Merge pull request #226117 from sdgilley/sdg-move-v1-files

move to v1 folder

Author: Court72

articles/machine-learning/.openpublishing.redirection.machine-learning.json

@@ -1,5 +1,10 @@
 {
     "redirections": [
+    {
+    "source_path_from_root": "/articles/machine-learning/how-to-train-with-custom-image.md",
+    "redirect_url": "/azure/machine-learning/v1/how-to-train-with-custom-image",
+    "redirect_document_id": true
+    },
     {
         "source_path_from_root": "/articles/machine-learning/how-to-monitor-tensorboard.md",
         "redirect_url": "/azure/machine-learning/v1/how-to-monitor-tensorboard",

articles/machine-learning/concept-environments.md

@@ -117,7 +117,6 @@ If you provide your own images, you are responsible for updating them.
 For more information on the base images, see the following links:
 
 * [Azure Machine Learning base images](https://github.com/Azure/AzureML-Containers) GitHub repository.
-* [Train a model using a custom image](how-to-train-with-custom-image.md).
 * [Deploy a TensorFlow model using a custom container](how-to-deploy-custom-container.md)
 
 ## Next steps

articles/machine-learning/how-to-secure-workspace-vnet.md

@@ -87,7 +87,7 @@ When your Azure Machine Learning workspace is configured with a private endpoint
 When ACR is behind a virtual network, Azure Machine Learning can’t use it to directly build Docker images. Instead, the compute cluster is used to build the images.
 
 > [!IMPORTANT]
-> The compute cluster used to build Docker images needs to be able to access the package repositories that are used to train and deploy your models. You may need to add network security rules that allow access to public repos, [use private Python packages](how-to-use-private-python-packages.md), or use [custom Docker images](how-to-train-with-custom-image.md) that already include the packages.
+> The compute cluster used to build Docker images needs to be able to access the package repositories that are used to train and deploy your models. You may need to add network security rules that allow access to public repos, [use private Python packages](how-to-use-private-python-packages.md), or use [custom Docker images](v1/how-to-train-with-custom-image.md) that already include the packages.
 
 > [!WARNING]
 > If your Azure Container Registry uses a private endpoint or service endpoint to communicate with the virtual network, you cannot use a managed identity with an Azure Machine Learning compute cluster.

articles/machine-learning/v1/how-to-migrate-from-estimators-to-scriptrunconfig.md

@@ -73,7 +73,7 @@ myenv.environment_variables = {"MESSAGE":"Hello from Azure Machine Learning"}
 For information on configuring and managing Azure ML environments, see:
 * [How to use environments](how-to-use-environments.md)
 * [Curated environments](../resource-curated-environments.md)
-* [Train with a custom Docker image](../how-to-train-with-custom-image.md)
+* [Train with a custom Docker image](how-to-train-with-custom-image.md)
 
 ## Using data for training
 ### Datasets

articles/machine-learning/v1/how-to-secure-workspace-vnet.md

@@ -88,7 +88,7 @@ When your Azure Machine Learning workspace is configured with a private endpoint
 When ACR is behind a virtual network, Azure Machine Learning can’t use it to directly build Docker images. Instead, the compute cluster is used to build the images.
 
 > [!IMPORTANT]
-> The compute cluster used to build Docker images needs to be able to access the package repositories that are used to train and deploy your models. You may need to add network security rules that allow access to public repos, [use private Python packages](how-to-use-private-python-packages.md), or use [custom Docker images](../how-to-train-with-custom-image.md) that already include the packages.
+> The compute cluster used to build Docker images needs to be able to access the package repositories that are used to train and deploy your models. You may need to add network security rules that allow access to public repos, [use private Python packages](how-to-use-private-python-packages.md), or use [custom Docker images](how-to-train-with-custom-image.md) that already include the packages.
 
 > [!WARNING]
 > If your Azure Container Registry uses a private endpoint or service endpoint to communicate with the virtual network, you cannot use a managed identity with an Azure Machine Learning compute cluster.

articles/machine-learning/v1/how-to-train-pytorch.md

@@ -190,7 +190,7 @@ pytorch_env.docker.base_image = 'mcr.microsoft.com/azureml/openmpi3.1.2-cuda10.1
 ```
 
 > [!TIP]
-> Optionally, you can just capture all your dependencies directly in a custom Docker image or Dockerfile, and create your environment from that. For more information, see [Train with custom image](../how-to-train-with-custom-image.md).
+> Optionally, you can just capture all your dependencies directly in a custom Docker image or Dockerfile, and create your environment from that. For more information, see [Train with custom image](how-to-train-with-custom-image.md).
 
 For more information on creating and using environments, see [Create and use software environments in Azure Machine Learning](how-to-use-environments.md).
 

articles/machine-learning/v1/how-to-train-tensorflow.md

@@ -195,7 +195,7 @@ tf_env.docker.base_image = 'mcr.microsoft.com/azureml/openmpi3.1.2-cuda10.1-cudn
 ```
 
 > [!TIP]
-> Optionally, you can just capture all your dependencies directly in a custom Docker image or Dockerfile, and create your environment from that. For more information, see [Train with custom image](../how-to-train-with-custom-image.md).
+> Optionally, you can just capture all your dependencies directly in a custom Docker image or Dockerfile, and create your environment from that. For more information, see [Train with custom image](how-to-train-with-custom-image.md).
 
 For more information on creating and using environments, see [Create and use software environments in Azure Machine Learning](how-to-use-environments.md).
 

articles/machine-learning/how-to-train-with-custom-image.md renamed to articles/machine-learning/v1/how-to-train-with-custom-image.md

@@ -15,30 +15,30 @@ ms.custom: sdkv1, event-tier1-build-2022
 
 # Train a model by using a custom Docker image
 
-[!INCLUDE [sdk v1](../../includes/machine-learning-sdk-v1.md)]
+[!INCLUDE [sdk v1](../../../includes/machine-learning-sdk-v1.md)]
 
 In this article, learn how to use a custom Docker image when you're training models with Azure Machine Learning. You'll use the example scripts in this article to classify pet images by creating a convolutional neural network. 
 
-Azure Machine Learning provides a default Docker base image. You can also use Azure Machine Learning environments to specify a different base image, such as one of the maintained [Azure Machine Learning base images](https://github.com/Azure/AzureML-Containers) or your own [custom image](./how-to-deploy-custom-container.md). Custom base images allow you to closely manage your dependencies and maintain tighter control over component versions when running training jobs.
+Azure Machine Learning provides a default Docker base image. You can also use Azure Machine Learning environments to specify a different base image, such as one of the maintained [Azure Machine Learning base images](https://github.com/Azure/AzureML-Containers) or your own [custom image](../how-to-deploy-custom-container.md). Custom base images allow you to closely manage your dependencies and maintain tighter control over component versions when running training jobs.
 
 ## Prerequisites
 
 Run the code on either of these environments:
 
 * Azure Machine Learning compute instance (no downloads or installation necessary):
-  * Complete the [Quickstart: Get started with Azure Machine Learning](quickstart-create-resources.md) tutorial to create a dedicated notebook server preloaded with the SDK and the sample repository.
+  * Complete the [Quickstart: Get started with Azure Machine Learning](../quickstart-create-resources.md) tutorial to create a dedicated notebook server preloaded with the SDK and the sample repository.
 * Your own Jupyter Notebook server:
-  * Create a [workspace configuration file](how-to-configure-environment.md#local-and-dsvm-only-create-a-workspace-configuration-file).
+  * Create a [workspace configuration file](../how-to-configure-environment.md#local-and-dsvm-only-create-a-workspace-configuration-file).
   * Install the [Azure Machine Learning SDK](/python/api/overview/azure/ml/install). 
-  * Create an [Azure container registry](../container-registry/index.yml) or other Docker registry that's available on the internet.
+  * Create an [Azure container registry](../../container-registry/index.yml) or other Docker registry that's available on the internet.
 
 ## Set up a training experiment
 
 In this section, you set up your training experiment by initializing a workspace, defining your environment, and configuring a compute target.
 
 ### Initialize a workspace
 
-The [Azure Machine Learning workspace](concept-workspace.md) is the top-level resource for the service. It gives you a centralized place to work with all the artifacts that you create. In the Python SDK, you can access the workspace artifacts by creating a [`Workspace`](/python/api/azureml-core/azureml.core.workspace.workspace) object.
+The [Azure Machine Learning workspace](../concept-workspace.md) is the top-level resource for the service. It gives you a centralized place to work with all the artifacts that you create. In the Python SDK, you can access the workspace artifacts by creating a [`Workspace`](/python/api/azureml-core/azureml.core.workspace.workspace) object.
 
 Create a `Workspace` object from the config.json file that you created as a [prerequisite](#prerequisites).
 
@@ -105,15 +105,15 @@ fastai_env.docker.base_dockerfile = "./Dockerfile"
 > * Python 3.7+.
 > * A POSIX compliant shell available at /bin/sh is required in any container image used for training. 
 
-For more information about creating and managing Azure Machine Learning environments, see [Create and use software environments](how-to-use-environments.md). 
+For more information about creating and managing Azure Machine Learning environments, see [Create and use software environments](../how-to-use-environments.md). 
 
 ### Create or attach a compute target
 
-You need to create a [compute target](v1/concept-azure-machine-learning-architecture.md#compute-targets) for training your model. In this tutorial, you create `AmlCompute` as your training compute resource.
+You need to create a [compute target](concept-azure-machine-learning-architecture.md#compute-targets) for training your model. In this tutorial, you create `AmlCompute` as your training compute resource.
 
 Creation of `AmlCompute` takes a few minutes. If the `AmlCompute` resource is already in your workspace, this code skips the creation process.
 
-As with other Azure services, there are limits on certain resources (for example, `AmlCompute`) associated with the Azure Machine Learning service. For more information, see [Default limits and how to request a higher quota](how-to-manage-quotas.md).
+As with other Azure services, there are limits on certain resources (for example, `AmlCompute`) associated with the Azure Machine Learning service. For more information, see [Default limits and how to request a higher quota](../how-to-manage-quotas.md).
 
 ```python
 from azureml.core.compute import ComputeTarget, AmlCompute
@@ -148,7 +148,7 @@ 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/v1/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](v1/how-to-set-up-training-targets.md).
+Create a `ScriptRunConfig` resource to configure your job for running on the desired [compute target](how-to-set-up-training-targets.md).
 
 ```python
 from azureml.core import ScriptRunConfig
@@ -171,9 +171,9 @@ run.wait_for_completion(show_output=True)
 ```
 
 > [!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 an [.ignore file](concept-train-machine-learning-model.md#understand-what-happens-when-you-submit-a-training-job) or don't include it in the source directory. Instead, access your data by using a [datastore](/python/api/azureml-core/azureml.data).
+> 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 an [.ignore file](../concept-train-machine-learning-model.md#understand-what-happens-when-you-submit-a-training-job) or don't include it in the source directory. Instead, access your data by using a [datastore](/python/api/azureml-core/azureml.data).
 
 ## Next steps
 In this article, you trained a model by using a custom Docker image. See these other articles to learn more about Azure Machine Learning:
-* [Track run metrics](how-to-log-view-metrics.md) during training.
-* [Deploy a model](./how-to-deploy-custom-container.md) by using a custom Docker image.
+* [Track run metrics](../how-to-log-view-metrics.md) during training.
+* [Deploy a model](../how-to-deploy-custom-container.md) by using a custom Docker image.

articles/machine-learning/v1/toc.yml

@@ -241,7 +241,7 @@
       - name: PyTorch
         href: how-to-train-pytorch.md
       - name: Train with custom Docker image
-        href: ../how-to-train-with-custom-image.md
+        href: how-to-train-with-custom-image.md
       - name: Migrate from Estimators to ScriptRunConfig
         href: how-to-migrate-from-estimators-to-scriptrunconfig.md
       - name: Use Key Vault when training