train.py: renaming variable to n_gpus

train.py

@@ -28,10 +28,10 @@ def batchnorm_to_float(module):
     return module
 
 
-def reduce_tensor(tensor, num_gpus):
+def reduce_tensor(tensor, n_gpus):
     rt = tensor.clone()
     dist.all_reduce(rt, op=dist.reduce_op.SUM)
-    rt /= num_gpus
+    rt /= n_gpus
     return rt
 
 
@@ -135,7 +135,7 @@ def validate(model, criterion, valset, iteration, batch_size, n_gpus,
             y_pred = model(x)
             loss = criterion(y_pred, y)
             if distributed_run:
-                reduced_val_loss = reduce_tensor(loss.data, num_gpus).item()
+                reduced_val_loss = reduce_tensor(loss.data, n_gpus).item()
             else:
                 reduced_val_loss = loss.item()
             val_loss += reduced_val_loss
@@ -212,7 +212,7 @@ def train(output_directory, log_directory, checkpoint_path, warm_start, n_gpus,
 
             loss = criterion(y_pred, y)
             if hparams.distributed_run:
-                reduced_loss = reduce_tensor(loss.data, num_gpus).item()
+                reduced_loss = reduce_tensor(loss.data, n_gpus).item()
             else:
                 reduced_loss = loss.item()