Learn Editor: Update how-to-interactive-jobs.md

Author: joburges

articles/machine-learning/how-to-interactive-jobs.md

@@ -131,56 +131,56 @@ If you don't see the above options, make sure you have enabled the "Debug & moni
 
 # [Azure CLI](#tab/azurecli)
 
-1. Create a job yaml `job.yaml` with below sample content. Make sure to replace `your compute name` with your own value. If you want to use custom environment, follow the examples in [this tutorial](how-to-manage-environments-v2.md) to create a custom environment. 
-    ```dotnetcli
-    code: src 
-    command: 
-      python train.py 
-      # you can add a command like "sleep 1h" to reserve the compute resource is reserved after the script finishes running.
-    environment: azureml:AzureML-tensorflow-2.4-ubuntu18.04-py37-cuda11-gpu:41
-    compute: azureml:<your compute name>
-    services:
-        my_vs_code:
-          job_service_type: vs_code
-          nodes: all # For distributed jobs, use the `nodes` property to pick which node you want to enable interactive services on. If `nodes` are not selected, by default, interactive applications are only enabled on the head node. Values are "all", or compute node index (for ex. "0", "1" etc.)
-        my_tensor_board:
-          job_service_type: tensor_board
-          properties:
-            logDir: "output/tblogs" # relative path of Tensorboard logs (same as in your training script)
-          nodes: all
-        my_jupyter_lab:
-          job_service_type: jupyter_lab
-          nodes: all
-        my_ssh:
-         job_service_type: ssh
-         properties:
-           sshPublicKeys: <paste the entire pub key content>
-         nodes: all
-    ```
-    The `services` section specifies the training applications you want to interact with.  
-
-    You can put `sleep <specific time>` at the end of the command to specify the amount of time you want to reserve the compute resource. The format follows: 
-    * sleep 1s
-    * sleep 1m
-    * sleep 1h
-    * sleep 1d
-
-    You can also use the `sleep infinity` command that would keep the job alive indefinitely. 
-    
-    > [!NOTE]
+1. 1. Create a job yaml `job.yaml` with below sample content. Make sure to replace `your compute name` with your own value. If you want to use custom environment, follow the examples in [this tutorial](how-to-manage-environments-v2.md) to create a custom environment. 
+   ```dotnetcli
+       code: src 
+       command: 
+         python train.py 
+         # you can add a command like "sleep 1h" to reserve the compute resource is reserved after the script finishes running.
+       environment: azureml:AzureML-tensorflow-2.4-ubuntu18.04-py37-cuda11-gpu:41
+       compute: azureml:<your compute name>
+       services:
+           my_vs_code:
+             job_service_type: vs_code
+             nodes: all # For distributed jobs, use the `nodes` property to pick which node you want to enable interactive services on. If `nodes` are not selected, by default, interactive applications are only enabled on the head node. Values are "all", or compute node index (for ex. "0", "1" etc.)
+           my_tensor_board:
+             job_service_type: tensor_board
+             logDir: "output/tblogs" # relative path of Tensorboard logs (same as in your training script)
+             nodes: all
+           my_jupyter_lab:
+             job_service_type: jupyter_lab
+             nodes: all
+           my_ssh:
+            job_service_type: ssh
+            properties:
+              sshPublicKeys: <paste the entire pub key content>
+            nodes: all
+   ```
+
+       The `services` section specifies the training applications you want to interact with.  
+
+       You can put `sleep <specific time>` at the end of the command to specify the amount of time you want to reserve the compute resource. The format follows: 
+   * sleep 1s
+   * sleep 1m
+   * sleep 1h
+   * sleep 1d
+
+       You can also use the `sleep infinity` command that would keep the job alive indefinitely. 
+ 
+       > [!NOTE]
     > If you use `sleep infinity`, you will need to manually [cancel the job](./how-to-interactive-jobs.md#end-job) to let go of the compute resource (and stop billing). 
 
 2. Run command `az ml job create --file <path to your job yaml file> --workspace-name <your workspace name> --resource-group <your resource group name> --subscription <sub-id> `to submit your training job. For more details on running a job via CLIv2, check out this [article](./how-to-train-model.md). 
 
 ---
-
 ### Connect to endpoints
 # [Azure Machine Learning Studio](#tab/ui)
 To interact with your running job, click the button **Debug and monitor** on the job details page. 
 
 :::image type="content" source="media/interactive-jobs/debug-and-monitor.png" alt-text="Screenshot of interactive jobs debug and monitor panel location.":::
 
 
+
 Clicking the applications in the panel opens a new tab for the applications. You can access the applications only when they are in **Running** status and only the **job owner** is authorized to access the applications. If you're training on multiple nodes, you can pick the specific node you would like to interact with.
 
 :::image type="content" source="media/interactive-jobs/interactive-jobs-application-list.png" alt-text="Screenshot of interactive jobs right panel information. Information content will vary depending on the user's data.":::
@@ -206,7 +206,6 @@ You can find the reference documentation for these commands [here](/cli/azure/ml
 You can access the applications only when they are in **Running** status and only the **job owner** is authorized to access the applications. If you're training on multiple nodes, you can pick the specific node you would like to interact with by passing in the node index.
 
 ---
-
 ### Interact with the applications
 When you click on the endpoints to interact when your job, you're taken to the user container under your working directory, where you can access your code, inputs, outputs, and logs. If you run into any issues while connecting to the applications, the interactive capability and applications logs can be found from **system_logs->interactive_capability** under **Outputs + logs** tab.