source_model
and "flash" in model_name
and (
float(source_model.split("-")[1]) == float(splited_model_name[1])
and int(splited_model_name[-1]) > int(source_model.split("-")[-1])
)
):
source_model = model_name
except (ValueError, IndexError) as e:
print(f"Could not parse model name '{model.get('name')}'. Skipping. Error: {e}")
continue
if not source_model:
raise ValueError("Source model not found")
return source_model
[docs]
PROJECT_ID = os.environ.get("SYSTEM_TESTS_GCP_PROJECT", "default")
[docs]
DAG_ID = "gen_ai_generative_model_tuning_dag"
[docs]
GEMINI_API_KEY = "api_key"
[docs]
SOURCE_MODEL = "{{ task_instance.xcom_pull('get_actual_model') }}"
[docs]
TRAIN_DATASET = TuningDataset(
gcs_uri="gs://cloud-samples-data/ai-platform/generative_ai/gemini-1_5/text/sft_train_data.jsonl",
)
[docs]
TUNED_MODEL_DISPLAY_NAME = "my_tuned_gemini_model"
[docs]
TUNING_JOB_CONFIG = {"tuned_model_display_name": TUNED_MODEL_DISPLAY_NAME}
[docs]
TUNED_VIDEO_MODEL_DISPLAY_NAME = "my_tuned_gemini_video_model"
[docs]
TUNING_JOB_VIDEO_MODEL_CONFIG = {"tuned_model_display_name": TUNED_VIDEO_MODEL_DISPLAY_NAME}
[docs]
BUCKET_NAME = f"bucket_tuning_dag_{PROJECT_ID}"
[docs]
FILE_NAME = "video_tuning_dataset.jsonl"
[docs]
UPLOAD_FILE_PATH = str(Path(__file__).parent / "resources" / FILE_NAME)
[docs]
TRAIN_VIDEO_DATASET = TuningDataset(gcs_uri=f"gs://{BUCKET_NAME}/{FILE_NAME}")
with DAG(
dag_id=DAG_ID,
description="Sample DAG with generative model tuning tasks.",
schedule="@once",
start_date=datetime(2024, 1, 1),
catchup=False,
tags=["example", "vertex_ai", "generative_model"],
render_template_as_native_obj=True,
) as dag:
@task
[docs]
def get_gemini_api_key():
return get_secret(GEMINI_API_KEY)
get_gemini_api_key_task = get_gemini_api_key()
@task
def get_actual_model(key):
return _get_actual_model(key)
get_actual_model_task = get_actual_model(get_gemini_api_key_task)
create_bucket = GCSCreateBucketOperator(
task_id="create_bucket",
bucket_name=BUCKET_NAME,
project_id=PROJECT_ID,
)
upload_file = LocalFilesystemToGCSOperator(
task_id="upload_file",
src=UPLOAD_FILE_PATH,
dst=FILE_NAME,
bucket=BUCKET_NAME,
)
delete_bucket = GCSDeleteBucketOperator(task_id="delete_bucket", bucket_name=BUCKET_NAME)
# [START how_to_cloud_gen_ai_supervised_fine_tuning_train_operator]
sft_train_task = GenAISupervisedFineTuningTrainOperator(
task_id="sft_train_task",
project_id=PROJECT_ID,
location=REGION,
source_model=SOURCE_MODEL,
training_dataset=TRAIN_DATASET,
tuning_job_config=TUNING_JOB_CONFIG,
)
# [END how_to_cloud_gen_ai_supervised_fine_tuning_train_operator]
# [START how_to_cloud_gen_ai_supervised_fine_tuning_train_operator_for_video]
sft_video_task = GenAISupervisedFineTuningTrainOperator(
task_id="sft_train_video_task",
project_id=PROJECT_ID,
location=REGION,
source_model=SOURCE_MODEL,
training_dataset=TRAIN_VIDEO_DATASET,
tuning_job_config=TUNING_JOB_VIDEO_MODEL_CONFIG,
)
# [END how_to_cloud_gen_ai_supervised_fine_tuning_train_operator_for_video]
delete_bucket.trigger_rule = TriggerRule.ALL_DONE
(
get_gemini_api_key_task
>> get_actual_model_task
>> create_bucket
>> upload_file
>> [sft_train_task, sft_video_task]
>> delete_bucket
)
from tests_common.test_utils.watcher import watcher
# This test needs watcher in order to properly mark success/failure
# when "tearDown" task with trigger rule is part of the DAG
list(dag.tasks) >> watcher()
from tests_common.test_utils.system_tests 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)