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UvA DL Tutorials: Updating PL API to >1.8 #222

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Borda merged 22 commits into from
Mar 14, 2023
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UvA DL Tutorials: Updating PL API to >1.8 #222

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c8f99e9
update API PL 1.8 - remove on_epoch_end
phlippe Nov 26, 2022
1e49a48
Merge branch 'main' into main
Borda Jan 1, 2023
7bcf341
Merge branch 'main' into main
Borda Jan 3, 2023
c6337ce
Merge branch 'main' into main
Borda Jan 3, 2023
d037ea2
Merge branch 'Lightning-AI:main' into main
phlippe Jan 4, 2023
a5811f5
UvA DL Tutorials: Updating progress bar API to PL 1.8
phlippe Jan 4, 2023
59450b0
UvA DL Tutorials: Updating meta
phlippe Jan 4, 2023
6e7f03e
UvA DL Tutorials: Fixing typo in torch deterministic setup
phlippe Jan 4, 2023
a6657dd
UvA DL Tutorials: Update accelerator API to PL 1.8
phlippe Jan 4, 2023
456aad9
UvA DL Tutorial 3: Fixing matplotlib 3d plots
phlippe Jan 4, 2023
0ed1437
UvA DL Tutorial 9: Updating NF architecture
phlippe Jan 4, 2023
dffd0a4
[pre-commit.ci] auto fixes from pre-commit.com hooks
pre-commit-ci[bot] Jan 4, 2023
a3f88be
UvA DL Tutorial 3: Removing unused variable
phlippe Jan 4, 2023
9b8e2ab
UvA DL Tutorial 6: Removing PyG version constraint
phlippe Jan 4, 2023
c58b366
UvA DL Tutorials: Adding PL>=1.8 requirement
phlippe Jan 4, 2023
3d2c57f
Merge branch 'main' into main
Borda Jan 11, 2023
4181b74
Merge branch 'main' into main
awaelchli Mar 10, 2023
1fe24f8
debug
awaelchli Mar 10, 2023
e2bde92
nbval
Borda Mar 14, 2023
7b3358b
Merge branch 'main' into main
Borda Mar 14, 2023
f61f7ad
rev 13
Borda Mar 14, 2023
b22c65f
rev 10
Borda Mar 14, 2023
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UvA DL Tutorials: Update accelerator API to PL 1.8
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@phlippe
phlippe committed Jan 4, 2023
commit a6657dd9749df0c0212b58edd437a094c85051a3
3 changes: 2 additions & 1 deletion course_UvA-DL/04-inception-resnet-densenet/Inception_ResNet_DenseNet.py
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Expand Up @@ -348,7 +348,8 @@ def train_model(model_name, save_name=None, **kwargs):
trainer = pl.Trainer(
default_root_dir=os.path.join(CHECKPOINT_PATH, save_name), # Where to save models
# We run on a single GPU (if possible)
gpus=1 if str(device) == "cuda:0" else 0,
accelerator="gpu" if str(device).startswith("cuda") else "cpu",
devices=1,
# How many epochs to train for if no patience is set
max_epochs=180,
callbacks=[
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6 changes: 4 additions & 2 deletions course_UvA-DL/05-transformers-and-MH-attention/Transformers_MHAttention.py
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Expand Up @@ -979,7 +979,8 @@ def train_reverse(**kwargs):
trainer = pl.Trainer(
default_root_dir=root_dir,
callbacks=[ModelCheckpoint(save_weights_only=True, mode="max", monitor="val_acc")],
gpus=1 if str(device).startswith("cuda") else 0,
accelerator="gpu" if str(device).startswith("cuda") else "cpu",
devices=1,
max_epochs=10,
gradient_clip_val=5,
enable_progress_bar=True,
Expand Down Expand Up @@ -1439,7 +1440,8 @@ def train_anomaly(**kwargs):
trainer = pl.Trainer(
default_root_dir=root_dir,
callbacks=[ModelCheckpoint(save_weights_only=True, mode="max", monitor="val_acc")],
gpus=1 if str(device).startswith("cuda") else 0,
accelerator="gpu" if str(device).startswith("cuda") else "cpu",
devices=1,
max_epochs=100,
gradient_clip_val=2,
enable_progress_bar=True,
Expand Down
6 changes: 4 additions & 2 deletions course_UvA-DL/06-graph-neural-networks/GNN_overview.py
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Expand Up @@ -651,7 +651,8 @@ def train_node_classifier(model_name, dataset, **model_kwargs):
trainer = pl.Trainer(
default_root_dir=root_dir,
callbacks=[ModelCheckpoint(save_weights_only=True, mode="max", monitor="val_acc")],
gpus=AVAIL_GPUS,
accelerator="gpu" if AVAIL_GPUS > 0 else "cpu",
devices=max(1, AVAIL_GPUS),
max_epochs=200,
enable_progress_bar=False,
) # 0 because epoch size is 1
Expand Down Expand Up @@ -932,7 +933,8 @@ def train_graph_classifier(model_name, **model_kwargs):
trainer = pl.Trainer(
default_root_dir=root_dir,
callbacks=[ModelCheckpoint(save_weights_only=True, mode="max", monitor="val_acc")],
gpus=AVAIL_GPUS,
accelerator="gpu" if AVAIL_GPUS > 0 else "cpu",
devices=max(1, AVAIL_GPUS),
max_epochs=500,
enable_progress_bar=False,
)
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3 changes: 2 additions & 1 deletion course_UvA-DL/07-deep-energy-based-generative-models/Deep_Energy_Models.py
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Expand Up @@ -640,7 +640,8 @@ def train_model(**kwargs):
# Create a PyTorch Lightning trainer with the generation callback
trainer = pl.Trainer(
default_root_dir=os.path.join(CHECKPOINT_PATH, "MNIST"),
gpus=1 if str(device).startswith("cuda") else 0,
accelerator="gpu" if str(device).startswith("cuda") else "cpu",
devices=1,
max_epochs=60,
gradient_clip_val=0.1,
callbacks=[
Expand Down
3 changes: 2 additions & 1 deletion course_UvA-DL/08-deep-autoencoders/Deep_Autoencoders.py
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Expand Up @@ -385,7 +385,8 @@ def train_cifar(latent_dim):
# Create a PyTorch Lightning trainer with the generation callback
trainer = pl.Trainer(
default_root_dir=os.path.join(CHECKPOINT_PATH, "cifar10_%i" % latent_dim),
gpus=1 if str(device).startswith("cuda") else 0,
accelerator="gpu" if str(device).startswith("cuda") else "cpu",
devices=1,
max_epochs=500,
callbacks=[
ModelCheckpoint(save_weights_only=True),
Expand Down
3 changes: 2 additions & 1 deletion course_UvA-DL/09-normalizing-flows/NF_image_modeling.py
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Expand Up @@ -918,7 +918,8 @@ def train_flow(flow, model_name="MNISTFlow"):
# Create a PyTorch Lightning trainer
trainer = pl.Trainer(
default_root_dir=os.path.join(CHECKPOINT_PATH, model_name),
gpus=1 if torch.cuda.is_available() else 0,
accelerator="gpu" if str(device).startswith("cuda") else "cpu",
devices=1,
max_epochs=200,
gradient_clip_val=1.0,
callbacks=[
Expand Down
3 changes: 2 additions & 1 deletion course_UvA-DL/10-autoregressive-image-modeling/Autoregressive_Image_Modeling.py
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Expand Up @@ -677,7 +677,8 @@ def train_model(**kwargs):
# Create a PyTorch Lightning trainer with the generation callback
trainer = pl.Trainer(
default_root_dir=os.path.join(CHECKPOINT_PATH, "PixelCNN"),
gpus=1 if str(device).startswith("cuda") else 0,
accelerator="gpu" if str(device).startswith("cuda") else "cpu",
devices=1,
max_epochs=150,
callbacks=[
ModelCheckpoint(save_weights_only=True, mode="min", monitor="val_bpd"),
Expand Down
3 changes: 2 additions & 1 deletion course_UvA-DL/11-vision-transformer/Vision_Transformer.py
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Expand Up @@ -378,7 +378,8 @@ def test_step(self, batch, batch_idx):
def train_model(**kwargs):
trainer = pl.Trainer(
default_root_dir=os.path.join(CHECKPOINT_PATH, "ViT"),
gpus=1 if str(device) == "cuda:0" else 0,
accelerator="gpu" if str(device).startswith("cuda") else "cpu",
devices=1,
max_epochs=180,
callbacks=[
ModelCheckpoint(save_weights_only=True, mode="max", monitor="val_acc"),
Expand Down
3 changes: 2 additions & 1 deletion course_UvA-DL/12-meta-learning/Meta_Learning.py
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Expand Up @@ -555,7 +555,8 @@ def validation_step(self, batch, batch_idx):
def train_model(model_class, train_loader, val_loader, **kwargs):
trainer = pl.Trainer(
default_root_dir=os.path.join(CHECKPOINT_PATH, model_class.__name__),
gpus=1 if str(device) == "cuda:0" else 0,
accelerator="gpu" if str(device).startswith("cuda") else "cpu",
devices=1,
max_epochs=200,
callbacks=[
ModelCheckpoint(save_weights_only=True, mode="max", monitor="val_acc"),
Expand Down
9 changes: 6 additions & 3 deletions course_UvA-DL/13-contrastive-learning/SimCLR.py
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Expand Up @@ -357,7 +357,8 @@ def validation_step(self, batch, batch_idx):
def train_simclr(batch_size, max_epochs=500, **kwargs):
trainer = pl.Trainer(
default_root_dir=os.path.join(CHECKPOINT_PATH, "SimCLR"),
gpus=1 if str(device) == "cuda:0" else 0,
accelerator="gpu" if str(device).startswith("cuda") else "cpu",
devices=1,
max_epochs=max_epochs,
callbacks=[
ModelCheckpoint(save_weights_only=True, mode="max", monitor="val_acc_top5"),
Expand Down Expand Up @@ -540,7 +541,8 @@ def prepare_data_features(model, dataset):
def train_logreg(batch_size, train_feats_data, test_feats_data, model_suffix, max_epochs=100, **kwargs):
trainer = pl.Trainer(
default_root_dir=os.path.join(CHECKPOINT_PATH, "LogisticRegression"),
gpus=1 if str(device) == "cuda:0" else 0,
accelerator="gpu" if str(device).startswith("cuda") else "cpu",
devices=1,
max_epochs=max_epochs,
callbacks=[
ModelCheckpoint(save_weights_only=True, mode="max", monitor="val_acc"),
Expand Down Expand Up @@ -731,7 +733,8 @@ def test_step(self, batch, batch_idx):
def train_resnet(batch_size, max_epochs=100, **kwargs):
trainer = pl.Trainer(
default_root_dir=os.path.join(CHECKPOINT_PATH, "ResNet"),
gpus=1 if str(device) == "cuda:0" else 0,
accelerator="gpu" if str(device).startswith("cuda") else "cpu",
devices=1,
max_epochs=max_epochs,
callbacks=[
ModelCheckpoint(save_weights_only=True, mode="max", monitor="val_acc"),
Expand Down