📚 Fix & shorten tutorials

Author: o-laurent

auto_tutorials_source/tutorial_bayesian.py

@@ -84,9 +84,10 @@ def optim_lenet(model: nn.Module) -> dict:
 # We mock the arguments for the trainer
 with ArgvContext(
     "file.py",
-    "--max_epochs 1",
-    "--enable_progress_bar=False",
-    "--verbose=False",
+    "--max_epochs",
+    "1",
+    "--enable_progress_bar",
+    "False",
 ):
     args = init_args(datamodule=MNISTDataModule)
 

auto_tutorials_source/tutorial_scaler.py

@@ -71,12 +71,15 @@
 #
 # When computing the ECE, you need to provide the likelihoods associated with the inputs.
 # To do this, just call PyTorch's softmax.
+#
+# To avoid lengthy computations (without GPU), we restrict the calibration computation to a subset
+# of the test set.
 
 from torch.utils.data import DataLoader, random_split
 
 # Split datasets
 dataset = dm.test
-cal_dataset, test_dataset = random_split(dataset, [1000, len(dataset) - 1000])
+cal_dataset, test_dataset, other = random_split(dataset, [1000, 1000, len(dataset) - 2000])
 test_dataloader = DataLoader(test_dataset, batch_size=32)
 
 # Initialize the ECE