Merge branch 'register-component-ui' of https://github.com/likebupt/azure-docs-pr into amlregcomp0423

Author: lgayhardt

articles/machine-learning/how-to-create-component-pipelines-ui.md

@@ -8,7 +8,7 @@ ms.subservice: core
 ms.author: keli19
 author: likebupt
 ms.reviewer: lagayhar
-ms.date:  05/10/2022
+ms.date:  03/27/2022
 ms.topic: how-to
 ms.custom: devplatv2, designer, event-tier1-build-2022, ignite-2022
 ---
@@ -34,8 +34,6 @@ In this article, you'll learn how to create and run [machine learning pipelines]
     cd azureml-examples/cli/jobs/pipelines-with-components/
     ```
 
-## Register component in your workspace
-
 >[!Note]
 > Designer supports two type of components, classic prebuilt components and custom components. These two types of components are not compatible.  
 >
@@ -44,23 +42,34 @@ In this article, you'll learn how to create and run [machine learning pipelines]
 >
 >Custom components allow you to provide your own code as a component. It supports sharing across workspaces and seamless authoring across Studio, CLI, and SDK interfaces.
 >
->This article applies to custom components. 
+>This article applies to **custom components**. 
+
+## Register component in your workspace
 
-To build pipeline using components in UI, you need to register components to your workspace first. You can use CLI or SDK to register components to your workspace, so that you can share and reuse the component within the workspace. Registered components support automatic versioning so you can update the component but assure that pipelines that require an older version will continue to work.  
+To build pipeline using components in UI, you need to register components to your workspace first. You can use UI, CLI or SDK to register components to your workspace, so that you can share and reuse the component within the workspace. Registered components support automatic versioning so you can update the component but assure that pipelines that require an older version will continue to work.  
 
-In the example below take using CLI for example. If you want to learn more about how to build a component, see [Create and run pipelines using components with  CLI](how-to-create-component-pipelines-cli.md).
+The example below will use UI to register components, and the component source files are in the [`cli/jobs/pipelines-with-components/basics/1b_e2e_registered_components` directory](https://github.com/Azure/azureml-examples/tree/main/cli/jobs/pipelines-with-components/basics/1b_e2e_registered_components) of the [`azureml-examples` repository](https://github.com/Azure/azureml-examples). You need to clone the repo to local at first.
 
-1. From the `cli/jobs/pipelines-with-components/basics` directory of the [`azureml-examples` repository](https://github.com/Azure/azureml-examples), navigate to the `1b_e2e_registered_components` subdirectory.
+1. In you AzureML workspace, navigate to **Components** page and select **New Component**. 
 
-1. Register the components to Azure Machine Learning workspace using following commands. Learn more about [ML components](concept-component.md).
+:::image type="content" source="./media/how-to-create-component-pipelines-ui/register-component-entry-button.png" alt-text="Screenshot showing register entry button in component page." lightbox ="./media/how-to-create-component-pipelines-ui/register-component-entry-button.png":::
 
-    ```CLI
-    az ml component create --file train.yml
-    az ml component create --file score.yml
-    az ml component create --file eval.yml
-    ```
+1. This example will use `train.yml` [in the directory](https://github.com/Azure/azureml-examples/tree/main/cli/jobs/pipelines-with-components/basics/1b_e2e_registered_components). The YAML file defines the name, type, interface including inputs and outputs, code, environment and command of this component. The code of this component `train.py` is under `./train_src` folder which describes the execution logic of this component. You can learn more about the component schema [here](reference-yaml-component-command.md).
+
+>[!Note]
+> When register components in UI, `code` defined in the component YAML file can only point to the current folder where YAML file locates or the subfolders, which means you cannot specify `../` for `code` as UI cannot recognize the parent directory.
+> `additional_includes` can only point to the current or sub folder.
+
+
+1. Select Upload from **Folder**, and select the `1b_e2e_registered_components` foler to upload. Select `train.yml` from the drop down list below.
+
+:::image type="content" source="./media/how-to-create-component-pipelines-ui/upload-from-local-folder.png" alt-text="Screenshot showing upload from local folder." lightbox ="./media/how-to-create-component-pipelines-ui/upload-from-local-folder.png":::
+
+1. Select **Next** in the bottom, and you can confirm the details of this component. Once you have confirmed, select **Create** to finish the registration process.
+
+1. Repeat the steps above to register Score and Eval component using `score.yml` and `eval.yml` as well.
 
-1. After register component successfully, you can see your component in the studio UI.
+1. After register the 3 components successfully, you can see your components in the studio UI.
 
 :::image type="content" source="./media/how-to-create-component-pipelines-ui/component-page.png" alt-text="Screenshot showing registered component in component page." lightbox ="./media/how-to-create-component-pipelines-ui/component-page.png":::
 

articles/machine-learning/media/how-to-create-component-pipelines-ui/register-component-entry-button.png

@@ -121,6 +121,15 @@ If no value is specified at runtime, `learning_rate` and `learning_rate_schedule
 python train.py --training_data some_input_path --max_epocs 10 --learning_rate 0.01 --learning_rate_schedule time-based --model_output some_output_path
 ```
 
+## Common errors and recommendation
+
+Following are some common errors and corresponding recommended suggestions when you define a component.
+
+| Key | Errors | Recommendation | 
+| --- | ---- | ----------- |
+|command|1. Only optional inputs can be in `$[[]]`<br>2. Using `\` to make a new line is not supported in command.<br>3. Inputs or outputs are not found.|1. Check that all the inputs or outputs used in command are already defined in the `inputs` and `outputs` sections, and use the correct format for optional inputs `$[[]]` or required ones `${{}}`.<br>2. Do not use `\` to make a new line.|
+|environment|1. No definition exists for environment `{envName}` version `{envVersion}`. <br>2. No environment exists for name `{envName}`, version `{envVersion}`.<br>3. Could not find asset with ID `{envAssetId}`. |1. Make sure the environment name and version you refer in the component definition exists. <br>2. You need to specify the version if you refer to a registered environment.|
+|inputs/outputs|1. Inputs/outputs names conflict with system reserved parameters.<br>2. Deplicated names of inputs or outputs.|1. Do not use any of these reserved parameters as your inputs/outputs name: `path`, `ld_library_path`, `user`, `logname`, `home`, `pwd`, `shell`.<br>2. Make sure names of inputs and outputs are not duplicated.|
 
 ## Next steps