diff --git a/CHANGELOG.md b/CHANGELOG.md
index 01076b46ec073..00bd7c84b0fb9 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -172,6 +172,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Enabled automatic parameters tying for TPUs ([#9525](https://github.com/PyTorchLightning/pytorch-lightning/pull/9525))
 
 
+- Added support for `torch.autograd.set_detect_anomaly` through `Trainer` constructor argument `detect_anomaly` ([#9848](https://github.com/PyTorchLightning/pytorch-lightning/pull/9848))
+
+
 ### Changed
 
 - `pytorch_lightning.loggers.neptune.NeptuneLogger` is now consistent with new [neptune-client](https://github.com/neptune-ai/neptune-client) API ([#6867](https://github.com/PyTorchLightning/pytorch-lightning/pull/6867)).
diff --git a/pytorch_lightning/trainer/trainer.py b/pytorch_lightning/trainer/trainer.py
index fa018a345262b..af225e708e343 100644
--- a/pytorch_lightning/trainer/trainer.py
+++ b/pytorch_lightning/trainer/trainer.py
@@ -177,6 +177,7 @@ def __init__(
         move_metrics_to_cpu: bool = False,
         multiple_trainloader_mode: str = "max_size_cycle",
         stochastic_weight_avg: bool = False,
+        detect_anomaly: bool = False,
     ):
         r"""
         Customize every aspect of training via flags.
@@ -223,6 +224,8 @@ def __init__(
                 Default: ``os.getcwd()``.
                 Can be remote file paths such as `s3://mybucket/path` or 'hdfs://path/'
 
+            detect_anomaly: Enable anomaly detection for the autograd engine.
+
             deterministic: If ``True``, sets whether PyTorch operations must use deterministic algorithms.
                 Default: ``False``.
 
@@ -488,6 +491,7 @@ def __init__(
             track_grad_norm,
             terminate_on_nan,
         )
+        self._detect_anomaly: bool = detect_anomaly
         self._setup_on_init(num_sanity_val_steps)
 
         # configure tuner
@@ -1184,7 +1188,8 @@ def _run_train(self) -> None:
         self.reset_train_val_dataloaders(model)
 
         self.fit_loop.trainer = self
-        self.fit_loop.run()
+        with torch.autograd.set_detect_anomaly(self._detect_anomaly):
+            self.fit_loop.run()
 
     def _run_evaluate(self) -> _EVALUATE_OUTPUT:
         if not self.is_global_zero and self.progress_bar_callback is not None:
diff --git a/tests/trainer/test_trainer.py b/tests/trainer/test_trainer.py
index 021812464e972..acb0c10df6c63 100644
--- a/tests/trainer/test_trainer.py
+++ b/tests/trainer/test_trainer.py
@@ -2079,3 +2079,19 @@ def test_step(self, *args, **kwargs):
     trainer.validate(model)
     trainer.test(model)
     trainer.predict(model)
+
+
+def test_detect_anomaly_nan(tmpdir):
+    class NanModel(BoringModel):
+        def training_step(self, batch, batch_idx):
+            output = super().training_step(batch, batch_idx)
+            output["loss"] = output["loss"] * torch.tensor(float("nan"))
+            return output
+
+    model = NanModel()
+    trainer = Trainer(default_root_dir=tmpdir, detect_anomaly=True)
+    with pytest.raises(RuntimeError, match=r"returned nan values in its 0th output."):
+        with pytest.warns(
+            UserWarning, match=r".*Error detected in.* Traceback of forward call that caused the error.*"
+        ):
+            trainer.fit(model)