Merge branch 'main' into pyproject

Author: Borda

.github/workflows/ci_checks.yml

@@ -15,4 +15,4 @@ concurrency:
 jobs:
 
   check-schema:
-    uses: Lightning-AI/utilities/.github/workflows/check-schema.yml@v0.5.0
+    uses: Lightning-AI/utilities/.github/workflows/check-schema.yml@v0.7.1

.github/workflows/ci_docs.yml

@@ -99,9 +99,13 @@ jobs:
 
       - name: Make Documentation
         working-directory: ./_docs
-        run: |
-          # First run the same pipeline as Read-The-Docs
-          make html --debug SPHINXOPTS="-W --keep-going" -b linkcheck
+        run: make html --debug SPHINXOPTS="-W --keep-going"
+
+      - name: Check External Links (Optional)
+        working-directory: ./_docs
+        run: make --jobs $(nproc) linkcheck
+        # ToDO: comment on PR if any link failed
+        continue-on-error: true
 
       - name: Upload built docs
         uses: actions/upload-artifact@v3

.gitignore

@@ -129,3 +129,9 @@ dmypy.json
 .pyre/
 
 .idea/
+
+# data artifacts
+logs/
+lightning_logs/
+cifar-10-batches-py
+*.tar.gz

.pre-commit-config.yaml

@@ -36,7 +36,7 @@ repos:
         args: [--in-place, --wrap-summaries=115, --wrap-descriptions=120]
 
   - repo: https://github.com/PyCQA/isort
-    rev: 5.11.4
+    rev: 5.12.0
     hooks:
       - id: isort
 

_requirements/default.txt

@@ -1,6 +1,6 @@
-setuptools==65.*
+setuptools==67.4.0
 matplotlib>=3.0.0, <3.4.0
-ipython[notebook]>=8.0.0, <8.9.0
+ipython[notebook]>=8.0.0, <8.12.0
 torch>=1.8.1, <1.14.0
-pytorch-lightning>=1.4, <1.9
+pytorch-lightning>=1.4, <2.0.0
 torchmetrics>=0.7, <0.12

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

@@ -1,14 +1,15 @@
 title: "Tutorial 1: Introduction to PyTorch"
 author: Phillip Lippe
 created: 2021-08-27
-updated: 2021-11-29
+updated: 2023-03-14
 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.
   This notebook is part of a lecture series on Deep Learning at the University of Amsterdam.
   The full list of tutorials can be found at https://uvadlc-notebooks.rtfd.io.
 requirements:
   - matplotlib
+  - lightning>=2.0.0rc0
 accelerator:
   - CPU
   - GPU

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

@@ -25,18 +25,18 @@
 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
-from IPython.display import set_matplotlib_formats
 from matplotlib.colors import to_rgba
 from torch import Tensor
 from tqdm.notebook import tqdm  # Progress bar
 
-set_matplotlib_formats("svg", "pdf")
+matplotlib_inline.backend_inline.set_matplotlib_formats("svg", "pdf")  # For export
 
 # %% [markdown]
 # ## The Basics of PyTorch
@@ -185,7 +185,7 @@
 print("X2 (after)", x2)
 
 # %% [markdown]
-# In-place operations are usually marked with a underscore postfix (e.g. "add_" instead of "add").
+# In-place operations are usually marked with a underscore postfix (for example `torch.add_` instead of `torch.add`).
 #
 # Another common operation aims at changing the shape of a tensor.
 # A tensor of size (2,3) can be re-organized to any other shape with the same number of elements (e.g. a tensor of size (6), or (3,2), ...).
@@ -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: 2023-03-14
 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.
@@ -14,6 +14,7 @@ requirements:
   - torchvision
   - matplotlib
   - seaborn
+  - lightning>=2.0.0rc0
 accelerator:
   - CPU
   - GPU

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
@@ -19,14 +22,11 @@
 import torch.optim as optim
 import torch.utils.data as data
 import torchvision
-
-# %matplotlib inline
-from IPython.display import set_matplotlib_formats
 from torchvision import transforms
 from torchvision.datasets import FashionMNIST
 from tqdm.notebook import tqdm
 
-set_matplotlib_formats("svg", "pdf")  # For export
+matplotlib_inline.backend_inline.set_matplotlib_formats("svg", "pdf")  # For export
 sns.set()
 
 # %% [markdown]
@@ -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: 2023-03-14
 license: CC BY-SA
 tags:
   - Image
@@ -18,6 +18,7 @@ requirements:
   - torchvision
   - matplotlib
   - seaborn
+  - lightning>=2.0.0rc0
 accelerator:
   - CPU
   - GPU