antoniocali commented on a change in pull request #10578:
URL: https://github.com/apache/airflow/pull/10578#discussion_r483426666
##########
File path: tests/providers/google/cloud/operators/test_mlengine.py
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@@ -350,6 +350,45 @@ def test_success_create_training_job(self, mock_hook):
project_id='test-project', job=self.TRAINING_INPUT,
use_existing_job_fn=ANY
)
+ @patch('airflow.providers.google.cloud.operators.mlengine.MLEngineHook')
+ def test_success_create_training_job_with_master_config(self, mock_hook):
+ custom_training_default_args: dict =
copy.deepcopy(self.TRAINING_DEFAULT_ARGS)
+ custom_training_default_args['scaleTier'] = 'CUSTOM'
Review comment:
I'm trying to understand how to write correctly the test honestly
This is the class
[class
TestMLEngineTrainingOperator](https://github.com/apache/airflow/blob/93aceab53ae9552f77f3bddf1b51d3e06c988cd2/tests/providers/google/cloud/operators/test_mlengine.py#L306)
If you take a look at the class there are 2 dictionary
`TRAINING_DEFAULT_ARGS` and `TRAINING_INPUT`, both of them contain a
scale_tier/scaleTier parameter.
The thing is that I have to overwrite that to CUSTOM to be able to run a
masterType and masterConfig.
if you look at the [Rest
Documentation](https://cloud.google.com/ai-platform/training/docs/reference/rest/v1/projects.jobs#traininginput),
it shows a trainingInput config, where scaleTier appears -> As much as I know
from my understanding, all of Google Operators at the end just do a HookBase
Operator that does a rest api call.
I'm tring to fully understand why the test fails, anyway I've prepared a new
commit and trying to verify. Thank you @mik-laj for your support, and if you
have a better idea how to solve it please let me know
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