test_mlflow.py

Author: KAVYANSHTYAGI

tests/tests_pytorch/loggers/test_mlflow.py

@@ -427,35 +427,3 @@ def test_set_tracking_uri(mlflow_mock):
     mlflow_mock.set_tracking_uri.assert_not_called()
     _ = logger.experiment
     mlflow_mock.set_tracking_uri.assert_called_with("the_tracking_uri")
-
-def test_mlflowlogger_metric_deduplication(monkeypatch):
-    import types
-    from lightning.pytorch.loggers.mlflow import MLFlowLogger
-
-    # Dummy MLflow client to record log_batch calls
-    logged_metrics = []
-    class DummyMlflowClient:
-        def log_batch(self, run_id, metrics, **kwargs):
-            logged_metrics.extend(metrics)
-        def set_tracking_uri(self, uri): pass
-        def create_run(self, experiment_id, tags): 
-            class Run: info = types.SimpleNamespace(run_id="dummy_run_id")
-            return Run()
-        def get_run(self, run_id):
-            class Run: info = types.SimpleNamespace(experiment_id="dummy_experiment_id")
-            return Run()
-        def get_experiment_by_name(self, name): return None
-        def create_experiment(self, name, artifact_location=None): return "dummy_experiment_id"
-
-    # Patch the MLFlowLogger to use DummyMlflowClient
-    monkeypatch.setattr("mlflow.tracking.MlflowClient", lambda *a, **k: DummyMlflowClient())
-
-    logger = MLFlowLogger(experiment_name="test_exp")
-    logger.log_metrics({'foo': 1.0}, step=5)
-    logger.log_metrics({'foo': 1.0}, step=5)  # duplicate
-
-    # Only the first metric should be logged
-    assert len(logged_metrics) == 1
-    assert logged_metrics[0].key == "foo"
-    assert logged_metrics[0].value == 1.0
-    assert logged_metrics[0].step == 5