diff --git a/ignite/engine/engine.py b/ignite/engine/engine.py
index a013fda29b1..f4333eee324 100644
--- a/ignite/engine/engine.py
+++ b/ignite/engine/engine.py
@@ -125,6 +125,23 @@ def compute_mean_std(engine, batch):
     _state_dict_all_req_keys = ("epoch_length", "max_epochs")
     _state_dict_one_of_opt_keys = ("iteration", "epoch")
 
+    class debug_mode(EventEnum):
+        DEBUG_NONE = 0
+        DEBUG_EVENTS = 1
+        DEBUG_OUTPUT = 2
+        DEBUG_GRADS = 4
+
+        def __iter__(self) -> Iterator:
+            return iter(self.name)
+
+        def __int__(self) -> str:
+            return self.value
+
+    DEBUG_NONE = debug_mode.DEBUG_NONE
+    DEBUG_EVENTS = debug_mode.DEBUG_EVENTS
+    DEBUG_OUTPUT = debug_mode.DEBUG_OUTPUT
+    DEBUG_GRADS = debug_mode.DEBUG_GRADS
+
     # Flag to disable engine._internal_run as generator feature for BC
     interrupt_resume_enabled = True
 
@@ -143,6 +160,8 @@ def __init__(self, process_function: Callable[["Engine", Any], Any]):
         self._dataloader_iter: Optional[Iterator[Any]] = None
         self._init_iter: Optional[int] = None
 
+        self.debug_level = 0
+
         self.register_events(*Events)
 
         if self._process_function is None:
@@ -425,6 +444,28 @@ def _fire_event(self, event_name: Any, *event_args: Any, **event_kwargs: Any) ->
             first, others = ((args[0],), args[1:]) if (args and args[0] == self) else ((), args)
             func(*first, *(event_args + others), **kwargs)
 
+    def debug(self, level: debug_mode = DEBUG_NONE, config: Union[Dict, Any] = None) -> None:
+        if isinstance(level, int):
+            raise ValueError(
+                f"Unknown event name '{level}'. Level should be combinations of Engine.DEBUG_NONE, "
+                f"Engine.DEBUG_EVENTS, Engine.DEBUG_OUTPUT, Engine.DEBUG_GRADS"
+            )
+        self.lr = config["optimizer"].param_groups[0]["lr"]
+        self.layer = config["layer"]
+
+        log = ""
+        for item in level:
+            if item == Engine.DEBUG_NONE:
+                log += ""
+            elif item == Engine.DEBUG_EVENTS:
+                log += f"{self.state.epoch} | {self.state.iteration}, Firing handlers for event {self.last_event_name} "
+            elif item == Engine.DEBUG_OUTPUT:
+                log += f"Loss : {self.state.output}, LR : {self.lr} "
+            elif item == Engine.DEBUG_GRADS:
+                log += f"Gradients : {self.layer.weight.grad} "
+
+        self.logger.debug(log)
+
     def fire_event(self, event_name: Any) -> None:
         """Execute all the handlers associated with given event.
 
diff --git a/ignite/engine/events.py b/ignite/engine/events.py
index ef76babdc5b..4bf83ddbb69 100644
--- a/ignite/engine/events.py
+++ b/ignite/engine/events.py
@@ -445,6 +445,7 @@ def __init__(self, **kwargs: Any) -> None:
         self.batch: Optional[int] = None
         self.metrics: Dict[str, Any] = {}
         self.dataloader: Optional[Union[DataLoader, Iterable[Any]]] = None
+        self.debug_config: Dict[str, Any] = {}
         self.seed: Optional[int] = None
         self.times: Dict[str, Optional[float]] = {
             Events.EPOCH_COMPLETED.name: None,
diff --git a/tests/ignite/engine/test_engine.py b/tests/ignite/engine/test_engine.py
index 994eb49f72b..161a4ee5ab5 100644
--- a/tests/ignite/engine/test_engine.py
+++ b/tests/ignite/engine/test_engine.py
@@ -1388,3 +1388,76 @@ def check_iter_epoch():
     state = engine.run(data, max_epochs=max_epochs)
     assert state.iteration == max_epochs * len(data) and state.epoch == max_epochs
     assert num_calls_check_iter_epoch == 1
+
+
+def test_engine_debug():
+    import torch.nn.functional as F
+    from torch import nn
+    from torch.optim import SGD
+    from torch.utils.data import DataLoader
+    from torchvision.datasets import MNIST
+    from torchvision.transforms import Compose, ToTensor
+
+    from ignite.engine import create_supervised_trainer
+
+    class Net(nn.Module):
+        def __init__(self):
+            super(Net, self).__init__()
+            self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
+            self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
+            self.conv2_drop = nn.Dropout2d()
+            self.fc1 = nn.Linear(320, 50)
+            self.fc2 = nn.Linear(50, 10)
+
+        def forward(self, x):
+            x = F.relu(F.max_pool2d(self.conv1(x), 2))
+            x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
+            x = x.view(-1, 320)
+            x = F.relu(self.fc1(x))
+            x = F.dropout(x, training=self.training)
+            x = self.fc2(x)
+            return F.log_softmax(x, dim=-1)
+
+    def _test():
+        train_loader = DataLoader(
+            MNIST(download=True, root=".", transform=Compose([ToTensor()]), train=True),
+            batch_size=64,
+            shuffle=True,
+        )
+
+        model = Net()
+        device = "cpu"
+        log_interval = 10
+        epochs = 10
+
+        if torch.cuda.is_available():
+            device = "cuda"
+
+        model.to(device)  # Move model before creating optimizer
+        optimizer = SGD(model.parameters(), lr=0.01, momentum=0.5)
+        criterion = nn.NLLLoss()
+        trainer = create_supervised_trainer(model, optimizer, criterion, device=device)
+        debug_config = {}
+        debug_config["optimizer"] = optimizer
+        debug_config["layer"] = model.fc2
+
+        def log_training_debug_events(engine):
+            trainer.debug(level=Engine.DEBUG_EVENTS, config=debug_config)
+
+        def log_training_debug_outputs(engine):
+            trainer.debug(level=Engine.DEBUG_OUTPUT, config=debug_config)
+
+        def log_training_debug_grads(engine):
+            trainer.debug(level=Engine.DEBUG_GRADS, config=debug_config)
+
+        def log_training_debug_int(engine):
+            with pytest.raises(
+                ValueError,
+                match=r"Unknown event name '2'. Level should be combinations of Engine.DEBUG_NONE, "
+                r"Engine.DEBUG_EVENTS, Engine.DEBUG_OUTPUT, Engine.DEBUG_GRADS",
+            ):
+                trainer.debug(level=2, config=debug_config)
+
+        trainer.run(train_loader, max_epochs=epochs)
+
+    _test()