diff --git a/flash/core/optimizers/optimizers.py b/flash/core/optimizers/optimizers.py
index 778f4ba247..5c5f9816a5 100644
--- a/flash/core/optimizers/optimizers.py
+++ b/flash/core/optimizers/optimizers.py
@@ -1,3 +1,4 @@
+from functools import partial
 from inspect import isclass
 from typing import Callable, List
 
@@ -17,7 +18,16 @@
             _optimizers.append(_optimizer)
 
     for fn in _optimizers:
-        _OPTIMIZERS_REGISTRY(fn, name=fn.__name__.lower())
+        name = fn.__name__.lower()
+        if name == "sgd":
+
+            def wrapper(fn, parameters, lr=None, **kwargs):
+                if lr is None:
+                    raise TypeError("The `learning_rate` argument is required when the optimizer is SGD.")
+                return fn(parameters, lr, **kwargs)
+
+            fn = partial(wrapper, fn)
+        _OPTIMIZERS_REGISTRY(fn, name=name)
 
 
 if _TORCH_OPTIMIZER_AVAILABLE:
diff --git a/tests/core/test_model.py b/tests/core/test_model.py
index e71f15582a..ce3f62fc44 100644
--- a/tests/core/test_model.py
+++ b/tests/core/test_model.py
@@ -374,6 +374,11 @@ def test_optimizer_learning_rate():
     ClassificationTask(model, optimizer="test", learning_rate=10).configure_optimizers()
     mock_optimizer.assert_called_once_with(mock.ANY, lr=10)
 
+    mock_optimizer.reset_mock()
+
+    with pytest.raises(TypeError, match="The `learning_rate` argument is required"):
+        ClassificationTask(model, optimizer="sgd").configure_optimizers()
+
 
 @pytest.mark.skipif(not _TORCH_OPTIMIZER_AVAILABLE, reason="torch_optimizer isn't installed.")
 @pytest.mark.parametrize("optim", ["Yogi"])