sentence case titles & formatting

docs/how-to/3rd-party/magma-install.rst

@@ -1,8 +1,6 @@
+*******************************************************************************************
 MAGMA installation for ROCm
-###########################
-
-MAGMA for ROCm
-**************
+*******************************************************************************************
 
 Matrix Algebra on GPU and Multicore Architectures (MAGMA) is a
 collection of next-generation dense linear algebra libraries that is designed
@@ -14,7 +12,7 @@ more information, refer to
 `https://icl.utk.edu/magma/index.html <https://icl.utk.edu/magma/index.html>`_.
 
 Using MAGMA for PyTorch
-=======================
+======================================
 
 Tensor is fundamental to deep-learning techniques because it provides extensive
 representational functionalities and math operations. This data structure is
@@ -23,7 +21,7 @@ with a variety of solutions including driver routines, computational routines,
 BLAS routines, auxiliary routines, and utility routines.
 
 Building MAGMA from source
-==========================
+========================================
 
 To build MAGMA from the source, follow these steps:
 

docs/how-to/3rd-party/pytorch-install.rst

@@ -1,5 +1,6 @@
+**********************************************************************************
 Installing PyTorch for ROCm
-###########################
+**********************************************************************************
 
 `PyTorch <https://pytorch.org/>`_ is an open-source tensor library designed for deep learning. PyTorch on
 ROCm provides mixed-precision and large-scale training using our
@@ -22,7 +23,7 @@ For hardware, software, and third-party framework compatibility between ROCm and
 .. _using-docker-with-pytorch-pre-installed:
 
 Using a Docker image with PyTorch pre-installed
-***********************************************
+===============================================================
 
 1. Download the latest public `PyTorch Docker image <https://hub.docker.com/r/rocm/pytorch>`_.
 
@@ -50,7 +51,7 @@ Using a Docker image with PyTorch pre-installed
 .. _using-wheels-package:
 
 Using a wheels package
-**********************
+===============================================================
 
 PyTorch supports the ROCm platform by providing tested wheels packages. To access this feature, go
 to `pytorch.org/get-started/locally/ <https://pytorch.org/get-started/locally/>`_. For the correct
@@ -61,7 +62,7 @@ wheels command, you must select 'Linux', 'Python', 'pip', and 'ROCm' in the matr
    **Option 1:**
 
    a. Download a base Docker image with the correct user-space ROCm version.
-      
+
       .. list-table::
           :header-rows: 1
 
@@ -163,7 +164,7 @@ wheels command, you must select 'Linux', 'Python', 'pip', and 'ROCm' in the matr
 .. _using-pytorch-rocm-docker-image:
 
 Using the PyTorch ROCm base Docker image
-****************************************
+===============================================================
 
 The pre-built base Docker image has all dependencies installed, including:
 
@@ -243,7 +244,7 @@ scripts to determine the configuration of the build environment.
 .. _using-pytorch-upstream-docker-image:
 
 Using the PyTorch upstream Docker file
-**************************************
+===============================================================
 
 If you don't want to use a prebuilt base Docker image, you can build a custom base Docker image
 using scripts from the PyTorch repository. This uses a standard Docker image from operating system
@@ -346,7 +347,7 @@ maintainers and installs all the required dependencies, including:
        echo $? # should return 0 if success
 
 Testing the PyTorch installation
-********************************
+===============================================================
 
 You can use PyTorch unit tests to validate your PyTorch installation. If you used a
 **prebuilt PyTorch Docker image from AMD ROCm DockerHub** or installed an
@@ -407,7 +408,7 @@ If you want to manually run unit tests to validate your PyTorch installation ful
    You can replace ``test_nn.py`` with any other test set.
 
 Running a basic PyTorch example
-*******************************
+===============================================================
 
 The PyTorch examples repository provides basic examples that exercise the functionality of your
 framework.
@@ -420,7 +421,7 @@ Two of our favorite testing databases are:
   **visual object recognition**.
 
 MNIST PyTorch example
-=====================
+---------------------------------------------------------------------------------------------------------
 
 1. Clone the PyTorch examples repository.
 
@@ -452,7 +453,7 @@ MNIST PyTorch example
        Test set: Average loss: 0.0252, Accuracy: 9921/10000 (99%)
 
 ImageNet PyTorch example
-========================
+---------------------------------------------------------------------------------------------------------
 
 1. Clone the PyTorch examples repository (if you didn't already do this step in the preceding MNIST example).
 

docs/how-to/3rd-party/tensorflow-install.rst

@@ -1,8 +1,6 @@
+****************************************************************************************
 Installing TensorFlow for ROCm
-##############################
-
-TensorFlow
-**********
+****************************************************************************************
 
 TensorFlow is an open-source library for solving machine-learning,
 deep-learning, and artificial-intelligence problems. It can be used to solve
@@ -31,12 +29,12 @@ development.
       - Last three versions at ROCm release.
 
 Installing TensorFlow
-=====================
+===============================================
 
 The following sections contain options for installing TensorFlow.
 
 Option 1: using a Docker image
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+-------------------------------------------------------------------------------
 
 To install ROCm on bare metal, follow
 :doc:`/tutorial/install-overview`. The recommended option to
@@ -62,7 +60,7 @@ Follow these steps:
        --security-opt seccomp=unconfined rocm/tensorflow:latest
 
 Option 2: using a wheels package
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+-------------------------------------------------------------------------------
 
 To install TensorFlow using the wheels package, follow these steps:
 
@@ -174,7 +172,7 @@ To install TensorFlow using the wheels package, follow these steps:
        `https://github.com/ROCmSoftwarePlatform/tensorflow-upstream/blob/develop-upstream/rocm_docs/tensorflow-rocm-release.md <https://github.com/ROCmSoftwarePlatform/tensorflow-upstream/blob/develop-upstream/rocm_docs/tensorflow-rocm-release.md>`_
 
 Test the TensorFlow installation
-================================
+=======================================
 
 To test the installation of TensorFlow, run the container image as specified in
 the previous section Installing TensorFlow. Ensure you have access to the Python
@@ -185,7 +183,7 @@ shell in the Docker container.
     python3 -c 'import tensorflow' 2> /dev/null && echo ‘Success’ || echo ‘Failure’
 
 Run a basic TensorFlow example
-==============================
+======================================
 
 The TensorFlow examples repository provides basic examples that exercise the
 framework's functionality. The MNIST database is a collection of handwritten

docs/how-to/amdgpu-install.rst

@@ -1,5 +1,6 @@
-Installation via AMDGPU Script
-##############################
+*************************************************************************************
+Installation via AMDGPU installer
+*************************************************************************************
 
 ``amdgpu-install`` is a tool that helps you install and update AMDGPU and ROCm
 and its components.
@@ -13,14 +14,14 @@ and its components.
 .. _amdgpu-install-installation:
 
 Installation
-************
+=================================================
 
 Installation of ``amdgpu-install`` differs slightly depending on the OS and its package manager.
 
 Make sure that the :doc:`/how-to/prerequisites` are met before installing.
 
 Ubuntu
-======
+--------------------------------------------------------------------
 
 .. datatemplate:nodata::
 
@@ -38,7 +39,7 @@ Ubuntu
         {% endfor %}
 
 Red Hat Enterprise Linux
-========================
+--------------------------------------------------------------------
 
 .. datatemplate:nodata::
 
@@ -54,7 +55,7 @@ Red Hat Enterprise Linux
         {% endfor %}
 
 SUSE Linux Enterprise
-=====================
+--------------------------------------------------------------------
 
 .. datatemplate:nodata::
 
@@ -68,11 +69,8 @@ SUSE Linux Enterprise
                 sudo zypper --no-gpg-checks install https://repo.radeon.com/amdgpu-install/|amdgpu_version|/sle/{{ os_version }}/amdgpu-install-|amdgpu_install_version|.noarch.rpm
         {% endfor %}
 
-Usage
-*****
-
-Use case groups
-===============
+Use cases
+=================================================
 
 Instead of installing individual applications or libraries the installer script
 groups packages into specific use cases, matching typical workflows and
@@ -120,14 +118,14 @@ output below.
     - HIP development libraries
 
 
-Upgrading
-=========
+Upgrading ROCm
+=================================================
 
 The upgrade procedure with the installer script is exactly the same as
 :ref:`installing for first time use. <amdgpu-install-installation>`
 
 Installing ROCm packages
-==========================
+=================================================
 
 To install use cases specific to your requirements, use the installer
 amdgpu-install as follows:
@@ -158,8 +156,8 @@ amdgpu-install as follows:
 
       sudo amdgpu-install --usecase=workstation,rocm
 
-Uninstalling
-============
+Uninstalling ROCm
+=================================================
 
 To uninstall all ROCm packages and the kernel-mode driver the following commands
 can be used.
@@ -184,7 +182,7 @@ can be used.
       sudo amdgpu-install --uninstall --rocmrelease=all
 
 Multi-versioned ROCm Installation
-=================================
+=================================================
 
 By default (without the --rocmrelease option) the installer script will install
 packages in the single-version layout.
@@ -205,11 +203,11 @@ for:
 
 - :ref:`Ubuntu <ubuntu-register-rocm>`
 
-Aditional options
-*****************
+Additional options
+=================================================
 
 Unattended installation
-=======================
+------------------------------------------------------------------------
 
 Adding ``-y`` as a parameter to amdgpu-install skips user prompts (for
 automation). For example:
@@ -219,7 +217,7 @@ automation). For example:
     amdgpu-install -y --usecase=rocm
 
 Skipping kernel mode driver installation
-========================================
+------------------------------------------------------------------------
 
 The installer script tries to install the kernel mode driver along with the
 requested use cases. This might be unnecessary as in the case of docker

docs/how-to/docker.rst

@@ -1,19 +1,20 @@
-Deploy ROCm Docker Containers
-#############################
+********************************************************************************
+Install ROCm Docker containers
+********************************************************************************
 
 Prerequisites
-*************
+==========================================
 
 Docker containers share the kernel with the host operating system, therefore the
 ROCm kernel-mode driver must be installed on the host. Please refer to
 :doc:`/tutorial/install-overview` on installing ``amdgpu-dkms``. The other
 user-space parts (like the HIP-runtime or math libraries) of the ROCm stack will
 be loaded from the container image and don't need to be installed to the host.
 
-(docker-access-gpus-in-container)=
+.. _docker-access-gpus-in-container:
 
 Accessing GPUs in containers
-****************************
+==========================================
 
 In order to access GPUs in a container (to run applications using HIP, OpenCL or
 OpenMP offloading) explicit access to the GPUs must be granted.
@@ -43,10 +44,10 @@ can be exposed to the new container:
 Note that this gives more access than strictly required, as it also exposes the
 other device files found in that folder to the container.
 
-(docker-restrict-gpus)=
+.. _docker-restrict-gpus:
 
 Restricting a container to a subset of the GPUs
-===============================================
+-------------------------------------------------------------------------------------------------
 
 If a ``/dev/dri/renderD`` device is not exposed to a container then it cannot use
 the GPU associated with it; this allows to restrict a container to any subset of
@@ -60,7 +61,7 @@ like:
     docker run --device /dev/kfd --device /dev/dri/renderD128 --device /dev/dri/renderD130 <image>
 
 Additional options
-==================
+-------------------------------------------------------------------------------------------------
 
 The performance of an application can vary depending on the assignment of GPUs
 and CPUs to the task. Typically, ``numactl`` is installed as part of many HPC
@@ -78,10 +79,10 @@ This option is recommended for Docker Containers running HPC applications.
     docker run --device /dev/kfd --device /dev/dri --security-opt seccomp=unconfined ...
 
 Docker images in the ROCm ecosystem
-***********************************
+=======================================================
 
 Base images
-===========
+-------------------------------------------------------------------------------------------------
 
 The `ROCm Docker repository <https://github.com/RadeonOpenCompute/ROCm-docker>`_ hosts images useful for users
 wishing to build their own containers leveraging ROCm. The built images are
@@ -90,7 +91,7 @@ available from `Docker Hub <https://hub.docker.com/u/rocm>`_. In particular
 applications, but does not include any libraries.
 
 Applications
-============
+-------------------------------------------------------------------------------------------------
 
 AMD provides pre-built images for various GPU-ready applications through
 `Infinity Hub <https://www.amd.com/en/technologies/infinity-hub>`_.