correct PyTorch spelling in documentation and comments

pages/faq.md

@@ -25,7 +25,7 @@ probabilities are, what a classifier is, what overfitting means etc. This corres
 course [02450](https://kurser.dtu.dk/course/02450). The actual focus of the course is not on machine learning models,
 but we will be using these basic concepts throughout the exercises.
 
-Additionally, we recommend basic knowledge about deep learning and how to code in [Pytorch](https://pytorch.org/),
+Additionally, we recommend basic knowledge about deep learning and how to code in [PyTorch](https://pytorch.org/),
 corresponding to the curriculum covered in [02456](https://kurser.dtu.dk/course/02456). From prior experience, we know
 that not all students have gained knowledge about deep learning models before this course, and we will be covering the
 basics of how to code in PyTorch in one of the

pages/overview.md

@@ -15,8 +15,8 @@ a different stack of tools that better fits your needs. Regardless of the stack,
 
 | Framework | Description |
 |-----------|-------------|
-| ![Pytorch](../figures/icons/pytorch.png){ width="50" } | **Pytorch** is the backbone of our code, it provides the computational engine and the data structures that we need to define our data structures. |
-| ![Pytorch Lightning](../figures/icons/lightning.png){ width="50" } | **Pytorch lightning** is a framework that provides a high-level interface to Pytorch. It provides a lot of functionality that we need to train our models, such as logging, checkpointing, early stopping, etc. such that we do not have to implement it ourselves. It also allows us to scale our models to multiple GPUs and multiple nodes. |
+| ![PyTorch](../figures/icons/pytorch.png){ width="50" } | **PyTorch** is the backbone of our code, it provides the computational engine and the data structures that we need to define our data structures. |
+| ![PyTorch Lightning](../figures/icons/lightning.png){ width="50" } | **PyTorch lightning** is a framework that provides a high-level interface to PyTorch. It provides a lot of functionality that we need to train our models, such as logging, checkpointing, early stopping, etc. such that we do not have to implement it ourselves. It also allows us to scale our models to multiple GPUs and multiple nodes. |
 | ![Conda](../figures/icons/conda.png){ width="50" } | We control the dependencies and Python interpreter using **Conda** that enables us to construct reproducible virtual environments |
 | ![Hydra](../figures/icons/hydra.png){ width="50" } | For configuring our experiments we use **Hydra** that allows us to define a hierarchical configuration structure config files |
 | ![Wandb](../figures/icons/w&b.png){ width="50" } | Using **Weights and Bias** allows us to track and log any values and hyperparameters for our experiments |

pages/projects.md

@@ -33,12 +33,12 @@ group yet.
 We strive to keep the tools thought in this course as open-source as possible. The great thing about the open-source
 community is that whatever problem you are working on, there is probably some package out there that can get you
 at least 10% of the way. For the project, we want to enforce this point and you are required to include some third-party
-package, that is neither Pytorch or one of the tools already covered in the course, into your project.
+package, that is neither PyTorch or one of the tools already covered in the course, into your project.
 
-If you have no idea what framework to include, the [Pytorch ecosystem](https://pytorch.org/ecosystem/) is a great place
-for finding open-source frameworks that can help you accelerate your own projects where Pytorch is the backengine. All
-tools in the ecosystem should work greatly together with Pytorch. However, it is important to note that the ecosystem is
-not a complete list of all the awesome packages that exist to extend the functionality of Pytorch. If you are still
+If you have no idea what framework to include, the [PyTorch ecosystem](https://pytorch.org/ecosystem/) is a great place
+for finding open-source frameworks that can help you accelerate your own projects where PyTorch is the backengine. All
+tools in the ecosystem should work greatly together with PyTorch. However, it is important to note that the ecosystem is
+not a complete list of all the awesome packages that exist to extend the functionality of PyTorch. If you are still
 missing inspiration for frameworks to use, we highly recommend these three that have been used in previous years of the
 course:
 
@@ -51,7 +51,7 @@ course:
     texts such as classification, information extraction, question answering, summarization, translation, text generation,
     etc in 100+ languages. Its aim is to make cutting-edge NLP easier to use for everyone.
 
-* [Pytorch-Geometric](https://github.com/rusty1s/pytorch_geometric). PyTorch Geometric (PyG) is a geometric deep
+* [PyTorch-Geometric](https://github.com/rusty1s/pytorch_geometric). PyTorch Geometric (PyG) is a geometric deep
     learning. It consists of various methods for deep learning on graphs and other irregular structures, also known as
     geometric deep learning, from a variety of published papers.
 
@@ -188,7 +188,7 @@ point on the checklist for the exam.
     you can optimize your code
 * [ ] Use Weights & Biases to log training progress and other important metrics/artifacts in your code. Additionally,
     consider running a hyperparameter optimization sweep.
-* [ ] Use Pytorch-lightning (if applicable) to reduce the amount of boilerplate in your code
+* [ ] Use PyTorch-lightning (if applicable) to reduce the amount of boilerplate in your code
 
 ### Week 2
 

pages/timeplan.md

@@ -30,7 +30,7 @@ especially with a focus on making everything reproducible.
 
 Date | Day       | Presentation topic                                                 | Frameworks                           | Format
 -----|-----------|--------------------------------------------------------------------|--------------------------------------|-----------
-6/1/25  | Monday    | [Deep learning software📝](../slides/DeepLearningSoftware.pdf)     | Terminal, Conda, IDE, Pytorch        | [Exercises](../s1_development_environment/README.md)
+6/1/25  | Monday    | [Deep learning software📝](../slides/DeepLearningSoftware.pdf)     | Terminal, Conda, IDE, PyTorch        | [Exercises](../s1_development_environment/README.md)
 7/1/25  | Tuesday   | [MLOps: what is it?📝](../slides/IntroToMLOps.pdf)                 | Git, CookieCutter, Pep8, DVC         | [Exercises](../s2_organisation_and_version_control/README.md)
 8/1/25  | Wednesday | [Reproducibility📝](../slides/ReproducibilityAndSoftware.pdf)      | Docker, Hydra                        | [Exercises](../s3_reproducibility/README.md)
 9/1/25  | Thursday  | [Debugging📝](../slides/DebuggingML.pdf)                           | Debugger, Profiler, Wandb, Lightning | [Exercises](../s4_debugging_and_logging/README.md)
@@ -60,7 +60,7 @@ important topic if we ever want our applications to be used by many people at th
 Date | Day       | Presentation topic                                           | Frameworks                               | Format
 -----|-----------|--------------------------------------------------------------|------------------------------------------|----------
 20/1/25 | Monday    | [Monitoring📝](../slides/Monitoring.pdf)                     | Evidently AI, Prometheus, GCP Monitoring |  [Exercises](../s8_monitoring/README.md)
-21/1/25 | Tuesday   | [Scalable applications📝](../slides/ScalingApplications.pdf) | Pytorch, Lightning                       | [Exercises](../s9_scalable_applications/README.md)
+21/1/25 | Tuesday   | [Scalable applications📝](../slides/ScalingApplications.pdf) | PyTorch, Lightning                       | [Exercises](../s9_scalable_applications/README.md)
 22/1/25 | Wednesday | Company presentation (TBA)                                    | -                                       | [Projects](projects.md)
 23/1/25 | Thursday  | No lecture                                                    | -                                       | [Projects](projects.md)
 24/1/25 | Friday    | No lecture                                                    | -                                       | [Projects](projects.md)

reports/README.md

@@ -65,7 +65,7 @@ curriculum in this course. Therefore, we do not expect at all that you have chec
       you can optimize your code
 * [ ] Use Weights & Biases to log training progress and other important metrics/artifacts in your code. Additionally,
       consider running a hyperparameter optimization sweep.
-* [ ] Use Pytorch-lightning (if applicable) to reduce the amount of boilerplate in your code
+* [ ] Use PyTorch-lightning (if applicable) to reduce the amount of boilerplate in your code
 
 ### Week 2
 

s10_extra/high_performance_clusters.md

@@ -144,7 +144,7 @@ of cluster. For the purpose of this exercise we are going to see how we can run
 
     1. First we need to load the correct version of CUDA. A cluster system often contains multiple versions of specific
         software to suit the needs of all their users, and it is the users that are in charge of *loading* the correct
-        software during job submission. The only extra software that needs to be loaded for most Pytorch applications
+        software during job submission. The only extra software that needs to be loaded for most PyTorch applications
         are a CUDA module. You can check which modules are available on the cluster with
 
         ```bash

s10_extra/quantization.md

@@ -41,7 +41,7 @@ after training. There are good reasons for doing both:
 
     ??? success "Solution"
 
-        We are here going to assume that you are either loading from a `onnx` model or alternatively loading a Pytorch
+        We are here going to assume that you are either loading from a `onnx` model or alternatively loading a PyTorch
         Lightning checkpoint:
 
         ```python

s1_development_environment/README.md

@@ -25,7 +25,7 @@
 
 - ![](../figures/icons/pytorch.png){align=right : style="height:100px;width:100px"}
 
-    Refresh your Pytorch skills and implement a simple deep-learning model.
+    Refresh your PyTorch skills and implement a simple deep-learning model.
 
     [:octicons-arrow-right-24: M4: Deep Learning Software](deep_learning_software.md)
 

s1_development_environment/deep_learning_software.md

@@ -31,26 +31,26 @@ of when they were published):
 ![Logo](../figures/pytorch.png){ align=right width="130"}
 ![Logo](../figures/jax.png){ align=right width="200"}
 
-* [Tensorflow](https://github.com/tensorflow/tensorflow)
+* [TensorFlow](https://github.com/tensorflow/tensorflow)
 
-* [Pytorch](https://github.com/pytorch/pytorch)
+* [PyTorch](https://github.com/pytorch/pytorch)
 
 * [JAX](https://github.com/google/jax)
 
-We won't go into a longer discussion on which framework is best, as it is pointless. Pytorch and Tensorflow have been
+We won't go into a longer discussion on which framework is best, as it is pointless. PyTorch and TensorFlow have been
 around for the longest and therefore have bigger communities and feature sets at this point in time. They are both very
 similar in the sense that they both have features directed against research and production. JAX is kind of the new kid
-on the block, which in many ways improves on Pytorch and Tensorflow, but is still not as mature as the other frameworks.
+on the block, which in many ways improves on PyTorch and TensorFlow, but is still not as mature as the other frameworks.
 As the frameworks use different kinds of programming principles (object-oriented vs. functional programming), comparing
 them is essentially meaningless.
 
-In this course, we have chosen to work with Pytorch because we find it a bit more intuitive and it is the framework that
+In this course, we have chosen to work with PyTorch because we find it a bit more intuitive and it is the framework that
 we use for our day-to-day research life. Additionally, as of right now, it is absolutely the
 [dominating framework](https://www.assemblyai.com/blog/pytorch-vs-tensorflow-in-2022/) for published models, research
 papers, and [competition winners](https://blog.mlcontests.com/p/winning-at-competitive-ml-in-2022?s=w).
 
-The intention behind this set of exercises is to bring everyone's Pytorch skills up to date. If you already are a
-Pytorch-Jedi, feel free to pass the first set of exercises, but I recommend that you still complete it. The exercises
+The intention behind this set of exercises is to bring everyone's PyTorch skills up to date. If you already are a
+PyTorch-Jedi, feel free to pass the first set of exercises, but I recommend that you still complete it. The exercises
 are, in large part, taken directly from the
 [deep learning course at Udacity](https://github.com/udacity/deep-learning-v2-pytorch). Note that these exercises are
 given as notebooks, which is the last time we are going to use them actively in the course. Instead, after this set of
@@ -81,13 +81,13 @@ important to have a basic understanding of the concepts.
     [here](https://code.visualstudio.com/docs/datascience/jupyter-notebooks)
 
 2. Complete the
-    [Tensors in Pytorch](https://github.com/SkafteNicki/dtu_mlops/tree/main/s1_development_environment/exercise_files/1_Tensors_in_PyTorch.ipynb)
-    notebook. It focuses on the basic manipulation of Pytorch tensors. You can pass this notebook if you are comfortable
+    [Tensors in PyTorch](https://github.com/SkafteNicki/dtu_mlops/tree/main/s1_development_environment/exercise_files/1_Tensors_in_PyTorch.ipynb)
+    notebook. It focuses on the basic manipulation of PyTorch tensors. You can pass this notebook if you are comfortable
     doing this.
 
 3. Complete the
-    [Neural Networks in Pytorch](https://github.com/SkafteNicki/dtu_mlops/tree/main/s1_development_environment/exercise_files/2_Neural_Networks_in_PyTorch.ipynb)
-    notebook. It focuses on building a very simple neural network using the Pytorch `nn.Module` interface.
+    [Neural Networks in PyTorch](https://github.com/SkafteNicki/dtu_mlops/tree/main/s1_development_environment/exercise_files/2_Neural_Networks_in_PyTorch.ipynb)
+    notebook. It focuses on building a very simple neural network using the PyTorch `nn.Module` interface.
 
 4. Complete the
     [Training Neural Networks](https://github.com/SkafteNicki/dtu_mlops/tree/main/s1_development_environment/exercise_files/3_Training_Neural_Networks.ipynb)
@@ -147,7 +147,7 @@ important to have a basic understanding of the concepts.
         sum([prod(p.shape) for p in neural_net.parameters()])
         ```
 
-3. A working training loop in Pytorch should have these three function calls: `optimizer.zero_grad()`,
+3. A working training loop in PyTorch should have these three function calls: `optimizer.zero_grad()`,
     `loss.backward()`, `optimizer.step()`. Explain what would happen in the training loop (or implement it) if you
     forgot each of the function calls.
 
@@ -222,7 +222,7 @@ future as you start to add more and more features. As subgoals, please fulfill t
 
         1. :man_raising_hand: The `.pt` files are nothing else than a `.pickle` file in disguise. The
             `torch.save/torch.load` function is essentially a wrapper around the `pickle` module in Python, which
-            produces serialized files. However, it is convention to use `.pt` to indicate that the file contains Pytorch
+            produces serialized files. However, it is convention to use `.pt` to indicate that the file contains PyTorch
             tensors.
 
         We have additionally in the solution added functionality for plotting the images together with the labels for

s3_reproducibility/docker.md

@@ -395,17 +395,17 @@ beneficial for you to download.
         If it does not work, try redoing the steps.
 
     3. We should hopefully have a working setup now for running Nvidia accelerated docker containers. The next step is
-        to get Pytorch inside our container, such that our Pytorch implementation also correctly identifies the GPU.
+        to get PyTorch inside our container, such that our PyTorch implementation also correctly identifies the GPU.
         Luckily for us, Nvidia provides a set of docker images for GPU-optimized software for AI, HPC and visualizations
         through their [NGC Catalog](https://docs.nvidia.com/ngc/ngc-catalog-user-guide/index.html#what-is-nvidia-ngc).
-        The containers that have to do with Pytorch can be seen
+        The containers that have to do with PyTorch can be seen
         [here](https://docs.nvidia.com/deeplearning/frameworks/pytorch-release-notes/index.html). Try pulling the latest:
 
         ```bash
         docker pull nvcr.io/nvidia/pytorch:22.07-py3
         ```
 
-        It may take some time because the NGC images include a lot of other software for optimizing Pytorch
+        It may take some time because the NGC images include a lot of other software for optimizing PyTorch
         applications. It may be possible for you to find other images for running GPU-accelerated applications that have
         a smaller memory footprint, but NGC is the recommended and supported way.
 

s3_reproducibility/exercise_files/vae_mnist.py

@@ -1,4 +1,4 @@
-"""Adapted from https://github.com/Jackson-Kang/Pytorch-VAE-tutorial/blob/master/01_Variational_AutoEncoder.ipynb.
+"""Adapted from https://github.com/Jackson-Kang/PyTorch-VAE-tutorial/blob/master/01_Variational_AutoEncoder.ipynb.
 
 A simple implementation of Gaussian MLP Encoder and Decoder trained on MNIST
 """

s3_reproducibility/exercise_files/vae_solution/vae_mnist.py

@@ -1,4 +1,4 @@
-"""Adapted from https://github.com/Jackson-Kang/Pytorch-VAE-tutorial/blob/master/01_Variational_AutoEncoder.ipynb.
+"""Adapted from https://github.com/Jackson-Kang/PyTorch-VAE-tutorial/blob/master/01_Variational_AutoEncoder.ipynb.
 
 A simple implementation of Gaussian MLP Encoder and Decoder trained on MNIST
 """