Merge pull request #107036 from msebolt/azurecli-update-pr2

azurecli updates

Author: Jak-MS

articles/machine-learning/data-science-virtual-machine/dsvm-pools.md

@@ -11,7 +11,6 @@ author: vijetajo
 ms.author: vijetaj
 ms.topic: conceptual
 ms.date: 12/10/2018
-
 ---
 
 # Create a shared pool of Data Science Virtual Machines
@@ -32,11 +31,13 @@ You can find a sample Azure Resource Manager template that creates a scale set w
 
 You can create the scale set from the Azure Resource Manager template by specifying values for the parameter file in the Azure CLI:
 
-```
+```azurecli-interactive
 az group create --name [[NAME OF RESOURCE GROUP]] --location [[ Data center. For eg: "West US 2"]
 az group deployment create --resource-group  [[NAME OF RESOURCE GROUP ABOVE]]  --template-uri https://raw.githubusercontent.com/Azure/DataScienceVM/master/Scripts/CreateDSVM/Ubuntu/dsvm-vmss-cluster.json --parameters @[[PARAMETER JSON FILE]]
 ```
+
 The preceding commands assume you have:
+
 * A copy of the parameter file with the values specified for your instance of the scale set.
 * The number of VM instances.
 * Pointers to the Azure Files share.
@@ -54,18 +55,3 @@ Virtual machine scale sets support autoscaling. You can set rules about when to
 
 * [Set up a common Identity](dsvm-common-identity.md)
 * [Securely store credentials to access cloud resources](dsvm-secure-access-keys.md)
-
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articles/machine-learning/data-science-virtual-machine/dsvm-secure-access-keys.md

@@ -23,10 +23,9 @@ One way to secure credentials is to use Windows Installer (MSI) in combination w
 
 The documentation about managed identities for Azure resources and Key Vault comprises a comprehensive resource for in-depth information on these services. The rest of this article walks through the basic use of MSI and Key Vault on the Data Science Virtual Machine (DSVM) to access Azure resources. 
 
-## Create a managed identity on the DSVM 
+## Create a managed identity on the DSVM
 
-
-```
+```azurecli-interactive
 # Prerequisite: You have already created a Data Science VM in the usual way.
 
 # Create an identity principal for the VM.
@@ -35,9 +34,9 @@ az vm assign-identity -g <Resource Group Name> -n <Name of the VM>
 az resource list -n <Name of the VM> --query [*].identity.principalId --out tsv
 ```
 
-
 ## Assign Key Vault access permissions to a VM principal
-```
+
+```azurecli-interactive
 # Prerequisite: You have already created an empty Key Vault resource on Azure by using the Azure portal or Azure CLI.
 
 # Assign only get and set permissions but not the capability to list the keys.
@@ -46,7 +45,7 @@ az keyvault set-policy --object-id <Principal ID of the DSVM from previous step>
 
 ## Access a secret in the key vault from the DSVM
 
-```
+```bash
 # Get the access token for the VM.
 x=`curl http://localhost:50342/oauth2/token --data "resource=https://vault.azure.net" -H Metadata:true`
 token=`echo $x | python -c "import sys, json; print(json.load(sys.stdin)['access_token'])"`
@@ -57,7 +56,7 @@ curl https://<Vault Name>.vault.azure.net/secrets/SQLPasswd?api-version=2016-10-
 
 ## Access storage keys from the DSVM
 
-```
+```bash
 # Prerequisite: You have granted your VMs MSI access to use storage account access keys based on instructions at https://docs.microsoft.com/azure/active-directory/managed-service-identity/tutorial-linux-vm-access-storage. This article describes the process in more detail.
 
 y=`curl http://localhost:50342/oauth2/token --data "resource=https://management.azure.com/" -H Metadata:true`
@@ -66,6 +65,7 @@ curl https://management.azure.com/subscriptions/<SubscriptionID>/resourceGroups/
 
 # Now you can access the data in the storage account from the retrieved storage account keys.
 ```
+
 ## Access the key vault from Python
 
 ```python
@@ -97,7 +97,7 @@ print("My secret value is {}".format(secret.value))
 
 ## Access the key vault from Azure CLI
 
-```
+```azurecli-interactive
 # With managed identities for Azure resources set up on the DSVM, users on the DSVM can use Azure CLI to perform the authorized functions. The following commands enable access to the key vault from Azure CLI without requiring login to an Azure account.
 # Prerequisites: MSI is already set up on the DSVM as indicated earlier. Specific permissions, like accessing storage account keys, reading specific secrets, and writing new secrets, are provided to the MSI.
 

articles/machine-learning/how-to-configure-environment.md

@@ -27,7 +27,6 @@ The following table shows each development environment covered in this article,
 | [Azure Databricks](#aml-databricks) | Ideal for running large-scale intensive machine learning workflows on the scalable Apache Spark platform. | Overkill for experimental machine learning, or smaller-scale experiments and workflows. Additional cost incurred for Azure Databricks. See [pricing details](https://azure.microsoft.com/pricing/details/databricks/). |
 | [The Data Science Virtual Machine (DSVM)](#dsvm) | Similar to the cloud-based compute instance (Python and the SDK are pre-installed), but with additional popular data science and machine learning tools pre-installed. Easy to scale and combine with other custom tools and workflows. | A slower getting started experience compared to the cloud-based compute instance. |
 
-
 This article also provides additional usage tips for the following tools:
 
 * [Jupyter Notebooks](#jupyter): If you're already using the Jupyter Notebook, the SDK has some extras that you should install.
@@ -55,7 +54,6 @@ The Azure Machine Learning [compute instance (preview)](concept-compute-instance
 
 There is nothing to install or configure for a compute instance.  Create one anytime from within your Azure Machine Learning workspace. Provide just a name and specify an Azure VM type. Try it now with this [Tutorial: Setup environment and workspace](tutorial-1st-experiment-sdk-setup.md).
 
-
 Learn more about [compute instances](concept-compute-instance.md).
 
 To stop incurring compute charges, [stop the compute instance](tutorial-1st-experiment-sdk-train.md#clean-up-resources).
@@ -91,7 +89,7 @@ To use the DSVM as a development environment:
 
         * To create an Ubuntu Data Science Virtual Machine, use the following command:
 
-            ```azurecli
+            ```azurecli-interactive
             # create a Ubuntu DSVM in your resource group
             # note you need to be at least a contributor to the resource group in order to execute this command successfully
             # If you need to create a new resource group use: "az group create --name YOUR-RESOURCE-GROUP-NAME --location YOUR-REGION (For example: westus2)"
@@ -100,7 +98,7 @@ To use the DSVM as a development environment:
 
         * To create a Windows Data Science Virtual Machine, use the following command:
 
-            ```azurecli
+            ```azurecli-interactive
             # create a Windows Server 2016 DSVM in your resource group
             # note you need to be at least a contributor to the resource group in order to execute this command successfully
             az vm create --resource-group YOUR-RESOURCE-GROUP-NAME --name YOUR-VM-NAME --image microsoft-dsvm:dsvm-windows:server-2016:latest --admin-username YOUR-USERNAME --admin-password YOUR-PASSWORD --authentication-type password
@@ -110,13 +108,13 @@ To use the DSVM as a development environment:
 
     * For Ubuntu DSVM:
 
-        ```shell
+        ```bash
         conda activate py36
         ```
 
     * For Windows DSVM:
 
-        ```shell
+        ```bash
         conda activate AzureML
         ```
 
@@ -141,35 +139,35 @@ When you're using a local computer (which might also be a remote virtual machine
 
     Run the following command to create the environment.
 
-    ```shell
+    ```bash
     conda create -n myenv python=3.6.5
     ```
 
     Then activate the environment.
 
-    ```shell
+    ```bash
     conda activate myenv
     ```
 
     This example creates an environment using python 3.6.5, but any specific subversions can be chosen. SDK compatibility may not be guaranteed with certain major versions (3.5+ is recommended), and it's recommended to try a different version/subversion in your Anaconda environment if you run into errors. It will take several minutes to create the environment while components and packages are downloaded.
 
 1. Run the following commands in your new environment to enable environment-specific IPython kernels. This will ensure expected kernel and package import behavior when working with Jupyter Notebooks within Anaconda environments:
 
-    ```shell
+    ```bash
     conda install notebook ipykernel
     ```
 
     Then run the following command to create the kernel:
 
-    ```shell
+    ```bash
     ipython kernel install --user --name myenv --display-name "Python (myenv)"
     ```
 
 1. Use the following commands to install packages:
 
     This command installs the base Azure Machine Learning SDK with notebook and `automl` extras. The `automl` extra is a large install, and can be removed from the brackets if you don't intend to run automated machine learning experiments. The `automl` extra also includes the Azure Machine Learning Data Prep SDK by default as a dependency.
 
-    ```shell
+    ```bash
     pip install azureml-sdk[notebooks,automl]
     ```
 
@@ -182,20 +180,19 @@ When you're using a local computer (which might also be a remote virtual machine
    >
    >   `pip install --upgrade azureml-sdk\[notebooks,automl\]`
 
-
    It will take several minutes to install the SDK. For more information on installation options, see the [install guide](https://docs.microsoft.com/python/api/overview/azure/ml/install?view=azure-ml-py).
 
 1. Install other packages for your machine learning experimentation.
 
     Use either of the following commands and replace *\<new package>* with the package you want to install. Installing packages via `conda install` requires that the package is part of the current channels (new channels can be added in Anaconda Cloud).
 
-    ```shell
+    ```bash
     conda install <new package>
     ```
 
     Alternatively, you can install packages via `pip`.
 
-    ```shell
+    ```bash
     pip install <new package>
     ```
 
@@ -209,19 +206,19 @@ To enable these components in your Jupyter Notebook environment:
 
 1. Open an Anaconda prompt and activate your environment.
 
-    ```shell
+    ```bash
     conda activate myenv
     ```
 
 1. Clone [the GitHub repository](https://aka.ms/aml-notebooks) for a set of sample notebooks.
 
-    ```CLI
+    ```bash
     git clone https://github.com/Azure/MachineLearningNotebooks.git
     ```
 
 1. Launch the Jupyter Notebook server with the following command:
 
-    ```shell
+    ```bash
     jupyter notebook
     ```
 
@@ -241,7 +238,6 @@ To enable these components in your Jupyter Notebook environment:
 
 1. To configure the Jupyter Notebook to use your Azure Machine Learning workspace, go to the [Create a workspace configuration file](#workspace) section.
 
-
 ### <a id="vscode"></a>Visual Studio Code
 
 Visual Studio Code is a very popular cross platform code editor that supports an extensive set of programming languages and tools through extensions available in the [Visual Studio marketplace](https://marketplace.visualstudio.com/vscode). The [Azure Machine Learning extension](https://marketplace.visualstudio.com/items?itemName=ms-toolsai.vscode-ai) installs the [Python extension](https://marketplace.visualstudio.com/items?itemName=ms-python.python) for coding in all types of Python environments (virtual, Anaconda, etc.). In addition, it provides convenience features for working with Azure Machine Learning resources and running Azure Machine Learning experiments all without leaving Visual Studio Code.
@@ -324,7 +320,7 @@ Once the cluster is running, [create a library](https://docs.databricks.com/user
    + In AutoML config, when using Azure Databricks add the following parameters:
        1. ```max_concurrent_iterations``` is based on number of worker nodes in your cluster.
         2. ```spark_context=sc``` is based on the default spark context.
-   + Or, if you have an old SDK version, deselect it from cluster’s installed libs and move to trash. Install the new SDK version and restart the cluster. If there is an issue after the restart, detach and reattach your cluster.
+   + Or, if you have an old SDK version, deselect it from cluster's installed libs and move to trash. Install the new SDK version and restart the cluster. If there is an issue after the restart, detach and reattach your cluster.
 
 If install was successful, the imported library should look like one of these:
 
@@ -388,7 +384,6 @@ You can create the configuration file in three ways:
 
     This code writes the configuration file to the *.azureml/config.json* file.
 
-
 ## Next steps
 
 - [Train a model](tutorial-train-models-with-aml.md) on Azure Machine Learning with the MNIST dataset

articles/machine-learning/how-to-deploy-fpga-web-service.md

@@ -17,7 +17,7 @@ ms.custom: seodec18
 # What are field-programmable gate arrays (FPGA) and how to deploy
 [!INCLUDE [applies-to-skus](../../includes/aml-applies-to-basic-enterprise-sku.md)]
 
-This article provides an introduction to field-programmable gate arrays (FPGA), and shows you how to deploy your models using Azure Machine Learning to an Azure FPGA. 
+This article provides an introduction to field-programmable gate arrays (FPGA), and shows you how to deploy your models using Azure Machine Learning to an Azure FPGA.
 
 FPGAs contain an array of programmable logic blocks, and a hierarchy of reconfigurable interconnects. The interconnects allow these blocks to be configured in various ways after manufacturing. Compared to other chips, FPGAs provide a combination of programmability and performance.
 
@@ -48,7 +48,7 @@ FPGAs on Azure supports:
 
 + Image classification and recognition scenarios
 + TensorFlow deployment
-+ Intel FPGA hardware 
++ Intel FPGA hardware
 
 These DNN models are currently available:
   - ResNet 50
@@ -77,20 +77,17 @@ The following scenarios use FPGAs:
 
 + [Land cover mapping](https://blogs.technet.microsoft.com/machinelearning/2018/05/29/how-to-use-fpgas-for-deep-learning-inference-to-perform-land-cover-mapping-on-terabytes-of-aerial-images/)
 
-
-
-## Example: Deploy models on FPGAs 
+## Example: Deploy models on FPGAs
 
 You can deploy a model as a web service on FPGAs with Azure Machine Learning Hardware Accelerated Models. Using FPGAs provides ultra-low latency inference, even with a single batch size. Inference, or model scoring, is the phase where the deployed model is used for prediction, most commonly on production data.
 
-
 ### Prerequisites
 
 - An Azure subscription.  If you do not have one, create a free account before you begin. Try the [free or paid version of Azure Machine Learning](https://aka.ms/AMLFree) today.
 
 - FPGA quota. Use the Azure CLI to check whether you have quota:
 
-    ```shell
+    ```azurecli-interactive
     az vm list-usage --location "eastus" -o table --query "[?localName=='Standard PBS Family vCPUs']"
     ```
 
@@ -113,7 +110,7 @@ You can deploy a model as a web service on FPGAs with Azure Machine Learning Har
  
 - The Python SDK for hardware-accelerated models:
 
-    ```shell
+    ```bash
     pip install --upgrade azureml-accel-models
     ```
 

articles/machine-learning/how-to-manage-workspace-cli.md

@@ -32,7 +32,7 @@ In this article, you learn how to create an Azure Machine Learning workspace usi
 
 There are several ways that you can authenticate to your Azure subscription from the CLI. The most basic is to interactively authenticate using a browser. To authenticate interactively, open a command line or terminal and use the following command:
 
-```azurecli
+```azurecli-interactive
 az login
 ```
 
@@ -146,13 +146,13 @@ To create a workspace that uses existing resources, you must provide the ID for
 
     1. Install the application insights extension:
 
-        ```bash
+        ```azurecli-interactive
         az extension add -n application-insights
         ```
 
     2. Get the ID of your application insight service:
 
-        ```bash
+        ```azurecli-interactive
         az monitor app-insights component show --app <application-insight-name> -g <resource-group-name> --query "id"
         ```
 

articles/machine-learning/resource-known-issues.md

@@ -123,7 +123,7 @@ You will not be able to deploy models on FPGAs until you have requested and been
 ## Automated machine learning
 
 Tensor Flow
-Automated machine learning does not currently support tensor flow version 1.13. Installing this version will cause package dependencies to stop working. We are working to fix this issue in a future release. 
+Automated machine learning does not currently support tensor flow version 1.13. Installing this version will cause package dependencies to stop working. We are working to fix this issue in a future release.
 
 ### Experiment Charts
 
@@ -145,7 +145,7 @@ script_params = {
 ```
 
 If you don't include the leading forward slash, '/',  you'll need to prefix the working directory e.g.
-`/mnt/batch/.../tmp/dataset` on the compute target to indicate where you want the dataset to be mounted. 
+`/mnt/batch/.../tmp/dataset` on the compute target to indicate where you want the dataset to be mounted.
 
 ### Fail to read Parquet file from HTTP or ADLS Gen 2
 
@@ -205,14 +205,14 @@ If you see this error when you use automated machine learning, run the two follo
 
 If you see this error when you use automated machine learning:
 
-1. Run this command to install two packages in your Azure Databricks cluster: 
+1. Run this command to install two packages in your Azure Databricks cluster:
 
-   ```
+   ```bash
    scikit-learn==0.19.1
    pandas==0.22.0
    ```
 
-1. Detach and then reattach the cluster to your notebook. 
+1. Detach and then reattach the cluster to your notebook.
 
 If these steps don't solve the issue, try restarting the cluster.
 
@@ -265,11 +265,11 @@ If you receive an error `Unable to upload project files to working directory in
 
 If you are using file share for other workloads, such as data transfer, the recommendation is to use blobs so that file share is free to be used for submitting runs. You may also split the workload between two different workspaces.
 
-## Webservices in Azure Kubernetes Service failures 
+## Webservices in Azure Kubernetes Service failures
 
 Many webservice failures in Azure Kubernetes Service can be debugged by connecting to the cluster using `kubectl`. You can get the `kubeconfig.json` for an Azure Kubernetes Service Cluster by running
 
-```bash
+```azurecli-interactive
 az aks get-credentials -g <rg> -n <aks cluster name>
 ```
 
@@ -313,7 +313,7 @@ Known issues with labeling projects.
 
 ### Only datasets created on blob datastores can be used
 
-This is a known limitation of the current release. 
+This is a known limitation of the current release.
 
 ### After creation, the project shows "Initializing" for a long time