UvA DL Tutorials: Fixing typo in torch deterministic setup

course_UvA-DL/01-introduction-to-pytorch/.meta.yml

@@ -1,7 +1,7 @@
 title: "Tutorial 1: Introduction to PyTorch"
 author: Phillip Lippe
 created: 2021-08-27
-updated: 2021-11-29
+updated: 2023-01-04
 license: CC BY-SA
 description: |
   This tutorial will give a short introduction to PyTorch basics, and get you setup for writing your own neural networks.

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

@@ -455,7 +455,7 @@
 
 # Additionally, some operations on a GPU are implemented stochastic for efficiency
 # We want to ensure that all operations are deterministic on GPU (if used) for reproducibility
-torch.backends.cudnn.determinstic = True
+torch.backends.cudnn.deterministic = True
 torch.backends.cudnn.benchmark = False
 
 # %% [markdown]

course_UvA-DL/02-activation-functions/.meta.yml

@@ -1,7 +1,7 @@
 title: "Tutorial 2: Activation Functions"
 author: Phillip Lippe
 created: 2021-08-27
-updated: 2021-08-27
+updated: 2023-01-04
 license: CC BY-SA
 description: |
   In this tutorial, we will take a closer look at (popular) activation functions and investigate their effect on optimization properties in neural networks.

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

@@ -64,7 +64,7 @@ def set_seed(seed):
 
 # Additionally, some operations on a GPU are implemented stochastic for efficiency
 # We want to ensure that all operations are deterministic on GPU (if used) for reproducibility
-torch.backends.cudnn.determinstic = True
+torch.backends.cudnn.deterministic = True
 torch.backends.cudnn.benchmark = False
 
 # Fetching the device that will be used throughout this notebook

course_UvA-DL/03-initialization-and-optimization/.meta.yml

@@ -1,7 +1,7 @@
 title: "Tutorial 3: Initialization and Optimization"
 author: Phillip Lippe
 created: 2021-08-27
-updated: 2021-11-29
+updated: 2023-01-04
 license: CC BY-SA
 tags:
   - Image

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

@@ -47,7 +47,7 @@
 pl.seed_everything(42)
 
 # Ensure that all operations are deterministic on GPU (if used) for reproducibility
-torch.backends.cudnn.determinstic = True
+torch.backends.cudnn.deterministic = True
 torch.backends.cudnn.benchmark = False
 
 # Fetching the device that will be used throughout this notebook

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

@@ -49,7 +49,7 @@
 pl.seed_everything(42)
 
 # Ensure that all operations are deterministic on GPU (if used) for reproducibility
-torch.backends.cudnn.determinstic = True
+torch.backends.cudnn.deterministic = True
 torch.backends.cudnn.benchmark = False
 
 device = torch.device("cuda:0") if torch.cuda.is_available() else torch.device("cpu")

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

@@ -61,7 +61,7 @@
 pl.seed_everything(42)
 
 # Ensure that all operations are deterministic on GPU (if used) for reproducibility
-torch.backends.cudnn.determinstic = True
+torch.backends.cudnn.deterministic = True
 torch.backends.cudnn.benchmark = False
 
 device = torch.device("cuda:0") if torch.cuda.is_available() else torch.device("cpu")

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

@@ -39,7 +39,7 @@
 pl.seed_everything(42)
 
 # Ensure that all operations are deterministic on GPU (if used) for reproducibility
-torch.backends.cudnn.determinstic = True
+torch.backends.cudnn.deterministic = True
 torch.backends.cudnn.benchmark = False
 
 # %% [markdown]

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

@@ -44,7 +44,7 @@
 pl.seed_everything(42)
 
 # Ensure that all operations are deterministic on GPU (if used) for reproducibility
-torch.backends.cudnn.determinstic = True
+torch.backends.cudnn.deterministic = True
 torch.backends.cudnn.benchmark = False
 
 device = torch.device("cuda:0") if torch.cuda.is_available() else torch.device("cpu")

course_UvA-DL/08-deep-autoencoders/.meta.yml

@@ -1,7 +1,7 @@
 title: "Tutorial 8: Deep Autoencoders"
 author: Phillip Lippe
 created: 2021-07-12
-updated: 2021-07-12
+updated: 2023-01-04
 license: CC BY-SA
 build: 0
 tags:

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

@@ -41,7 +41,7 @@
 pl.seed_everything(42)
 
 # Ensure that all operations are deterministic on GPU (if used) for reproducibility
-torch.backends.cudnn.determinstic = True
+torch.backends.cudnn.deterministic = True
 torch.backends.cudnn.benchmark = False
 
 device = torch.device("cuda:0") if torch.cuda.is_available() else torch.device("cpu")

course_UvA-DL/09-normalizing-flows/.meta.yml

@@ -1,7 +1,7 @@
 title: "Tutorial 9: Normalizing Flows for Image Modeling"
 author: Phillip Lippe
 created: 2021-06-07
-updated: 2021-06-16
+updated: 2023-01-04
 license: CC BY-SA
 build: 0
 tags:

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

@@ -44,7 +44,7 @@
 pl.seed_everything(42)
 
 # Ensure that all operations are deterministic on GPU (if used) for reproducibility
-torch.backends.cudnn.determinstic = True
+torch.backends.cudnn.deterministic = True
 torch.backends.cudnn.benchmark = False
 
 # Fetching the device that will be used throughout this notebook

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: 2021-07-12
+updated: 2023-01-04
 license: CC BY-SA
 build: 0
 tags:

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.determinstic = True
+torch.backends.cudnn.deterministic = True
 torch.backends.cudnn.benchmark = False
 
 # Fetching the device that will be used throughout this notebook

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

@@ -39,7 +39,7 @@
 pl.seed_everything(42)
 
 # Ensure that all operations are deterministic on GPU (if used) for reproducibility
-torch.backends.cudnn.determinstic = True
+torch.backends.cudnn.deterministic = True
 torch.backends.cudnn.benchmark = False
 
 device = torch.device("cuda:0") if torch.cuda.is_available() else torch.device("cpu")

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

@@ -60,7 +60,7 @@
 pl.seed_everything(42)
 
 # Ensure that all operations are deterministic on GPU (if used) for reproducibility
-torch.backends.cudnn.determinstic = True
+torch.backends.cudnn.deterministic = True
 torch.backends.cudnn.benchmark = False
 
 device = torch.device("cuda:0") if torch.cuda.is_available() else torch.device("cpu")

course_UvA-DL/13-contrastive-learning/SimCLR.py

@@ -70,7 +70,7 @@
 pl.seed_everything(42)
 
 # Ensure that all operations are deterministic on GPU (if used) for reproducibility
-torch.backends.cudnn.determinstic = True
+torch.backends.cudnn.deterministic = True
 torch.backends.cudnn.benchmark = False
 
 device = torch.device("cuda:0") if torch.cuda.is_available() else torch.device("cpu")