diff --git a/submission_runner.py b/submission_runner.py
index 632cb450b..3ef30ffba 100644
--- a/submission_runner.py
+++ b/submission_runner.py
@@ -378,25 +378,27 @@ def train_once(
         workload.eval_period_time_sec or train_state['training_complete']):
 
       # Prepare for evaluation (timed).
-      with profiler.profile('Prepare for eval'):
-        del batch
-        prepare_for_eval_start_time = get_time()
-        optimizer_state, model_params, model_state = prepare_for_eval(
-            workload=workload,
-            current_param_container=model_params,
-            current_params_types=workload.model_params_types,
-            model_state=model_state,
-            hyperparameters=hyperparameters,
-            loss_type=workload.loss_type,
-            optimizer_state=optimizer_state,
-            eval_results=eval_results,
-            global_step=global_step,
-            rng=prep_eval_rng)
-        prepare_for_eval_end_time = get_time()
-
-      # Update sumbission time.
-      train_state['accumulated_submission_time'] += (
-          prepare_for_eval_end_time - prepare_for_eval_start_time)
+      if prepare_for_eval is not None:
+
+        with profiler.profile('Prepare for eval'):
+          del batch
+          prepare_for_eval_start_time = get_time()
+          optimizer_state, model_params, model_state = prepare_for_eval(
+              workload=workload,
+              current_param_container=model_params,
+              current_params_types=workload.model_params_types,
+              model_state=model_state,
+              hyperparameters=hyperparameters,
+              loss_type=workload.loss_type,
+              optimizer_state=optimizer_state,
+              eval_results=eval_results,
+              global_step=global_step,
+              rng=prep_eval_rng)
+          prepare_for_eval_end_time = get_time()
+
+        # Update sumbission time.
+        train_state['accumulated_submission_time'] += (
+            prepare_for_eval_end_time - prepare_for_eval_start_time)
 
       # Check if time is remaining,
       # use 3x the runtime budget for the self-tuning ruleset.
@@ -548,7 +550,7 @@ def score_submission_on_workload(workload: spec.Workload,
   init_optimizer_state = submission_module.init_optimizer_state
   update_params = submission_module.update_params
   data_selection = submission_module.data_selection
-  prepare_for_eval = submission_module.prepare_for_eval
+  prepare_for_eval = getattr(submission_module, 'prepare_for_eval', None)
   try:
     global_batch_size = submission_module.get_batch_size(workload_name)
   except ValueError: