remove deprecated test (#3820)

tests/core/test_results.py

@@ -119,63 +119,6 @@ def test_result_obj_predictions(tmpdir, test_option, do_train, gpus):
     assert len(predictions) == len(dm.mnist_test)
 
 
-@pytest.mark.skipif(torch.cuda.device_count() < 2, reason="test requires multi-GPU machine")
-def test_result_obj_predictions_ddp_spawn(tmpdir):
-    seed_everything(4321)
-
-    distributed_backend = 'ddp_spawn'
-    option = 0
-
-    import os
-    os.environ['CUDA_VISIBLE_DEVICES'] = '0,1'
-
-    dm = TrialMNISTDataModule(tmpdir)
-
-    prediction_file = Path(tmpdir) / 'predictions.pt'
-
-    model = EvalModelTemplate(learning_rate=0.005)
-    model.test_option = option
-    model.prediction_file = prediction_file.as_posix()
-    model.test_step = model.test_step_result_preds
-    model.test_step_end = None
-    model.test_epoch_end = None
-    model.test_end = None
-
-    prediction_files = [Path(tmpdir) / 'predictions_rank_0.pt', Path(tmpdir) / 'predictions_rank_1.pt']
-    for prediction_file in prediction_files:
-        if prediction_file.exists():
-            prediction_file.unlink()
-
-    trainer = Trainer(
-        default_root_dir=tmpdir,
-        max_epochs=3,
-        weights_summary=None,
-        deterministic=True,
-        distributed_backend=distributed_backend,
-        gpus=[0, 1]
-    )
-
-    # Prediction file shouldn't exist yet because we haven't done anything
-    # assert not model.prediction_file.exists()
-
-    result = trainer.fit(model, dm)
-    assert result == 1
-    result = trainer.test(datamodule=dm)
-    result = result[0]
-    assert result['test_loss'] < 0.6
-    assert result['test_acc'] > 0.8
-
-    dm.setup('test')
-
-    # check prediction file now exists and is of expected length
-    size = 0
-    for prediction_file in prediction_files:
-        assert prediction_file.exists()
-        predictions = torch.load(prediction_file)
-        size += len(predictions)
-    assert size == len(dm.mnist_test)
-
-
 def test_result_gather_stack():
     """ Test that tensors get concatenated when they all have the same shape. """
     outputs = [