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Merge branch 'master' of github.com:clearlinux/clear-linux-documentation into rtd-theme
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Kevin Putnam
@@ -16,7 +16,8 @@ Description
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Kubernetes is an open source system for automating deployment, scaling, and
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management of containerized applications. It groups containers that make up
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an application into logical units for easy management and discovery.
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an application into logical units for easy management and discovery. Get up
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and running quickly with our `Cloud native setup automation`_.
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Kata Containers\* kata-runtime adheres to
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:abbr:`OCI (Open Container Initiative*)` guidelines and works seamlessly with
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@@ -229,6 +230,10 @@ Related topics
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Read the Kubernetes documentation to learn more about:
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* Deploying Kubernetes with a `cloud-native-setup`_
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* :ref:`Kubernetes best practices <kubernetes-bp>`
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* `Understanding basic Kubernetes architecture`_
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* `Deploying an application to your cluster`_
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@@ -237,11 +242,12 @@ Read the Kubernetes documentation to learn more about:
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* `Joining your nodes`_
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Cloud native setup automation (optional)
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****************************************
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Cloud native setup automation
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*****************************
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Clone the `cloud-native-setup`_ repository on your system and follow the
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instructions. This repository includes helper scripts to automate configuration.
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Optional: Clone the `cloud-native-setup`_ repository on your system and
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follow the instructions. This repository includes helper scripts to automate
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configuration.
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Package configuration customization (optional)
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**********************************************
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@@ -293,12 +299,6 @@ commands as a shell script to configure all of these services in one step:
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EOF
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done
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Next steps
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**********
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:ref:`kubernetes-bp`
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Troubleshooting
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***************
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@@ -412,4 +412,4 @@ Troubleshooting
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.. _documentation: https://clearlinux.org/documentation/clear-linux
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.. _cloud-native-setup: https://github.com/clearlinux/cloud-native-setup
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.. _cloud-native-setup: https://github.com/clearlinux/cloud-native-setup/tree/master/clr-k8s-examples
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@@ -3,13 +3,17 @@
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TensorFlow\* machine learning on |CL-ATTR|
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##########################################
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This tutorial will demonstrate the installation and execusion of a TensorFlow\*
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This tutorial demonstrates the installation and execution of a TensorFlow\*
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machine learning example on |CL-ATTR|. It uses a Jupyter\* Notebook and MNIST
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data for handwriting recognition.
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The initial steps will have you set up a Jupyter kernel and run a Notebook
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The initial steps show how to set up a Jupyter kernel and run a Notebook
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on a bare-metal |CL| system.
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.. contents::
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:local:
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:depth: 1
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Prerequisites
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*************
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@@ -23,12 +27,12 @@ Before you install any new packages, update |CL| with the following command:
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sudo swupd update
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Once your system is updated, add the following bundles to your system:
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After your system is updated, add the following bundles to your system:
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* `machine-learning-web-ui`: This bundle contains the Jupyter application.
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* :command:`machine-learning-web-ui`: This bundle contains the Jupyter application.
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* `machine-learning-basic`: This bundle contains TensorFlow and other useful
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tools.
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* :command:`machine-learning-basic`: This bundle contains TensorFlow
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and other useful tools.
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To install the bundles, run the following commands in your :file:`$HOME`
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directory:
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@@ -39,7 +43,7 @@ directory:
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sudo swupd bundle-add machine-learning-basic
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Set up a Jupyter notebook
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Set up a Jupyter Notebook
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*************************
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With all required packages and libraries installed, set up the file structure
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@@ -68,8 +72,8 @@ for the Jupyter Notebook.
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directory.
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.. note::
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After installing the `machine-learning basic` bundle, you can find the
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example code under
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After installing the :command:`machine-learning basic` bundle, you can find the example code under
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:file:`/usr/share/doc/tensorflow/MNIST_example.ipynb`.
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@@ -91,7 +95,7 @@ The files needed are:
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Run the Jupyter machine learning example code
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*********************************************
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With |CL|, Jupyter, and TensorFlow installed and configured, we can
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With |CL|, Jupyter, and TensorFlow installed and configured, you can
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run the example code.
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#. Go to the :file:`($HOME)/Notebooks` directory and start Jupyter with the
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@@ -104,7 +108,8 @@ run the example code.
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jupyter notebook
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The Jupyter server starts and opens a web browser showing the Jupyter file
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manager with a list of files in the current directory, see figure 1.
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manager with a list of files in the current directory, as shown in
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Figure 1.
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.. figure:: figures/machine-learning-1.png
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:alt: Jupyter file manager
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@@ -112,15 +117,15 @@ run the example code.
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Figure 1: The Jupyter file manager shows the list of available files.
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#. Click on the :file:`Handwriting` directory. The :file:`MNIST_example.ipynb`
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file created earlier should be listed there, see figure 2.
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file created earlier should be listed there, as shown in Figure 2.
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.. figure:: figures/machine-learning-2.png
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:alt: Example file within the Jupyter file manager
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Figure 2: The example file within the Jupyter file manager.
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#. To run the hand writing example, click on the :file:`MNIST_example.ipynb`
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file to load the notebook, see figure 3.
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#. To run the handwriting example, click on the :file:`MNIST_example.ipynb`
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file to load the notebook, as shown in Figure 3.
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.. figure:: figures/machine-learning-3.png
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:alt: The loaded MNIST_example notebook
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@@ -132,7 +137,7 @@ run the example code.
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move to the next.
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#. Select the :guilabel:`In [2]` cell and click the |run-cell| button to load
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the MNIST data. The successful output is shown on figure 4.
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the MNIST data. The successful output is shown on Figure 4.
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.. figure:: figures/machine-learning-4.png
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:alt: Successful import of MNIST data
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@@ -140,15 +145,16 @@ run the example code.
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Figure 4: Output after successfully importing the MNIST data.
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After the MNIST data was successfully downloaded and extracted into the
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After the MNIST data is successfully downloaded and extracted into the
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:file:`mnist` directory within the :file:`($HOME)/Notebooks/Handwriting`
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directory, four .gz files are present and the four data sets were created:
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directory, four .gz files are present and the four data sets are created:
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`trainX`, `trainY`, `testX` and `testY`.
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#. To inspect the imported data, the function in :guilabel:`In [3]` first
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instructs Jupyter to reshape the data into an array of 28 x 28 images and to
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plot the area in a 28 x 28 grid. Click the |run-cell| button twice to show
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the first two digits in the `trainX` dataset, see figure 5.
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the first two digits in the `trainX` dataset. An example is shown in
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Figure 5.
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.. figure:: figures/machine-learning-5.png
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:alt: Function to reshape data.
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@@ -158,17 +164,17 @@ run the example code.
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#. The :guilabel:`In [4]` cell defines the neural network. It provides the
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inputs, defines the hidden layers, runs the training model, and sets up
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the output layer, see figure 6. Click the |run-cell| button four times to
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perform these operations.
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the output layer, as shown in Figure 6. Click the |run-cell| button four
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times to perform these operations.
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.. figure:: figures/machine-learning-6.png
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:alt: Defining, building and training the neural network model
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Figure 6: Defining, building and training the neural network model.
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Figure 6: Defining, building, and training the neural network model.
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#. To test the accuracy of the prediction the system makes, select the
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#. To test the accuracy of the prediction that the system makes, select the
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:guilabel:`In [8]` cell and click the |run-cell| button. In this example,
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the number 6 was predicted with a 99% accuracy, see figure 7.
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the number 6 was predicted with a 99% accuracy, as shown in Figure 7.
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.. figure:: figures/machine-learning-7.png
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:alt: Prediction example
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@@ -184,7 +190,7 @@ run the example code.
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#. To check the accuracy for the whole dataset, select the :guilabel:`In [10]`
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cell and click the |run-cell| button. Our example's accuracy is
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calculated as 97.17%, see figure 8.
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calculated as 97.17%, as shown in Figure 8.
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.. figure:: figures/machine-learning-8.png
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:alt: System's accuracy
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