Merge pull request #209339 from sdgilley/sdg-v2

Stacyrch140 · web-flow · commit 350ea25be9e3 · 2022-08-26T13:29:37.000-04:00
move how-to-set-up-compute-targets
diff --git a/articles/machine-learning/.openpublishing.redirection.machine-learning.json b/articles/machine-learning/.openpublishing.redirection.machine-learning.json
@@ -1303,7 +1303,12 @@
     },
     {
         "source_path_from_root": "/articles/machine-learning/service/how-to-set-up-training-targets.md",
-        "redirect_url": "/azure/machine-learning/how-to-set-up-training-targets",
+        "redirect_url": "/azure/machine-learning/v1/how-to-set-up-training-targets",
+        "redirect_document_id": false
+    },
+    {
+        "source_path_from_root": "/articles/machine-learning/how-to-set-up-training-targets.md",
+        "redirect_url": "/azure/machine-learning/v1/how-to-set-up-training-targets",
         "redirect_document_id": true
     },
     {
diff --git a/articles/machine-learning/concept-automated-ml.md b/articles/machine-learning/concept-automated-ml.md
@@ -182,8 +182,6 @@ Using **Azure Machine Learning**, you can design and run your automated ML train
     
 1. **Specify the source and format of the labeled training data**: Numpy arrays or Pandas dataframe
 
-1. **Configure the compute target for model training**, such as your [local computer, Azure Machine Learning Computes, remote VMs, or Azure Databricks](how-to-set-up-training-targets.md).
-
 1. **Configure the automated machine learning parameters** that determine how many iterations over different models, hyperparameter settings, advanced preprocessing/featurization, and what metrics to look at when determining the best model.  
 1. **Submit the training job.**
 
diff --git a/articles/machine-learning/concept-compute-target.md b/articles/machine-learning/concept-compute-target.md
@@ -31,7 +31,6 @@ Azure Machine Learning has varying support across different compute targets. A t
 
 [!INCLUDE [aml-compute-target-train](../../includes/aml-compute-target-train.md)]
 
-Learn more about how to [submit a training job to a compute target](how-to-set-up-training-targets.md).
 
 ## <a name="deploy"></a> Compute targets for inference
 
@@ -171,5 +170,4 @@ For more information, see [set up compute targets for model training and deploym
 ## Next steps
 
 Learn how to:
-* [Use a compute target to train your model](how-to-set-up-training-targets.md)
 * [Deploy your model to a compute target](how-to-deploy-managed-online-endpoints.md)
diff --git a/articles/machine-learning/concept-deep-learning-vs-machine-learning.md b/articles/machine-learning/concept-deep-learning-vs-machine-learning.md
@@ -142,5 +142,3 @@ The following articles show you more options for using open-source deep learning
 - [Classify handwritten digits by using a TensorFlow model](./how-to-train-tensorflow.md?WT.mc_id=docs-article-lazzeri) 
 
 - [Classify handwritten digits by using a TensorFlow estimator and Keras](./how-to-train-keras.md?WT.mc_id=docs-article-lazzeri)
-
-- [Classify handwritten digits by using a Chainer model](./how-to-set-up-training-targets.md?WT.mc_id=docs-article-lazzeri)
diff --git a/articles/machine-learning/concept-distributed-training.md b/articles/machine-learning/concept-distributed-training.md
@@ -46,6 +46,5 @@ In model parallelism, worker nodes only need to synchronize the shared parameter
 
 ## Next steps
 
-* Learn how to [use compute targets for model training](how-to-set-up-training-targets.md) with the Python SDK.
 * For a technical example, see the [reference architecture  scenario](/azure/architecture/reference-architectures/ai/training-deep-learning).
 * Find tips for MPI, TensorFlow, and PyTorch in the [Distributed GPU training guide](how-to-train-distributed-gpu.md) 
diff --git a/articles/machine-learning/concept-model-management-and-deployment.md b/articles/machine-learning/concept-model-management-and-deployment.md
@@ -146,7 +146,7 @@ Microsoft Power BI supports using machine learning models for data analytics. Fo
 
 Machine Learning gives you the capability to track the end-to-end audit trail of all your machine learning assets by using metadata. For example:
 
-- Machine Learning [integrates with Git](how-to-set-up-training-targets.md#gitintegration) to track information on which repository, branch, and commit your code came from.
+- Machine Learning [integrates with Git](concept-train-model-git-integration.md) to track information on which repository, branch, and commit your code came from.
 - [Machine Learning datasets](how-to-create-register-datasets.md) help you track, profile, and version data.
 - [Interpretability](how-to-machine-learning-interpretability.md) allows you to explain your models, meet regulatory compliance, and understand how models arrive at a result for specific input.
 - Machine Learning Job history stores a snapshot of the code, data, and computes used to train a model.
diff --git a/articles/machine-learning/concept-train-machine-learning-model.md b/articles/machine-learning/concept-train-machine-learning-model.md
@@ -48,7 +48,7 @@ You may start with a run configuration for your local computer, and then switch
 * [What is a run configuration?](v1/concept-azure-machine-learning-architecture.md#run-configurations)
 * [Tutorial: Train your first ML model](tutorial-1st-experiment-sdk-train.md)
 * [Examples: Jupyter Notebook and Python examples of training models](https://github.com/Azure/azureml-examples)
-* [How to: Configure a training run](how-to-set-up-training-targets.md)
+* [How to: Configure a training run](v1/how-to-set-up-training-targets.md)
 
 ### Automated Machine Learning
 
@@ -116,4 +116,4 @@ You can use the VS Code extension to run and manage your training jobs. See the
 
 ## Next steps
 
-Learn how to [Configure a training run](how-to-set-up-training-targets.md).
+Learn how to [Configure a training run](v1/how-to-set-up-training-targets.md).
diff --git a/articles/machine-learning/concept-train-model-git-integration.md b/articles/machine-learning/concept-train-model-git-integration.md
@@ -192,4 +192,4 @@ run.properties['azureml.git.commit']
 
 ## Next steps
 
-* [Use compute targets for model training](how-to-set-up-training-targets.md)
+* [Use compute targets for model training](v1/how-to-set-up-training-targets.md)
diff --git a/articles/machine-learning/how-to-attach-kubernetes-anywhere.md b/articles/machine-learning/how-to-attach-kubernetes-anywhere.md
@@ -437,7 +437,7 @@ In the above example, replace `<instance_type_name>` with the name of the instan
 ## Next steps
 
 - [Train models with CLI v2](how-to-train-cli.md)
-- [Train models with Python SDK](how-to-set-up-training-targets.md)
+- [Train models with Python SDK](how-to-train-sdk.md)
 - [Deploy model with an online endpoint (CLI v2)](./how-to-deploy-managed-online-endpoints.md)
 - [Use batch endpoint for batch scoring (CLI v2)](./how-to-use-batch-endpoint.md)
 
diff --git a/articles/machine-learning/how-to-create-attach-compute-cluster.md b/articles/machine-learning/how-to-create-attach-compute-cluster.md
@@ -318,5 +318,5 @@ If your Azure Machine Learning compute cluster appears stuck at resizing (0 -> 0
 
 Use your compute cluster to:
 
-* [Submit a training run](how-to-set-up-training-targets.md) 
+* [Submit a training run](v1/how-to-set-up-training-targets.md) 
 * [Run batch inference](./tutorial-pipeline-batch-scoring-classification.md).
diff --git a/articles/machine-learning/how-to-create-attach-compute-studio.md b/articles/machine-learning/how-to-create-attach-compute-studio.md
@@ -87,7 +87,7 @@ To detach your compute use the following steps:
 
 ## Next steps
 
-* Use the compute resource to [submit a training run](how-to-set-up-training-targets.md).
+* Use the compute resource to [submit a training run](how-to-train-sdk.md).
 * Learn how to [efficiently tune hyperparameters](how-to-tune-hyperparameters.md) to build better models.
 * Once you have a trained model, learn [how and where to deploy models](how-to-deploy-managed-online-endpoints.md).
 * [Use Azure Machine Learning with Azure Virtual Networks](./how-to-network-security-overview.md)
diff --git a/articles/machine-learning/how-to-create-manage-compute-instance.md b/articles/machine-learning/how-to-create-manage-compute-instance.md
@@ -542,4 +542,4 @@ To create a compute instance, you'll need permissions for the following actions:
 * [Access the compute instance terminal](how-to-access-terminal.md)
 * [Create and manage files](how-to-manage-files.md)
 * [Update the compute instance to the latest VM image](concept-vulnerability-management.md#compute-instance)
-* [Submit a training job](how-to-set-up-training-targets.md)
+* [Submit a training job](v1/how-to-set-up-training-targets.md)
diff --git a/articles/machine-learning/how-to-customize-compute-instance.md b/articles/machine-learning/how-to-customize-compute-instance.md
@@ -131,4 +131,3 @@ Logs from the setup script execution appear in the logs folder in the compute in
 * [Access the compute instance terminal](how-to-access-terminal.md)
 * [Create and manage files](how-to-manage-files.md)
 * [Update the compute instance to the latest VM image](concept-vulnerability-management.md#compute-instance)
-* [Submit a training run](how-to-set-up-training-targets.md)
diff --git a/articles/machine-learning/how-to-generate-automl-training-code.md b/articles/machine-learning/how-to-generate-automl-training-code.md
@@ -417,7 +417,7 @@ For more information about AzureML environments, see [the Environment class docu
 
 Since the generated code isn’t driven by automated ML anymore, instead of creating an `AutoMLConfig` and then passing it to `experiment.submit()`, you need to create a [`ScriptRunConfig`](/python/api/azureml-core/azureml.core.scriptrunconfig) and provide the generated code (script.py) to it. 
 
-The following example contains the parameters and regular dependencies needed to run `ScriptRunConfig`, such as compute, environment, etc. For more information on how to use ScriptRunConfig, see [Configure and submit training runs](how-to-set-up-training-targets.md).
+The following example contains the parameters and regular dependencies needed to run `ScriptRunConfig`, such as compute, environment, etc. For more information on how to use ScriptRunConfig, see [Configure and submit training runs](v1/how-to-set-up-training-targets.md).
 
 ```python
 from azureml.core import ScriptRunConfig
diff --git a/articles/machine-learning/how-to-link-synapse-ml-workspaces.md b/articles/machine-learning/how-to-link-synapse-ml-workspaces.md
@@ -190,5 +190,5 @@ ws.compute_targets['Synapse Spark pool alias']
 
 * [How to data wrangle with Azure Synapse (preview)](v1/how-to-data-prep-synapse-spark-pool.md).
 * [How to use Apache Spark in your machine learning pipeline with Azure Synapse (preview)](v1/how-to-use-synapsesparkstep.md)
-* [Train a model](how-to-set-up-training-targets.md).
+* [Train a model](v1/how-to-set-up-training-targets.md).
 * [How to securely integrate Azure Synapse and Azure Machine Learning workspaces](how-to-private-endpoint-integration-synapse.md).
diff --git a/articles/machine-learning/how-to-manage-optimize-cost.md b/articles/machine-learning/how-to-manage-optimize-cost.md
@@ -93,7 +93,7 @@ Azure Machine Learning Compute supports reserved instances inherently. If you pu
 
 ## Train locally
 
-When prototyping and running training jobs that are small enough to run on your local computer, consider training locally. Using the Python SDK, setting your compute target to `local` executes your script locally. For more information, see [Configure and submit training jobs](how-to-set-up-training-targets.md#select-a-compute-target).
+When prototyping and running training jobs that are small enough to run on your local computer, consider training locally. Using the Python SDK, setting your compute target to `local` executes your script locally. 
 
 Visual Studio Code provides a full-featured environment for developing your machine learning applications. Using the Azure Machine Learning visual Visual Studio Code extension and Docker, you can run and debug locally. For more information, see [interactive debugging with Visual Studio Code](how-to-debug-visual-studio-code.md).
 
diff --git a/articles/machine-learning/how-to-migrate-from-estimators-to-scriptrunconfig.md b/articles/machine-learning/how-to-migrate-from-estimators-to-scriptrunconfig.md
@@ -30,7 +30,7 @@ This article covers common considerations when migrating from Estimators to Scri
 Azure Machine Learning documentation and samples have been updated to use [ScriptRunConfig](/python/api/azureml-core/azureml.core.script_run_config.scriptrunconfig) for job configuration and submission.
 
 For information on using ScriptRunConfig, refer to the following documentation:
-* [Configure and submit training jobs](how-to-set-up-training-targets.md)
+* [Configure and submit training jobs](v1/how-to-set-up-training-targets.md)
 * [Configuring PyTorch training jobs](how-to-train-pytorch.md)
 * [Configuring TensorFlow training jobs](how-to-train-tensorflow.md)
 * [Configuring scikit-learn training jobs](how-to-train-scikit-learn.md)
@@ -129,4 +129,4 @@ src.run_config
 
 ## Next steps
 
-* [Configure and submit training jobs](how-to-set-up-training-targets.md)
+* [Configure and submit training jobs](v1/how-to-set-up-training-targets.md)
diff --git a/articles/machine-learning/how-to-secure-training-vnet.md b/articles/machine-learning/how-to-secure-training-vnet.md
@@ -231,7 +231,7 @@ except ComputeTargetException:
 
 ---
 
-When the creation process finishes, you train your model by using the cluster in an experiment. For more information, see [Select and use a compute target for training](how-to-set-up-training-targets.md).
+When the creation process finishes, you train your model by using the cluster in an experiment. 
 
 [!INCLUDE [low-pri-note](../../includes/machine-learning-low-pri-vm.md)]
 
@@ -339,7 +339,7 @@ If you don't want to use the default outbound rules and you do want to limit the
 
 ### Attach the VM or HDInsight cluster
 
-Attach the VM or HDInsight cluster to your Azure Machine Learning workspace. For more information, see [Set up compute targets for model training](how-to-set-up-training-targets.md).
+Attach the VM or HDInsight cluster to your Azure Machine Learning workspace. For more information, see [Manage compute resources for model training and deployment in studio](how-to-create-attach-compute-studio.md).
 
 ## Next steps
 
diff --git a/articles/machine-learning/how-to-train-keras.md b/articles/machine-learning/how-to-train-keras.md
@@ -191,7 +191,7 @@ src = ScriptRunConfig(source_directory=script_folder,
                       environment=keras_env)
 ```
 
-For more information on configuring jobs with ScriptRunConfig, see [Configure and submit training runs](how-to-set-up-training-targets.md).
+For more information on configuring jobs with ScriptRunConfig, see [Configure and submit training runs](v1/how-to-set-up-training-targets.md).
 
 > [!WARNING]
 > If you were previously using the TensorFlow estimator to configure your Keras training jobs, please note that Estimators have been deprecated as of the 1.19.0 SDK release. With Azure ML SDK >= 1.15.0, ScriptRunConfig is the recommended way to configure training jobs, including those using deep learning frameworks. For common migration questions, see the [Estimator to ScriptRunConfig migration guide](how-to-migrate-from-estimators-to-scriptrunconfig.md).
diff --git a/articles/machine-learning/how-to-train-pytorch.md b/articles/machine-learning/how-to-train-pytorch.md
@@ -209,7 +209,7 @@ src = ScriptRunConfig(source_directory=project_folder,
 > [!WARNING]
 > Azure Machine Learning runs training scripts by copying the entire source directory. If you have sensitive data that you don't want to upload, use a [.ignore file](how-to-save-write-experiment-files.md#storage-limits-of-experiment-snapshots) or don't include it in the source directory . Instead, access your data using an Azure ML [dataset](v1/how-to-train-with-datasets.md).
 
-For more information on configuring jobs with ScriptRunConfig, see [Configure and submit training runs](how-to-set-up-training-targets.md).
+For more information on configuring jobs with ScriptRunConfig, see [Configure and submit training runs](v1/how-to-set-up-training-targets.md).
 
 > [!WARNING]
 > If you were previously using the PyTorch estimator to configure your PyTorch training jobs, please note that Estimators have been deprecated as of the 1.19.0 SDK release. With Azure ML SDK >= 1.15.0, ScriptRunConfig is the recommended way to configure training jobs, including those using deep learning frameworks. For common migration questions, see the [Estimator to ScriptRunConfig migration guide](how-to-migrate-from-estimators-to-scriptrunconfig.md).
diff --git a/articles/machine-learning/how-to-train-tensorflow.md b/articles/machine-learning/how-to-train-tensorflow.md
@@ -220,7 +220,7 @@ src = ScriptRunConfig(source_directory=script_folder,
 > [!WARNING]
 > Azure Machine Learning runs training scripts by copying the entire source directory. If you have sensitive data that you don't want to upload, use a [.ignore file](how-to-save-write-experiment-files.md#storage-limits-of-experiment-snapshots) or don't include it in the source directory . Instead, access your data using an Azure ML [dataset](v1/how-to-train-with-datasets.md).
 
-For more information on configuring jobs with ScriptRunConfig, see [Configure and submit training runs](how-to-set-up-training-targets.md).
+For more information on configuring jobs with ScriptRunConfig, see [Configure and submit training runs](v1/how-to-set-up-training-targets.md).
 
 > [!WARNING]
 > If you were previously using the TensorFlow estimator to configure your TensorFlow training jobs, please note that Estimators have been deprecated as of the 1.19.0 SDK release. With Azure ML SDK >= 1.15.0, ScriptRunConfig is the recommended way to configure training jobs, including those using deep learning frameworks. For common migration questions, see the [Estimator to ScriptRunConfig migration guide](how-to-migrate-from-estimators-to-scriptrunconfig.md).
diff --git a/articles/machine-learning/how-to-train-with-custom-image.md b/articles/machine-learning/how-to-train-with-custom-image.md
@@ -147,7 +147,7 @@ print(compute_target.get_status().serialize())
 
 For this tutorial, use the training script *train.py* on [GitHub](https://github.com/Azure/azureml-examples/blob/main/python-sdk/workflows/train/fastai/pets/src/train.py). In practice, you can take any custom training script and run it, as is, with Azure Machine Learning.
 
-Create a `ScriptRunConfig` resource to configure your job for running on the desired [compute target](how-to-set-up-training-targets.md).
+Create a `ScriptRunConfig` resource to configure your job for running on the desired [compute target](v1/how-to-set-up-training-targets.md).
 
 ```python
 from azureml.core import ScriptRunConfig
diff --git a/articles/machine-learning/how-to-use-mlflow-cli-runs.md b/articles/machine-learning/how-to-use-mlflow-cli-runs.md
@@ -183,8 +183,7 @@ For details about how to log metrics, parameters and artifacts in a run using ML
 
 [!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.
-
+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 [What are compute targets in Azure Machine Learning?](concept-compute-target.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.
 
diff --git a/articles/machine-learning/toc.yml b/articles/machine-learning/toc.yml
@@ -382,9 +382,6 @@
           items:
           - name: Train with SDK v2 (preview)
             href: how-to-train-sdk.md
-          - name: Configure & submit training run
-            displayName: run config, script run config, scriptrunconfig, compute target, dsvm, Data Science Virtual Machine, local, cluster, ACI, container instance, Databricks, data lake, lake, HDI, HDInsight
-            href: how-to-set-up-training-targets.md
           - name: Tune hyperparameters
             displayName: parameter, hyperparameter, hyperdrive
             href: how-to-tune-hyperparameters.md
diff --git a/articles/machine-learning/v1/concept-automated-ml-v1.md b/articles/machine-learning/v1/concept-automated-ml-v1.md
@@ -188,7 +188,7 @@ Using **Azure Machine Learning**, you can design and run your automated ML train
     
 1. **Specify the source and format of the labeled training data**: Numpy arrays or Pandas dataframe
 
-1. **Configure the compute target for model training**, such as your [local computer, Azure Machine Learning Computes, remote VMs, or Azure Databricks with SDK v1](../how-to-set-up-training-targets.md).
+1. **Configure the compute target for model training**, such as your [local computer, Azure Machine Learning Computes, remote VMs, or Azure Databricks with SDK v1](how-to-set-up-training-targets.md).
 
 1. **Configure the automated machine learning parameters** that determine how many iterations over different models, hyperparameter settings, advanced preprocessing/featurization, and what metrics to look at when determining the best model.  
 1. **Submit the training job.**
diff --git a/articles/machine-learning/v1/concept-azure-machine-learning-architecture.md b/articles/machine-learning/v1/concept-azure-machine-learning-architecture.md
@@ -107,7 +107,7 @@ A run configuration defines how a script should be run in a specified compute ta
 
 A run configuration can be persisted into a file inside the directory that contains your training script.   Or it can be constructed as an in-memory object and used to submit a run.
 
-For example run configurations, see [Configure a training run](../how-to-set-up-training-targets.md).
+For example run configurations, see [Configure a training run](how-to-set-up-training-targets.md).
 
 ### Snapshots
 
diff --git a/articles/machine-learning/v1/concept-model-management-and-deployment.md b/articles/machine-learning/v1/concept-model-management-and-deployment.md
@@ -146,7 +146,7 @@ Microsoft Power BI supports using machine learning models for data analytics. Fo
 
 Azure ML gives you the capability to track the end-to-end audit trail of all of your ML assets by using metadata.
 
-- Azure ML [integrates with Git](../how-to-set-up-training-targets.md#gitintegration) to track information on which repository / branch / commit your code came from.
+- Azure ML [integrates with Git](../concept-train-model-git-integration.md) to track information on which repository / branch / commit your code came from.
 - [Azure ML Datasets](how-to-create-register-datasets.md) help you track, profile, and version data.
 - [Interpretability](../how-to-machine-learning-interpretability.md) allows you to explain your models, meet regulatory compliance, and understand how models arrive at a result for given input.
 - Azure ML Run history stores a snapshot of the code, data, and computes used to train a model.
diff --git a/articles/machine-learning/v1/how-to-access-data.md b/articles/machine-learning/v1/how-to-access-data.md
@@ -303,5 +303,5 @@ Azure Data Factory provides efficient and resilient data transfer with more than
 ## Next steps
 
 * [Create an Azure machine learning dataset](how-to-create-register-datasets.md)
-* [Train a model](../how-to-set-up-training-targets.md)
+* [Train a model](how-to-set-up-training-targets.md)
 * [Deploy a model](how-to-deploy-and-where.md)
diff --git a/articles/machine-learning/v1/how-to-attach-compute-targets.md b/articles/machine-learning/v1/how-to-attach-compute-targets.md
diff --git a/articles/machine-learning/v1/how-to-connect-data-ui.md b/articles/machine-learning/v1/how-to-connect-data-ui.md
diff --git a/articles/machine-learning/v1/how-to-create-attach-compute-cluster.md b/articles/machine-learning/v1/how-to-create-attach-compute-cluster.md
diff --git a/articles/machine-learning/v1/how-to-create-manage-compute-instance.md b/articles/machine-learning/v1/how-to-create-manage-compute-instance.md
diff --git a/articles/machine-learning/v1/how-to-data-prep-synapse-spark-pool.md b/articles/machine-learning/v1/how-to-data-prep-synapse-spark-pool.md
diff --git a/articles/machine-learning/v1/how-to-set-up-training-targets.md b/articles/machine-learning/v1/how-to-set-up-training-targets.md
diff --git a/articles/machine-learning/v1/how-to-tune-hyperparameters-v1.md b/articles/machine-learning/v1/how-to-tune-hyperparameters-v1.md
diff --git a/articles/machine-learning/v1/how-to-use-mlflow.md b/articles/machine-learning/v1/how-to-use-mlflow.md
diff --git a/articles/machine-learning/v1/toc.yml b/articles/machine-learning/v1/toc.yml

Original file line number	Diff line number	Diff line change
`@@ -1303,7 +1303,12 @@`
`1303`	`1303`	`},`
`1304`	`1304`	`{`
`1305`	`1305`	`"source_path_from_root": "/articles/machine-learning/service/how-to-set-up-training-targets.md",`
`1306`		`- "redirect_url": "/azure/machine-learning/how-to-set-up-training-targets",`
	`1306`	`+ "redirect_url": "/azure/machine-learning/v1/how-to-set-up-training-targets",`
	`1307`	`+ "redirect_document_id": false`
	`1308`	`+ },`
	`1309`	`+ {`
	`1310`	`+ "source_path_from_root": "/articles/machine-learning/how-to-set-up-training-targets.md",`
	`1311`	`+ "redirect_url": "/azure/machine-learning/v1/how-to-set-up-training-targets",`
`1307`	`1312`	`"redirect_document_id": true`
`1308`	`1313`	`},`
`1309`	`1314`	`{`
Original file line number	Diff line number	Diff line change
`@@ -192,4 +192,4 @@ run.properties['azureml.git.commit']`
`192`	`192`
`193`	`193`	`## Next steps`
`194`	`194`
`195`		`-* [Use compute targets for model training](how-to-set-up-training-targets.md)`
	`195`	`+* [Use compute targets for model training](v1/how-to-set-up-training-targets.md)`