From 25819fccd9983e265e5e9d5867ec50aaf538717d Mon Sep 17 00:00:00 2001 From: DougTW Date: Fri, 2 Aug 2019 13:23:03 -0700 Subject: [PATCH 1/2] machine-learning.rst: minor editorial changes (#674) * Incremental commit 1: minor editorial revisons. Signed-off-by: DougTW * machine-learning.rst: Minor editorial changes. Signed-off-by: DougTW * Applies :command: directive for bundles and capitalizes Figures. Signed-off-by: Michael Vincerra --- .../machine-learning/machine-learning.rst | 54 ++++++++++--------- 1 file changed, 30 insertions(+), 24 deletions(-) diff --git a/source/clear-linux/tutorials/machine-learning/machine-learning.rst b/source/clear-linux/tutorials/machine-learning/machine-learning.rst index d07e0243..de491bb7 100644 --- a/source/clear-linux/tutorials/machine-learning/machine-learning.rst +++ b/source/clear-linux/tutorials/machine-learning/machine-learning.rst @@ -3,13 +3,17 @@ TensorFlow\* machine learning on |CL-ATTR| ########################################## -This tutorial will demonstrate the installation and execusion of a TensorFlow\* +This tutorial demonstrates the installation and execution of a TensorFlow\* machine learning example on |CL-ATTR|. It uses a Jupyter\* Notebook and MNIST data for handwriting recognition.  -The initial steps will have you set up a Jupyter kernel and run a Notebook +The initial steps show how to set up a Jupyter kernel and run a Notebook on a bare-metal |CL| system. +.. contents:: + :local: + :depth: 1 + Prerequisites ************* @@ -23,12 +27,12 @@ Before you install any new packages, update |CL| with the following command: sudo swupd update -Once your system is updated, add the following bundles to your system: +After your system is updated, add the following bundles to your system: -* `machine-learning-web-ui`: This bundle contains the Jupyter application. +* :command:`machine-learning-web-ui`: This bundle contains the Jupyter application. -* `machine-learning-basic`: This bundle contains TensorFlow and other useful - tools. +* :command:`machine-learning-basic`: This bundle contains TensorFlow + and other useful tools. To install the bundles, run the following commands in your :file:`$HOME` directory: @@ -39,7 +43,7 @@ directory: sudo swupd bundle-add machine-learning-basic -Set up a Jupyter notebook +Set up a Jupyter Notebook ************************* With all required packages and libraries installed, set up the file structure @@ -68,8 +72,8 @@ for the Jupyter Notebook. directory. .. note:: - After installing the `machine-learning basic` bundle, you can find the - example code under + + After installing the :command:`machine-learning basic` bundle, you can find the example code under :file:`/usr/share/doc/tensorflow/MNIST_example.ipynb`. @@ -91,7 +95,7 @@ The files needed are: Run the Jupyter machine learning example code ********************************************* -With |CL|, Jupyter, and TensorFlow installed and configured, we can +With |CL|, Jupyter, and TensorFlow installed and configured, you can run the example code. #. Go to the :file:`($HOME)/Notebooks` directory and start Jupyter with the @@ -104,7 +108,8 @@ run the example code. jupyter notebook The Jupyter server starts and opens a web browser showing the Jupyter file - manager with a list of files in the current directory, see figure 1. + manager with a list of files in the current directory, as shown in + Figure 1. .. figure:: figures/machine-learning-1.png :alt: Jupyter file manager @@ -112,15 +117,15 @@ run the example code. Figure 1: The Jupyter file manager shows the list of available files. #. Click on the :file:`Handwriting` directory. The :file:`MNIST_example.ipynb` - file created earlier should be listed there, see figure 2. + file created earlier should be listed there, as shown in Figure 2. .. figure:: figures/machine-learning-2.png :alt: Example file within the Jupyter file manager Figure 2: The example file within the Jupyter file manager. -#. To run the hand writing example, click on the :file:`MNIST_example.ipynb` - file to load the notebook, see figure 3. +#. To run the handwriting example, click on the :file:`MNIST_example.ipynb` + file to load the notebook, as shown in Figure 3. .. figure:: figures/machine-learning-3.png :alt: The loaded MNIST_example notebook @@ -132,7 +137,7 @@ run the example code. move to the next. #. Select the :guilabel:`In [2]` cell and click the |run-cell| button to load - the MNIST data. The successful output is shown on figure 4. + the MNIST data. The successful output is shown on Figure 4. .. figure:: figures/machine-learning-4.png :alt: Successful import of MNIST data @@ -140,15 +145,16 @@ run the example code. Figure 4: Output after successfully importing the MNIST data. - After the MNIST data was successfully downloaded and extracted into the + After the MNIST data is successfully downloaded and extracted into the :file:`mnist` directory within the :file:`($HOME)/Notebooks/Handwriting` - directory, four .gz files are present and the four data sets were created: + directory, four .gz files are present and the four data sets are created: `trainX`, `trainY`, `testX` and `testY`. #. To inspect the imported data, the function in :guilabel:`In [3]` first instructs Jupyter to reshape the data into an array of 28 x 28 images and to plot the area in a 28 x 28 grid. Click the |run-cell| button twice to show - the first two digits in the `trainX` dataset, see figure 5. + the first two digits in the `trainX` dataset. An example is shown in + Figure 5. .. figure:: figures/machine-learning-5.png :alt: Function to reshape data. @@ -158,17 +164,17 @@ run the example code. #. The :guilabel:`In [4]` cell defines the neural network. It provides the inputs, defines the hidden layers, runs the training model, and sets up - the output layer, see figure 6. Click the |run-cell| button four times to - perform these operations. + the output layer, as shown in Figure 6. Click the |run-cell| button four + times to perform these operations. .. figure:: figures/machine-learning-6.png :alt: Defining, building and training the neural network model - Figure 6: Defining, building and training the neural network model. + Figure 6: Defining, building, and training the neural network model. -#. To test the accuracy of the prediction the system makes, select the +#. To test the accuracy of the prediction that the system makes, select the :guilabel:`In [8]` cell and click the |run-cell| button. In this example, - the number 6 was predicted with a 99% accuracy, see figure 7. + the number 6 was predicted with a 99% accuracy, as shown in Figure 7. .. figure:: figures/machine-learning-7.png :alt: Prediction example @@ -184,7 +190,7 @@ run the example code. #. To check the accuracy for the whole dataset, select the :guilabel:`In [10]` cell and click the |run-cell| button. Our example's accuracy is - calculated as 97.17%, see figure 8. + calculated as 97.17%, as shown in Figure 8. .. figure:: figures/machine-learning-8.png :alt: System's accuracy From 075cb998377bac41ab7ed13ce82ca958c7b4d92e Mon Sep 17 00:00:00 2001 From: michael vincerra <37549381+mvincerx@users.noreply.github.com> Date: Fri, 2 Aug 2019 16:24:36 -0700 Subject: [PATCH 2/2] Adds cloud-native-setup in related topics; corrects link. (#691) Signed-off-by: Michael Vincerra Adds sentence ref to cloud-native-setup in Desc. Signed-off-by: Michael Vincerra --- .../tutorials/kubernetes/kubernetes.rst | 24 +++++++++---------- 1 file changed, 12 insertions(+), 12 deletions(-) diff --git a/source/clear-linux/tutorials/kubernetes/kubernetes.rst b/source/clear-linux/tutorials/kubernetes/kubernetes.rst index f5d40a4d..93c8d68c 100644 --- a/source/clear-linux/tutorials/kubernetes/kubernetes.rst +++ b/source/clear-linux/tutorials/kubernetes/kubernetes.rst @@ -16,7 +16,8 @@ Description Kubernetes is an open source system for automating deployment, scaling, and management of containerized applications. It groups containers that make up -an application into logical units for easy management and discovery. +an application into logical units for easy management and discovery. Get up +and running quickly with our `Cloud native setup automation`_. Kata Containers\* kata-runtime adheres to :abbr:`OCI (Open Container Initiative*)` guidelines and works seamlessly with @@ -229,6 +230,10 @@ Related topics Read the Kubernetes documentation to learn more about: +* Deploying Kubernetes with a `cloud-native-setup`_ + +* :ref:`Kubernetes best practices ` + * `Understanding basic Kubernetes architecture`_ * `Deploying an application to your cluster`_ @@ -237,11 +242,12 @@ Read the Kubernetes documentation to learn more about: * `Joining your nodes`_ -Cloud native setup automation (optional) -**************************************** +Cloud native setup automation +***************************** -Clone the `cloud-native-setup`_ repository on your system and follow the -instructions. This repository includes helper scripts to automate configuration. +Optional: Clone the `cloud-native-setup`_ repository on your system and +follow the instructions. This repository includes helper scripts to automate +configuration. Package configuration customization (optional) ********************************************** @@ -293,12 +299,6 @@ commands as a shell script to configure all of these services in one step: EOF done -Next steps -********** - -:ref:`kubernetes-bp` - - Troubleshooting *************** @@ -412,4 +412,4 @@ Troubleshooting .. _documentation: https://clearlinux.org/documentation/clear-linux -.. _cloud-native-setup: https://github.com/clearlinux/cloud-native-setup +.. _cloud-native-setup: https://github.com/clearlinux/cloud-native-setup/tree/master/clr-k8s-examples