Update how-to-use-mlflow-cli-runs.md

Author: santiagxf

articles/machine-learning/how-to-use-mlflow-cli-runs.md

@@ -51,28 +51,43 @@ Tracking using MLflow with Azure Machine Learning lets you store the logged metr
 
 ### Set up tracking environment
 
-To track a run that is not running on Azure Machine Learning compute (from now on referred to as *"local compute"*), you need to point your local compute to the Azure Machine Learning MLflow Tracking URI. 
-
+To track a run that is not running on Azure Machine Learning compute, you need to point MLflow to the Azure Machine Learning MLflow Tracking URI. 
 
 > [!NOTE]
 > When running on Azure Compute (Azure Notebooks, Jupyter Notebooks hosted on Azure Compute Instances or Compute Clusters) you don't have to configure the tracking URI. It's automatically configured for you.
- 
-# [Using the Azure ML SDK v2](#tab/azuremlsdk)
+
+1. Getting the Azure Machine Learning Tracking URI:
+
+# [Bash](#tab/cli)
+
+[!INCLUDE [cli v2](../../includes/machine-learning-cli-v2.md)]
+
+1. Login and configure your workspace:
+
+```bash
+az account set --subscription <subscription>
+az configure --defaults workspace=<workspace> group=<resource-group> location=<location> 
+```
+
+1. You can get the tracking URI using the `az ml workspace` command:
+
+```bash
+MLFLOW_TRACKING_URI=$(az ml workspace show --query mlflow_tracking_uri | sed 's/"//g') 
+```
+
+# [Python](#tab/python)
 
 [!INCLUDE [sdk v2](../../includes/machine-learning-sdk-v2.md)]
 
-You can get the Azure ML MLflow tracking URI using the [Azure Machine Learning SDK v2 for Python](concept-v2.md). Ensure you have the library `azure-ai-ml` installed in the cluster you are using. The following sample gets the unique MLFLow tracking URI associated with your workspace. Then the method [`set_tracking_uri()`](https://mlflow.org/docs/latest/python_api/mlflow.html#mlflow.set_tracking_uri) points the MLflow tracking URI to that URI.
+You can get the Azure ML MLflow tracking URI using the [Azure Machine Learning SDK v2 for Python](concept-v2.md). Ensure you have the library `azure-ai-ml` installed in the compute you are using. The following sample gets the unique MLFLow tracking URI associated with your workspace.
 
-1. Using the workspace configuration file:
+1. Login into your workspace using the `MLClient`. The easier way to do that is by using the workspace config file:
 
-    ```Python
+    ```python
     from azure.ai.ml import MLClient
     from azure.identity import DefaultAzureCredential
-    import mlflow
 
     ml_client = MLClient.from_config(credential=DefaultAzureCredential())
-    azureml_mlflow_uri = ml_client.workspaces.get(ml_client.workspace_name).mlflow_tracking_uri
-    mlflow.set_tracking_uri(azureml_mlflow_uri)
     ```
 
     > [!TIP]
@@ -81,68 +96,81 @@ You can get the Azure ML MLflow tracking URI using the [Azure Machine Learning S
     > 2. Click on the uper-right corner of the page -> Download config file.
     > 3. Save the file `config.json` in the same directory where you are working on.
 
-1. Using the subscription ID, resource group name and workspace name:
+1. Alternatively, you can use the subscription ID, resource group name and workspace name to get it:
 
-    ```Python
+    ```python
     from azure.ai.ml import MLClient
     from azure.identity import DefaultAzureCredential
-    import mlflow
 
     #Enter details of your AzureML workspace
     subscription_id = '<SUBSCRIPTION_ID>'
     resource_group = '<RESOURCE_GROUP>'
-    workspace_name = '<AZUREML_WORKSPACE_NAME>'
+    workspace_name = '<AML_WORKSPACE_NAME>'
 
     ml_client = MLClient(credential=DefaultAzureCredential(),
                          subscription_id=subscription_id, 
                          resource_group_name=resource_group)
-
-    azureml_mlflow_uri = ml_client.workspaces.get(workspace_name).mlflow_tracking_uri
-    mlflow.set_tracking_uri(azureml_mlflow_uri)
     ```
 
     > [!IMPORTANT]
     > `DefaultAzureCredential` will try to pull the credentials from the available context. If you want to specify credentials in a different way, for instance using the web browser in an interactive way, you can use `InteractiveBrowserCredential` or any other method available in `azure.identity` package.
 
-# [Using an environment variable](#tab/environ)
+1. Get the Azure Machine Learning Tracking URI:
 
-[!INCLUDE [cli v2](../../includes/machine-learning-cli-v2.md)]
+    ```python
+    mlflow_tracking_uri = ml_client.workspaces.get(ml_client.workspace_name).mlflow_tracking_uri
+    ```
 
-Another option is to set one of the MLflow environment variables [MLFLOW_TRACKING_URI](https://mlflow.org/docs/latest/tracking.html#logging-to-a-tracking-server) directly in your terminal. 
+# [Studio](#tab/studio)
 
-```Azure CLI
-export MLFLOW_TRACKING_URI=$(az ml workspace show --query mlflow_tracking_uri | sed 's/"//g') 
-```
+Use the Azure Machine Learning portal to get the tracking URI:
 
->[!IMPORTANT]
-> Make sure you are logged in to your Azure account on your local machine, otherwise the tracking URI returns an empty string. If you are using any Azure ML compute the tracking environment and experiment name is already configured.
+1. Open the [Azure Machine Learning studio portal](https://ml.azure.com) and log in using your credentials.
+1. In the upper right corner, click on the name of your workspace to show the __Directory + Subscription + Workspace__ blade.
+1. Click on __View all properties in Azure Portal__.
+1. On the __Essentials__ section, you will find the property __MLflow tracking URI__.
 
-# [Building the MLflow tracking URI](#tab/build)
+# [Manually](#tab/manual)
 
 The Azure Machine Learning Tracking URI can be constructed using the subscription ID, region of where the resource is deployed, resource group name and workspace name. The following code sample shows how:
 
+> [!WARNING]
+> If you are working in a private link-enabled workspace, the MLflow endpoint will also use a private link to communicate with Azure Machine Learning. As a consequence, the tracking URI will look different as proposed here. On those cases, you need to get the tracking URI using the Azure ML SDK or CLI v2.
+
 ```python
-import mlflow
+region = "<LOCATION>"
+subscription_id = '<SUBSCRIPTION_ID>'
+resource_group = '<RESOURCE_GROUP>'
+workspace_name = '<AML_WORKSPACE_NAME>'
+
+mlflow_tracking_uri = f"azureml://{region}.api.azureml.ms/mlflow/v1.0/subscriptions/{subscription_id}/resourceGroups/{resource_group}/providers/Microsoft.MachineLearningServices/workspaces/{workspace_name}"
+```
+
+---
+
+1. Configuring the tracking URI:
+
+There are two main ways to configure your environment to work with Azure Machine Learning with Mlflow.
+
+# [Using an environment variable](#tab/environ)
 
-region = ""
-subscription_id = ""
-resource_group = ""
-workspace_name = ""
+You can set the MLflow environment variables [MLFLOW_TRACKING_URI](https://mlflow.org/docs/latest/tracking.html#logging-to-a-tracking-server) in your compute to make any interaction with MLflow in that compute to point by default to Azure Machine Learning. 
 
-azureml_mlflow_uri = f"azureml://{region}.api.azureml.ms/mlflow/v1.0/subscriptions/{subscription_id}/resourceGroups/{resource_group}/providers/Microsoft.MachineLearningServices/workspaces/{workspace_name}"
-mlflow.set_tracking_uri(azureml_mlflow_uri)
+```bash
+MLFLOW_TRACKING_URI=$(az ml workspace show --query mlflow_tracking_uri | sed 's/"//g') 
 ```
 
-> [!WARNING]
-> If you are working in a private link-enabled workspace, the MLflow endpoint will also use a private link to communicate with Azure Machine Learning. As a consequence, the tracking URI will look different as proposed here. On those cases, you need to get the tracking URI using the Azure ML SDK or CLI v2.
+# [Using MLflow SDK](#tab/mlflow)
 
----
+Then the method [`set_tracking_uri()`](https://mlflow.org/docs/latest/python_api/mlflow.html#mlflow.set_tracking_uri) points the MLflow tracking URI to that URI.
 
-> [!NOTE]
-> You can also get this URL by: 
-> 1. Navigate to [Azure ML studio](https://ml.azure.com)
-> 2. Click on the upper-right corner of the page -> View all properties in Azure Portal -> MLflow tracking URI.
-> 3. Copy the URI and use it with the method `mlflow.set_tracking_uri`.
+```python
+import mlflow
+
+mlflow.set_tracking_uri(mlflow_tracking_uri)
+```
+
+---
 
 ### Set experiment name