Merge pull request #220124 from shouryaj/patch-9

Update pre-reqs to use preview panel

Author: denrea

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

@@ -27,6 +27,7 @@ Interactive training is supported on **Azure Machine Learning Compute Clusters**
 
 ## Prerequisites
 - Review [getting started with training on Azure Machine Learning](./how-to-train-model.md).
+- To use this feature in Azure Machine Learning Studio, enable the "Debug & monitor your training jobs" flight via the [preview panel](./how-to-enable-preview-features.md#how-do-i-enable-preview-features).
 - To use **VS Code**, [follow this guide](how-to-setup-vs-code.md) to set up the Azure Machine Learning extension.
 - Make sure your job environment has the `openssh-server` and `ipykernel ~=6.0` packages installed (all Azure Machine Learning curated training environments have these packages installed by default).
 - Interactive applications can't be enabled on distributed training runs where the distribution type is anything other than Pytorch, Tensorflow or MPI. Custom distributed training setup (configuring multi-node training without using the above distribution frameworks) is not currently supported.
@@ -69,6 +70,7 @@ By specifying interactive applications at job creation, you can connect directly
 
 6. Review and create the job.
 
+If you don't see the above options, make sure you have enabled the "Debug & monitor your training jobs" flight via the [preview panel](./how-to-enable-preview-features.md#how-do-i-enable-preview-features).
 
 # [Python SDK](#tab/python)
 1. Define the interactive services you want to use for your job. Make sure to replace `your compute name` with your own value. If you want to use your own custom environment, follow the examples in [this tutorial](how-to-manage-environments-v2.md) to create a custom environment. 
@@ -183,7 +185,7 @@ Clicking the applications in the panel opens a new tab for the applications. You
 
 :::image type="content" source="media/interactive-jobs/interactive-jobs-right-panel.png" alt-text="Screenshot of interactive jobs right panel information. Information content will vary depending on the users data":::
 
-It might take a few minutes to start the job and the training applications specified during job creation.
+It might take a few minutes to start the job and the training applications specified during job creation. If you don't see the above options, make sure you have enabled the "Debug & monitor your training jobs" flight via the [preview panel](./how-to-enable-preview-features.md#how-do-i-enable-preview-features).
 
 # [Python SDK](#tab/python)
 - Once the job is submitted, you can use `ml_client.jobs.show_services("<job name>", <compute node index>)` to view the interactive service endpoints.
@@ -208,7 +210,7 @@ You can access the applications only when they are in **Running** status and onl
 ### 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.
 
-:::image type="content" source="./media/interactive-jobs/interactive-jobs-logs.png" alt-text="Screenshot of interactive jobs interactive logs panel location.":::
+:::image type="content" source="./media/interactive-jobs/interactive-logs.png" alt-text="Screenshot of interactive jobs interactive logs panel location.":::
 
 - You can open a terminal from Jupyter Lab and start interacting within the job container. You can also directly iterate on your training script with Jupyter Lab. 
 
@@ -221,6 +223,8 @@ When you click on the endpoints to interact when your job, you're taken to the u
 - If you have logged tensorflow events for your job, you can use TensorBoard to monitor the metrics when your job is running.
 
   :::image type="content" source="./media/interactive-jobs/tensorboard-open.png" alt-text="Screenshot of interactive jobs tensorboard panel when first opened. This information will vary depending upon customer data":::
+  
+If you don't see the above options, make sure you have enabled the "Debug & monitor your training jobs" flight via the [preview panel](./how-to-enable-preview-features.md#how-do-i-enable-preview-features).
 
 ### End job
 Once you're done with the interactive training, you can also go to the job details page to cancel the job which will release the compute resource. Alternatively, use `az ml job cancel -n <your job name>` in the CLI or `ml_client.job.cancel("<job name>")` in the SDK.