Tabify wheels installation

ROCm · Jun 5, 2024 · 6dccd3b · 6dccd3b
1 parent b59d9de
commit 6dccd3b
Showing 1 changed file with 64 additions and 38 deletions.
diff --git a/docs/how-to/3rd-party/tensorflow-install.rst b/docs/how-to/3rd-party/tensorflow-install.rst
@@ -6,9 +6,9 @@
 Installing TensorFlow for ROCm
 ****************************************************************************************
 
-TensorFlow is an open-source library for solving machine learning,
+`TensorFlow <https://www.tensorflow.org>`_ is an open-source library for solving machine learning,
 deep learning, and AI problems. It can solve many
-problems across different sectors and industries, but primarily focuses on
+problems across different sectors and industries but primarily focuses on
 neural network training and inference. It is one of the most popular and
 in-demand frameworks and is very active in open-source contribution and
 development.
@@ -29,8 +29,8 @@ To install ROCm on bare metal, follow
 :doc:`/tutorial/install-overview`. The recommended option to
 get a TensorFlow environment is through Docker.
 
-Using Docker provides portability and access to a prebuilt Docker image that
-has been rigorously tested within AMD. This can also save compilation time and
+Using Docker provides portability and access to a prebuilt Docker image
+rigorously tested within AMD. This can also save compilation time and
 should perform as tested and mitigate potential installation issues.
 
 Follow these steps:
@@ -54,48 +54,73 @@ Follow these steps:
 Using a wheels package
 -------------------------------------------------------------------------------
 
-#. Ensure your environment is using a supported Python version for the desired ``tensorflow-rocm`` wheel.
+Follow these steps to install ROCm on Linux using a ready-made Python wheel.
 
-   a. To check compatible Python versions, navigate to the desired ROCm release version
-      in `<https://repo.radeon.com/rocm/manylinux/>`__.
+The following table associates ROCm versions with their supported TensorFlow versions.
 
-   b. In the versioned ROCm release directory, take note of the Python versions associated with the desired
-      wheel version indicated by ``cp`` in the wheels' file names.
-
-      For example, ``tensorflow_rocm-2.15.0-cp310-cp310-manylinux2014_x86_64.whl`` targets Python 3.10, as indicated by
-      ``cp310``.
+.. list-table::
+    :header-rows: 1
 
-#. To install TensorFlow, use the following command. Replace ``[wheel-version]`` and the ROCm version ``[major.minor]``
-   as appropriate.
+    * - ROCm version
+      - TensorFlow version
+    * - 6.1.x
+      - .. list-table::
 
-   .. code-block:: shell
+           * - 2.13.1
+             - 2.14.0
+             - 2.15.0
 
-      pip install --user tensorflow-rocm==[wheel-version] -f https://repo.radeon.com/rocm/manylinux/rocm-rel-[major.minor] --upgrade
+    * - 6.0.x
+      - .. list-table::
 
-   * ``[wheel-version]`` indicates the TensorFlow version.
+           * - 2.12.1
+             - 2.13.1
+             - 2.14.0
 
-   * ``[major.minor]`` indicates the `ROCm release version <https://repo.radeon.com/rocm/manylinux/>`_; for instance,
-     ``6.1``.
-   |
+.. note::
 
-   .. _install-tensorflow-versions:
+   As of ROCm 6.1, ``tensorflow-rocm`` packages are found at `<https://repo.radeon.com/rocm/manylinux>`__.
+   Before ROCm 6.1, packages were found at `<https://pypi.org/project/tensorflow-rocm>`__.
 
-   The following table associates ROCm versions with their supported TensorFlow versions.
+.. tab-set::
 
-   .. list-table::
-       :header-rows: 1
+   .. tab-item:: ROCm 6.1 and later
 
-       * - ROCm version
-         - TensorFlow version
-       * - 6.1.x
-         - 2.13.1, 2.14.0, 2.15.0
-       * - 6.0.x
-         - 2.12.1, 2.13.1, 2.14.0
+      #. Ensure your environment uses a supported Python version for the desired ``tensorflow-rocm`` wheel.
 
-.. note::
+         a. To check compatible Python versions, navigate to the desired ROCm release version
+            in `<https://repo.radeon.com/rocm/manylinux/>`__.
 
-   As of ROCm 6.1, ``tensorflow-rocm`` packages are found at `<https://repo.radeon.com/rocm/manylinux>`__.
-   Prior to ROCm 6.1, packages were found at `<https://pypi.org/project/tensorflow-rocm>`_.
+         b. In the versioned ROCm release directory, take note of the Python versions associated with the desired
+            wheel version indicated by ``cp`` in the wheels' file names.
+
+            For example, ``tensorflow_rocm-2.15.0-cp310-cp310-manylinux2014_x86_64.whl`` targets Python 3.10, as indicated by
+            ``cp310``.
+
+      #. To install TensorFlow, use the following command. Replace ``[wheel-version]`` and the ROCm version ``[major.minor]``
+         as appropriate.
+
+         .. code-block:: shell
+
+            pip install --user tensorflow-rocm==[wheel-version] -f https://repo.radeon.com/rocm/manylinux/rocm-rel-[major.minor] --upgrade
+
+         * ``[wheel-version]`` indicates the TensorFlow version.
+
+         * ``[major.minor]`` indicates the `ROCm release version <https://repo.radeon.com/rocm/manylinux/>`_; for instance,
+           ``6.1``.
+
+   .. tab-item:: Prior to ROCm 6.1
+
+      #. Ensure your environment uses a supported Python version for the desired ``tensorflow-rocm`` wheel.
+
+      #. To install TensorFlow, use the following command. Replace ``[wheel-version]`` with the appropriate
+         TensorFlow version.
+
+         .. code-block:: shell
+
+            pip install --user tensorflow-rocm==[wheel-version] --upgrade
+
+.. _install-tensorflow-versions:
 
 For details on ``tensorflow-rocm`` wheels and ROCm version compatibility, refer to
 `<https://github.com/ROCm/tensorflow-upstream/blob/develop-upstream/rocm_docs/tensorflow-rocm-release.md>`__.
@@ -116,10 +141,11 @@ shell in the Docker container.
 Running a basic TensorFlow example
 ======================================
 
-To quickly validate your TensorFlow environment, let's run a basic TensorFlow example.
+Run a basic TensorFlow example to validate your TensorFlow enviroment.
 
-The MNIST dataset is a collection of handwritten digits that may be used to train a Convolutional Neural Network (CNN)
-for handwriting recognition. This dataset is included with your TensorFlow installation.
+The MNIST dataset is a collection of handwritten digits that may be used to
+train a convolutional neural network (CNN) for handwriting recognition. This
+dataset is included with your TensorFlow installation.
 
 Run the following sample code to load the MNIST dataset, then train and evaluate it.
 
@@ -128,7 +154,7 @@ Run the following sample code to load the MNIST dataset, then train and evaluate
    import tensorflow as tf
    print("TensorFlow version:", tf.__version__)
    mnist = tf.keras.datasets.mnist
-   
+
    (x_train, y_train), (x_test, y_test) = mnist.load_data()
    x_train, x_test = x_train / 255.0, x_test / 255.0
    model = tf.keras.models.Sequential([
@@ -147,7 +173,7 @@ Run the following sample code to load the MNIST dataset, then train and evaluate
    model.fit(x_train, y_train, epochs=5)
    model.evaluate(x_test,  y_test, verbose=2)
 
-If successful, you should see the following output indicating the image classifier is now trained to around 98% accuracy
+If successful, you should see the following output indicating the image classifier is now trained to around 98 percent accuracy
 on this dataset.
 
 .. image:: ../../data/install/tensorflow-install/tensorflow-test-output.png