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The [Azure Machine Learning SDK for Python](https://docs.microsoft.com/python/api/overview/azure/ml/intro?view=azure-ml-py) and [Machine Learning CLI](reference-azure-machine-learning-cli.md) provide various methods to monitor, organize, and manage your runs for training and experimentation.
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The [Azure Machine Learning SDK for Python](https://docs.microsoft.com/python/api/overview/azure/ml/intro?view=azure-ml-py), [Machine Learning CLI](reference-azure-machine-learning-cli.md), and [Azure Machine Learning studio](https://ml.azure.com) provide various methods to monitor, organize, and manage your runs for training and experimentation.
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This article shows examples of the following tasks:
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@@ -101,6 +100,27 @@ To start a run of your experiment, use the following steps:
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For more information, see [az ml run submit-script](https://docs.microsoft.com/cli/azure/ext/azure-cli-ml/ml/run?view=azure-cli-latest#ext-azure-cli-ml-az-ml-run-submit-script).
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### Using Azure Machine Learning studio
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To start a submit a pipeline run in the designer (preview), use the following steps:
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1. Set a default compute target for your pipeline.
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1. Select **Run** at the top of the pipeline canvas.
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1. Select an Experiment to group your pipeline runs.
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You can also resubmit pipeline runs using the same parameters and settings in the studio.
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1. In the **Pipeline** section of the studio, select a pipeline run number.
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1. Select the **Child runs** tab.
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1. Select **Resubmit**.
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> [!TIP]
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> You can **Clone** a pipeline run to create an editable pipeline draft. This lets you use a previous pipeline run as a starting point for a new pipeline.
For more information, see [az ml run show](https://docs.microsoft.com/cli/azure/ext/azure-cli-ml/ml/run?view=azure-cli-latest#ext-azure-cli-ml-az-ml-run-show).
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### Using Azure Machine Learning studio
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To view the number of active runs for your experiment in the studio.
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1. Navigate to the **Experiments** section..
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1. Select an experiment.
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In the experiment page, you can see the number of active compute targets and the duration for each run.
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1. Select a specific run number.
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1. In the **Logs** tab, you can find diagnostic and error logs for your pipeline run.
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## Cancel or fail runs
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If you notice a mistake orif your run is taking too long to finish, you can cancel the run.
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For more information, see [az ml run cancel](https://docs.microsoft.com/cli/azure/ext/azure-cli-ml/ml/run?view=azure-cli-latest#ext-azure-cli-ml-az-ml-run-cancel).
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### Using Azure Machine Learning studio
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To cancel a run in the studio, using the following steps:
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1. Go to the running pipeline in either the **Experiments**or**Pipelines** section.
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1. Select the pipeline run number you want to cancel.
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1. In the toolbar, select **Cancel**
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## Create child runs
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Create child runs to group together related runs, such asfor different hyperparameter-tuning iterations.
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For more information on querying Azure CLI results, see [Query Azure CLI command output](https://docs.microsoft.com/cli/azure/query-azure-cli?view=azure-cli-latest).
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### Using Azure Machine Learning studio
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1. Navigate to the **Pipelines** section.
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1. Use the search bar to filter pipelines using tags, descriptions, experiment names, and submitter name.
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## Example notebooks
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The following notebooks demonstrate the concepts in this article:
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