Merge pull request #268905 from Blackmist/environment-update

Environment update

Author: prmerger-automator[bot]

articles/machine-learning/concept-azure-machine-learning-v2.md

@@ -272,13 +272,13 @@ In custom environments, you're responsible for setting up your environment and i
 
 ### [Python SDK](#tab/sdk)
 
-To create an environment using Python SDK v2, see [Create an environment](how-to-manage-environments-v2.md?tabs=python#create-an-environment).
+To create an environment using Python SDK v2, see [Create an environment](how-to-manage-environments-v2.md?tabs=python#create-a-custom-environment).
 
 This [Jupyter notebook](https://github.com/Azure/azureml-examples/blob/main/sdk/python/assets/environment/environment.ipynb) shows more ways to create custom environments using SDK v2.
 
 ### [Azure CLI](#tab/cli)
 
-To create an environment using CLI v2, see [Create an environment](how-to-manage-environments-v2.md?tabs=cli#create-an-environment).
+To create an environment using CLI v2, see [Create an environment](how-to-manage-environments-v2.md?tabs=cli#create-a-custom-environment).
 
 For more information, see [environment YAML schema](reference-yaml-environment.md).
 

articles/machine-learning/concept-environments.md

@@ -42,7 +42,7 @@ You use system-managed environments when you want [conda](https://conda.io/docs/
 
 You can create environments from clients like the Azure Machine Learning Python SDK, Azure Machine Learning CLI, Environments page in Azure Machine Learning studio, and [VS Code extension](how-to-manage-resources-vscode.md#create-environment). Every client allows you to customize the base image, Dockerfile, and Python layer if needed.
 
-For specific code samples, see the "Create an environment" section of [How to use environments](how-to-manage-environments-v2.md#create-an-environment). 
+For specific code samples, see the "Create an environment" section of [How to use environments](how-to-manage-environments-v2.md#create-a-custom-environment). 
 
 Environments are also easily managed through your workspace, which allows you to:
 

articles/machine-learning/how-to-deploy-online-endpoints.md

@@ -408,15 +408,15 @@ In this example, we specify the `path` (where to upload files from) inline. The
 
 For registration, you can extract the YAML definitions of `model` and `environment` into separate YAML files and use the commands `az ml model create` and `az ml environment create`. To learn more about these commands, run `az ml model create -h` and `az ml environment create -h`.
 
-For more information on registering your model as an asset, see [Register your model as an asset in Machine Learning by using the CLI](how-to-manage-models.md#register-your-model-as-an-asset-in-machine-learning-by-using-the-cli). For more information on creating an environment, see [Manage Azure Machine Learning environments with the CLI & SDK (v2)](how-to-manage-environments-v2.md#create-an-environment).
+For more information on registering your model as an asset, see [Register your model as an asset in Machine Learning by using the CLI](how-to-manage-models.md#register-your-model-as-an-asset-in-machine-learning-by-using-the-cli). For more information on creating an environment, see [Manage Azure Machine Learning environments with the CLI & SDK (v2)](how-to-manage-environments-v2.md#create-a-custom-environment).
 
 # [Python](#tab/python)
 
 In this example, we specify the `path` (where to upload files from) inline. The SDK automatically uploads the files and registers the model and environment. As a best practice for production, you should register the model and environment and specify the registered name and version separately in the codes.
 
 For more information on registering your model as an asset, see [Register your model as an asset in Machine Learning by using the SDK](how-to-manage-models.md#register-your-model-as-an-asset-in-machine-learning-by-using-the-sdk).
 
-For more information on creating an environment, see [Manage Azure Machine Learning environments with the CLI & SDK (v2)](how-to-manage-environments-v2.md#create-an-environment).
+For more information on creating an environment, see [Manage Azure Machine Learning environments with the CLI & SDK (v2)](how-to-manage-environments-v2.md#create-a-custom-environment).
 
 # [Studio](#tab/azure-studio)
 

articles/machine-learning/how-to-manage-environments-v2.md

@@ -71,13 +71,13 @@ To connect to the workspace, you need identifier parameters - a subscription, re
 
 There are two types of environments in Azure Machine Learning: curated and custom environments. Curated environments are predefined environments containing popular ML frameworks and tooling. Custom environments are user-defined and can be created via `az ml environment create`.
 
-Curated environments are provided by Azure Machine Learning and are available in your workspace by default. Azure Machine Learning routinely updates these environments with the latest framework version releases and maintains them for bug fixes and security patches. They're backed by cached Docker images, which reduce job preparation cost and model deployment time.
+Curated environments are provided by Azure Machine Learning and are available by default. Azure Machine Learning routinely updates these environments with the latest framework version releases and maintains them for bug fixes and security patches. They're backed by cached Docker images, which reduce job preparation cost and model deployment time.
 
-You can use these curated environments out of the box for training or deployment by referencing a specific environment using the `azureml:<curated-environment-name>:<version>` or `azureml:<curated-environment-name>@latest` syntax. You can also use them as reference for your own custom environments by modifying the Dockerfiles that back these curated environments.
+You can use these curated environments out of the box for training or deployment by referencing a specific version or latest version of the environment. Use the following syntax: `azureml://registries/azureml/environment/<curated-environment-name>/versions/<version-number>` or `azureml://registries/azureml/environment/<curated-environment-name>/labels/latest`. You can also use them as a reference for your own custom environments by modifying the Dockerfiles that back these curated environments.
 
 You can see the set of available curated environments in the Azure Machine Learning studio UI, or by using the CLI (v2) via `az ml environment list`.
 
-## Create an environment
+## Create a custom environment
 
 You can define an environment from a Docker image, a Docker build context, and a conda specification with Docker image. 
 
@@ -297,7 +297,7 @@ ml_client.environments.archive(name="docker-image-example", version="1")
 
 # [Azure CLI](#tab/cli)
 
-To use an environment for a training job, specify the `environment` field of the job YAML configuration. You can either reference an existing registered Azure Machine Learning environment via `environment: azureml:<environment-name>:<environment-version>` or `environment: azureml:<environment-name>@latest` (to reference the latest version of an environment), or define an environment specification inline. If defining an environment inline, don't specify the `name` and `version` fields, as these environments are treated as "unregistered" environments and aren't tracked in your environment asset registry.
+To use a **custom environment** for a training job, specify the `environment` field of the job YAML configuration. You can either reference an existing registered Azure Machine Learning environment via `environment: azureml:<environment-name>:<environment-version>` or `environment: azureml:<environment-name>@latest` (to reference the latest version of an environment), or define an environment specification inline. If defining an environment inline, don't specify the `name` and `version` fields, as these environments are treated as "unregistered" environments and aren't tracked in your environment asset registry.
 
 # [Python SDK](#tab/python)
 

articles/machine-learning/how-to-safely-rollout-online-endpoints.md

@@ -252,7 +252,7 @@ In the `blue-deployment.yaml` file, we specify the `path` (where to upload files
 
 For registration, you can extract the YAML definitions of `model` and `environment` into separate YAML files and use the commands `az ml model create` and `az ml environment create`. To learn more about these commands, run `az ml model create -h` and `az ml environment create -h`.
 
-For more information on registering your model as an asset, see [Register your model as an asset in Machine Learning by using the CLI](how-to-manage-models.md#register-your-model-as-an-asset-in-machine-learning-by-using-the-cli). For more information on creating an environment, see [Manage Azure Machine Learning environments with the CLI & SDK (v2)](how-to-manage-environments-v2.md#create-an-environment).
+For more information on registering your model as an asset, see [Register your model as an asset in Machine Learning by using the CLI](how-to-manage-models.md#register-your-model-as-an-asset-in-machine-learning-by-using-the-cli). For more information on creating an environment, see [Manage Azure Machine Learning environments with the CLI & SDK (v2)](how-to-manage-environments-v2.md#create-a-custom-environment).
 
 # [Python](#tab/python)
 
@@ -284,7 +284,7 @@ The following table describes the attributes of a `deployment`:
 
     For more information on registering your model as an asset, see [Register your model as an asset in Machine Learning by using the SDK](how-to-manage-models.md#register-your-model-as-an-asset-in-machine-learning-by-using-the-sdk).
 
-    For more information on creating an environment, see [Manage Azure Machine Learning environments with the CLI & SDK (v2)](how-to-manage-environments-v2.md#create-an-environment).
+    For more information on creating an environment, see [Manage Azure Machine Learning environments with the CLI & SDK (v2)](how-to-manage-environments-v2.md#create-a-custom-environment).
 
     > [!NOTE]
     > To create a deployment for a Kubernetes online endpoint, use the `KubernetesOnlineDeployment` class.

articles/machine-learning/how-to-train-distributed-gpu.md

@@ -41,7 +41,7 @@ Azure Machine Learning offers an [MPI job](https://www.mcs.anl.gov/research/proj
 
 To run distributed training using MPI, follow these steps:
 
-1. Use an Azure Machine Learning environment with the preferred deep learning framework and MPI. Azure Machine Learning provides [curated environments](resource-curated-environments.md) for popular frameworks. Or [create a custom environment](how-to-manage-environments-v2.md#create-an-environment) with the preferred deep learning framework and MPI.
+1. Use an Azure Machine Learning environment with the preferred deep learning framework and MPI. Azure Machine Learning provides [curated environments](resource-curated-environments.md) for popular frameworks. Or [create a custom environment](how-to-manage-environments-v2.md#create-a-custom-environment) with the preferred deep learning framework and MPI.
 1. Define  a `command` with `instance_count`. `instance_count` should be equal to the number of GPUs per node for per-process-launch, or set to 1 (the default) for per-node-launch if the user script is responsible for launching the processes per node.
 1. Use the `distribution` parameter of the `command` to specify settings for `MpiDistribution`.