diff --git a/codes/trainer/ExtensibleTrainer.py b/codes/trainer/ExtensibleTrainer.py
index 24e5fa72..af131394 100644
--- a/codes/trainer/ExtensibleTrainer.py
+++ b/codes/trainer/ExtensibleTrainer.py
@@ -121,6 +121,12 @@ class ExtensibleTrainer(BaseModel):
                     # Do NOT be tempted to put find_unused_parameters=True here. It will not work in the current incarnation of this trainer.
                     # Use all of your parameters in training, or delete them!
                     dnet = DistributedDataParallel(anet, device_ids=[torch.cuda.current_device()])
+                    # DDP graphs cannot be used with gradient checkpointing unless you use find_unused_parameters=True,
+                    # which does not work with this trainer (as stated above). However, if the graph is not subject
+                    # to control flow alterations, you can set this option to allow gradient checkpointing. Beware that
+                    # if you are wrong about control flow, DDP will not train all your model parameters! User beware!
+                    if opt_get(opt, ['ddp_static_graph'], False):
+                        dnet._set_static_graph()
             else:
                 dnet = DataParallel(anet, device_ids=opt['gpu_ids'])
             if self.is_train: