bash fix

Author: msebolt

articles/machine-learning/data-science-virtual-machine/dsvm-secure-access-keys.md

@@ -45,7 +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
 
-```cmd
+```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'])"`
@@ -56,7 +56,7 @@ curl https://<Vault Name>.vault.azure.net/secrets/SQLPasswd?api-version=2016-10-
 
 ## Access storage keys from the DSVM
 
-```cmd
+```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`

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

@@ -112,13 +112,13 @@ To use the DSVM as a development environment:
 
     * For Ubuntu DSVM:
 
-        ```cmd
+        ```bash
         conda activate py36
         ```
 
     * For Windows DSVM:
 
-        ```cmd
+        ```bash
         conda activate AzureML
         ```
 
@@ -143,35 +143,35 @@ When you're using a local computer (which might also be a remote virtual machine
 
     Run the following command to create the environment.
 
-    ```cmd
+    ```bash
     conda create -n myenv python=3.6.5
     ```
 
     Then activate the environment.
 
-    ```cmd
+    ```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:
 
-    ```cmd
+    ```bash
     conda install notebook ipykernel
     ```
 
     Then run the following command to create the kernel:
 
-    ```cmd
+    ```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.
 
-    ```cmd
+    ```bash
     pip install azureml-sdk[notebooks,automl]
     ```
 
@@ -190,13 +190,13 @@ When you're using a local computer (which might also be a remote virtual machine
 
     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).
 
-    ```cmd
+    ```bash
     conda install <new package>
     ```
 
     Alternatively, you can install packages via `pip`.
 
-    ```cmd
+    ```bash
     pip install <new package>
     ```
 
@@ -210,19 +210,19 @@ To enable these components in your Jupyter Notebook environment:
 
 1. Open an Anaconda prompt and activate your environment.
 
-    ```cmd
+    ```bash
     conda activate myenv
     ```
 
 1. Clone [the GitHub repository](https://aka.ms/aml-notebooks) for a set of sample notebooks.
 
-    ```cmd
+    ```bash
     git clone https://github.com/Azure/MachineLearningNotebooks.git
     ```
 
 1. Launch the Jupyter Notebook server with the following command:
 
-    ```cmd
+    ```bash
     jupyter notebook
     ```
 

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

@@ -110,7 +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:
 
-    ```cmd
+    ```bash
     pip install --upgrade azureml-accel-models
     ```
 

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

@@ -55,7 +55,7 @@ pip install --upgrade azureml-sdk[notebooks,automl] --ignore-installed PyYAML
 Anaconda's Python 3.7.4 distribution has a bug that breaks azureml-sdk install. This issue is discussed in this [GitHub Issue](https://github.com/ContinuumIO/anaconda-issues/issues/11195)
 This can be worked around by creating a new Conda Environment using this command:
 
-```cmd
+```bash
 conda create -n <env-name> python=3.7.3
 ```
 
@@ -156,7 +156,7 @@ If you see this error when you use automated machine learning:
 
 1. Run this command to install two packages in your Azure Databricks cluster:
 
-   ```cmd
+   ```bash
    scikit-learn==0.19.1
    pandas==0.22.0
    ```
@@ -249,7 +249,7 @@ compute_target.wait_for_completion(show_output=True)
 
 If you no longer have the SSL certificate and private key, or you are using a certificate generated by Azure Machine Learning, you can retrieve the files prior to detaching the cluster by connecting to the cluster using `kubectl` and retrieving the secret `azuremlfessl`.
 
-```cmd
+```bash
 kubectl get secret/azuremlfessl -o yaml
 ```
 

articles/mariadb/tutorial-design-database-using-portal.md

@@ -96,7 +96,7 @@ Use the [mysql command-line tool](https://dev.mysql.com/doc/refman/5.7/en/mysql.
 
 Enter the command to connect:
 
-```cmd
+```bash
 mysql -h mydemoserver.mariadb.database.azure.com -u myadmin@mydemoserver -p
 ```
 

includes/aml-dsvm-server.md

@@ -15,7 +15,7 @@ ms.date: 01/25/2019
 
 1. Clone [the GitHub repository](https://aka.ms/aml-notebooks).
 
-    ```cmd
+    ```bash
     git clone https://github.com/Azure/MachineLearningNotebooks.git
     ```
 
@@ -29,6 +29,6 @@ ms.date: 01/25/2019
 
 1. Start the notebook server from your cloned directory.
 
-    ```cmd
+    ```bash
     jupyter notebook
     ```

includes/aml-your-server.md

@@ -19,12 +19,12 @@ ms.date: 09/26/2019
 
 1. Clone [the GitHub repository](https://aka.ms/aml-notebooks).
 
-    ```cmd
+    ```bash
     git clone https://github.com/Azure/MachineLearningNotebooks.git
     ```
 
 1. Start the notebook server from your cloned directory.
 
-    ```cmd
+    ```bash
     jupyter notebook
     ```