Merge pull request #208261 from Blackmist/v1-v2

moving v1 articles to v1 folder

Author: v-regandowner

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

@@ -1,5 +1,20 @@
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+    {
+        "source_path_from_root": "/articles/machine-learning/how-to-prebuilt-docker-images-inference-python-extensibility.md",
+        "redirect_url": "/azure/machine-learning/v1/how-to-prebuilt-docker-images-inference-python-extensibility",
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…xtend-prebuilt-docker-image-inference.md …xtend-prebuilt-docker-image-inference.mdarticles/machine-learning/how-to-extend-prebuilt-docker-image-inference.md renamed to articles/machine-learning/v1/how-to-extend-prebuilt-docker-image-inference.md articles/machine-learning/how-to-extend-prebuilt-docker-image-inference.md renamed to articles/machine-learning/v1/how-to-extend-prebuilt-docker-image-inference.md

@@ -15,7 +15,7 @@ ms.custom: deploy, docker, prebuilt
 
 # Extend a prebuilt Docker image
 
-In some cases, the [prebuilt Docker images for model inference](concept-prebuilt-docker-images-inference.md) and [extensibility](./how-to-prebuilt-docker-images-inference-python-extensibility.md) solutions for Azure Machine Learning may not meet your inference service needs.
+In some cases, the [prebuilt Docker images for model inference](../concept-prebuilt-docker-images-inference.md) and [extensibility](./how-to-prebuilt-docker-images-inference-python-extensibility.md) solutions for Azure Machine Learning may not meet your inference service needs.
 
 In this case, you can use a Dockerfile to create a new image, using one of the prebuilt images as the starting point. By extending from an existing prebuilt Docker image, you can use the Azure Machine Learning network stack and libraries without creating an image from scratch.
 
@@ -26,7 +26,7 @@ Using a Dockerfile allows for full customization of the image before deployment.
 The main tradeoff for this approach is that an extra image build will take place during deployment, which slows down the deployment process. If you can use the [Python package extensibility](./how-to-prebuilt-docker-images-inference-python-extensibility.md) method, deployment will be faster.
 ## Prerequisites
 
-* An Azure Machine Learning workspace. For a tutorial on creating a workspace, see [Get started with Azure Machine Learning](quickstart-create-resources.md).
+* An Azure Machine Learning workspace. For a tutorial on creating a workspace, see [Get started with Azure Machine Learning](../quickstart-create-resources.md).
 * Familiarity with authoring a [Dockerfile](https://docs.docker.com/engine/reference/builder/).
 * Either a local working installation of [Docker](https://www.docker.com/), including the `docker` CLI, **OR** an Azure Container Registry (ACR) associated with your Azure Machine Learning workspace.
 
@@ -53,7 +53,7 @@ docker build -f <above dockerfile> -t <image_name>:<tag> .
 > [!TIP]
 > More details about `docker build` can be found here in the [Docker documentation](https://docs.docker.com/engine/reference/commandline/build/).
 
-If the `docker build` command isn't available locally, use the Azure Container Registry ACR for your Azure Machine Learning Workspace to build the Docker image in the cloud. For more information, see [Tutorial: Build and deploy container images with Azure Container Registry](../container-registry/container-registry-tutorial-quick-task.md).
+If the `docker build` command isn't available locally, use the Azure Container Registry ACR for your Azure Machine Learning Workspace to build the Docker image in the cloud. For more information, see [Tutorial: Build and deploy container images with Azure Container Registry](/azure/container-registry/container-registry-tutorial-quick-task).
 
 > [!IMPORTANT]
 > Microsoft recommends that you first validate that your Dockerfile works locally before trying to create a custom base image via Azure Container Registry.
@@ -144,8 +144,8 @@ ENV AZUREML_MODEL_DIR=/var/azureml-app/azureml-models
 
 To use a Dockerfile with the Azure Machine Learning Python SDK, see the following documents:
 
-* [Use your own local Dockerfile](how-to-use-environments.md#use-your-own-dockerfile)
-* [Use a pre-built Docker image and create a custom base image](how-to-use-environments.md#use-a-prebuilt-docker-image)
+* [Use your own local Dockerfile](../how-to-use-environments.md#use-your-own-dockerfile)
+* [Use a pre-built Docker image and create a custom base image](../how-to-use-environments.md#use-a-prebuilt-docker-image)
 
 To learn more about deploying a model, see [How to deploy a model](how-to-deploy-and-where.md).
 

…images-inference-python-extensibility.md …images-inference-python-extensibility.mdarticles/machine-learning/how-to-prebuilt-docker-images-inference-python-extensibility.md renamed to articles/machine-learning/v1/how-to-prebuilt-docker-images-inference-python-extensibility.md articles/machine-learning/how-to-prebuilt-docker-images-inference-python-extensibility.md renamed to articles/machine-learning/v1/how-to-prebuilt-docker-images-inference-python-extensibility.md

@@ -15,9 +15,9 @@ ms.custom: deploy, docker, prebuilt, sdkv1, event-tier1-build-2022
 
 # Python package extensibility for prebuilt Docker images (preview)
 
-[!INCLUDE [sdk v1](../../includes/machine-learning-sdk-v1.md)]
+[!INCLUDE [sdk v1](../../../includes/machine-learning-sdk-v1.md)]
 
-The [prebuilt Docker images for model inference](concept-prebuilt-docker-images-inference.md) contain packages for popular machine learning frameworks. There are two methods that can be used to add Python packages __without rebuilding the Docker image__:
+The [prebuilt Docker images for model inference](../concept-prebuilt-docker-images-inference.md) contain packages for popular machine learning frameworks. There are two methods that can be used to add Python packages __without rebuilding the Docker image__:
 
 * [Dynamic installation](#dynamic): This approach uses a [requirements](https://pip.pypa.io/en/stable/cli/pip_install/#requirements-file-format) file to automatically restore Python packages when the Docker container boots.
 
@@ -32,8 +32,8 @@ The [prebuilt Docker images for model inference](concept-prebuilt-docker-images-
 
 ## Prerequisites
 
-* An Azure Machine Learning workspace. For a tutorial on creating a workspace, see [Get started with Azure Machine Learning](quickstart-create-resources.md).
-* Familiarity with using Azure Machine Learning [environments](how-to-use-environments.md).
+* An Azure Machine Learning workspace. For a tutorial on creating a workspace, see [Get started with Azure Machine Learning](../quickstart-create-resources.md).
+* Familiarity with using Azure Machine Learning [environments](../how-to-use-environments.md).
 * Familiarity with [Where and how to deploy models](how-to-deploy-and-where.md) with Azure Machine Learning.
 
 <a id="dynamic"></a>
@@ -46,7 +46,7 @@ To extend your prebuilt docker container image through a requirements.txt, follo
 
 1. Create a `requirements.txt` file alongside your `score.py` script.
 2. Add **all** of your required packages to the `requirements.txt` file.
-3. Set the `AZUREML_EXTRA_REQUIREMENTS_TXT` environment variable in your Azure Machine Learning [environment](how-to-use-environments.md) to the location of `requirements.txt` file.
+3. Set the `AZUREML_EXTRA_REQUIREMENTS_TXT` environment variable in your Azure Machine Learning [environment](../how-to-use-environments.md) to the location of `requirements.txt` file.
 
 Once deployed, the packages will automatically be restored for your score script.
 
@@ -152,7 +152,7 @@ Here are some things that may cause this problem:
     | Solution  | Create a `requirements.txt` that installs the specified packages when the container starts. | Create a local Python environment with all of the dependencies. Mount this directory into container at runtime. |
     | Package Installation           | No extra installation (assuming pip already installed)                                                                                                          | Virtual environment or conda environment installation.                                                                                   |
     | Virtual environment Setup              | No extra setup of virtual environment required, as users can pull the current local user environment with pip freeze as needed to create the `requirements.txt`. | Need to set up a clean virtual environment, may take extra steps depending on the current user local environment.                        |
-    | [Debugging](how-to-inference-server-http.md)                 | Easy to set up and debug server, since dependencies are clearly listed. | Unclean virtual environment could cause problems when debugging of server. For example, it may not be clear if errors come from the environment or user code. |
+    | [Debugging](../how-to-inference-server-http.md)                 | Easy to set up and debug server, since dependencies are clearly listed. | Unclean virtual environment could cause problems when debugging of server. For example, it may not be clear if errors come from the environment or user code. |
     | Consistency during scaling out | Not consistent as dependent on external PyPi packages and users pinning their dependencies. These external downloads could be flaky.                                 | Relies solely on user environment, so no consistency issues.                                                                             |
 
 * Why are my `requirements.txt` and mounted dependencies directory not found in the container?
@@ -166,7 +166,7 @@ Here are some things that may cause this problem:
 
 ## Best Practices
 
-* Refer to the [Load registered model](./v1/how-to-deploy-advanced-entry-script.md#load-registered-models) docs. When you register a model directory, don't include your scoring script, your mounted dependencies directory, or `requirements.txt` within that directory.
+* Refer to the [Load registered model](how-to-deploy-advanced-entry-script.md#load-registered-models) docs. When you register a model directory, don't include your scoring script, your mounted dependencies directory, or `requirements.txt` within that directory.
 
 
 * For more information on how to load a registered or local model, see [Where and how to deploy](how-to-deploy-and-where.md?tabs=azcli#define-a-dummy-entry-script).
@@ -180,6 +180,6 @@ For example, if both the requirements.txt and score script is in **my_folder**,
 
 ## Next steps
 
-To learn more about deploying a model, see [How to deploy a model](./v1/how-to-deploy-and-where.md).
+To learn more about deploying a model, see [How to deploy a model](how-to-deploy-and-where.md).
 
 To learn how to troubleshoot prebuilt docker image deployments, see [how to troubleshoot prebuilt Docker image deployments](how-to-troubleshoot-prebuilt-docker-image-inference.md).

…shoot-prebuilt-docker-image-inference.md …shoot-prebuilt-docker-image-inference.mdarticles/machine-learning/how-to-troubleshoot-prebuilt-docker-image-inference.md renamed to articles/machine-learning/v1/how-to-troubleshoot-prebuilt-docker-image-inference.md articles/machine-learning/how-to-troubleshoot-prebuilt-docker-image-inference.md renamed to articles/machine-learning/v1/how-to-troubleshoot-prebuilt-docker-image-inference.md

@@ -24,7 +24,7 @@ Learn how to troubleshoot problems you may see when using prebuilt docker images
 If model deployment fails, you won't see logs in [Azure Machine Learning studio](https://ml.azure.com/) and `service.get_logs()` will return None.
 If there is a problem in the init() function of score.py, `service.get_logs()` will return logs for the same.
 
-So you'll need to run the container locally using one of the commands shown below and replace `<MCR-path>` with an image path. For a list of the images and paths, see [Prebuilt Docker images for inference](concept-prebuilt-docker-images-inference.md).
+So you'll need to run the container locally using one of the commands shown below and replace `<MCR-path>` with an image path. For a list of the images and paths, see [Prebuilt Docker images for inference](../concept-prebuilt-docker-images-inference.md).
 
 ### Mounting extensibility solution
 
@@ -44,11 +44,11 @@ docker run -it -v $(pwd):/var/azureml-app -e AZUREML_EXTRA_REQUIREMENTS_TXT="req
 
 ## Enable local debugging
 
-The local inference server allows you to quickly debug your entry script (`score.py`). In case the underlying score script has a bug, the server will fail to initialize or serve the model. Instead, it will throw an exception & the location where the issues occurred. [Learn more about Azure Machine Learning inference HTTP Server](how-to-inference-server-http.md)
+The local inference server allows you to quickly debug your entry script (`score.py`). In case the underlying score script has a bug, the server will fail to initialize or serve the model. Instead, it will throw an exception & the location where the issues occurred. [Learn more about Azure Machine Learning inference HTTP Server](../how-to-inference-server-http.md)
 
 ## For common model deployment issues
 
-For problems when deploying a model from Azure Machine Learning to Azure Container Instances (ACI) or Azure Kubernetes Service (AKS), see [Troubleshoot model deployment](./v1/how-to-troubleshoot-deployment.md).
+For problems when deploying a model from Azure Machine Learning to Azure Container Instances (ACI) or Azure Kubernetes Service (AKS), see [Troubleshoot model deployment](how-to-troubleshoot-deployment.md).
 
 ## init() or run() failing to write a file
 

…bility/dynamic-install-python-extend.svg …bility/dynamic-install-python-extend.svgarticles/machine-learning/media/how-to-prebuilt-docker-images-inference-python-extensibility/dynamic-install-python-extend.svg renamed to articles/machine-learning/v1/media/how-to-prebuilt-docker-images-inference-python-extensibility/dynamic-install-python-extend.svg articles/machine-learning/media/how-to-prebuilt-docker-images-inference-python-extensibility/dynamic-install-python-extend.svg renamed to articles/machine-learning/v1/media/how-to-prebuilt-docker-images-inference-python-extensibility/dynamic-install-python-extend.svg

@@ -233,11 +233,11 @@
     - name: Prebuilt Docker images
       items: 
         - name: Python extensibility
-          href: ../how-to-prebuilt-docker-images-inference-python-extensibility.md
+          href: how-to-prebuilt-docker-images-inference-python-extensibility.md
         - name: Dockerfile extensibility
-          href: ../how-to-extend-prebuilt-docker-image-inference.md
+          href: how-to-extend-prebuilt-docker-image-inference.md
         - name: Troubleshoot prebuilt docker images
-          href: ../how-to-troubleshoot-prebuilt-docker-image-inference.md
+          href: how-to-troubleshoot-prebuilt-docker-image-inference.md
     - name: Monitor web services
       items: 
         - name: Collect & evaluate model data