Update course tutorials to Lightning 2.0

course_UvA-DL/01-introduction-to-pytorch/Introduction_to_PyTorch.py

@@ -1,15 +1,3 @@
-# ---
-# jupyter:
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-
 # %% [markdown]
 # <div class="center-wrapper"><div class="video-wrapper"><iframe src="https://www.youtube.com/embed/wnKZZgFQY-E" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe></div></div>
 # Welcome to our PyTorch tutorial for the Deep Learning course 2020 at the University of Amsterdam!
@@ -37,13 +25,13 @@
 import time
 
 import matplotlib.pyplot as plt
+
+# %matplotlib inline
+import matplotlib_inline.backend_inline
 import numpy as np
 import torch
 import torch.nn as nn
 import torch.utils.data as data
-
-# %matplotlib inline
-import matplotlib_inline.backend_inline
 from matplotlib.colors import to_rgba
 from torch import Tensor
 from tqdm.notebook import tqdm  # Progress bar

course_UvA-DL/02-activation-functions/Activation_Functions.py

@@ -11,6 +11,9 @@
 from urllib.error import HTTPError
 
 import matplotlib.pyplot as plt
+
+# %matplotlib inline
+import matplotlib_inline.backend_inline
 import numpy as np
 import seaborn as sns
 import torch
@@ -19,9 +22,6 @@
 import torch.optim as optim
 import torch.utils.data as data
 import torchvision
-
-# %matplotlib inline
-import matplotlib_inline.backend_inline
 from torchvision import transforms
 from torchvision.datasets import FashionMNIST
 from tqdm.notebook import tqdm

course_UvA-DL/03-initialization-and-optimization/Initialization_and_Optimization.py

@@ -1,15 +1,3 @@
-# ---
-# jupyter:
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-
 # %% [markdown]
 # <div class="center-wrapper"><div class="video-wrapper"><iframe src="https://www.youtube.com/embed/X5m7bC4xCLY" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe></div></div>
 # In the first half of the notebook, we will review different initialization techniques, and go step by step from the simplest initialization to methods that are nowadays used in very deep networks.
@@ -25,17 +13,17 @@
 import urllib.request
 from urllib.error import HTTPError
 
+import lightning as L
 import matplotlib.pyplot as plt
+
+# %matplotlib inline
+import matplotlib_inline.backend_inline
 import numpy as np
-import lightning as L
 import seaborn as sns
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
 import torch.utils.data as data
-
-# %matplotlib inline
-import matplotlib_inline.backend_inline
 from matplotlib import cm
 from torchvision import transforms
 from torchvision.datasets import FashionMNIST
@@ -952,7 +940,7 @@ def plot_curve(
     fig = plt.figure()
     ax = fig.gca()
     if plot_3d:
-        ax = fig.add_subplot(projection='3d')
+        ax = fig.add_subplot(projection="3d")
 
     x = torch.arange(x_range[0], x_range[1], (x_range[1] - x_range[0]) / 100.0)
     y = torch.arange(y_range[0], y_range[1], (y_range[1] - y_range[0]) / 100.0)

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

@@ -1,15 +1,3 @@
-# ---
-# jupyter:
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-# ---
-
 # %% [markdown]
 # <div class="center-wrapper"><div class="video-wrapper"><iframe src="https://www.youtube.com/embed/vjSSyGxlczs" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe></div></div>
 # Let's start with importing our standard libraries here.
@@ -20,10 +8,11 @@
 from types import SimpleNamespace
 from urllib.error import HTTPError
 
+import lightning as L
 import matplotlib
 import matplotlib.pyplot as plt
+import matplotlib_inline.backend_inline
 import numpy as np
-import lightning as L
 import seaborn as sns
 import tabulate
 import torch
@@ -34,10 +23,8 @@
 
 # %matplotlib inline
 from IPython.display import HTML, display
-import matplotlib_inline.backend_inline
-
-from PIL import Image
 from lightning.pytorch.callbacks import LearningRateMonitor, ModelCheckpoint
+from PIL import Image
 from torchvision import transforms
 from torchvision.datasets import CIFAR10
 

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

@@ -34,13 +34,14 @@
 from functools import partial
 from urllib.error import HTTPError
 
+# PyTorch Lightning
+import lightning as L
+
 # Plotting
 import matplotlib
 import matplotlib.pyplot as plt
+import matplotlib_inline.backend_inline
 import numpy as np
-
-# PyTorch Lightning
-import lightning as L
 import seaborn as sns
 
 # PyTorch
@@ -52,7 +53,6 @@
 
 # Torchvision
 import torchvision
-import matplotlib_inline.backend_inline
 from lightning.pytorch.callbacks import ModelCheckpoint
 from torchvision import transforms
 from torchvision.datasets import CIFAR100

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

@@ -634,26 +634,27 @@ 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.
 
 
 # %%
 def train_node_classifier(model_name, dataset, **model_kwargs):
-    pl.seed_everything(42)
+    L.seed_everything(42)
     node_data_loader = geom_data.DataLoader(dataset, batch_size=1)
 
     # Create a PyTorch Lightning trainer
     root_dir = os.path.join(CHECKPOINT_PATH, "NodeLevel" + model_name)
     os.makedirs(root_dir, exist_ok=True)
-    trainer = pl.Trainer(
+    trainer = L.Trainer(
         default_root_dir=root_dir,
         callbacks=[ModelCheckpoint(save_weights_only=True, mode="max", monitor="val_acc")],
-        gpus=AVAIL_GPUS,
+        accelerator="auto",
+        devices=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
 
@@ -663,7 +664,7 @@ def train_node_classifier(model_name, dataset, **model_kwargs):
         print("Found pretrained model, loading...")
         model = NodeLevelGNN.load_from_checkpoint(pretrained_filename)
     else:
-        pl.seed_everything()
+        L.seed_everything()
         model = NodeLevelGNN(
             model_name=model_name, c_in=dataset.num_node_features, c_out=dataset.num_classes, **model_kwargs
         )

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

@@ -9,13 +9,16 @@
 import urllib.request
 from urllib.error import HTTPError
 
+# PyTorch Lightning
+import lightning as L
+
 # Plotting
 import matplotlib
 import matplotlib.pyplot as plt
-import numpy as np
 
-# PyTorch Lightning
-import lightning as L
+# %matplotlib inline
+import matplotlib_inline.backend_inline
+import numpy as np
 
 # PyTorch
 import torch
@@ -25,9 +28,6 @@
 
 # Torchvision
 import torchvision
-
-# %matplotlib inline
-import matplotlib_inline.backend_inline
 from lightning.pytorch.callbacks import Callback, LearningRateMonitor, ModelCheckpoint
 from torchvision import transforms
 from torchvision.datasets import MNIST

course_UvA-DL/08-deep-autoencoders/Deep_Autoencoders.py

@@ -1,15 +1,3 @@
-# ---
-# jupyter:
-#   jupytext:
-#     cell_metadata_filter: -all
-#     formats: ipynb,py:percent
-#     text_representation:
-#       extension: .py
-#       format_name: percent
-#       format_version: '1.3'
-#       jupytext_version: 1.14.5
-# ---
-
 # %% [markdown]
 # <div class="center-wrapper"><div class="video-wrapper"><iframe src="https://www.youtube.com/embed/E2d8NRYt2e4" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe></div></div>
 
@@ -18,17 +6,17 @@
 import urllib.request
 from urllib.error import HTTPError
 
+import lightning as L
 import matplotlib
 import matplotlib.pyplot as plt
-import lightning as L
+import matplotlib_inline.backend_inline
 import seaborn as sns
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
 import torch.optim as optim
 import torch.utils.data as data
 import torchvision
-import matplotlib_inline.backend_inline
 from lightning.pytorch.callbacks import Callback, LearningRateMonitor, ModelCheckpoint
 from torch.utils.tensorboard import SummaryWriter
 from torchvision import transforms

course_UvA-DL/09-normalizing-flows/NF_image_modeling.py

@@ -10,10 +10,11 @@
 import urllib.request
 from urllib.error import HTTPError
 
+import lightning as L
 import matplotlib
 import matplotlib.pyplot as plt
+import matplotlib_inline.backend_inline
 import numpy as np
-import lightning as L
 import seaborn as sns
 import tabulate
 import torch
@@ -23,9 +24,8 @@
 import torch.utils.data as data
 import torchvision
 from IPython.display import HTML, display
-import matplotlib_inline.backend_inline
-from matplotlib.colors import to_rgb
 from lightning.pytorch.callbacks import LearningRateMonitor, ModelCheckpoint
+from matplotlib.colors import to_rgb
 from torch import Tensor
 from torchvision import transforms
 from torchvision.datasets import MNIST

course_UvA-DL/10-autoregressive-image-modeling/Autoregressive_Image_Modeling.py

@@ -36,20 +36,21 @@
 import urllib.request
 from urllib.error import HTTPError
 
+import lightning as L
+
 # Imports for plotting
 import matplotlib.pyplot as plt
+import matplotlib_inline.backend_inline
 import numpy as np
-import lightning as L
 import seaborn as sns
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
 import torch.optim as optim
 import torch.utils.data as data
 import torchvision
-import matplotlib_inline.backend_inline
-from matplotlib.colors import to_rgb
 from lightning.pytorch.callbacks import LearningRateMonitor, ModelCheckpoint
+from matplotlib.colors import to_rgb
 from torch import Tensor
 from torchvision import transforms
 from torchvision.datasets import MNIST

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

@@ -7,17 +7,17 @@
 import urllib.request
 from urllib.error import HTTPError
 
+import lightning as L
 import matplotlib
 import matplotlib.pyplot as plt
-import lightning as L
+import matplotlib_inline.backend_inline
 import seaborn as sns
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
 import torch.optim as optim
 import torch.utils.data as data
 import torchvision
-import matplotlib_inline.backend_inline
 from lightning.pytorch.callbacks import LearningRateMonitor, ModelCheckpoint
 from torchvision import transforms
 from torchvision.datasets import CIFAR10

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

@@ -25,19 +25,19 @@
 from statistics import mean, stdev
 from urllib.error import HTTPError
 
+import lightning as L
 import matplotlib
 import matplotlib.pyplot as plt
+import matplotlib_inline.backend_inline
 import numpy as np
-import lightning as L
 import seaborn as sns
 import torch
 import torch.nn.functional as F
 import torch.optim as optim
 import torch.utils.data as data
 import torchvision
-import matplotlib_inline.backend_inline
-from PIL import Image
 from lightning.pytorch.callbacks import LearningRateMonitor, ModelCheckpoint
+from PIL import Image
 from torchvision import transforms
 from torchvision.datasets import CIFAR100, SVHN
 from tqdm.auto import tqdm