rev 10

Author: Borda

course_UvA-DL/10-autoregressive-image-modeling/.meta.yml

@@ -1,7 +1,7 @@
 title: "Tutorial 10: Autoregressive Image Modeling"
 author: Phillip Lippe
 created: 2021-07-12
-updated: 2023-01-04
+updated: 2021-07-12
 license: CC BY-SA
 build: 0
 tags:
@@ -18,6 +18,5 @@ requirements:
  - torchvision
  - matplotlib
  - seaborn
- - pytorch-lightning>=1.8
 accelerator:
  - GPU

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

@@ -68,7 +68,7 @@
 pl.seed_everything(42)
 
 # Ensure that all operations are deterministic on GPU (if used) for reproducibility
-torch.backends.cudnn.deterministic = True
+torch.backends.cudnn.determinstic = True
 torch.backends.cudnn.benchmark = False
 
 # Fetching the device that will be used throughout this notebook
@@ -677,8 +677,7 @@ 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"),
- accelerator="gpu" if str(device).startswith("cuda") else "cpu",
- devices=1,
+ gpus=1 if str(device).startswith("cuda") else 0,
  max_epochs=150,
  callbacks=[
  ModelCheckpoint(save_weights_only=True, mode="min", monitor="val_bpd"),