fix the example of pytorch_lightning_mnist.py

CHANGELOG.md

@@ -18,6 +18,8 @@ The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/).
 
 ### Fixed
 
+- fix the example of pytorch_lightning_mnist.py ([#3245](https://github.com/horovod/horovod/pull/3245))
+
 ## [v0.23.0] - 2021-10-06
 
 ### Added

examples/pytorch/pytorch_lightning_mnist.py

@@ -24,22 +24,10 @@
                     help='input batch size for testing (default: 1000)')
 parser.add_argument('--epochs', type=int, default=10, metavar='N',
                     help='number of epochs to train (default: 10)')
-parser.add_argument('--lr', type=float, default=0.01, metavar='LR',
-                    help='learning rate (default: 0.01)')
-parser.add_argument('--momentum', type=float, default=0.5, metavar='M',
-                    help='SGD momentum (default: 0.5)')
 parser.add_argument('--no-cuda', action='store_true', default=False,
                     help='disables CUDA training')
 parser.add_argument('--seed', type=int, default=42, metavar='S',
                     help='random seed (default: 42)')
-parser.add_argument('--log-interval', type=int, default=10, metavar='N',
-                    help='how many batches to wait before logging training status')
-parser.add_argument('--fp16-allreduce', action='store_true', default=False,
-                    help='use fp16 compression during allreduce')
-parser.add_argument('--use-adasum', action='store_true', default=False,
-                    help='use adasum algorithm to do reduction')
-parser.add_argument('--gradient-predivide-factor', type=float, default=1.0,
-                    help='apply gradient predivide factor in optimizer (default: 1.0)')
 parser.add_argument('--data-dir',
                     help='location of the training dataset in the local filesystem (will be downloaded if needed)')
 
@@ -205,7 +193,7 @@ def on_train_end(self, trainer, model):
         callbacks = [MyDummyCallback(), ModelCheckpoint(dirpath=ckpt_path)]
 
         trainer = Trainer(accelerator='horovod',
-                          gpus=(1 if torch.cuda.is_available() else 0),
+                          gpus=(1 if args.cuda else 0),
                           callbacks=callbacks,
                           max_epochs=epochs,
                           limit_train_batches=train_percent,
@@ -214,6 +202,7 @@ def on_train_end(self, trainer, model):
                           num_sanity_val_steps=0)
 
         trainer.fit(model)
-
+        if args.cuda:
+            model = model.cuda()
         test()