Merge pull request #199524 from ssalgadodev/runsToJobsPart11

runs

Author: GitHubber17

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

@@ -258,20 +258,21 @@ client.download_artifacts(run_id, "helloworld.txt", ".")
 
 For more details about how to retrieve information from experiments and runs in Azure Machine Learning using MLflow view [Manage experiments and runs with MLflow](how-to-track-experiments-mlflow.md).
 
+
 ## Manage models
 
-Register and track your models with the [Azure Machine Learning model registry](concept-model-management-and-deployment.md#register-package-and-deploy-models-from-anywhere), which supports the MLflow model registry. Azure Machine Learning models are aligned with the MLflow model schema making it easy to export and import these models across different workflows. The MLflow-related metadata, such as run ID, is also tracked with the registered model for traceability. Users can submit training runs, register, and deploy models produced from MLflow runs.
+Register and track your models with the [Azure Machine Learning model registry](concept-model-management-and-deployment.md#register-package-and-deploy-models-from-anywhere), which supports the MLflow model registry. Azure Machine Learning models are aligned with the MLflow model schema making it easy to export and import these models across different workflows. The MLflow-related metadata, such as run ID, is also tracked with the registered model for traceability. Users can submit training jobs, register, and deploy models produced from MLflow runs.
 
 If you want to deploy and register your production ready model in one step, see [Deploy and register MLflow models](how-to-deploy-mlflow-models.md).
 
-To register and view a model from a run, use the following steps:
+To register and view a model from a job, use the following steps:
 
-1. Once a run is complete, call the [`register_model()`](https://mlflow.org/docs/latest/python_api/mlflow.html#mlflow.register_model) method.
+1. Once a job is complete, call the [`register_model()`](https://mlflow.org/docs/latest/python_api/mlflow.html#mlflow.register_model) method.
 
 
 
     ```Python
-    # the model folder produced from a run is registered. This includes the MLmodel file, model.pkl and the conda.yaml.
+    # the model folder produced from a job is registered. This includes the MLmodel file, model.pkl and the conda.yaml.
     model_path = "model"
     model_uri = 'runs:/{}/{}'.format(run_id, model_path) 
     mlflow.register_model(model_uri,"registered_model_name")
@@ -287,7 +288,7 @@ To register and view a model from a run, use the following steps:
 
     ![model-schema](./media/how-to-use-mlflow-cli-runs/mlflow-model-schema.png)
 
-1. Select MLmodel to see the MLmodel file generated by the run.
+1. Select MLmodel to see the MLmodel file generated by the job.
 
     ![MLmodel-schema](./media/how-to-use-mlflow-cli-runs/mlmodel-view.png)
 

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

@@ -16,14 +16,14 @@ ms.custom: designer
 
 Use pipeline parameters to build flexible pipelines in the designer. Pipeline parameters let you dynamically set values at runtime to encapsulate pipeline logic and reuse assets.
 
-Pipeline parameters are especially useful when resubmitting a pipeline run, [retraining models](how-to-retrain-designer.md), or [performing batch predictions](how-to-run-batch-predictions-designer.md).
+Pipeline parameters are especially useful when resubmitting a pipeline job, [retraining models](how-to-retrain-designer.md), or [performing batch predictions](how-to-run-batch-predictions-designer.md).
 
 In this article, you learn how to do the following:
 
 > [!div class="checklist"]
 > * Create pipeline parameters
 > * Delete and manage pipeline parameters
-> * Trigger pipeline runs while adjusting pipeline parameters
+> * Trigger pipeline jobs while adjusting pipeline parameters
 
 ## Prerequisites
 
@@ -96,7 +96,7 @@ In this section, you will learn how to attach and detach component parameter to
 
 ### Attach component parameter to pipeline parameter
 
-You can attach the same component parameters of duplicated components to the same pipeline parameter if you want to alter the value at one time when triggering the pipeline run.
+You can attach the same component parameters of duplicated components to the same pipeline parameter if you want to alter the value at one time when triggering the pipeline job.
 
 The following example has duplicated **Clean Missing Data** component. For each **Clean Missing Data** component, attach **Replacement value** to pipeline parameter **replace-missing-value**:
 
@@ -159,18 +159,18 @@ Use the following steps to delete a component pipeline parameter:
     > [!NOTE]
     > Deleting a pipeline parameter will cause all attached component parameters to be detached and the value of detached component parameters will keep current pipeline parameter value.     
 
-## Trigger a pipeline run with pipeline parameters 
+## Trigger a pipeline job with pipeline parameters 
 
-In this section, you learn how to submit a pipeline run while setting pipeline parameters.
+In this section, you learn how to submit a pipeline job while setting pipeline parameters.
 
-### Resubmit a pipeline run
+### Resubmit a pipeline job
 
-After submitting a pipeline with pipeline parameters, you can resubmit a pipeline run with different parameters:
+After submitting a pipeline with pipeline parameters, you can resubmit a pipeline job with different parameters:
 
-1. Go to pipeline detail page. In the **Pipeline run overview** window, you can check current pipeline parameters and values.
+1. Go to pipeline detail page. In the **Pipeline job overview** window, you can check current pipeline parameters and values.
 
 1. Select **Resubmit**.
-1. In the **Setup pipeline run**, specify your new pipeline parameters. 
+1. In the **Setup pipeline job**, specify your new pipeline parameters. 
 
 ![Screenshot that shows resubmit pipeline with pipeline parameters](media/how-to-use-pipeline-parameter/resubmit-pipeline-run.png)
 

articles/machine-learning/how-to-use-reinforcement-learning.md

@@ -26,7 +26,7 @@ In this article you learn how to:
 > * Set up an experiment
 > * Define head and worker nodes
 > * Create an RL estimator
-> * Submit an experiment to start a run
+> * Submit an experiment to start a job
 > * View results
 
 This article is based on the [RLlib Pong example](https://aka.ms/azureml-rl-pong) that can be found in the Azure Machine Learning notebook [GitHub repository](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/reinforcement-learning/README.md).
@@ -102,7 +102,7 @@ ws = Workspace.from_config()
 
 ### Create a reinforcement learning experiment
 
-Create an [experiment](/python/api/azureml-core/azureml.core.experiment.experiment) to track your reinforcement learning run. In Azure Machine Learning, experiments are logical collections of related trials to organize run logs, history, outputs, and more.
+Create an [experiment](/python/api/azureml-core/azureml.core.experiment.experiment) to track your reinforcement learning job. In Azure Machine Learning, experiments are logical collections of related trials to organize job logs, history, outputs, and more.
 
 ```python
 experiment_name='rllib-pong-multi-node'
@@ -212,7 +212,7 @@ else:
 
 Use the [ReinforcementLearningEstimator](/python/api/azureml-contrib-reinforcementlearning/azureml.contrib.train.rl.reinforcementlearningestimator) to submit a training job to Azure Machine Learning.
 
-Azure Machine Learning uses estimator classes to encapsulate run configuration information. This lets you specify how to configure a script execution. 
+Azure Machine Learning uses estimator classes to encapsulate job configuration information. This lets you specify how to configure a script execution. 
 
 ### Define a worker configuration
 
@@ -312,7 +312,7 @@ rl_estimator = ReinforcementLearningEstimator(
     cluster_coordination_timeout_seconds=3600,
 
     # Maximum time for the whole Ray job to run
-    # This will cut off the run after an hour
+    # This will cut off the job after an hour
     max_run_duration_seconds=3600,
 
     # Allow the docker container Ray runs in to make full use
@@ -396,7 +396,7 @@ def on_train_result(info):
         value=info["result"]["episodes_total"])
 ```
 
-## Submit a run
+## Submit a job
 
 [Run](/python/api/azureml-core/azureml.core.run%28class%29) handles the run history of in-progress or complete jobs. 
 
@@ -408,7 +408,7 @@ run = exp.submit(config=rl_estimator)
 
 ## Monitor and view results
 
-Use the Azure Machine Learning Jupyter widget to see the status of your runs in real time. The widget shows two child runs: one for head and one for workers. 
+Use the Azure Machine Learning Jupyter widget to see the status of your jobs in real time. The widget shows two child jobs: one for head and one for workers. 
 
 ```python
 from azureml.widgets import RunDetails
@@ -418,15 +418,15 @@ run.wait_for_completion()
 ```
 
 1. Wait for the widget to load.
-1. Select the head run in the list of runs.
+1. Select the head job in the list of jobs.
 
-Select **Click here to see the run in Azure Machine Learning studio** for additional run information in the studio. You can access this information while the run is in progress or after it completes.
+Select **Click here to see the job in Azure Machine Learning studio** for additional job information in the studio. You can access this information while the job is in progress or after it completes.
 
-![Line graph showing how run details widget](./media/how-to-use-reinforcement-learning/pong-run-details-widget.png)
+![Line graph showing how job details widget](./media/how-to-use-reinforcement-learning/pong-run-details-widget.png)
 
 The **episode_reward_mean** plot shows the mean number of points scored per training epoch. You can see that the training agent initially performed poorly, losing its matches without scoring a single point (shown by a reward_mean of -21). Within 100 iterations, the training agent learned to beat the computer opponent by an average of 18 points.
 
-If you browse logs of the child run, you can see the evaluation results recorded in driver_log.txt file. You may need to wait several minutes before these metrics become available on the Run page.
+If you browse logs of the child job, you can see the evaluation results recorded in driver_log.txt file. You may need to wait several minutes before these metrics become available on the Job page.
 
 In short work, you have learned to configure multiple compute resources to train a reinforcement learning agent to play Pong very well against a computer opponent.
 

articles/machine-learning/how-to-use-secrets-in-runs.md

@@ -1,7 +1,7 @@
 ---
 title: Authentication secrets in training
 titleSuffix: Azure Machine Learning
-description: Learn how to pass secrets to training runs in secure fashion using the Azure Key Vault for your workspace.
+description: Learn how to pass secrets to training jobs in secure fashion using the Azure Key Vault for your workspace.
 services: machine-learning
 author: rastala
 ms.author: roastala
@@ -13,19 +13,19 @@ ms.topic: how-to
 ms.custom: sdkv1, event-tier1-build-2022
 ---
 
-# Use authentication credential secrets in Azure Machine Learning training runs
+# Use authentication credential secrets in Azure Machine Learning training jobs
 
 [!INCLUDE [sdk v1](../../includes/machine-learning-sdk-v1.md)]
 
-In this article, you learn how to use secrets in training runs securely. Authentication information such as your user name and password are secrets. For example, if you connect to an external database in order to query training data, you would need to pass your username and password to the remote run context. Coding such values into training scripts in cleartext is insecure as it would expose the secret. 
+In this article, you learn how to use secrets in training jobs securely. Authentication information such as your user name and password are secrets. For example, if you connect to an external database in order to query training data, you would need to pass your username and password to the remote job context. Coding such values into training scripts in cleartext is insecure as it would expose the secret. 
 
-Instead, your Azure Machine Learning workspace has an associated resource called a [Azure Key Vault](../key-vault/general/overview.md). Use this Key Vault to pass secrets to remote runs securely through a set of APIs in the Azure Machine Learning Python SDK.
+Instead, your Azure Machine Learning workspace has an associated resource called a [Azure Key Vault](../key-vault/general/overview.md). Use this Key Vault to pass secrets to remote jobs securely through a set of APIs in the Azure Machine Learning Python SDK.
 
 The standard flow for using secrets is:
  1. On local computer, log in to Azure and connect to your workspace.
  2. On local computer, set a secret in Workspace Key Vault.
- 3. Submit a remote run.
- 4. Within the remote run, get the secret from Key Vault and use it.
+ 3. Submit a remote job.
+ 4. Within the remote job, get the secret from Key Vault and use it.
 
 ## Set secrets
 
@@ -56,10 +56,10 @@ You can list secret names using the [`list_secrets()`](/python/api/azureml-core/
 
 In your local code, you can use the [`get_secret()`](/python/api/azureml-core/azureml.core.keyvault.keyvault#get-secret-name-) method to get the secret value by name.
 
-For runs submitted the [`Experiment.submit`](/python/api/azureml-core/azureml.core.experiment.experiment#submit-config--tags-none----kwargs-)  , use the [`get_secret()`](/python/api/azureml-core/azureml.core.run.run#get-secret-name-) method with the [`Run`](/python/api/azureml-core/azureml.core.run%28class%29) class. Because a submitted run is aware of its workspace, this method shortcuts the Workspace instantiation and returns the secret value directly.
+For jobs submitted the [`Experiment.submit`](/python/api/azureml-core/azureml.core.experiment.experiment#submit-config--tags-none----kwargs-)  , use the [`get_secret()`](/python/api/azureml-core/azureml.core.run.run#get-secret-name-) method with the [`Run`](/python/api/azureml-core/azureml.core.run%28class%29) class. Because a submitted run is aware of its workspace, this method shortcuts the Workspace instantiation and returns the secret value directly.
 
 ```python
-# Code in submitted run
+# Code in submitted job
 from azureml.core import Experiment, Run
 
 run = Run.get_context()

articles/machine-learning/how-to-use-sweep-in-pipeline.md

@@ -67,15 +67,15 @@ Below code snippet shows how to enable sweep for `train_model`.
 
 After you submit a pipeline job, the SDK or CLI widget will give you a web URL link to Studio UI. The link will guide you to the pipeline graph view by default.
 
-To check details of the sweep step, double click the sweep step and navigate to the **child run** tab in the panel on the right.
+To check details of the sweep step, double click the sweep step and navigate to the **child job** tab in the panel on the right.
 
-:::image type="content" source="./media/how-to-use-sweep-in-pipeline/pipeline-view.png" alt-text="Screenshot of the pipeline with child run and the train_model node highlighted." lightbox= "./media/how-to-use-sweep-in-pipeline/pipeline-view.png":::
+:::image type="content" source="./media/how-to-use-sweep-in-pipeline/pipeline-view.png" alt-text="Screenshot of the pipeline with child job and the train_model node highlighted." lightbox= "./media/how-to-use-sweep-in-pipeline/pipeline-view.png":::
 
-This will link you to the sweep job page as seen in the below screenshot. Navigate to **child run** tab, here you can see the metrics of all child runs and list of all child runs.
+This will link you to the sweep job page as seen in the below screenshot. Navigate to **child job** tab, here you can see the metrics of all child jobs and list of all child jobs.
 
-:::image type="content" source="./media/how-to-use-sweep-in-pipeline/sweep-job.png" alt-text="Screenshot of the job page on the child runs tab." lightbox= "./media/how-to-use-sweep-in-pipeline/sweep-job.png":::
+:::image type="content" source="./media/how-to-use-sweep-in-pipeline/sweep-job.png" alt-text="Screenshot of the job page on the child jobs tab." lightbox= "./media/how-to-use-sweep-in-pipeline/sweep-job.png":::
 
-If a child runs failed, select the name of that child run to enter detail page of that specific child run (see screenshot below). The useful debug information is under **Outputs + Logs**.
+If a child jobs failed, select the name of that child job to enter detail page of that specific child job (see screenshot below). The useful debug information is under **Outputs + Logs**.
 
 :::image type="content" source="./media/how-to-use-sweep-in-pipeline/child-run.png" alt-text="Screenshot of the output + logs tab of a child run." lightbox= "./media/how-to-use-sweep-in-pipeline/child-run.png":::
 

articles/machine-learning/how-to-use-synapsesparkstep.md

@@ -198,9 +198,9 @@ sdf.coalesce(1).write\
 .csv(args.output_dir)
 ```
 
-This "data preparation" script doesn't do any real data transformation, but illustrates how to retrieve data, convert it to a spark dataframe, and how to do some basic Apache Spark manipulation. You can find the output in Azure Machine Learning Studio by opening the child run, choosing the **Outputs + logs** tab, and opening the `logs/azureml/driver/stdout` file, as shown in the following figure.
+This "data preparation" script doesn't do any real data transformation, but illustrates how to retrieve data, convert it to a spark dataframe, and how to do some basic Apache Spark manipulation. You can find the output in Azure Machine Learning Studio by opening the child job, choosing the **Outputs + logs** tab, and opening the `logs/azureml/driver/stdout` file, as shown in the following figure.
 
-:::image type="content" source="media/how-to-use-synapsesparkstep/synapsesparkstep-stdout.png" alt-text="Screenshot of Studio showing stdout tab of child run":::
+:::image type="content" source="media/how-to-use-synapsesparkstep/synapsesparkstep-stdout.png" alt-text="Screenshot of Studio showing stdout tab of child job":::
 
 ## Use the `SynapseSparkStep` in a pipeline
 
@@ -244,7 +244,7 @@ pipeline_run = pipeline.submit('synapse-pipeline', regenerate_outputs=True)
 
 The above code creates a pipeline consisting of the data preparation step on Apache Spark pools powered by Azure Synapse Analytics (`step_1`) and the training step (`step_2`). Azure calculates the execution graph by examining the data dependencies between the steps. In this case, there's only a straightforward dependency that `step2_input` necessarily requires `step1_output`.
 
-The call to `pipeline.submit` creates, if necessary, an Experiment called `synapse-pipeline` and asynchronously begins a Run within it. Individual steps within the pipeline are run as Child Runs of this main run and can be monitored and reviewed in the Experiments page of Studio.
+The call to `pipeline.submit` creates, if necessary, an Experiment called `synapse-pipeline` and asynchronously begins a Job within it. Individual steps within the pipeline are run as Child Jobs of this main job and can be monitored and reviewed in the Experiments page of Studio.
 
 ## Next steps