Merge pull request #193732 from MicrosoftDocs/release-preview-aml-cli-v2-refresh

Release preview aml cli v2 refresh--scheduled release at 10am of 4/04

Author: huypub

.openpublishing.publish.config.json

@@ -441,6 +441,12 @@
       "branch": "main",
       "branch_mapping": {}
     },
+    {
+      "path_to_root": "azureml-examples-march-cli-preview",
+      "url": "https://github.com/azure/azureml-examples",
+      "branch": "march-cli-preview",
+      "branch_mapping": {}
+    },
     {
       "path_to_root": "terraform",
       "url": "https://github.com/Azure/terraform",

.openpublishing.redirection.json

@@ -43538,6 +43538,16 @@
       "redirect_url": "reference-yaml-endpoint-managed-online",
       "redirect_document_id": true
     },
+    {
+      "source_path_from_root": "/articles/machine-learning/reference-yaml-job-component.md",
+      "redirect_url": "reference-yaml-job-command",
+      "redirect_document_id": true
+    },
+    {
+      "source_path_from_root": "/articles/machine-learning/reference-yaml-dataset.md",
+      "redirect_url": "reference-yaml-data",
+      "redirect_document_id": true
+    },   
     {
       "source_path_from_root": "/articles/store-sendgrid-nodejs-how-to-send-email.md",
       "redirect_url": "https://docs.sendgrid.com/for-developers/partners/microsoft-azure-2021#create-a-twilio-sendgrid-account",

articles/machine-learning/azure-machine-learning-release-notes-cli-v2.md

@@ -8,20 +8,54 @@ ms.subservice: core
 ms.topic: reference
 ms.author: minxia
 author: mx-iao
-ms.date: 11/03/2021
-ms.custom: cliv2
+ms.date: 03/14/2022
 ---
 
 # Azure Machine Learning CLI (v2) release notes
 
 [!INCLUDE [cli v2](../../includes/machine-learning-cli-v2.md)]
+[!INCLUDE [cli v2 how to update](../../includes/machine-learning-cli-v2-update-note.md)]
 
 
 In this article, learn about Azure Machine Learning CLI (v2) releases.
 
 __RSS feed__: Get notified when this page is updated by copying and pasting the following URL into your feed reader:
 `https://docs.microsoft.com/api/search/rss?search=%22Azure+machine+learning+release+notes-v2%22&locale=en-us`
 
+## 2022-03-14
+
+### Azure Machine Learning CLI (v2) v2.2.0
+
+- `az ml job`
+  - For all job types, flattened the `code` section of the YAML schema. Instead of `code.local_path` to specify the path to the source code directory, it is now just `code`
+  - For all job types, changed the schema for defining data inputs to the job in the job YAML. Instead of specifying the data path using either the `file` or `folder` fields, use the `path` field to specify either a local path, a URI to a cloud path containing the data, or a reference to an existing registered Azure ML data asset via `path: azureml:<data_name>:<data_version>`. Also specify the `type` field to clarify whether the data source is a single file (`uri_file`) or a folder (`uri_folder`). If `type` field is omitted, it defaults to `type: uri_folder`. For more information, see the section of any of the [job YAML references](reference-yaml-job-command.md) that discuss the schema for specifying input data.
+  - In the [sweep job YAML schema](reference-yaml-job-sweep.md), changed the `sampling_algorithm` field from a string to an object in order to support additional configurations for the random sampling algorithm type
+  - Removed the component job YAML schema. With this release, if you want to run a command job inside a pipeline that uses a component, just specify the component to the `component` field of the command job YAML definition. 
+  - For all job types, added support for referencing the latest version of a nested asset in the job YAML configuration. When referencing a registered environment or data asset to use as input in a job, you can alias by latest version rather than having to explicitly specify the version. For example: `environment: azureml:AzureML-Minimal@latest`
+  - For pipeline jobs, introduced the `${{ parent }}` context for binding inputs and outputs between steps in a pipeline. For more information, see [Expression syntax for binding inputs and outputs between steps in a pipeline job](reference-yaml-core-syntax.md#binding-inputs-and-outputs-between-steps-in-a-pipeline-job).
+  - Added support for downloading named outputs of job via the `--output-name` argument for the `az ml job download` command
+- `az ml data`
+  - Deprecated the `az ml dataset` subgroup, now using `az ml data` instead
+  - There are two types of data that can now be created, either from a single file source (`type: uri_file`) or a folder (`type: uri_folder`). When creating the data asset, you can either specify the data source from a local file / folder or from a URI to a cloud path location. See the [data YAML schema](reference-yaml-data.md) for the full schema
+- `az ml environment`
+  - In the [environment YAML schema](reference-yaml-environment.md), renamed the `build.local_path` field to `build.path`
+  - Removed the `build.context_uri` field, the URI of the uploaded build context location will be accessible via `build.path` when the environment is returned
+- `az ml model`
+  - In the [model YAML schema](reference-yaml-model.md), `model_uri` and `local_path` fields removed and consolidated to one `path` field that can take either a local path or a cloud path URI. `model_format` field renamed to `type`; the default type is `custom_model`, but you can specify one of the other types (`mlflow_model`, `triton_model`) to use the model in no-code deployment scenarios
+  - For `az ml model create`, `--model-uri` and `--local-path` arguments removed and consolidated to one `--path` argument that can take either a local path or a cloud path URI 
+  - Added the `az ml model download` command to download a model's artifact files
+- `az ml online-deployment`
+  - In the [online deployment YAML schema](reference-yaml-deployment-managed-online.md), flattened the `code` section of the `code_configuration` field. Instead of `code_configuration.code.local_path` to specify the path to the source code directory containing the scoring files, it is now just `code_configuration.code`
+  - Added an `environment_variables` field to the online deployment YAML schema to support configuring environment variables for an online deployment
+- `az ml batch-deployment`
+  - In the [batch deployment YAML schema](reference-yaml-deployment-batch.md), flattened the `code` section of the `code_configuration` field. Instead of `code_configuration.code.local_path` to specify the path to the source code directory containing the scoring files, it is now just `code_configuration.code`
+- `az ml component`
+  - Flattened the `code` section of the [command component YAML schema](reference-yaml-component-command.md). Instead of `code.local_path` to specify the path to the source code directory, it is now just `code`
+  -  Added support for referencing the latest version of a registered environment to use in the component YAML configuration. When referencing a registered environment, you can alias by latest version rather than having to explicitly specify the version. For example: `environment: azureml:AzureML-Minimal@latest`
+  -  Renamed the component input and output type value from `path` to `uri_folder` for the `type` field when defining a component input or output
+- Removed the `delete` commands for assets (model, component, data, environment). The existing delete functionality is only a soft delete, so the `delete` commands will be reintroduced in a later release once hard delete is supported
+- Added support for archiving and restoring assets (model, component, data, environment) and jobs, e.g. `az ml model archive` and `az ml model restore`. You can now archive assets and jobs, which will hide the archived entity from list queries (e.g. `az ml model list`).
+
 ## 2021-10-04
 
 ### Azure Machine Learning CLI (v2) v2.0.2
@@ -48,18 +82,18 @@ __RSS feed__: Get notified when this page is updated by copying and pasting the
   - Added new `model_format` property to Model for no-code deployment scenarios
 - `az ml dataset`
   - Renamed `az ml data` subgroup to `az ml dataset`
-  - Updated [dataset YAML schema](reference-yaml-dataset.md)
+  - Updated dataset YAML schema
 - `az ml component`
   - Added the `az ml component` commands for managing Azure ML components
   - Added support for command components ([command component YAML schema](reference-yaml-component-command.md))
 - `az ml online-endpoint`
   - `az ml endpoint` subgroup split into two separate groups: `az ml online-endpoint` and `az ml batch-endpoint`
-  - Updated [online endpoint YAML schema](reference-yaml-endpoint-managed-online.md)
+  - Updated [online endpoint YAML schema](reference-yaml-endpoint-online.md)
   - Added support for local endpoints for dev/test scenarios
   - Added interactive VSCode debugging support for local endpoints (added the `--vscode-debug` flag to `az ml batch-endpoint create/update`)
 - `az ml online-deployment`
   - `az ml deployment` subgroup split into two separate groups: `az ml online-deployment` and `az ml batch-deployment`
-  - Updated [managed online deployment YAML schema](reference-yaml-endpoint-managed-online.md)
+  - Updated [managed online deployment YAML schema](reference-yaml-deployment-managed-online.md)
   - Added autoscaling support via integration with Azure Monitor Autoscale
   - Added support for updating multiple online deployment properties in the same update operation
   - Added support for performing concurrent operations on deployments under the same endpoint

articles/machine-learning/concept-endpoints.md

@@ -8,15 +8,16 @@ ms.subservice: mlops
 ms.topic: conceptual
 ms.author: seramasu
 author: rsethur
-ms.reviewer: laobri
+ms.reviewer: larryfr
 ms.custom: devplatv2, ignite-fall-2021
-ms.date: 12/22/2021
+ms.date: 03/31/2022
 #Customer intent: As an MLOps administrator, I want to understand what a managed endpoint is and why I need it.
 ---
 
 # What are Azure Machine Learning endpoints (preview)? 
 
 [!INCLUDE [preview disclaimer](../../includes/machine-learning-preview-generic-disclaimer.md)]
+[!INCLUDE [cli v2 how to update](../../includes/machine-learning-cli-v2-update-note.md)]
 
 Use Azure Machine Learning endpoints (preview) to streamline model deployments for both real-time and batch inference deployments. Endpoints provide a unified interface to invoke and manage model deployments across compute types.