hotfix: Fix documentation (#135)

* build: Upgrade documentation packages

* refactor: Reduce mkdocs warnings

* build: Upgrade version

Author: lorenzomammana

CHANGELOG.md

@@ -2,6 +2,17 @@
 # Changelog
 All notable changes to this project will be documented in this file.
 
+### [2.3.1]
+
+#### Fixed
+
+- Fix documentation deployment not working properly after the latest updates
+
+#### Updated
+
+- Update mkdocs related packages to the latest versions
+- Reduce the amount of warnings generated by mkdocs
+
 ### [2.3.0]
 
 #### Updated

mkdocs.yml

@@ -77,12 +77,12 @@ plugins:
   - mkdocstrings:
       handlers:
         python:
-          import:
+          inventories:
             - https://docs.python.org/3/objects.inv
             - https://mkdocstrings.github.io/objects.inv
             - https://mkdocstrings.github.io/griffe/objects.inv
             - https://numpy.org/doc/stable/objects.inv
-            - https://pytorch.org/docs/master/objects.inv
+            - https://pytorch.org/docs/stable/objects.inv
           options:
             docstring_style: google
             docstring_options:

poetry.lock

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "quadra"
-version = "2.3.0"
+version = "2.3.1"
 description = "Deep Learning experiment orchestration library"
 authors = [
 	"Federico Belotti <[email protected]>",
@@ -123,14 +123,14 @@ pytest_env = "~1.1"
 optional = true
 
 [tool.poetry.group.docs.dependencies]
-mkdocs = "1.5.2"
+mkdocs = "~1.6"
 mkdocs_literate_nav = "0.6.0"
 mkdocs_section_index = "0.3.6"
-mkdocstrings = "0.23.0"
-mkdocs_autorefs = "0.5.0"
+mkdocstrings = "~0.28"
+mkdocs_autorefs = "~1.4"
 mkdocs_gen_files = "0.5.0"
 mkdocs_material = "9.2.8"
-mkdocstrings_python = "1.6.2"
+mkdocstrings_python = "~1.16"
 mkdocs_material_extensions = "1.1.1"
 mike = "1.1.2"
 cairosvg = "2.7.0"

pyproject.toml

@@ -1,4 +1,4 @@
-__version__ = "2.3.0"
+__version__ = "2.3.1"
 
 
 def get_version():

quadra/__init__.py

@@ -46,7 +46,7 @@ def _scale_batch_size(
             however in practise a few are needed
         init_val: initial batch size to start the search with
         max_trials: max number of increases in batch size done before
-           algorithm is terminated
+            algorithm is terminated
         batch_arg_name: name of the attribute that stores the batch size.
             It is expected that the user has provided a model or datamodule that has a hyperparameter
             with that name. We will look for this attribute name in the following places

quadra/callbacks/lightning.py

@@ -75,9 +75,9 @@ class TwoSetAugmentationDataset(Dataset):
             return the original image.
 
     Example:
-        >>> images[0] = global_transform[0](original_image)
-        >>> images[1] = global_transform[1](original_image)
-        >>> images[2:] = local_transform(s)(original_image)
+        >>> `images[0] = global_transform[0](original_image)`
+        >>> `images[1] = global_transform[1](original_image)`
+        >>> `images[2:] = local_transform(s)(original_image)`
     """
 
     def __init__(

quadra/datasets/ssl.py

@@ -324,7 +324,7 @@ def _safe_export_half_precision_onnx(
     onnx_config: DictConfig,
     input_shapes: list[Any],
     input_names: list[str],
-):
+) -> bool:
     """Check that the exported half precision ONNX model does not contain NaN values. If it does, attempt to export
     the model with a more stable export and overwrite the original model.
 

quadra/utils/export.py

@@ -43,7 +43,7 @@ def imagenette_dataset(tmp_path: Path) -> str:
 
     Args:
         tmp_path: Path to temporary directory
-        request: Pytest SubRequest object
+
     Yields:
         Path to imagenette dataset folder
     """