Merge pull request #199420 from ssalgadodev/runsToJobs5

Runs to jobs

Author: PRMerger12

articles/machine-learning/how-to-high-availability-machine-learning.md

@@ -162,7 +162,7 @@ Jobs in Azure Machine Learning are defined by a job specification. This specific
 
 When your primary workspace becomes unavailable, you can switch over the secondary workspace to continue experimentation and development. Azure Machine Learning does not automatically submit jobs to the secondary workspace if there is an outage. Update your code configuration to point to the new workspace resource. We recommend to avoiding hardcoding workspace references. Instead, use a [workspace config file](how-to-configure-environment.md#workspace) to minimize manual user steps when changing workspaces. Make sure to also update any automation, such as continuous integration and deployment pipelines to the new workspace.
 
-Azure Machine Learning cannot sync or recover artifacts or metadata between workspace instances. Dependent on your application deployment strategy, you might have to move artifacts or recreate experimentation inputs such as dataset objects in the failover workspace in order to continue run submission. In case you have configured your primary workspace and secondary workspace resources to share associated resources with geo-replication enabled, some objects might be directly available to the failover workspace. For example, if both workspaces share the same docker images, configured datastores, and Azure Key Vault resources. The following diagram shows a configuration where two workspaces share the same images (1), datastores (2), and Key Vault (3).
+Azure Machine Learning cannot sync or recover artifacts or metadata between workspace instances. Dependent on your application deployment strategy, you might have to move artifacts or recreate experimentation inputs such as dataset objects in the failover workspace in order to continue job submission. In case you have configured your primary workspace and secondary workspace resources to share associated resources with geo-replication enabled, some objects might be directly available to the failover workspace. For example, if both workspaces share the same docker images, configured datastores, and Azure Key Vault resources. The following diagram shows a configuration where two workspaces share the same images (1), datastores (2), and Key Vault (3).
 
 ![Reference resource configuration](./media/how-to-high-availability-machine-learning/bcdr-resource-configuration.png)
 
@@ -183,7 +183,7 @@ The following artifacts can be exported and imported between workspaces by using
 
 > [!TIP]
 > * __Registered datasets__ cannot be downloaded or moved. This includes datasets generated by Azure ML, such as intermediate pipeline datasets. However datasets that refer to a shared file location that both workspaces can access, or where the underlying data storage is replicated, can be registered on both workspaces. Use the [az ml dataset register](/cli/azure/ml(v1)/dataset#ml-az-ml-dataset-register) to register a dataset.
-> * __Run outputs__ are stored in the default storage account associated with a workspace. While run outputs might become inaccessible from the studio UI in the case of a service outage, you can directly access the data through the storage account. For more information on working with data stored in blobs, see [Create, download, and list blobs with Azure CLI](../storage/blobs/storage-quickstart-blobs-cli.md).
+> * __Job outputs__ are stored in the default storage account associated with a workspace. While job outputs might become inaccessible from the studio UI in the case of a service outage, you can directly access the data through the storage account. For more information on working with data stored in blobs, see [Create, download, and list blobs with Azure CLI](../storage/blobs/storage-quickstart-blobs-cli.md).
 
 ## Recovery options
 

articles/machine-learning/how-to-log-pipelines-application-insights.md

@@ -11,6 +11,7 @@ ms.date: 10/21/2021
 ms.topic: how-to
 ms.custom: devx-track-python, sdkv1, event-tier1-build-2022
 ---
+
 # Collect machine learning pipeline log files in Application Insights for alerts and debugging
 
 [!INCLUDE [sdk v1](../../includes/machine-learning-sdk-v1.md)]

articles/machine-learning/how-to-log-view-metrics.md

@@ -34,7 +34,7 @@ Logs can help you diagnose errors and warnings, or track performance metrics lik
 
 
 > [!TIP]
-> This article shows you how to monitor the model training process. If you're 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).
+> This article shows you how to monitor the model training process. If you're 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).
 
 ## Prerequisites
 
@@ -58,7 +58,7 @@ The following table describes how to log specific value types:
 |Log numpy metrics or PIL image objects|`mlflow.log_image(img, 'figure.png')`||
 |Log matlotlib plot or image file|` mlflow.log_figure(fig, "figure.png")`||
 
-## Log a training run with MLflow
+## Log a training job with MLflow
 
 To set up for logging with MLflow, import `mlflow` and set the tracking URI:
 
@@ -76,16 +76,16 @@ ws = Workspace.from_config()
 mlflow.set_tracking_uri(ws.get_mlflow_tracking_uri())
 ```
 
-### Interactive runs
+### Interactive jobs
 
 When training interactively, such as in a Jupyter Notebook, use the following pattern:
 
 1. Create or set the active experiment. 
-1. Start the run.
+1. Start the job.
 1. Use logging methods to log metrics and other information.
-1. End the run.
+1. End the job.
 
-For example, the following code snippet demonstrates setting the tracking URI, creating an experiment, and then logging during a run
+For example, the following code snippet demonstrates setting the tracking URI, creating an experiment, and then logging during a job
 
 ```python
 from mlflow.tracking import MlflowClient
@@ -132,7 +132,7 @@ For remote training runs, the tracking URI and experiment are set automatically.
 
 To save the model from a training run, use the `log_model()` API for the framework you're working with. For example, [mlflow.sklearn.log_model()](https://mlflow.org/docs/latest/python_api/mlflow.sklearn.html#mlflow.sklearn.log_model). For frameworks that MLflow doesn't support, see [Convert custom models to MLflow](how-to-convert-custom-model-to-mlflow.md).
 
-## View run information
+## View job information
 
 You can view the logged information using MLflow through the [MLflow.entities.Run](https://mlflow.org/docs/latest/python_api/mlflow.entities.html#mlflow.entities.Run) object. After a training job completes, you can retrieve it using the [MlFlowClient()](https://mlflow.org/docs/latest/python_api/mlflow.tracking.html#mlflow.tracking.MlflowClient):
 
@@ -159,22 +159,22 @@ params = finished_mlflow_run.data.params
 
 <a name="view-the-experiment-in-the-web-portal"></a>
 
-## View run metrics in the studio UI
+## View job metrics in the studio UI
 
-You can browse completed run records, including logged metrics, in the [Azure Machine Learning studio](https://ml.azure.com).
+You can browse completed job records, including logged metrics, in the [Azure Machine Learning studio](https://ml.azure.com).
 
-Navigate to the **Experiments** tab. To view all your runs in your Workspace across Experiments, select the **All runs** tab. You can drill down on runs for specific Experiments by applying the Experiment filter in the top menu bar.
+Navigate to the **Jobs** tab. To view all your jobs in your Workspace across Experiments, select the **All jobs** tab. You can drill down on jobs for specific Experiments by applying the Experiment filter in the top menu bar.
 
 For the individual Experiment view, select the **All experiments** tab. On the experiment run dashboard, you can see tracked metrics and logs for each run. 
 
-You can also edit the run list table to select multiple runs and display either the last, minimum, or maximum logged value for your runs. Customize your charts to compare the logged metrics values and aggregates across multiple runs. You can plot multiple metrics on the y-axis of your chart and customize your x-axis to plot your logged metrics. 
+You can also edit the job list table to select multiple jobs and display either the last, minimum, or maximum logged value for your jobs. Customize your charts to compare the logged metrics values and aggregates across multiple jobs. You can plot multiple metrics on the y-axis of your chart and customize your x-axis to plot your logged metrics. 
 
 
-### View and download log files for a run 
+### View and download log files for a job 
 
 Log files are an essential resource for debugging the Azure ML workloads. After submitting a training job, drill down to a specific run to view its logs and outputs:  
 
-1. Navigate to the **Experiments** tab.
+1. Navigate to the **Jobs** tab.
 1. Select the runID for a specific run.
 1. Select **Outputs and logs** at the top of the page.
 2. Select **Download all** to download all your logs into a zip folder.

articles/machine-learning/how-to-machine-learning-fairness-aml.md

@@ -207,8 +207,8 @@ The following example shows how to use the fairness package. We will upload mode
     If you complete the previous steps (uploading generated fairness insights to Azure Machine Learning), you can view the fairness dashboard in [Azure Machine Learning studio](https://ml.azure.com). This dashboard is the same visualization dashboard provided in Fairlearn, enabling you to analyze the disparities among your sensitive feature's subgroups (e.g., male vs. female).
     Follow one of these paths to access the visualization dashboard in Azure Machine Learning studio:
 
-    * **Experiments pane (Preview)**
-    1. Select **Experiments** in the left pane to see a list of experiments that you've run on Azure Machine Learning.
+    * **Jobs pane (Preview)**
+    1. Select **Jobs** in the left pane to see a list of experiments that you've run on Azure Machine Learning.
     1. Select a particular experiment to view all the runs in that experiment.
     1. Select a run, and then the **Fairness** tab to the explanation visualization dashboard.
     1. Once landing on the **Fairness** tab, click on a **fairness id** from the menu on the right.

articles/machine-learning/how-to-manage-environments-in-studio.md

@@ -32,7 +32,7 @@ For a high-level overview of how environments work in Azure Machine Learning, se
 
 ## Browse curated environments
 
-Curated environments contain collections of Python packages and are available in your workspace by default. These environments are backed by cached Docker images which reduces the run preparation cost and support training and inferencing scenarios. 
+Curated environments contain collections of Python packages and are available in your workspace by default. These environments are backed by cached Docker images which reduces the job preparation cost and support training and inferencing scenarios. 
 
 Click on an environment to see detailed information about its contents. For more information, see [Azure Machine Learning curated environments](resource-curated-environments.md). 
 

articles/machine-learning/how-to-use-pipeline-ui.md

@@ -87,7 +87,7 @@ If your pipeline fails or gets stuck on a node, first view the logs.
 
     The **user_logs folder** contains information about user code generated logs. This folder is open by default, and the **std_log.txt** log is selected. The **std_log.txt** is where your code's logs (for example, print statements) show up.
 
-    The **system_logs folder** contains logs generated by Azure Machine Learning. Learn more about [how to view and download log files for a run](how-to-log-view-metrics.md#view-and-download-log-files-for-a-run).
+    The **system_logs folder** contains logs generated by Azure Machine Learning. Learn more about [how to view and download log files for a run](how-to-log-view-metrics.md#view-and-download-log-files-for-a-job).
 
     :::image type="content" source="./media/how-to-use-pipeline-ui/view-user-log.png" alt-text="Screenshot showing the user logs of a node." lightbox= "./media/how-to-use-pipeline-ui/view-user-log.png":::