def test_log_metrics_string_value_raise_error(self):
aiplatform.init(
project=_TEST_PROJECT, location=_TEST_LOCATION, experiment=_TEST_EXPERIMENT
)
aiplatform.start_run(_TEST_RUN)
with pytest.raises(TypeError):
aiplatform.log_metrics({"test": "string"})
def test_log_metrics(
self, update_artifact_mock,
):
aiplatform.init(
project=_TEST_PROJECT, location=_TEST_LOCATION, experiment=_TEST_EXPERIMENT
)
aiplatform.start_run(_TEST_RUN)
aiplatform.log_metrics(_TEST_METRICS)
updated_artifact = GapicArtifact(
name=_TEST_ARTIFACT_NAME,
display_name=_TEST_ARTIFACT_ID,
schema_title=constants.SYSTEM_METRICS,
schema_version=constants.SCHEMA_VERSIONS[constants.SYSTEM_METRICS],
metadata=_TEST_METRICS,
)
update_artifact_mock.assert_called_once_with(artifact=updated_artifact)
def test_experiment_logging(self, shared_state):
# Truncating the name because of resource id constraints from the service
experiment_name = self._make_display_name("experiment")[:56]
aiplatform.init(
project=e2e_base._PROJECT,
location=e2e_base._LOCATION,
experiment=experiment_name,
)
shared_state["resources"] = [
aiplatform.metadata.metadata_service._experiment
]
# Truncating the name because of resource id constraints from the service
run_name = self._make_display_name("run")[:56]
aiplatform.start_run(run_name)
shared_state["resources"].extend([
aiplatform.metadata.metadata_service._run,
aiplatform.metadata.metadata_service._metrics,
])
aiplatform.log_params(PARAMS)
aiplatform.log_metrics(METRICS)
df = aiplatform.get_experiment_df()
true_df_dict = {
f"metric.{key}": value
for key, value in METRICS.items()
}
for key, value in PARAMS.items():
true_df_dict[f"param.{key}"] = value
true_df_dict["experiment_name"] = experiment_name
true_df_dict["run_name"] = run_name
assert true_df_dict == df.to_dict("records")[0]