Bump kedro-datasets to 2.0.0

.readthedocs.yml

@@ -8,20 +8,13 @@ version: 2
 build:
   os: ubuntu-22.04
   tools:
-    python: "3.8"
+    python: "3.9"
     nodejs: "19"
   apt_packages:
     - libasound2
   jobs:
     post_create_environment:
       - npm install -g @mermaid-js/mermaid-cli
-    pre_install:
-    # pip==23.2 breaks pip-tools<7.0, and pip-tools>=7.0 does not support Python 3.7
-    # pip==23.3 breaks dependency resolution
-      - python -m pip install -U "pip>=21.2,<23.2"
-    # These are technically installation steps, due to RTD's limit we need to inject the installation earlier.
-      - python -m pip install --upgrade --no-cache-dir sphinx readthedocs-sphinx-ext
-      - python -m pip install --upgrade --upgrade-strategy only-if-needed --no-cache-dir .[docs,test]
     pre_build:
       - pip freeze
       - python -m sphinx -WETan -j auto -D language=en -b linkcheck -d _build/doctrees docs/source _build/linkcheck
@@ -32,15 +25,9 @@ sphinx:
   configuration: docs/source/conf.py
   fail_on_warning: true
 
-# Build documentation with MkDocs
-# mkdocs:
-# configuration: mkdocs.yml
-
-# Optionally set the version of Python and requirements required to build your docs
-# python:
-#   install:
-#     - method: pip
-#       path: .
-#       extra_requirements:
-#         - docs
-#         - test
+python:
+  install:
+    - method: pip
+      path: .
+      extra_requirements:
+        - docs

RELEASE.md

@@ -17,6 +17,7 @@
 * Removed `pip-tools` as a dependency.
 * Accepted path-like filepaths more broadly for datasets.
 * Removed support for defining the `layer` attribute at top-level within DataCatalog.
+* Bumped `kedro-datasets` to latest `2.0.0`.
 
 ## Breaking changes to the API
 * Renamed the `data_sets` argument and the `_data_sets` attribute in `Catalog` and their references to `datasets` and `_datasets` respectively.

docs/source/conf.py

@@ -68,7 +68,7 @@
 
 intersphinx_mapping = {
     "kedro-viz": ("https://docs.kedro.org/projects/kedro-viz/en/v6.6.1/", None),
-    "kedro-datasets": ("https://docs.kedro.org/projects/kedro-datasets/en/kedro-datasets-1.8.0/", None),
+    "kedro-datasets": ("https://docs.kedro.org/projects/kedro-datasets/en/kedro-datasets-2.0.0/", None),
 }
 
 # The suffix(es) of source filenames.

docs/source/data/advanced_data_catalog_usage.md

@@ -11,7 +11,7 @@ From version **`2.0.0`** of `kedro-datasets`, all dataset names have changed to
 
 To use the `DataCatalog` API, construct a `DataCatalog` object programmatically in a file like `catalog.py`.
 
-In the following code, we use several pre-built data loaders documented in the {doc}`kedro-datasets documentation<kedro-datasets:kedro_datasets>`.
+In the following code, we use several pre-built data loaders documented in the {py:mod}`kedro-datasets documentation <kedro-datasets:kedro_datasets>`.
 
 ```python
 from kedro.io import DataCatalog

docs/source/data/data_catalog.md

@@ -34,7 +34,7 @@ shuttles:
 
 Kedro supports a range of connectors, for CSV files, Excel spreadsheets, Parquet files, Feather files, HDF5 files, JSON documents, pickled objects, SQL tables, SQL queries, and more. They are supported using libraries such as pandas, PySpark, NetworkX, and Matplotlib.
 
-{doc}`The kedro-datasets package documentation<kedro-datasets:kedro_datasets>` contains a comprehensive list of all available file types.
+{py:mod}`The kedro-datasets package documentation <kedro-datasets:kedro_datasets>` contains a comprehensive list of all available file types.
 
 ### Dataset `filepath`
 

docs/source/data/how_to_create_a_custom_dataset.md

@@ -1,6 +1,6 @@
 # Advanced: Tutorial to create a custom dataset
 
-{doc}`Kedro supports many datasets<kedro-datasets:kedro_datasets>` out of the box, but you may find that you need to create a custom dataset. For example, you may need to handle a proprietary data format or filesystem in your pipeline, or perhaps you have found a particular use case for a dataset that Kedro does not support. This tutorial explains how to create a custom dataset to read and save image data.
+{py:mod}`Kedro supports many datasets <kedro-datasets:kedro_datasets>` out of the box, but you may find that you need to create a custom dataset. For example, you may need to handle a proprietary data format or filesystem in your pipeline, or perhaps you have found a particular use case for a dataset that Kedro does not support. This tutorial explains how to create a custom dataset to read and save image data.
 
 ## AbstractDataset
 

docs/source/data/index.md

@@ -3,7 +3,7 @@
 
 In a Kedro project, the Data Catalog is a registry of all data sources available for use by the project. The catalog is stored in a YAML file (`catalog.yml`) that maps the names of node inputs and outputs as keys in the `DataCatalog` class.
 
-The {doc}`kedro-datasets<kedro-datasets:kedro_datasets>` package offers built-in datasets for common file types and file systems.
+The {py:mod}`kedro-datasets <kedro-datasets:kedro_datasets>` package offers built-in datasets for common file types and file systems.
 
 We first introduce the basic sections of `catalog.yml`, which is the file used to register data sources for a Kedro project.
 

docs/source/extend_kedro/common_use_cases.md

@@ -4,15 +4,15 @@
 
 ## Use Case 1: How to add extra behaviour to Kedro's execution timeline
 
-The execution timeline of a Kedro pipeline can be thought of as a sequence of actions performed by various Kedro library components, such as the {doc}`datasets<kedro-datasets:kedro_datasets>`, [DataCatalog](/kedro.io.DataCatalog), [Pipeline](/kedro.pipeline.Pipeline), [Node](/kedro.pipeline.node.Node) and [KedroContext](/kedro.framework.context.KedroContext).
+The execution timeline of a Kedro pipeline can be thought of as a sequence of actions performed by various Kedro library components, such as the {py:mod}`datasets <kedro-datasets:kedro_datasets>`, [DataCatalog](/kedro.io.DataCatalog), [Pipeline](/kedro.pipeline.Pipeline), [Node](/kedro.pipeline.node.Node) and [KedroContext](/kedro.framework.context.KedroContext).
 
 At different points in the lifecycle of these components, you might want to add extra behaviour: for example, you could add extra computation for profiling purposes _before_ and _after_ a node runs, or _before_ and _after_ the I/O actions of a dataset, namely the `load` and `save` actions.
 
 This can now achieved by using [Hooks](../hooks/introduction.md), to define the extra behaviour and when in the execution timeline it should be introduced.
 
 ## Use Case 2: How to integrate Kedro with additional data sources
 
-You can use {doc}`datasets<kedro-datasets:kedro_datasets>` to interface with various different data sources. If the data source you plan to use is not supported out of the box by Kedro, you can [create a custom dataset](../data/how_to_create_a_custom_dataset.md).
+You can use {py:mod}`datasets <kedro-datasets:kedro_datasets>` to interface with various different data sources. If the data source you plan to use is not supported out of the box by Kedro, you can [create a custom dataset](../data/how_to_create_a_custom_dataset.md).
 
 ## Use Case 3: How to add or modify CLI commands
 

docs/source/faq/faq.md

@@ -10,7 +10,7 @@ This is a growing set of technical FAQs. The [product FAQs on the Kedro website]
 
 ## Kedro documentation
 * {doc}`Where can I find the documentation about Kedro-Viz<kedro-viz:kedro-viz_visualisation>`?
-* {doc}`Where can I find the documentation for Kedro's datasets<kedro-datasets:kedro_datasets>`?
+* {py:mod}`Where can I find the documentation for Kedro's datasets <kedro-datasets:kedro_datasets>`?
 
 ## Working with Jupyter
 

docs/source/get_started/kedro_concepts.md

@@ -57,7 +57,7 @@
 
 The Kedro Data Catalog is the registry of all data sources that the project can use to manage loading and saving data. It maps the names of node inputs and outputs as keys in a `DataCatalog`, a Kedro class that can be specialised for different types of data storage.
 
-{doc}`Kedro provides different built-in datasets<kedro-datasets:kedro_datasets>` for numerous file types and file systems, so you don’t have to write the logic for reading/writing data.
+{py:mod}`Kedro provides different built-in datasets <kedro-datasets:kedro_datasets>` for numerous file types and file systems, so you don’t have to write the logic for reading/writing data.
 
 ## Kedro project directory structure
 

docs/source/tutorial/set_up_data.md

@@ -118,7 +118,7 @@ When you have finished, close `ipython` session with `exit()`.
 
 ### Custom data
 
-{doc}`Kedro supports numerous datasets<kedro-datasets:kedro_datasets>` out of the box, but you can also add support for any proprietary data format or filesystem.
+{py:mod}`Kedro supports numerous datasets <kedro-datasets:kedro_datasets>` out of the box, but you can also add support for any proprietary data format or filesystem.
 
 You can find further information about [how to add support for custom datasets](../data/how_to_create_a_custom_dataset.md) in specific documentation covering advanced usage.
 

pyproject.toml

@@ -79,14 +79,12 @@ test = [
     "pytest-mock>=1.7.1, <2.0",
     "pytest-xdist[psutil]~=2.2.1",
     "pytest~=7.2",
-    "s3fs>=0.3.0, <0.5",  # Needs to be at least 0.3.0 to make use of `cachable` attribute on S3FileSystem.
+    "s3fs>=2021.4, <2024.1",  # Upper bound set arbitrarily, to be reassessed in early 2024
     "semver",
     "trufflehog~=2.1",
 ]
 docs = [
-    # docutils>=0.17 changed the HTML
-    # see https://github.com/readthedocs/sphinx_rtd_theme/issues/1115
-    "docutils==0.16",
+    "docutils<0.18",
     "sphinx~=5.3.0",
     "sphinx_rtd_theme==1.2.0",
     # Regression on sphinx-autodoc-typehints 1.21
@@ -98,7 +96,6 @@ docs = [
     "sphinxcontrib-mermaid~=0.7.1",
     "myst-parser~=1.0.0",
     "Jinja2<3.1.0",
-    "kedro-datasets[all]~=1.8.0",
 ]
 all = [ "kedro[test,docs]" ]