diff --git a/benchmark/train/configs/financial.yaml b/benchmark/train/configs/financial.yaml
index f98e5675..2641f339 100644
--- a/benchmark/train/configs/financial.yaml
+++ b/benchmark/train/configs/financial.yaml
@@ -45,3 +45,4 @@ optim:
   optimizer: adam
   base_lr: 0.001
   max_epoch: 50
+  early_stopping: False
diff --git a/benchmark/train/main.py b/benchmark/train/main.py
index c757b35c..4da419b2 100644
--- a/benchmark/train/main.py
+++ b/benchmark/train/main.py
@@ -5,6 +5,7 @@
 import os
 import pytorch_lightning as pl
 from pytorch_lightning import loggers as pl_loggers
+from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
 from torch_geometric import seed_everything
 from torch_geometric.graphgym.cmd_args import parse_args
 from torch_geometric.graphgym.config import (load_cfg, get_fname)
@@ -57,11 +58,30 @@
 
         # Training
         gpus = 1 if cfg.device != 'cpu' and torch.cuda.is_available() else 0
+        if cfg.optim.early_stopping:
+            patience = cfg.optim.patience
+            if patience is None:
+                patience = cfg.optim.max_epoch // 5
+            early_stopping = EarlyStopping(monitor="val_loss",
+                                           min_delta=cfg.optim.min_delta,
+                                           patience=patience, mode="min")
+            ckpt = ModelCheckpoint(save_top_k=1, monitor='val_loss',
+                                   mode='min')
+            callbacks = [early_stopping, ckpt]
+        else:
+            callbacks = None
+
         trainer = pl.Trainer(gpus=gpus, log_every_n_steps=1,
-                             max_epochs=cfg.optim.max_epoch, logger=tb_logger)
+                             max_epochs=cfg.optim.max_epoch, logger=tb_logger,
+                             callbacks=callbacks)
         # visualization
         visualize_scalar_distribution(
             dataset[cfg.dataset.target_table].y,
             os.path.join(tb_logger.log_dir, "target_distribution.png"),
         )
         trainer.fit(model, loaders[0], loaders[1])
+        # test
+        print('Validation on best epoch:')
+        trainer.validate(ckpt_path='best', dataloaders=loaders[1])
+        print('Test on best epoch:')
+        trainer.test(ckpt_path='best', dataloaders=loaders[2])
diff --git a/kumo/config/config.py b/kumo/config/config.py
index 52df3957..990daa63 100644
--- a/kumo/config/config.py
+++ b/kumo/config/config.py
@@ -53,6 +53,12 @@ def set_cfg(cfg):
     # Restriction: the split column has to be in the prediction target table.
     cfg.dataset.split_column = None
 
+    # early stopping configs
+    cfg.optim.early_stopping = False
+    cfg.optim.min_delta = 0.001
+    # if None, set patience = total num epochs / 10
+    cfg.optim.patience = None
+
     # Overwrite GraphGym scheduler
     # (might improve default optimizer after more training experiences
     # on databases)
diff --git a/kumo/model/model_builder.py b/kumo/model/model_builder.py
index 240c2324..16849d19 100644
--- a/kumo/model/model_builder.py
+++ b/kumo/model/model_builder.py
@@ -148,6 +148,30 @@ def training_step(self, batch, batch_idx):
             self._eval_regression(y, pred_value)
         return loss
 
+    @torch.no_grad()
+    def _eval_metric(self, pred_value, label, loss, prefix="val"):
+        """ Validation and test evaluation metrics. """
+        if self.cfg.dataset.task_type == "classification":
+            pred_int = self._get_pred_int(pred_value)
+            acc = round(
+                accuracy_score(label.cpu(), pred_int.cpu()),
+                self.cfg.round,
+            )
+            auc = self._eval_auroc(label, pred_value)
+
+            self.log(f'{prefix}_acc', acc, on_step=False, on_epoch=True,
+                     prog_bar=True)
+            if auc is not None:
+                self.log(f'{prefix}_auc', auc, on_step=False, on_epoch=True,
+                         prog_bar=True)
+        elif self.cfg.dataset.task_type == "regression":
+            if self.current_epoch >= PLOT_EPOCH_THRESHOLD and \
+                    loss < self.val_best_loss:
+                self.plot_error_distribution(pred_value, label, metric="err",
+                                             prefix=prefix)
+                self.val_best_loss = loss
+            self._eval_regression(label, pred_value, prefix=prefix)
+
     def validation_step(self, batch, batch_idx):
         """"""
         feat_dict, discrete_feat_dict, edge_index_dict = (
@@ -162,26 +186,24 @@ def validation_step(self, batch, batch_idx):
                              mask)
         loss, pred_value = compute_loss(pred, y)
 
-        if self.cfg.dataset.task_type == "classification":
-            pred_int = self._get_pred_int(pred_value)
-            acc = round(
-                accuracy_score(y.detach().cpu(), pred_int.cpu()),
-                self.cfg.round,
-            )
-            auc = self._eval_auroc(y, pred_value)
+        self._eval_metric(pred_value, y, loss, prefix="val")
+        return loss
 
-            self.log("val_acc", acc, on_step=False, on_epoch=True,
-                     prog_bar=True)
-            if auc is not None:
-                self.log("val_auc", auc, on_step=False, on_epoch=True,
-                         prog_bar=True)
-        elif self.cfg.dataset.task_type == "regression":
-            if self.current_epoch >= PLOT_EPOCH_THRESHOLD and \
-                    loss < self.val_best_loss:
-                self.plot_error_distribution(pred_value, y, metric="err",
-                                             prefix="val")
-                self.val_best_loss = loss
-            self._eval_regression(y, pred_value, prefix="val")
+    def test_step(self, batch, batch_idx):
+        """"""
+        feat_dict, discrete_feat_dict, edge_index_dict = (
+            batch.feat_dict,
+            batch.discrete_feat_dict,
+            batch.edge_index_dict,
+        )
+        y = batch[self.target_table].y
+        mask = batch[self.target_table].test_mask
+
+        pred, y = self.model(feat_dict, edge_index_dict, discrete_feat_dict, y,
+                             mask)
+        loss, pred_value = compute_loss(pred, y)
+
+        self._eval_metric(pred_value, y, loss, prefix="test")
         return loss
 
     def _configure_scheduler(self, optimizer, scheduler, min_lr=0.0001):