explicitly set num_workers=0 in examples

README.md

@@ -333,11 +333,11 @@ from disent.schedule import CyclicSchedule
 
 # create the dataset & dataloaders
 # - ToImgTensorF32 transforms images from numpy arrays to tensors and performs checks
-# - if you use `num_workers` in the DataLoader, the make sure to wrap `trainer.fit`
-#   with `if __name__ == '__main__': ...`
+# - if you use `num_workers != 0` in the DataLoader, the make sure to
+#   wrap `trainer.fit` with `if __name__ == '__main__': ...`
 data = XYObjectData()
 dataset = DisentDataset(dataset=data, sampler=SingleSampler(), transform=ToImgTensorF32())
-dataloader = DataLoader(dataset=dataset, batch_size=128, shuffle=True)
+dataloader = DataLoader(dataset=dataset, batch_size=128, shuffle=True, num_workers=0)
 
 # create the BetaVAE model
 # - adjusting the beta, learning rate, and representation size.

docs/examples/mnist_example.py

@@ -24,10 +24,10 @@ def __getitem__(self, index):
 dataset_test  =               MNIST(data_folder, train=False, download=True, transform=ToImgTensorF32())
 
 # create the dataloaders
-# - if you use `num_workers` in the DataLoader, the make sure to wrap `trainer.fit`
-#   with `if __name__ == '__main__': ...`
-dataloader_train = DataLoader(dataset=dataset_train, batch_size=128, shuffle=True)
-dataloader_test  = DataLoader(dataset=dataset_test,  batch_size=128, shuffle=True)
+# - if you use `num_workers != 0` in the DataLoader, the make sure to
+#   wrap `trainer.fit` with `if __name__ == '__main__': ...`
+dataloader_train = DataLoader(dataset=dataset_train, batch_size=128, shuffle=True, num_workers=0)
+dataloader_test  = DataLoader(dataset=dataset_test,  batch_size=128, shuffle=True, num_workers=0)
 
 # create the model
 module = AdaVae(

docs/examples/overview_dataset_loader.py

@@ -7,7 +7,7 @@
 # prepare the data
 data = XYObjectData(grid_size=4, min_square_size=1, max_square_size=2, square_size_spacing=1, palette='rgb_1')
 dataset = DisentDataset(data, sampler=GroundTruthPairOrigSampler(), transform=ToImgTensorF32())
-dataloader = DataLoader(dataset=dataset, batch_size=4, shuffle=True)
+dataloader = DataLoader(dataset=dataset, batch_size=4, shuffle=True, num_workers=0)
 
 # iterate over single epoch
 for batch in dataloader:

docs/examples/overview_framework_adagvae.py

@@ -13,7 +13,7 @@
 # prepare the data
 data = XYObjectData()
 dataset = DisentDataset(data, GroundTruthPairOrigSampler(), transform=ToImgTensorF32())
-dataloader = DataLoader(dataset=dataset, batch_size=4, shuffle=True)
+dataloader = DataLoader(dataset=dataset, batch_size=4, shuffle=True, num_workers=0)
 
 # create the pytorch lightning system
 module: pl.LightningModule = AdaVae(

docs/examples/overview_framework_ae.py

@@ -12,7 +12,7 @@
 # prepare the data
 data = XYObjectData()
 dataset = DisentDataset(data, transform=ToImgTensorF32())
-dataloader = DataLoader(dataset=dataset, batch_size=4, shuffle=True)
+dataloader = DataLoader(dataset=dataset, batch_size=4, shuffle=True, num_workers=0)
 
 # create the pytorch lightning system
 module: pl.LightningModule = Ae(

docs/examples/overview_framework_betavae.py

@@ -12,7 +12,7 @@
 # prepare the data
 data = XYObjectData()
 dataset = DisentDataset(data, transform=ToImgTensorF32())
-dataloader = DataLoader(dataset=dataset, batch_size=4, shuffle=True)
+dataloader = DataLoader(dataset=dataset, batch_size=4, shuffle=True, num_workers=0)
 
 # create the pytorch lightning system
 module: pl.LightningModule = BetaVae(

docs/examples/overview_framework_betavae_scheduled.py

@@ -12,7 +12,7 @@
 # prepare the data
 data = XYObjectData()
 dataset = DisentDataset(data, transform=ToImgTensorF32())
-dataloader = DataLoader(dataset=dataset, batch_size=4, shuffle=True)
+dataloader = DataLoader(dataset=dataset, batch_size=4, shuffle=True, num_workers=0)
 
 # create the pytorch lightning system
 module: pl.LightningModule = BetaVae(

docs/examples/overview_metrics.py

@@ -11,7 +11,7 @@
 
 data = XYObjectData()
 dataset = DisentDataset(data, transform=ToImgTensorF32(), augment=None)
-dataloader = DataLoader(dataset=dataset, batch_size=32, shuffle=True)
+dataloader = DataLoader(dataset=dataset, batch_size=32, shuffle=True, num_workers=0)
 
 def make_vae(beta):
     return BetaVae(

docs/examples/readme_example.py

@@ -16,11 +16,11 @@
 
 # create the dataset & dataloaders
 # - ToImgTensorF32 transforms images from numpy arrays to tensors and performs checks
-# - if you use `num_workers` in the DataLoader, the make sure to wrap `trainer.fit`
-#   with `if __name__ == '__main__': ...`
+# - if you use `num_workers != 0` in the DataLoader, the make sure to
+#   wrap `trainer.fit` with `if __name__ == '__main__': ...`
 data = XYObjectData()
 dataset = DisentDataset(dataset=data, sampler=SingleSampler(), transform=ToImgTensorF32())
-dataloader = DataLoader(dataset=dataset, batch_size=128, shuffle=True)
+dataloader = DataLoader(dataset=dataset, batch_size=128, shuffle=True, num_workers=0)
 
 # create the BetaVAE model
 # - adjusting the beta, learning rate, and representation size.

tests/test_frameworks.py

@@ -105,7 +105,7 @@ def test_frameworks(Framework, cfg_kwargs, Data):
 
     data = XYObjectData() if (Data is None) else Data()
     dataset = DisentDataset(data, DataSampler(), transform=ToImgTensorF32())
-    dataloader = DataLoader(dataset=dataset, batch_size=4, shuffle=True)
+    dataloader = DataLoader(dataset=dataset, batch_size=4, shuffle=True, num_workers=0)
 
     framework = Framework(
         model=AutoEncoder(
@@ -176,7 +176,7 @@ def test_ada_vae_similarity():
 
     data = XYObjectData()
     dataset = DisentDataset(data, sampler=RandomSampler(num_samples=2), transform=ToImgTensorF32())
-    dataloader = DataLoader(dataset, num_workers=0, batch_size=3)
+    dataloader = DataLoader(dataset, batch_size=3, num_workers=0)
 
     model = AutoEncoder(
         encoder=EncoderLinear(x_shape=data.x_shape, z_size=25, z_multiplier=2),