diff --git a/dags/test-train-pytorch.py b/dags/test-train-pytorch.py
index 95fa60f..d9e4a5c 100644
--- a/dags/test-train-pytorch.py
+++ b/dags/test-train-pytorch.py
@@ -38,6 +38,10 @@ def run_training_node_func(rank, world_size):
   import socket
   from datetime import datetime
   import time
+  import sys
+
+  # Redirect NCCL stderr to stdout so Airflow captures it
+  os.dup2(sys.stdout.fileno(), sys.stderr.fileno())
 
   # CRITICAL: Set NCCL environment variables BEFORE importing torch
   os.environ['NCCL_DEBUG'] = 'TRACE'
@@ -262,9 +266,12 @@ def run_training_node_func(rank, world_size):
   batch_size = 32
   num_batches = 5
 
+  training_start = time.time()
+
   for epoch in range(num_epochs):
       ddp_model.train()
       epoch_loss = 0.0
+      epoch_start = time.time()
 
       for batch_idx in range(num_batches):
           torch.manual_seed(epoch * num_batches + batch_idx)
@@ -278,22 +285,24 @@ def run_training_node_func(rank, world_size):
           optimizer.step()
 
           epoch_loss += loss.item()
+          print(f"[{rank}] Epoch {epoch+1}/{num_epochs} Batch {batch_idx+1}/{num_batches} | Loss: {loss.item():.6f}")
 
       avg_loss = epoch_loss / num_batches
+      epoch_time = time.time() - epoch_start
 
       # Synchronize loss across ranks
       loss_tensor = torch.tensor([avg_loss]).to(device)
       dist.all_reduce(loss_tensor, op=dist.ReduceOp.AVG)
       global_avg_loss = loss_tensor.item()
 
-      if rank == 0:
-          print(f"[{rank}] Epoch {epoch+1}/{num_epochs} | Global Avg Loss: {global_avg_loss:.6f}")
+      print(f"[{rank}] Epoch {epoch+1}/{num_epochs} DONE | Local Avg: {avg_loss:.6f} | Global Avg: {global_avg_loss:.6f} | Time: {epoch_time:.3f}s")
+
+  total_time = time.time() - training_start
 
   print(f"\n[{rank}] {'='*60}")
   print(f"[{rank}] Training Complete!")
+  print(f"[{rank}] Total training time: {total_time:.3f}s")
   print(f"[{rank}] {'='*60}")
-
-  # STEP 8: Cleanup
   dist.destroy_process_group()
   print(f"[{rank}] Process group destroyed. Finished at {datetime.now()}")