mik-laj commented on a change in pull request #7809: Run Dataflow for ML Engine
summary in venv
URL: https://github.com/apache/airflow/pull/7809#discussion_r396172233
##########
File path: airflow/providers/google/cloud/utils/mlengine_operator_utils.py
##########
@@ -225,20 +225,22 @@ def validate_err_and_count(summary):
metric_fn_encoded = base64.b64encode(dill.dumps(metric_fn,
recurse=True)).decode()
evaluate_summary = DataflowCreatePythonJobOperator(
task_id=(task_prefix + "-summary"),
- py_options=["-m"],
-
py_file="airflow.providers.google.cloud.utils.mlengine_prediction_summary",
+ py_file=os.path.join(os.path.dirname(__file__),
'mlengine_prediction_summary.py'),
dataflow_default_options=dataflow_options,
options={
"prediction_path": prediction_path,
"metric_fn_encoded": metric_fn_encoded,
"metric_keys": ','.join(metric_keys)
},
py_interpreter=py_interpreter,
+ py_requirements=[
+ 'apache-beam[gcp]>=2.14.0'
Review comment:
We install these libraries in an isolated environment outside of user code.
In this environment, we only run Airflow code. At this point, Airflow is not a
framework, but an application. We do not need an extension point here, i.e.
change versions. Version pinning is a good solution here to maintain stability.
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