Merge pull request #199523 from ssalgadodev/runsToJobsPart10

Runs to Jobs

Author: PRMerger18

articles/machine-learning/how-to-use-batch-endpoint.md

@@ -257,7 +257,7 @@ Follow the below steps to view the scoring results in Azure Storage Explorer whe
 
     :::code language="azurecli" source="~/azureml-examples-main/cli/batch-score.sh" ID="show_job_in_studio" :::
 
-1. In the graph of the run, select the `batchscoring` step.
+1. In the graph of the job, select the `batchscoring` step.
 1. Select the __Outputs + logs__ tab and then select **Show data outputs**.
 1. From __Data outputs__, select the icon to open __Storage Explorer__.
 

articles/machine-learning/how-to-use-batch-endpoints-studio.md

@@ -118,7 +118,7 @@ To change where the results are stored, providing a blob store and output path w
 
 ### Summary of all submitted jobs
 
-To see a summary of all the submitted jobs for an endpoint, select the endpoint and then select the **Runs** tab.
+To see a summary of all the submitted jobs for an endpoint, select the endpoint and then select the **Jobs** tab.
 
 :::image type="content" source="media/how-to-use-batch-endpoints-studio/summary-jobs.png" alt-text="Screenshot of summary of jobs submitted to a batch endpoint":::
 ## Check batch scoring results

articles/machine-learning/how-to-use-managed-identities.md

@@ -73,7 +73,7 @@ az ml workspace create -w <workspace name> \
 
 ### Let Azure Machine Learning service create workspace ACR
 
-If you don't bring your own ACR, Azure Machine Learning service will create one for you when you perform an operation that needs one. For example, submit a training run to Machine Learning Compute, build an environment, or deploy a web service endpoint. The ACR created by the workspace will have admin user enabled, and you need to disable the admin user manually.
+If you don't bring your own ACR, Azure Machine Learning service will create one for you when you perform an operation that needs one. For example, submit a training job to Machine Learning Compute, build an environment, or deploy a web service endpoint. The ACR created by the workspace will have admin user enabled, and you need to disable the admin user manually.
 
 [!INCLUDE [cli v2](../../includes/machine-learning-cli-v2.md)]
 
@@ -171,7 +171,7 @@ az role assignment create --assignee <principal ID> \
 --scope "/subscriptions/<subscription ID>/resourceGroups/<private ACR resource group>/providers/Microsoft.ContainerRegistry/registries/<private ACR name>"
 ```
 
-Finally, when submitting a training run, specify the base image location in the [environment definition](how-to-use-environments.md#use-existing-environments).
+Finally, when submitting a training job, specify the base image location in the [environment definition](how-to-use-environments.md#use-existing-environments).
 
 [!INCLUDE [sdk v1](../../includes/machine-learning-sdk-v1.md)]
 

articles/machine-learning/how-to-use-mlflow-azure-databricks.md

@@ -17,7 +17,7 @@ ms.custom: devx-track-python, sdkv1, event-tier1-build-2022
 
 In this article, learn how to enable MLflow to connect to Azure Machine Learning while working in an Azure Databricks workspace. You can leverage this configuration for tracking, model management and model deployment.
 
-[MLflow](https://www.mlflow.org) is an open-source library for managing the life cycle of your machine learning experiments. MLFlow Tracking is a component of MLflow that logs and tracks your training run metrics and model artifacts. Learn more about [Azure Databricks and MLflow](/azure/databricks/applications/mlflow/). 
+[MLflow](https://www.mlflow.org) is an open-source library for managing the life cycle of your machine learning experiments. MLFlow Tracking is a component of MLflow that logs and tracks your training job metrics and model artifacts. Learn more about [Azure Databricks and MLflow](/azure/databricks/applications/mlflow/). 
 
 See [MLflow and Azure Machine Learning](concept-mlflow.md) for additional MLflow and Azure Machine Learning functionality integrations.
 
@@ -30,6 +30,7 @@ If you have an MLflow Project to train with Azure Machine Learning, see [Train M
 * [Create an Azure Machine Learning Workspace](quickstart-create-resources.md).
     * See which [access permissions you need to perform your MLflow operations with your workspace](how-to-assign-roles.md#mlflow-operations).
 
+
 ## Install libraries
 
 To install libraries on your cluster, navigate to the **Libraries** tab and select **Install New**
@@ -167,10 +168,13 @@ When MLflow is configured to exclusively track experiments in Azure Machine Lear
 mlflow.set_experiment(experiment_name="experiment-name")
 ```
 
+In your training script, import `mlflow` to use the MLflow logging APIs, and start logging your job metrics. The following example, logs the epoch loss metric. 
+
 ## Logging models with MLflow
 
 After your model is trained, you can log it to the tracking server with the `mlflow.<model_flavor>.log_model()` method. `<model_flavor>`, refers to the framework associated with the model. [Learn what model flavors are supported](https://mlflow.org/docs/latest/models.html#model-api). In the following example, a model created with the Spark library MLLib is being registered:
 
+
 ```python
 mlflow.spark.log_model(model, artifact_path = "model")
 ```
@@ -272,5 +276,5 @@ The [Training models in Azure Databricks and deploying them on Azure ML](https:/
 ## Next steps
 * [Deploy MLflow models as an Azure web service](how-to-deploy-mlflow-models.md). 
 * [Manage your models](concept-model-management-and-deployment.md).
-* [Track experiment runs with MLflow and Azure Machine Learning](how-to-use-mlflow.md). 
+* [Track experiment jobs with MLflow and Azure Machine Learning](how-to-use-mlflow.md). 
 * Learn more about [Azure Databricks and MLflow](/azure/databricks/applications/mlflow/).

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

@@ -22,15 +22,15 @@ ms.devlang: azurecli
 
 In this article, learn how to enable [MLflow Tracking](https://mlflow.org/docs/latest/quickstart.html#using-the-tracking-api) to connect Azure Machine Learning as the backend of your MLflow experiments.
 
-[MLflow](https://www.mlflow.org) is an open-source library for managing the lifecycle of your machine learning experiments. MLflow Tracking is a component of MLflow that logs and tracks your training run metrics and model artifacts, no matter your experiment's environment--locally on your computer, on a remote compute target, a virtual machine, or an [Azure Databricks cluster](how-to-use-mlflow-azure-databricks.md).
+[MLflow](https://www.mlflow.org) is an open-source library for managing the lifecycle of your machine learning experiments. MLflow Tracking is a component of MLflow that logs and tracks your training job metrics and model artifacts, no matter your experiment's environment--locally on your computer, on a remote compute target, a virtual machine, or an [Azure Databricks cluster](how-to-use-mlflow-azure-databricks.md).
 
 See [MLflow and Azure Machine Learning](concept-mlflow.md) for all supported MLflow and Azure Machine Learning functionality including MLflow Project support (preview) and model deployment.
 
 > [!IMPORTANT]
 > When using the Azure Machine Learning SDK v2, no native logging is provided. Instead, use MLflow's tracking capabilities. For more information, see [How to log and view metrics (v2)](how-to-log-view-metrics.md).
  
 > [!TIP]
-> The information in this document is primarily for data scientists and developers who want to monitor the model training process. If you are an administrator interested in monitoring resource usage and events from Azure Machine Learning, such as quotas, completed training runs, or completed model deployments, see [Monitoring Azure Machine Learning](monitor-azure-machine-learning.md).
+> The information in this document is primarily for data scientists and developers who want to monitor the model training process. If you are an administrator interested in monitoring resource usage and events from Azure Machine Learning, such as quotas, completed training jobs, or completed model deployments, see [Monitoring Azure Machine Learning](monitor-azure-machine-learning.md).
 
 > [!NOTE] 
 > You can use the [MLflow Skinny client](https://github.com/mlflow/mlflow/blob/master/README_SKINNY.rst) which is a lightweight MLflow package without SQL storage, server, UI, or data science dependencies. This is recommended for users who primarily need the tracking and logging capabilities without importing the full suite of MLflow features including deployments.
@@ -44,6 +44,7 @@ See [MLflow and Azure Machine Learning](concept-mlflow.md) for all supported MLf
 * Install and [set up CLI (v2)](how-to-configure-cli.md#prerequisites) and make sure you install the ml extension.
 * Install and set up SDK(v2) for Python
 
+
 ## Track runs from your local machine or remote compute
 
 Tracking using MLflow with Azure Machine Learning lets you store the logged metrics and artifacts runs that were executed on your local machine into your Azure Machine Learning workspace.
@@ -52,6 +53,7 @@ Tracking using MLflow with Azure Machine Learning lets you store the logged metr
 
 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. 
 
+
 > [!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.
  
@@ -119,6 +121,7 @@ export MLFLOW_TRACKING_URI=$(az ml workspace show --query mlflow_tracking_uri |
 
 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:
 
+
 ```python
 import mlflow
 
@@ -157,9 +160,9 @@ You can also set one of the MLflow environment variables [MLFLOW_EXPERIMENT_NAME
 export MLFLOW_EXPERIMENT_NAME="experiment_with_mlflow"
 ```
 
-### Start training run
+### Start training job
 
-After you set the MLflow experiment name, you can start your training run with `start_run()`. Then use `log_metric()` to activate the MLflow logging API and begin logging your training run metrics.
+After you set the MLflow experiment name, you can start your training job with `start_run()`. Then use `log_metric()` to activate the MLflow logging API and begin logging your training job metrics.
 
 ```Python
 import os
@@ -172,21 +175,25 @@ with mlflow.start_run() as mlflow_run:
     mlflow.log_artifact("helloworld.txt")
 ```
 
+
 For details about how to log metrics, parameters and artifacts in a run using MLflow view [How to log and view metrics](how-to-log-view-metrics.md).
 
 ## Track jobs running on Azure Machine Learning
 
+
 [!INCLUDE [cli v2](../../includes/machine-learning-cli-v2.md)]
 
 Remote runs (jobs) let you train your models in a more robust and repetitive way. They can also leverage more powerful computes, such as Machine Learning Compute clusters. See [Use compute targets for model training](how-to-set-up-training-targets.md) to learn about different compute options.
 
+
 When submitting runs using jobs, Azure Machine Learning automatically configures MLflow to work with the workspace the job is running in. This means that there is no need to configure the MLflow tracking URI. On top of that, experiments are automatically named based on the details of the job.
 
 > [!IMPORTANT]
 > When submitting training jobs to Azure Machine Learning, you don't have to configure the MLflow tracking URI on your training logic as it is already configured for you.
 
 ### Creating a training routine
 
+
 First, you should create a `src` subdirectory and create a file with your training code in a `hello_world.py` file in the `src` subdirectory. All your training code will go into the `src` subdirectory, including `train.py`.
 
 The training code is taken from this [MLfLow example](https://github.com/Azure/azureml-examples/blob/main/cli/jobs/basics/src/hello-mlflow.py) in the Azure Machine Learning example repo. 
@@ -195,13 +202,15 @@ Copy this code into the file:
 
 :::code language="python" source="~/azureml-examples-main/cli/jobs/basics/src/hello-mlflow.py":::
 
+
 > [!NOTE]
 > Note how this sample don't contains the instructions `mlflow.start_run` nor `mlflow.set_experiment`. This is automatically done by Azure Machine Learning.
 
 ### Submitting the job
 
 Use the [Azure Machine Learning CLI (v2)](how-to-train-cli.md) to submit a remote run. When using the Azure Machine Learning CLI (v2), the MLflow tracking URI and experiment name are set automatically and directs the logging from MLflow to your workspace. Learn more about [logging Azure Machine Learning CLI (v2) experiments with MLflow](how-to-train-cli.md#model-tracking-with-mlflow) 
 
+
 Create a YAML file with your job definition in a `job.yml` file. This file should be created outside the `src` directory. Copy this code into the file:
 
 :::code language="azurecli" source="~/azureml-examples-main/cli/jobs/basics/hello-mlflow.yml":::
@@ -222,7 +231,7 @@ Retrieve run metric using MLflow [get_run()](https://mlflow.org/docs/latest/pyth
 ```Python
 from mlflow.tracking import MlflowClient
 
-# Use MlFlow to retrieve the run that was just completed
+# Use MlFlow to retrieve the job that was just completed
 client = MlflowClient()
 run_id = mlflow_run.info.run_id
 finished_mlflow_run = MlflowClient().get_run(run_id)
@@ -234,6 +243,7 @@ params = finished_mlflow_run.data.params
 print(metrics,tags,params)
 ```
 
+
 To view the artifacts of a run, you can use [MlFlowClient.list_artifacts()](https://mlflow.org/docs/latest/python_api/mlflow.tracking.html#mlflow.tracking.MlflowClient.list_artifacts)
 
 ```Python