documentation: switch to pydata theme

doc/conf.py

@@ -52,20 +52,32 @@
 # for source files.
 exclude_trees = ["_build"]
 
-pygments_style = "default"
+highlight_language = "python3"
+pygments_style = "sphinx"
 
 autoclass_content = "both"
 autodoc_default_flags = ["show-inheritance", "members", "undoc-members"]
 autodoc_member_order = "bysource"
 
-html_theme = "sphinx_rtd_theme"
+html_theme = "pydata_sphinx_theme"
+
+html_logo = "../branding/icon/sagemaker-icon.png"
+html_favicon = "../branding/icon/sagemaker-icon.png"
 
 html_theme_options = {
     "collapse_navigation": True,
-    "sticky_navigation": True,
     "navigation_depth": 6,
-    "includehidden": True,
-    "titles_only": False,
+    "icon_links": [
+        {
+            "name": "GitHub",
+            "url": "https://github.com/aws/sagemaker-python-sdk",
+            "icon": "fa-brands fa-square-github",
+            "type": "fontawesome",
+        }
+    ],
+    "logo": {
+        "text": "Amazon Sagemaker SDK",
+    },
 }
 
 
@@ -83,16 +95,11 @@
 
 html_css_files = [
     "https://cdn.datatables.net/1.10.23/css/jquery.dataTables.min.css",
+    "_static/theme_overrides.css",
+    "_static/pagination.css",
+    "_static/search_accessories.css",
 ]
 
-html_context = {
-    "css_files": [
-        "_static/theme_overrides.css",
-        "_static/pagination.css",
-        "_static/search_accessories.css",
-    ]
-}
-
 # Example configuration for intersphinx: refer to the Python standard library.
 intersphinx_mapping = {"http://docs.python.org/": None}
 

doc/frameworks/tensorflow/deploying_tensorflow_serving.rst

@@ -515,7 +515,7 @@ either, so you can simplify the layout at the same time:
 
 You should now have a directory structure like this:
 
-::
+.. code-block:: text
 
   └── multi
     ├── model1

doc/requirements.txt

@@ -1,5 +1,5 @@
-sphinx==3.1.2
-sphinx-rtd-theme==0.5.0
+sphinx==5.1.1
+pydata-sphinx-theme==0.14.1
 docutils==0.15.2
 packaging==20.9
 jinja2<3.1

doc/v2.rst

@@ -414,7 +414,7 @@ Usage
 
 Currently, the tool supports only converting one file at a time:
 
-.. code::
+.. code:: bash
 
     $ sagemaker-upgrade-v2 --in-file input.py --out-file output.py
     $ sagemaker-upgrade-v2 --in-file input.ipynb --out-file output.ipynb

src/sagemaker/feature_store/feature_processor/feature_processor.py

@@ -11,7 +11,7 @@
 # ANY KIND, either express or implied. See the License for the specific
 # language governing permissions and limitations under the License.
 """Feature Processor decorator for feature transformation functions."""
-from __future__ import absolute_import
+from __future__ import absolute_import, annotations
 
 from typing import Any, Callable, Dict, List, Optional, Sequence, Union
 
@@ -33,7 +33,7 @@
 
 def feature_processor(
     inputs: Sequence[
-        Union[FeatureGroupDataSource, CSVDataSource, ParquetDataSource, BaseDataSource]
+        FeatureGroupDataSource | CSVDataSource | ParquetDataSource | BaseDataSource
     ],
     output: str,
     target_stores: Optional[List[str]] = None,
@@ -79,8 +79,8 @@ def transform(input_feature_group, input_csv):
             return ...
 
     Args:
-        inputs (Sequence[Union[FeatureGroupDataSource, CSVDataSource, ParquetDataSource,
-            BaseDataSource]]): A list of data sources.
+        inputs (Sequence[FeatureGroupDataSource | CSVDataSource  | ParquetDataSource
+            | BaseDataSource]): A list of data sources.
         output (str): A Feature Group ARN to write results of this function to.
         target_stores (Optional[list[str]], optional): A list containing at least one of
             'OnlineStore' or 'OfflineStore'. If unspecified, data will be ingested to the enabled

src/sagemaker/jumpstart/estimator.py

@@ -251,8 +251,8 @@ def __init__(
                 (Default: None).
             model_channel_name (Optional[Union[str, PipelineVariable]]): Name of the channel where
                 'model_uri' will be downloaded. (Default: None).
-            metric_definitions (Optional[Union[list[dict[str, str], list[dict[str,
-                PipelineVariable]]]]): A list of dictionaries that defines the metric(s)
+            metric_definitions (Optional[Union[list[dict[str, str], list[dict[str,PipelineVariable]]]]):
+                A list of dictionaries that defines the metric(s)
                 used to evaluate the training jobs. Each dictionary contains two keys: 'Name'
                 for the name of the metric, and 'Regex' for the regular expression used to extract
                 the metric from the logs. This should be defined only for jobs that