fix some formatting issues

Author: rscohn2

scripts/oneapi.py

@@ -120,11 +120,16 @@ def sphinx(root, target):
     if not args.verbose:
         os.environ['LATEXMKOPTS'] = '--silent'
         os.environ['LATEXOPTS'] = '-interaction=nonstopmode -halt-on-error'
+    sphinx_args = ''
+    if not args.verbose:
+        sphinx_args += ' -q'
+    if args.a:
+        sphinx_args += ' -a'
     shell('%s -M %s %s %s %s' % (sphinx_build,
                                  target,
                                  join(root,source_dir),
                                  join(root,build_dir),
-                                 '' if args.verbose else '-q'))
+                                 sphinx_args))
 
 def get_env(var):
     return os.environ[var] if var in os.environ else ''
@@ -389,6 +394,7 @@ def main():
     parser.add_argument('--branch')
     parser.add_argument('--verbose', action='store_true')
     parser.add_argument('--dry-run', action='store_true')
+    parser.add_argument('-a', action='store_true')
     args = parser.parse_args()
 
     commands[args.action](args.root, args.action)

source/elements/oneDAL/source/introduction.rst

@@ -4,57 +4,79 @@ Introduction
 
 .. _data_analytics_pipeline:
 
-|dal_full_name| (|dal_short_name|) is a library that provides building blocks covering all stages of data analytics: data acquisition
-from a data source, preprocessing, transformation, data mining, modeling, validation, and decision making.
+|dal_full_name| (|dal_short_name|) is a library that provides building
+blocks covering all stages of data analytics: data acquisition from a
+data source, preprocessing, transformation, data mining, modeling,
+validation, and decision making.
 
 .. image:: _static/data_analytics_stages.png
   :width: 800
   :alt: Data analytis stages
 
-|dal_short_name| supports the concept of the end-to-end analytics when some of data analytics stages are performed on the
-edge devices (close to where the data is generated and where it is finally consumed). Specifically,
-|dal_short_name| Application Programming Interfaces (APIs) are agnostic about a particular cross-device
-communication technology and, therefore, can be used within different end-to-end analytics frameworks.
+|dal_short_name| supports the concept of the end-to-end analytics when
+some of data analytics stages are performed on the edge devices (close
+to where the data is generated and where it is finally
+consumed). Specifically, |dal_short_name| Application Programming
+Interfaces (APIs) are agnostic about a particular cross-device
+communication technology and, therefore, can be used within different
+end-to-end analytics frameworks.
 
 .. image:: _static/e2eframeworks.png
   :width: 800
   :alt: End to End Analytics Frameworks
 
 |dal_short_name| consists of the following major components:
 
- - The :ref:`Data Management <data_management>` component includes classes and utilities for data acquisition, initial preprocessing and normalization,
-   for data conversion into numeric formats (performed by one of supported Data Sources), and for model representation.
+ - The :ref:`Data Management <data_management>` component includes
+   classes and utilities for data acquisition, initial preprocessing
+   and normalization, for data conversion into numeric formats
+   (performed by one of supported Data Sources), and for model
+   representation.
 
- - The :ref:`Algorithms <algorithms>` component consists of classes that implement algorithms for data analysis (data mining) and data modeling
-   (training and prediction). These algorithms include clustering, classification, regression, and recommendation algorithms.
-   Algorithms support the following computation modes:
+ - The :ref:`Algorithms <algorithms>` component consists of classes
+   that implement algorithms for data analysis (data mining) and data
+   modeling (training and prediction). These algorithms include
+   clustering, classification, regression, and recommendation
+   algorithms.  Algorithms support the following computation modes:
 
-   - :ref:`Batch processing <Batch>`: algorithms work with the entire data set to produce the final result
+   - :ref:`Batch processing <Batch>`: algorithms work with the entire
+     data set to produce the final result
 
-   - :ref:`Online processing <Online>`: algorithms process a data set in blocks streamed into the device’s memory
+   - :ref:`Online processing <Online>`: algorithms process a data set
+     in blocks streamed into the device’s memory
 
-   - :ref:`Distributed processing <Distributed>`: algorithms operate on a data set distributed across several devices
-     (compute nodes)
+   - :ref:`Distributed processing <Distributed>`: algorithms operate
+     on a data set distributed across several devices (compute nodes)
 
-     Distributed algorithms in |dal_short_name| are abstracted from underlying cross-device communication technology,
-     which enables use of the library in a variety of multi-device computing and data transfer scenarios.
+     Distributed algorithms in |dal_short_name| are abstracted from
+     underlying cross-device communication technology, which enables
+     use of the library in a variety of multi-device computing and
+     data transfer scenarios.
 
-   Depending on the usage, algorithms operate both on actual data (data set) and data models:
+   Depending on the usage, algorithms operate both on actual data
+   (data set) and data models:
 
    - Analysis algorithms typically operate on data sets.
 
-   - Training algorithms typically operate on a data set to train an appropriate data model.
+   - Training algorithms typically operate on a data set to train an
+     appropriate data model.
 
-   - Prediction algorithms typically work with the trained data model and with a working data set.
+   - Prediction algorithms typically work with the trained data model
+     and with a working data set.
 
- - The **Utilities** component includes auxiliary functionality intended to be used for design of
- classes and implementation of methods such as memory allocators or type traits.
+ - The **Utilities** component includes auxiliary functionality
+   intended to be used for design of classes and implementation of
+   methods such as memory allocators or type traits.
 
- - The **Miscellaneous** component includes functionality intended to be used by |dal_short_name|
- algorithms and applications for algorithm customization and optimization on various stages of the
- analytical pipeline. Examples of such algorithms include solvers and random number generators.
+ - The **Miscellaneous** component includes functionality intended to
+   be used by |dal_short_name| algorithms and applications for
+   algorithm customization and optimization on various stages of the
+   analytical pipeline. Examples of such algorithms include solvers
+   and random number generators.
 
-Classes in Data Management, Algorithms, Utilities, and Miscellaneous components cover the most
-important usage scenarios and allow seamless implementation of complex data analytics workflows
-through direct API calls. At the same time, the library is an object-oriented framework that helps
-customize the API by redefining particular classes and methods of the library.
+Classes in Data Management, Algorithms, Utilities, and Miscellaneous
+components cover the most important usage scenarios and allow seamless
+implementation of complex data analytics workflows through direct API
+calls. At the same time, the library is an object-oriented framework
+that helps customize the API by redefining particular classes and
+methods of the library.

source/elements/oneVPL/source/index.rst

@@ -3,14 +3,14 @@
 
 .. _oneVPL-section:
 
-**Thank you for using oneAPI Video Processing Library.  Based on developer
-  feedback, we are revising oneVPL to make it even better.  Stay tuned for a new
-  oneVPL.  Thanks again!**
-
 ********************
 oneVPL |vpl_version|
 ********************
 
+**Thank you for using oneAPI Video Processing Library.  Based on developer
+  feedback, we are revising oneVPL to make it even better.  Stay tuned for a new
+  oneVPL.  Thanks again!**
+
 The |vpl_full_name| is a programming interface for video processing and video analytics,
 focusing on building portable media pipeline on CPU, GPU, Deep Learning (DL) 
 accelerators and FPGA. It aims for function and performance portability such that