GH-16186: Updating user guide pages for Cloud Integration to reflect makersaurus guidelines

h2o-docs/src/product/cloud-integration.rst

@@ -1,7 +1,7 @@
-Cloud Integration
------------------
+Cloud integration
+=================
 
-H2O is supported on a number of cloud environments, including
+H2O is supported on a number of cloud environments:
 
 - EC2 Instances and S3 Storage (RedHat AMI, Amazon Linux AMI, and Ubuntu AMI)
 - Amazon AWS
@@ -11,7 +11,10 @@ H2O is supported on a number of cloud environments, including
 - Nimbix Cloud
 - Kubernetes
 
-Refer to the following topics:
+H2O on cloud environments
+-------------------------
+
+Refer to the following topics for more information:
 
 .. toctree::
    :maxdepth: 1

h2o-docs/src/product/cloud-integration/aws.rst

@@ -1,6 +1,6 @@
-Using H2O with Amazon AWS
-~~~~~~~~~~~~~~~~~~~~~~~~~
+Use H2O with Amazon AWS
+=======================
 
 The old `H2O Artificial Intelligence instance <https://aws.amazon.com/marketplace/pp/prodview-76ewxkmzkvr3m>`_ is no longer available.
 
-Please see `Training and serving H2O models using Amazon SageMaker <https://aws.amazon.com/blogs/machine-learning/training-and-serving-h2o-models-using-amazon-sagemaker/>`_.
+Please see `Training and serving H2O models using Amazon SageMaker <https://aws.amazon.com/blogs/machine-learning/training-and-serving-h2o-models-using-amazon-sagemaker/>`_ for more information.

h2o-docs/src/product/cloud-integration/azure-dsvm.rst

@@ -1,64 +1,71 @@
-Using H2O with Microsoft Azure Linux Data Science VM
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+Use H2O with Microsoft Azure Linux Data Science VM
+==================================================
 
 The Data Science Virtual Machine (DSVM) for Linux is an Ubuntu-based virtual machine image that makes it easy to get started with deep learning on Azure. CNTK, TensorFlow, MXNet, Caffe, Caffe2, DIGITS, H2O, Keras, Theano, and Torch are built, installed, and configured, so they are ready to run immediately. The NVIDIA driver, CUDA, and cuDNN are also included. All frameworks are the GPU versions but work on the CPU as well. Many sample Jupyter notebooks are included.
 
-You can view a full list of installed tools for the Linux edition `here <https://docs.microsoft.com/en-us/azure/machine-learning/machine-learning-data-science-virtual-machine-overview>`__.
+You can `view a full list of installed tools for the Linux edition here <https://docs.microsoft.com/en-us/azure/machine-learning/machine-learning-data-science-virtual-machine-overview>`__.
 
-Follow the steps below to create the H2O Artificial Intelligence VM.
+Create the H2O Artificial Intelligence VM
+-----------------------------------------
 
-1. Log in to your Azure portal at `https://portal.azure.com <https://portal.azure.com>`__, and click the **New** button.  
+Follow these steps to create the H2O Artificial Intelligence VM.
+
+1. Log in to your `Azure portal <https://portal.azure.com>`__, and click **New**.  
 
   .. figure:: ../images/azurelin_new.png
-     :alt: Create a new VM
+     :alt: Create a new VM page on Microsoft Azure.
 
-2. Search in the Marketplace for “H2O”. The result will show all of the H2O offering in the Azure Marketplace. Select the Data Science Virtual Machine for Linux (Ubuntu), and click the **Create** button.
+2. Search in the Marketplace for “H2O”. The result will show all of the H2O offerings in the Azure Marketplace. Select the Data Science Virtual Machine for Linux (Ubuntu), and click **Create**.
 
   .. figure:: ../images/azurelin_h2o_dsvm.png
-     :alt: Data Science VM for Linux
+     :alt: Data Science VM for Linux panel outlining how DSVM for Linux works and which tools are contained in it.
 
 3. Follow the UI instructions and fill the Basic settings: 
 
-   - Username and Password that you will be using to connect to the VM
-   - Subscriptions where you want to create the VM
-   - The new resource group name
-   - The location where the VM is going to be created
-   - Size of the VM. 
-
-   Optional: For VMs with GPU, select the HDD disk option, and search for the N-Series VM. For more information, click `here <http://gpu.azure.com/>`__. 
+   - Username and Password that you will be using to connect to the VM.
+   - Subscriptions where you want to create the VM.
+   - The new resource group name.
+   - The location where the VM is going to be created.
+   - Size of the VM.
+   - (Optional for VMs with GPU) Select the HDD disk option, and search for the N-Series VM. See more on `GPU optimized virtual machine sizes <http://gpu.azure.com/>`__.
 
 4. Wait for the VM Solution to be created. The expected creation time is around 10 minutes.
 
-5. After your deployment is created, locate your network security group. Your Network Security Group is named by the default as **<your VM name>-nsg**. After you locate the security group, look at the inbound security rules.  
+5. Locate your network security group after your deployment is created. Your Network Security Group is named by the default as **<your VM name>-nsg**. After you locate the security group, look at the inbound security rules.  
 
   .. figure:: ../images/azurelin_inbound_secrules.png
-     :alt: Inbound security rules
+     :alt: Inbound and outbound security rules overview page with three inbound security rules.
 
 6. Click **Add** to add a new Inbound Security Rule, and create a TCP Rule with Port Range 54321. Click **OK** when you are done.
 
   .. figure:: ../images/azurelin_add_inbound_secrule.png
-     :alt: Add inbound security rule
+     :alt: Add inbound security rule panel where the new TCP rule with a port range of 54321 has been added.
 
-7. After the new Inbound Security Rule is added, then you are ready to start using your Linux DSVM with H2O. Simply connect to your Jupyter notebooks and look at the examples in the **h2o > python** folder to create your first H2O Model on Azure. 
+7. After the new Inbound Security Rule is added, you are ready to start using your Linux DSVM with H2O. Connect to your Jupyter notebooks and look at the examples in the **h2o > python** folder to create your first H2O Model on Azure. 
 
    1. Connect to Jupyter Notebook by going to **https://<<VM DNS Name or IP Address>>:8000/**.
    2. Connect to H2O Flow by going to **http://<<VM DNS Name or IP Address>>:54321/**.
 
-Troubleshooting Tips
-'''''''''''''''''''' 
+Tips for troubleshooting
+------------------------
 
-- Be aware that Azure subscriptions by default allow only 20 cores. If you get a Validation error on template deployment, this might be the cause. To increase the cores limit, follow these steps: `https://blogs.msdn.microsoft.com/girishp/2015/09/20/increasing-core-quota-limits-in-azure/ <https://blogs.msdn.microsoft.com/girishp/2015/09/20/increasing-core-quota-limits-in-azure/>`__
-- OS Disk by default is small (approx 30GB). In this case, you will have approximately 16GB of free space to start with. This is the same for all VM sizes. It is recommended that you add an SSD data disk by following these instructions: `https://azure.microsoft.com/en-us/documentation/articles/virtual-machines-linux-classic-attach-disk/ <https://azure.microsoft.com/en-us/documentation/articles/virtual-machines-linux-classic-attach-disk/>`__
-- Pick a VM size that provides RAM of at least 4x the size of your dataset. More information on Azure VM sizes is available here: `https://azure.microsoft.com/en-us/pricing/details/virtual-machines/ <https://azure.microsoft.com/en-us/pricing/details/virtual-machines/>`__
-- The included examples use a ``locate`` function for importing data. This function is not available to end users. For example:
+- Be aware that Azure subscriptions only allow 20 cores by default. If you get a Validation error on template deployment, this might be the cause. Follow these `steps to increase your core quota limits <https://blogs.msdn.microsoft.com/girishp/2015/09/20/increasing-core-quota-limits-in-azure/>`__.
+- OS Disk by default is small (approx 30GB). In this case, you will have approximately 16GB of free space to start with. This is the same for all VM sizes. Follow these `steps to add an SSD data disk <https://learn.microsoft.com/en-us/azure/devtest-labs/devtest-lab-attach-detach-data-disk>`__.
+- Pick a VM size that provides RAM of at least 4x the size of your dataset. See more about `Azure VM sizes <https://azure.microsoft.com/en-us/pricing/details/virtual-machines/>`__.
+- The previous examples use a ``locate`` function for importing data. This function is not available to end users. For example:
 
   :: 
 
     air_path = [_locate("smalldata/airlines/allyears2k_headers.zip")]
 
- To retrieve the datasets, use one of the following methods instead:
+  To retrieve the datasets, use one of the following methods instead:
+
+    - Replace this path with a pointer to an Amazon S3 file at **http://s3.amazonaws.com/h2o-public-test-data/..**. For example: 
+
+    ::
+
+      air_path="http://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip"
 
-  - Replace this path with a pointer to a raw github file at **https://raw.github.com/h2o.ai/h2o/master/..**. For example, ``air_path="https://raw.github.com/h2oai/h2o/master/smalldata/airlines/allyears2k_headers.zip"``
-  - Use ``wget`` to retrieve the files.
+    - Use ``wget`` to retrieve the files.
 
- A list of the datasets used in the examples along with their locate path is available in the **h2o > python > README** file.
+  A list of the datasets used in the examples along with their locate path is available in the **h2o > python > README** file.