diff --git a/harness/determined/exec/harness.py b/harness/determined/exec/harness.py
index ce15fadc788..92fafc50c70 100644
--- a/harness/determined/exec/harness.py
+++ b/harness/determined/exec/harness.py
@@ -210,55 +210,54 @@ def _run_deepspeed_trial(
 
     logger.debug("Starting harness.")
 
-    with maybe_periodic_stacktraces(info.trial._debug):
-        with det_ds.init(
-            hparams=info.trial.hparams,
-            exp_conf=info.trial._config,
-        ) as train_context:
-            fp16_compression = bool(info.trial._config["optimizations"]["gradient_compression"])
-            average_aggregated_gradients = bool(
-                info.trial._config["optimizations"]["average_aggregated_gradients"]
-            )
-
-            train_context._set_default_gradient_compression(fp16_compression)
-            train_context._set_default_average_aggregated_gradients(average_aggregated_gradients)
-            train_context._set_is_pre_trainer()
-
-            trial_inst = trial_class(train_context)
-
-            if train_context.distributed.size > 1 and not train_context.distributed.rank == 0:
-                log_level = logging.DEBUG if info.trial._debug else logging.WARNING
-                logging.getLogger().setLevel(log_level)
-
-            logger.info(
-                f"Creating {det_ds.DeepSpeedTrialController.__name__} with {trial_class.__name__}."
-            )
-
-            trainer = det_ds.Trainer(trial_inst, train_context)
-
-            if "global_batch_size" in info.trial.hparams:
-                global_batch_size = int(
-                    info.trial.hparams["global_batch_size"]
-                )  # type: Optional[int]
-            else:
-                global_batch_size = None
-
-            trainer.fit(
-                checkpoint_period=pytorch.TrainUnit._from_values(
-                    **info.trial._config["min_checkpoint_period"],
-                    global_batch_size=global_batch_size,
-                ),
-                validation_period=pytorch.TrainUnit._from_values(
-                    **info.trial._config["min_validation_period"],
-                    global_batch_size=global_batch_size,
-                ),
-                reporting_period=pytorch.Batch(info.trial._config["scheduling_unit"]),
-                checkpoint_policy=info.trial._config["checkpoint_policy"],
-                latest_checkpoint=info.latest_checkpoint,
-                step_zero_validation=info.trial._config["perform_initial_validation"],
-                test_mode=False,
-                profiling_enabled=bool(info.trial._config["profiling"]["enabled"]),
-            )
+    with det_ds.init(
+        hparams=info.trial.hparams,
+        exp_conf=info.trial._config,
+    ) as train_context:
+        fp16_compression = bool(info.trial._config["optimizations"]["gradient_compression"])
+        average_aggregated_gradients = bool(
+            info.trial._config["optimizations"]["average_aggregated_gradients"]
+        )
+
+        train_context._set_default_gradient_compression(fp16_compression)
+        train_context._set_default_average_aggregated_gradients(average_aggregated_gradients)
+        train_context._set_is_pre_trainer()
+
+        trial_inst = trial_class(train_context)
+
+        if train_context.distributed.size > 1 and not train_context.distributed.rank == 0:
+            log_level = logging.DEBUG if info.trial._debug else logging.WARNING
+            logging.getLogger().setLevel(log_level)
+
+        logger.info(
+            f"Creating {det_ds.DeepSpeedTrialController.__name__} with {trial_class.__name__}."
+        )
+
+        trainer = det_ds.Trainer(trial_inst, train_context)
+
+        if "global_batch_size" in info.trial.hparams:
+            global_batch_size = int(
+                info.trial.hparams["global_batch_size"]
+            )  # type: Optional[int]
+        else:
+            global_batch_size = None
+
+        trainer.fit(
+            checkpoint_period=pytorch.TrainUnit._from_values(
+                **info.trial._config["min_checkpoint_period"],
+                global_batch_size=global_batch_size,
+            ),
+            validation_period=pytorch.TrainUnit._from_values(
+                **info.trial._config["min_validation_period"],
+                global_batch_size=global_batch_size,
+            ),
+            reporting_period=pytorch.Batch(info.trial._config["scheduling_unit"]),
+            checkpoint_policy=info.trial._config["checkpoint_policy"],
+            latest_checkpoint=info.latest_checkpoint,
+            step_zero_validation=info.trial._config["perform_initial_validation"],
+            test_mode=False,
+            profiling_enabled=bool(info.trial._config["profiling"]["enabled"]),
+        )
 
     return 0
 
diff --git a/harness/determined/pytorch/deepspeed/_trainer.py b/harness/determined/pytorch/deepspeed/_trainer.py
index 55df67fe452..4f9ecd37581 100644
--- a/harness/determined/pytorch/deepspeed/_trainer.py
+++ b/harness/determined/pytorch/deepspeed/_trainer.py
@@ -236,7 +236,6 @@ def init(
     if local_training:
         trial_seed = None
         steps_completed = 0
-        debug_enabled = False
         num_gpus = len(gpu.get_gpu_uuids())
     else:
         assert cluster_info, "Unable to detect cluster info"
@@ -245,7 +244,6 @@ def init(
         exp_conf = cluster_info.trial._config
         steps_completed = cluster_info.trial._steps_completed
         num_gpus = len(cluster_info.gpu_uuids)
-        debug_enabled = cluster_info.trial._debug
 
         _set_random_seeds(trial_seed)
 
@@ -262,7 +260,6 @@ def init(
             num_gpus=num_gpus,
             exp_conf=exp_conf,
             steps_completed=steps_completed,
-            debug_enabled=debug_enabled,
             enable_tensorboard_logging=enable_tensorboard_logging,
         )