diff --git a/pytorch_job_wganSingleGen_ncc.yaml b/pytorch_job_wganSingleGen_ncc.yaml
index b980cec96d582d2027f729e672cf0caaded7cfa3..e1c5db6b7d00832a3f84a35985121568ca41ec18 100644
--- a/pytorch_job_wganSingleGen_ncc.yaml
+++ b/pytorch_job_wganSingleGen_ncc.yaml
@@ -40,7 +40,7 @@ spec:
                 limits:
                   nvidia.com/gpu: 1
     Worker:
-      replicas: 4
+      replicas: 2
       restartPolicy: OnFailure
       template:
         metadata:
diff --git a/wganSingleGen.py b/wganSingleGen.py
index 1ece4d0cce35c0e75c722d2c632008b2b2aa8e47..4a92e3a5d503c488cac692ee8762cc0cb1dce63d 100644
--- a/wganSingleGen.py
+++ b/wganSingleGen.py
@@ -274,9 +274,8 @@ def run(args):
     print('Critic trainable params:', sum(p.numel() for p in Crit_E_H.parameters() if p.requires_grad))
     print('Generator trainable params:', sum(p.numel() for p in Gen_E_H.parameters() if p.requires_grad))
     
-     if args.world_size > 1: 
-        Distributor = nn.parallel.DistributedDataParallel if use_cuda \
-            else nn.parallel.DistributedDataParallelCPU
+    if args.world_size > 1: 
+        Distributor = nn.parallel.DistributedDataParallel if use_cuda else nn.parallel.DistributedDataParallelCPU
         Crit_E_H = Distributor(Crit_E_H, device_ids=[args.local_rank], output_device=args.local_rank )
         Gen_E_H = Distributor(Gen_E_H, device_ids=[args.local_rank], output_device=args.local_rank )