Fix scripts and docs

.actions/assistant.py

@@ -650,7 +650,7 @@ def update_env_details(folder: str, base_path: str = DIR_NOTEBOOKS) -> str:
         req += meta.get("requirements", [])
         req = [r.strip() for r in req]
 
-        def _parse_package_name(pkg: str, keys: str = " <=>[]@", egg_name: str = "#egg=") -> str:
+        def _parse_package_name(pkg: str, keys: str = " !<=>[]@", egg_name: str = "#egg=") -> str:
             """Parsing just the package name."""
             if egg_name in pkg:
                 pkg = pkg[pkg.index(egg_name) + len(egg_name) :]

.github/workflows/ci_code-format.yml

@@ -16,6 +16,8 @@ jobs:
     steps:
       - uses: actions/checkout@v3
       - uses: actions/setup-python@v4
+        with:
+          python-version: "3.8"
 
       - name: set PY
         run: echo "PY=$(python -VV | sha256sum | cut -d' ' -f1)" >> $GITHUB_ENV

flash_tutorials/image_classification/image_classification.py

@@ -1,5 +1,3 @@
-#!/usr/bin/env python
-
 # %% [markdown]
 # In this tutorial, we'll go over the basics of lightning Flash by finetuning/predictin with an ImageClassifier on [Hymenoptera Dataset](https://www.kaggle.com/ajayrana/hymenoptera-data) containing ants and bees images.
 #
@@ -69,7 +67,7 @@
 # In this demo, we will limit the fine-tuning to run just one epoch using max_epochs=2.
 
 # %%
-trainer = flash.Trainer(max_epochs=3)
+trainer = flash.Trainer(max_epochs=1)
 
 
 # %% [markdown]

flash_tutorials/tabular_classification/tabular_classification.py

@@ -1,5 +1,3 @@
-#!/usr/bin/env python
-
 # %% [markdown]
 # In this notebook, we'll go over the basics of lightning Flash by training a TabularClassifier on [Titanic Dataset](https://www.kaggle.com/c/titanic).
 

flash_tutorials/text_classification/text_classification.py

@@ -1,5 +1,3 @@
-#!/usr/bin/env python
-
 # %% [markdown]
 # In this notebook, we'll go over the basics of lightning Flash by finetunig a TextClassifier on [IMDB Dataset](https://www.imdb.com/interfaces/).
 #
@@ -46,7 +44,7 @@
     train_file="data/imdb/train.csv",
     val_file="data/imdb/valid.csv",
     test_file="data/imdb/test.csv",
-    batch_size=4,
+    batch_size=512,  # just increased for the example to run fast
 )
 
 

lightning_examples/augmentation_kornia/.meta.yml

@@ -12,6 +12,7 @@ description: |
   mode without additional effort.
 requirements:
   - kornia
+  - pytorch-lightning !=1.8.0, !=1.8.0.post1  # skip for PermissionError: [Errno 13] Permission denied: 'command'
   - torchmetrics
   - torchvision
   - matplotlib

lightning_examples/augmentation_kornia/augmentation.py

@@ -25,7 +25,7 @@
 # %% [markdown]
 # ## Define Data Augmentations module
 #
-# [Kornia](https://kornia.github.io/) is low level Computer Vision library that provides a dedicated module
+# [Kornia](https://github.com/kornia/kornia) is low level Computer Vision library that provides a dedicated module
 # [`kornia.augmentation`](https://kornia.readthedocs.io/en/latest/augmentation.html) module implementing
 # en extensive set of data augmentation techniques for image and video.
 #

requirements/devel.txt

@@ -1,5 +1,5 @@
 virtualenv>=20.10
 jupytext>=1.10  # converting
 pytest>=6.0
-nbval>=0.9.6  # testing
+https://github.com/Borda/nbval/archive/refs/heads/master.zip  # testing with own fork with extended cell timeout
 papermill>=2.3.4  # render