UvA DL Tutorials: Updating progress bar API to PL 1.8

course_UvA-DL/04-inception-resnet-densenet/Inception_ResNet_DenseNet.py

@@ -357,7 +357,7 @@ def train_model(model_name, save_name=None, **kwargs):
             ),  # Save the best checkpoint based on the maximum val_acc recorded. Saves only weights and not optimizer
             LearningRateMonitor("epoch"),
         ],  # Log learning rate every epoch
-        progress_bar_refresh_rate=1,
+        enable_progress_bar=True,
     )  # In case your notebook crashes due to the progress bar, consider increasing the refresh rate
     trainer.logger._log_graph = True  # If True, we plot the computation graph in tensorboard
     trainer.logger._default_hp_metric = None  # Optional logging argument that we don't need

course_UvA-DL/05-transformers-and-MH-attention/Transformers_MHAttention.py

@@ -982,7 +982,7 @@ def train_reverse(**kwargs):
         gpus=1 if str(device).startswith("cuda") else 0,
         max_epochs=10,
         gradient_clip_val=5,
-        progress_bar_refresh_rate=1,
+        enable_progress_bar=True,
     )
     trainer.logger._default_hp_metric = None  # Optional logging argument that we don't need
 
@@ -1442,7 +1442,7 @@ def train_anomaly(**kwargs):
         gpus=1 if str(device).startswith("cuda") else 0,
         max_epochs=100,
         gradient_clip_val=2,
-        progress_bar_refresh_rate=1,
+        enable_progress_bar=True,
     )
     trainer.logger._default_hp_metric = None  # Optional logging argument that we don't need
 

course_UvA-DL/06-graph-neural-networks/GNN_overview.py

@@ -634,7 +634,7 @@ def test_step(self, batch, batch_idx):
 # Additionally to the Lightning module, we define a training function below.
 # As we have a single graph, we use a batch size of 1 for the data loader and share the same data loader for the train,
 # validation, and test set (the mask is picked inside the Lightning module).
-# Besides, we set the argument `progress_bar_refresh_rate` to zero as it usually shows the progress per epoch,
+# Besides, we set the argument `enable_progress_bar` to False as it usually shows the progress per epoch,
 # but an epoch only consists of a single step.
 # If you have downloaded the pre-trained models in the beginning of the tutorial, we load those instead of training from scratch.
 # Finally, we test the model and return the results.
@@ -653,7 +653,7 @@ def train_node_classifier(model_name, dataset, **model_kwargs):
         callbacks=[ModelCheckpoint(save_weights_only=True, mode="max", monitor="val_acc")],
         gpus=AVAIL_GPUS,
         max_epochs=200,
-        progress_bar_refresh_rate=0,
+        enable_progress_bar=False,
     )  # 0 because epoch size is 1
     trainer.logger._default_hp_metric = None  # Optional logging argument that we don't need
 
@@ -934,7 +934,7 @@ def train_graph_classifier(model_name, **model_kwargs):
         callbacks=[ModelCheckpoint(save_weights_only=True, mode="max", monitor="val_acc")],
         gpus=AVAIL_GPUS,
         max_epochs=500,
-        progress_bar_refresh_rate=0,
+        enable_progress_bar=False,
     )
     trainer.logger._default_hp_metric = None
 

course_UvA-DL/07-deep-energy-based-generative-models/Deep_Energy_Models.py

@@ -650,7 +650,7 @@ def train_model(**kwargs):
             OutlierCallback(),
             LearningRateMonitor("epoch"),
         ],
-        progress_bar_refresh_rate=1,
+        enable_progress_bar=True,
     )
     # Check whether pretrained model exists. If yes, load it and skip training
     pretrained_filename = os.path.join(CHECKPOINT_PATH, "MNIST.ckpt")

course_UvA-DL/11-vision-transformer/Vision_Transformer.py

@@ -384,7 +384,7 @@ def train_model(**kwargs):
             ModelCheckpoint(save_weights_only=True, mode="max", monitor="val_acc"),
             LearningRateMonitor("epoch"),
         ],
-        progress_bar_refresh_rate=1,
+        enable_progress_bar=True,
     )
     trainer.logger._log_graph = True  # If True, we plot the computation graph in tensorboard
     trainer.logger._default_hp_metric = None  # Optional logging argument that we don't need

course_UvA-DL/12-meta-learning/Meta_Learning.py

@@ -561,7 +561,7 @@ def train_model(model_class, train_loader, val_loader, **kwargs):
             ModelCheckpoint(save_weights_only=True, mode="max", monitor="val_acc"),
             LearningRateMonitor("epoch"),
         ],
-        progress_bar_refresh_rate=0,
+        enable_progress_bar=False,
     )
     trainer.logger._default_hp_metric = None
 

course_UvA-DL/13-contrastive-learning/SimCLR.py

@@ -363,7 +363,7 @@ def train_simclr(batch_size, max_epochs=500, **kwargs):
             ModelCheckpoint(save_weights_only=True, mode="max", monitor="val_acc_top5"),
             LearningRateMonitor("epoch"),
         ],
-        progress_bar_refresh_rate=1,
+        enable_progress_bar=True,
     )
     trainer.logger._default_hp_metric = None  # Optional logging argument that we don't need
 
@@ -546,7 +546,7 @@ def train_logreg(batch_size, train_feats_data, test_feats_data, model_suffix, ma
             ModelCheckpoint(save_weights_only=True, mode="max", monitor="val_acc"),
             LearningRateMonitor("epoch"),
         ],
-        progress_bar_refresh_rate=0,
+        enable_progress_bar=False,
         check_val_every_n_epoch=10,
     )
     trainer.logger._default_hp_metric = None
@@ -737,7 +737,7 @@ def train_resnet(batch_size, max_epochs=100, **kwargs):
             ModelCheckpoint(save_weights_only=True, mode="max", monitor="val_acc"),
             LearningRateMonitor("epoch"),
         ],
-        progress_bar_refresh_rate=1,
+        enable_progress_bar=True,
         check_val_every_n_epoch=2,
     )
     trainer.logger._default_hp_metric = None