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Posted to commits@airflow.apache.org by GitBox <gi...@apache.org> on 2022/10/06 14:16:36 UTC
[GitHub] [airflow] MrGeorgeOwl opened a new pull request, #26915: Rewrite system tests for ML Engine service
MrGeorgeOwl opened a new pull request, #26915:
URL: https://github.com/apache/airflow/pull/26915
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[GitHub] [airflow] boring-cyborg[bot] commented on pull request #26915: Rewrite system tests for ML Engine service
Posted by GitBox <gi...@apache.org>.
boring-cyborg[bot] commented on PR #26915:
URL: https://github.com/apache/airflow/pull/26915#issuecomment-1296546504
Awesome work, congrats on your first merged pull request!
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[GitHub] [airflow] potiuk merged pull request #26915: Rewrite system tests for ML Engine service
Posted by GitBox <gi...@apache.org>.
potiuk merged PR #26915:
URL: https://github.com/apache/airflow/pull/26915
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[GitHub] [airflow] bhirsz commented on a diff in pull request #26915: Rewrite system tests for ML Engine service
Posted by GitBox <gi...@apache.org>.
bhirsz commented on code in PR #26915:
URL: https://github.com/apache/airflow/pull/26915#discussion_r989702419
##########
tests/system/providers/google/cloud/ml_engine/example_mlengine.py:
##########
@@ -216,28 +222,38 @@
# [END howto_operator_gcp_mlengine_get_prediction]
# [START howto_operator_gcp_mlengine_delete_version]
- delete_version = MLEngineDeleteVersionOperator(
- task_id="delete-version", project_id=PROJECT_ID, model_name=MODEL_NAME, version_name="v1"
+ delete_version_v1 = MLEngineDeleteVersionOperator(
+ task_id="delete-version-v1",
+ project_id=PROJECT_ID,
+ model_name=MODEL_NAME,
+ version_name="v1",
+ trigger_rule=TriggerRule.ALL_DONE,
Review Comment:
keep trigger_rule out of doc example - as unnecessary addition:
# [START..
operator code..
# [END..
operator.trigger_rule = TriggerRule.ALL_DONE
##########
tests/system/providers/google/cloud/ml_engine/example_mlengine.py:
##########
@@ -216,28 +222,38 @@
# [END howto_operator_gcp_mlengine_get_prediction]
# [START howto_operator_gcp_mlengine_delete_version]
- delete_version = MLEngineDeleteVersionOperator(
- task_id="delete-version", project_id=PROJECT_ID, model_name=MODEL_NAME, version_name="v1"
+ delete_version_v1 = MLEngineDeleteVersionOperator(
+ task_id="delete-version-v1",
+ project_id=PROJECT_ID,
+ model_name=MODEL_NAME,
+ version_name="v1",
+ trigger_rule=TriggerRule.ALL_DONE,
Review Comment:
keep trigger_rule out of doc example - as unnecessary addition:
```
# [START..
operator code..
# [END..
operator.trigger_rule = TriggerRule.ALL_DONE
```
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[GitHub] [airflow] bhirsz commented on a diff in pull request #26915: Rewrite system tests for ML Engine service
Posted by GitBox <gi...@apache.org>.
bhirsz commented on code in PR #26915:
URL: https://github.com/apache/airflow/pull/26915#discussion_r989700523
##########
tests/system/providers/google/cloud/ml_engine/example_mlengine.py:
##########
@@ -37,70 +40,74 @@
MLEngineStartBatchPredictionJobOperator,
MLEngineStartTrainingJobOperator,
)
+from airflow.providers.google.cloud.transfers.local_to_gcs import LocalFilesystemToGCSOperator
from airflow.providers.google.cloud.utils import mlengine_operator_utils
+from airflow.utils.trigger_rule import TriggerRule
-PROJECT_ID = os.environ.get("GCP_PROJECT_ID", "example-project")
+DAG_ID = "example_gcp_mlengine"
+BASE_DIR = pathlib.Path(__file__).parent.resolve()
+PREDICT_FILE_NAME = 'predict.json'
+PATH_TO_PREDICT_FILE = BASE_DIR / PREDICT_FILE_NAME
-MODEL_NAME = os.environ.get("GCP_MLENGINE_MODEL_NAME", "model_name")
-
-SAVED_MODEL_PATH = os.environ.get("GCP_MLENGINE_SAVED_MODEL_PATH", "gs://INVALID BUCKET NAME/saved-model/")
-JOB_DIR = os.environ.get("GCP_MLENGINE_JOB_DIR", "gs://INVALID BUCKET NAME/keras-job-dir")
-PREDICTION_INPUT = os.environ.get(
- "GCP_MLENGINE_PREDICTION_INPUT", "gs://INVALID BUCKET NAME/prediction_input.json"
-)
+PROJECT_ID = os.environ.get("GCP_PROJECT_ID")
+ENV_ID = os.environ.get("SYSTEM_TESTS_ENV_ID")
+MODEL_NAME = os.environ.get("GCP_MLENGINE_MODEL_NAME", f"example_mlengine_model_{ENV_ID}")
Review Comment:
Just define constant: MODEL_NAME = f"example_mlengine_model_{ENV_ID}"
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[GitHub] [airflow] bhirsz commented on a diff in pull request #26915: Rewrite system tests for ML Engine service
Posted by GitBox <gi...@apache.org>.
bhirsz commented on code in PR #26915:
URL: https://github.com/apache/airflow/pull/26915#discussion_r989700784
##########
tests/system/providers/google/cloud/ml_engine/example_mlengine.py:
##########
@@ -37,70 +40,74 @@
MLEngineStartBatchPredictionJobOperator,
MLEngineStartTrainingJobOperator,
)
+from airflow.providers.google.cloud.transfers.local_to_gcs import LocalFilesystemToGCSOperator
from airflow.providers.google.cloud.utils import mlengine_operator_utils
+from airflow.utils.trigger_rule import TriggerRule
-PROJECT_ID = os.environ.get("GCP_PROJECT_ID", "example-project")
+DAG_ID = "example_gcp_mlengine"
+BASE_DIR = pathlib.Path(__file__).parent.resolve()
+PREDICT_FILE_NAME = 'predict.json'
+PATH_TO_PREDICT_FILE = BASE_DIR / PREDICT_FILE_NAME
-MODEL_NAME = os.environ.get("GCP_MLENGINE_MODEL_NAME", "model_name")
-
-SAVED_MODEL_PATH = os.environ.get("GCP_MLENGINE_SAVED_MODEL_PATH", "gs://INVALID BUCKET NAME/saved-model/")
-JOB_DIR = os.environ.get("GCP_MLENGINE_JOB_DIR", "gs://INVALID BUCKET NAME/keras-job-dir")
-PREDICTION_INPUT = os.environ.get(
- "GCP_MLENGINE_PREDICTION_INPUT", "gs://INVALID BUCKET NAME/prediction_input.json"
-)
+PROJECT_ID = os.environ.get("GCP_PROJECT_ID")
+ENV_ID = os.environ.get("SYSTEM_TESTS_ENV_ID")
+MODEL_NAME = os.environ.get("GCP_MLENGINE_MODEL_NAME", f"example_mlengine_model_{ENV_ID}")
+BUCKET_NAME = os.environ.get("BUCKET_NAME", f"example_mlengine_bucket_{ENV_ID}")
Review Comment:
BUCKET_NAME = f"example_mlengine_bucket_{ENV_ID}"
same for other variables - don't use env variables if it's not necessary.
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[GitHub] [airflow] bhirsz commented on a diff in pull request #26915: Rewrite system tests for ML Engine service
Posted by GitBox <gi...@apache.org>.
bhirsz commented on code in PR #26915:
URL: https://github.com/apache/airflow/pull/26915#discussion_r989701818
##########
tests/system/providers/google/cloud/ml_engine/example_mlengine.py:
##########
@@ -37,70 +40,74 @@
MLEngineStartBatchPredictionJobOperator,
MLEngineStartTrainingJobOperator,
)
+from airflow.providers.google.cloud.transfers.local_to_gcs import LocalFilesystemToGCSOperator
from airflow.providers.google.cloud.utils import mlengine_operator_utils
+from airflow.utils.trigger_rule import TriggerRule
-PROJECT_ID = os.environ.get("GCP_PROJECT_ID", "example-project")
+DAG_ID = "example_gcp_mlengine"
+BASE_DIR = pathlib.Path(__file__).parent.resolve()
+PREDICT_FILE_NAME = 'predict.json'
+PATH_TO_PREDICT_FILE = BASE_DIR / PREDICT_FILE_NAME
-MODEL_NAME = os.environ.get("GCP_MLENGINE_MODEL_NAME", "model_name")
-
-SAVED_MODEL_PATH = os.environ.get("GCP_MLENGINE_SAVED_MODEL_PATH", "gs://INVALID BUCKET NAME/saved-model/")
-JOB_DIR = os.environ.get("GCP_MLENGINE_JOB_DIR", "gs://INVALID BUCKET NAME/keras-job-dir")
-PREDICTION_INPUT = os.environ.get(
- "GCP_MLENGINE_PREDICTION_INPUT", "gs://INVALID BUCKET NAME/prediction_input.json"
-)
+PROJECT_ID = os.environ.get("GCP_PROJECT_ID")
+ENV_ID = os.environ.get("SYSTEM_TESTS_ENV_ID")
+MODEL_NAME = os.environ.get("GCP_MLENGINE_MODEL_NAME", f"example_mlengine_model_{ENV_ID}")
+BUCKET_NAME = os.environ.get("BUCKET_NAME", f"example_mlengine_bucket_{ENV_ID}")
+BUCKET_PATH = os.environ.get("BUCKET_PATH", f"gs://{BUCKET_NAME}")
+JOB_DIR = os.environ.get("GCP_MLENGINE_JOB_DIR", f"{BUCKET_PATH}/job-dir")
+SAVED_MODEL_PATH = os.environ.get("GCP_MLENGINE_SAVED_MODEL_PATH", f"{JOB_DIR}/")
+PREDICTION_INPUT = os.environ.get("GCP_MLENGINE_PREDICTION_INPUT", f"{BUCKET_PATH}/{PREDICT_FILE_NAME}")
PREDICTION_OUTPUT = os.environ.get(
- "GCP_MLENGINE_PREDICTION_OUTPUT", "gs://INVALID BUCKET NAME/prediction_output"
+ "GCP_MLENGINE_PREDICTION_OUTPUT", "gs://INVALID BUCKET NAME/prediction_output/"
+)
+TRAINER_URI = os.environ.get(
+ "GCP_MLENGINE_TRAINER_URI",
+ "gs://system-tests-resources/example_gcp_mlengine/trainer-0.1.tar.gz",
+)
+TRAINER_PY_MODULE = os.environ.get(
+ "GCP_MLENGINE_TRAINER_TRAINER_PY_MODULE",
+ "trainer.task",
)
-TRAINER_URI = os.environ.get("GCP_MLENGINE_TRAINER_URI", "gs://INVALID BUCKET NAME/trainer.tar.gz")
-TRAINER_PY_MODULE = os.environ.get("GCP_MLENGINE_TRAINER_TRAINER_PY_MODULE", "trainer.task")
+SUMMARY_TMP = os.environ.get("GCP_MLENGINE_DATAFLOW_TMP", f"{BUCKET_PATH}/tmp/")
+SUMMARY_STAGING = os.environ.get("GCP_MLENGINE_DATAFLOW_STAGING", f"{BUCKET_PATH}/staging/")
-SUMMARY_TMP = os.environ.get("GCP_MLENGINE_DATAFLOW_TMP", "gs://INVALID BUCKET NAME/tmp/")
-SUMMARY_STAGING = os.environ.get("GCP_MLENGINE_DATAFLOW_STAGING", "gs://INVALID BUCKET NAME/staging/")
+
+def generate_model_predict_input_data() -> list[int]:
+ return [i for i in range(0, 201, 10)]
with models.DAG(
- "example_gcp_mlengine",
+ dag_id=DAG_ID,
+ schedule="@once",
start_date=datetime(2021, 1, 1),
catchup=False,
- tags=['example'],
+ tags=['example', 'ml_engine'],
params={"model_name": MODEL_NAME},
) as dag:
- hyperparams: dict[str, Any] = {
- 'goal': 'MAXIMIZE',
- 'hyperparameterMetricTag': 'metric1',
- 'maxTrials': 30,
- 'maxParallelTrials': 1,
- 'enableTrialEarlyStopping': True,
- 'params': [],
- }
-
- hyperparams['params'].append(
- {
- 'parameterName': 'hidden1',
- 'type': 'INTEGER',
- 'minValue': 40,
- 'maxValue': 400,
- 'scaleType': 'UNIT_LINEAR_SCALE',
- }
+ create_bucket = GCSCreateBucketOperator(
+ task_id="create-bucket",
+ bucket_name=BUCKET_NAME,
)
- hyperparams['params'].append(
- {'parameterName': 'numRnnCells', 'type': 'DISCRETE', 'discreteValues': [1, 2, 3, 4]}
+ def write_predict_file(path_to_file: str):
+ predict_data = generate_model_predict_input_data()
+ with open(path_to_file, 'w') as file:
+ for p in predict_data:
+ file.write(f'{{"input_layer": [{p}]}}\n')
+
+ write_data = PythonOperator(
+ task_id="write-predict-data-file",
+ python_callable=write_predict_file,
+ op_args=(PATH_TO_PREDICT_FILE,),
)
Review Comment:
The code is fine but with the introduction of ``@task`` it's recommended more: https://airflow.apache.org/docs/apache-airflow/stable/howto/operator/python.html
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