diff --git a/torchtnt/runner/evaluate.py b/torchtnt/runner/evaluate.py
index ad90a045e5..a7b760b0c2 100644
--- a/torchtnt/runner/evaluate.py
+++ b/torchtnt/runner/evaluate.py
@@ -10,7 +10,7 @@
 import torch
 from torchtnt.runner.callback import Callback
 
-from torchtnt.runner.state import EntryPoint, PhaseState, State
+from torchtnt.runner.state import ActivePhase, EntryPoint, PhaseState, State
 from torchtnt.runner.unit import TEvalData, TEvalUnit
 from torchtnt.runner.utils import (
     _is_epoch_done,
@@ -89,6 +89,8 @@ def _evaluate_impl(
     eval_state = state.eval_state
     if not eval_state:
         raise RuntimeError("Expected eval_state to be initialized!")
+
+    state._active_phase = ActivePhase.EVALUATE
     logger.info(
         f"Started evaluate with max_steps_per_epoch={eval_state.max_steps_per_epoch}"
     )
diff --git a/torchtnt/runner/predict.py b/torchtnt/runner/predict.py
index 9c6eeb1f0c..6ad2ad44c5 100644
--- a/torchtnt/runner/predict.py
+++ b/torchtnt/runner/predict.py
@@ -10,7 +10,7 @@
 import torch
 from torchtnt.runner.callback import Callback
 
-from torchtnt.runner.state import EntryPoint, PhaseState, State
+from torchtnt.runner.state import ActivePhase, EntryPoint, PhaseState, State
 from torchtnt.runner.unit import TPredictData, TPredictUnit
 from torchtnt.runner.utils import (
     _is_epoch_done,
@@ -89,6 +89,8 @@ def _predict_impl(
     predict_state = state.predict_state
     if not predict_state:
         raise RuntimeError("Expected predict_state to be initialized!")
+
+    state._active_phase = ActivePhase.PREDICT
     logger.info(
         f"Started predict with max_steps_per_epoch={predict_state.max_steps_per_epoch}"
     )
diff --git a/torchtnt/runner/state.py b/torchtnt/runner/state.py
index 5378251c29..366a828481 100644
--- a/torchtnt/runner/state.py
+++ b/torchtnt/runner/state.py
@@ -27,12 +27,39 @@ def _check_loop_condition(name: str, val: Optional[int]) -> None:
 
 
 class EntryPoint(Enum):
+    """
+    Enum for the user-facing functions offered by the TorchTNT runner module.
+    - :py:func:`~torchtnt.runner.fit`
+    - :py:func:`~torchtnt.runner.train`
+    - :py:func:`~torchtnt.runner.evaluate`
+    - :py:func:`~torchtnt.runner.predict`
+    """
+
     FIT = auto()
     TRAIN = auto()
     EVALUATE = auto()
     PREDICT = auto()
 
 
+class ActivePhase(Enum):
+    """Enum for the currently active phase.
+
+    This class complements :class:`EntryPoint` by specifying the active phase for each function.
+    More than one phase value can be set while a :class:`EntryPoint` is running:
+        - ``EntryPoint.FIT`` - ``ActivePhase.{TRAIN,EVALUATE}``
+        - ``EntryPoint.TRAIN`` - ``ActivePhase.TRAIN``
+        - ``EntryPoint.EVALUATE`` - ``ActivePhase.EVALUATE``
+        - ``EntryPoint.PREDICT`` - ``ActivePhase.PREDICT``
+
+    This can be used within hooks such as :meth:`~torchtnt.runner.unit._OnExceptionMixin.on_exception`
+    to determine within which of training, evaluation, or prediction the hook is being called.
+    """
+
+    TRAIN = auto()
+    EVALUATE = auto()
+    PREDICT = auto()
+
+
 class PhaseState:
     """State for each phase (train, eval, predict)"""
 
@@ -116,11 +143,16 @@ def __init__(
         self._eval_state = eval_state
         self._predict_state = predict_state
         self._should_stop: bool = False
+        self._active_phase: ActivePhase = ActivePhase.TRAIN
 
     @property
     def entry_point(self) -> EntryPoint:
         return self._entry_point
 
+    @property
+    def active_phase(self) -> ActivePhase:
+        return self._active_phase
+
     @property
     def timer(self) -> Timer:
         return self._timer
diff --git a/torchtnt/runner/train.py b/torchtnt/runner/train.py
index f285412f99..0434d305ca 100644
--- a/torchtnt/runner/train.py
+++ b/torchtnt/runner/train.py
@@ -10,7 +10,7 @@
 import torch
 from torchtnt.runner.callback import Callback
 from torchtnt.runner.evaluate import _evaluate_impl
-from torchtnt.runner.state import EntryPoint, PhaseState, State
+from torchtnt.runner.state import ActivePhase, EntryPoint, PhaseState, State
 from torchtnt.runner.unit import TTrainData, TTrainUnit
 from torchtnt.runner.utils import (
     _is_done,
@@ -100,6 +100,7 @@ def _train_impl(
     logger.info(
         f"Started train with max_epochs={train_state.max_epochs}, max_steps={train_state.max_steps}, max_steps_per_epoch={train_state.max_steps_per_epoch}"
     )
+    state._active_phase = ActivePhase.TRAIN
 
     # Set all modules to train() mode
     # access modules made available through _AppStateMixin
@@ -166,6 +167,7 @@ def _train_epoch_impl(
     callbacks: List[Callback],
 ) -> None:
     logger.info("Started train epoch")
+    state._active_phase = ActivePhase.TRAIN
 
     # Set all modules to train() mode
     # access modules made available through _AppStateMixin