Skip to content

Commit 486400b

Browse files
TheMichaelHuTheMichaelHu
committed
clean up resources
1 parent c1161fa commit 486400b

File tree

1 file changed

+79
-71
lines changed

1 file changed

+79
-71
lines changed

‎tests/system/aiplatform/test_e2e_forecasting.py

+79-71
Original file line numberDiff line numberDiff line change
@@ -38,79 +38,87 @@ class TestEndToEndForecasting(e2e_base.TestEndToEnd):
3838
def test_end_to_end_forecasting(self, shared_state):
3939
"""Builds a dataset, trains models, and gets batch predictions."""
4040

41-
# Collection of resources generated by this test, to be deleted during
42-
# teardown.
43-
shared_state["resources"] = []
44-
4541
aiplatform.init(
4642
project=e2e_base._PROJECT,
4743
location=e2e_base._LOCATION,
4844
staging_bucket=shared_state["staging_bucket_name"],
4945
)
50-
51-
# Create and import to single managed dataset for both training jobs.
52-
ds = aiplatform.TimeSeriesDataset.create(
53-
display_name=self._make_display_name("dataset"),
54-
bq_source=[_TRAINING_DATASET_BQ_PATH],
55-
sync=False,
56-
create_request_timeout=180.0,
57-
)
58-
shared_state["resources"].extend([ds])
59-
60-
time_column = "date"
61-
time_series_identifier_column = "store_name"
62-
target_column = "sale_dollars"
63-
column_specs = {
64-
time_column: "timestamp",
65-
target_column: "numeric",
66-
"city": "categorical",
67-
"zip_code": "categorical",
68-
"county": "categorical",
69-
}
70-
71-
# Define both training jobs
72-
# TODO(humichael): Add seq2seq job.
73-
automl_job = aiplatform.AutoMLForecastingTrainingJob(
74-
display_name=self._make_display_name("train-housing-automl"),
75-
optimization_objective="minimize-rmse",
76-
column_specs=column_specs,
77-
)
78-
79-
# Kick off both training jobs, AutoML job will take approx one hour to
80-
# run.
81-
automl_model = automl_job.run(
82-
dataset=ds,
83-
target_column=target_column,
84-
time_column=time_column,
85-
time_series_identifier_column=time_series_identifier_column,
86-
available_at_forecast_columns=[time_column],
87-
unavailable_at_forecast_columns=[target_column],
88-
time_series_attribute_columns=["city", "zip_code", "county"],
89-
forecast_horizon=30,
90-
context_window=30,
91-
data_granularity_unit="day",
92-
data_granularity_count=1,
93-
budget_milli_node_hours=1000,
94-
model_display_name=self._make_display_name("automl-liquor-model"),
95-
sync=False,
96-
)
97-
shared_state["resources"].extend([automl_job, automl_model])
98-
99-
automl_batch_prediction_job = automl_model.batch_predict(
100-
job_display_name=self._make_display_name("automl-liquor-model"),
101-
instances_format="bigquery",
102-
machine_type="n1-standard-4",
103-
bigquery_source=_PREDICTION_DATASET_BQ_PATH,
104-
gcs_destination_prefix=(
105-
f'gs://{shared_state["staging_bucket_name"]}/bp_results/'
106-
),
107-
sync=False,
108-
)
109-
shared_state["resources"].append(automl_batch_prediction_job)
110-
111-
automl_batch_prediction_job.wait()
112-
113-
assert automl_job.state == pipeline_state.PipelineState.PIPELINE_STATE_SUCCEEDED
114-
assert (
115-
automl_batch_prediction_job.state == job_state.JobState.JOB_STATE_SUCCEEDED
116-
)
46+
try:
47+
# Create and import to single managed dataset for both training
48+
# jobs.
49+
ds = aiplatform.TimeSeriesDataset.create(
50+
display_name=self._make_display_name("dataset"),
51+
bq_source=[_TRAINING_DATASET_BQ_PATH],
52+
sync=False,
53+
create_request_timeout=180.0,
54+
)
55+
56+
time_column = "date"
57+
time_series_identifier_column = "store_name"
58+
target_column = "sale_dollars"
59+
column_specs = {
60+
time_column: "timestamp",
61+
target_column: "numeric",
62+
"city": "categorical",
63+
"zip_code": "categorical",
64+
"county": "categorical",
65+
}
66+
67+
# Define both training jobs
68+
# TODO(humichael): Add seq2seq job.
69+
automl_job = aiplatform.AutoMLForecastingTrainingJob(
70+
display_name=self._make_display_name("train-housing-automl"),
71+
optimization_objective="minimize-rmse",
72+
column_specs=column_specs,
73+
)
74+
75+
# Kick off both training jobs, AutoML job will take approx one hour
76+
# to run.
77+
automl_model = automl_job.run(
78+
dataset=ds,
79+
target_column=target_column,
80+
time_column=time_column,
81+
time_series_identifier_column=time_series_identifier_column,
82+
available_at_forecast_columns=[time_column],
83+
unavailable_at_forecast_columns=[target_column],
84+
time_series_attribute_columns=["city", "zip_code", "county"],
85+
forecast_horizon=30,
86+
context_window=30,
87+
data_granularity_unit="day",
88+
data_granularity_count=1,
89+
budget_milli_node_hours=1000,
90+
model_display_name=self._make_display_name("automl-liquor-model"),
91+
sync=False,
92+
)
93+
94+
automl_batch_prediction_job = automl_model.batch_predict(
95+
job_display_name=self._make_display_name("automl-liquor-model"),
96+
instances_format="bigquery",
97+
machine_type="n1-standard-4",
98+
bigquery_source=_PREDICTION_DATASET_BQ_PATH,
99+
gcs_destination_prefix=(
100+
f'gs://{shared_state["staging_bucket_name"]}/bp_results/'
101+
),
102+
sync=False,
103+
)
104+
105+
automl_batch_prediction_job.wait()
106+
107+
assert (
108+
automl_job.state
109+
== pipeline_state.PipelineState.PIPELINE_STATE_SUCCEEDED
110+
)
111+
assert (
112+
automl_batch_prediction_job.state
113+
== job_state.JobState.JOB_STATE_SUCCEEDED
114+
)
115+
finally:
116+
resources = [
117+
"ds",
118+
"automl_job",
119+
"automl_model",
120+
"automl_batch_prediction_job",
121+
]
122+
for resource in resources:
123+
if resource in locals():
124+
resource.delete()

0 commit comments

Comments
 (0)