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创建和管理 Azure 机器学习服务工作区Create and manage Azure Machine Learning service workspaces

本文将介绍如何在 Azure 门户中针对 Azure 机器学习服务创建、查看和删除 Azure 机器学习服务工作区In this article, you'll create, view, and delete Azure Machine Learning service workspaces in the Azure portal for Azure Machine Learning service. 您还可以创建和删除工作区使用 CLI使用 Python 代码通过 VS Code 扩展You can also create and delete workspaces using the CLI, with Python code or via the VS Code extension.

创建工作区Create a workspace

必须有 Azure 订阅,才能创建工作区。To create a workspace, you need an Azure subscription. 如果还没有 Azure 订阅,请在开始前创建免费帐户。If you don’t have an Azure subscription, create a free account before you begin. 立即试用 Azure 机器学习服务免费版或付费版Try the free or paid version of Azure Machine Learning service today.

  1. 使用将所使用的 Azure 订阅的凭据登录到 Azure 门户Sign in to the Azure portal by using the credentials for the Azure subscription you use.

    Azure 门户

  2. 在门户左上角选择“创建资源” 。In the upper-left corner of the portal, select Create a resource.

    在 Azure 门户中创建资源

  3. 在搜索栏中输入“机器学习” 。In the search bar, enter Machine Learning. 选择搜索结果“机器学习服务工作区” 。Select the Machine Learning service workspace search result.

    搜索工作区

  4. 在“ML 服务工作区”窗格中,选择“创建”以开始 。In the ML service workspace pane, select Create to begin.

    “创建”按钮

  5. 在“机器学习服务工作区”窗格中,配置工作区 。In the ML service workspace pane, configure your workspace.

    字段Field 说明Description
    工作区名称Workspace name 输入用于标识工作区的唯一名称。Enter a unique name that identifies your workspace. 本示例使用 docs-ws 。In this example, we use docs-ws. 名称在整个资源组中必须唯一。Names must be unique across the resource group. 使用易于记忆且区别于其他人所创建工作区的名称。Use a name that's easy to recall and differentiate from workspaces created by others.
    订阅Subscription 选择要使用的 Azure 订阅。Select the Azure subscription that you want to use.
    资源组Resource group 使用订阅中的现有资源组,或者输入一个名称,创建新的资源组。Use an existing resource group in your subscription, or enter a name to create a new resource group. 资源组保存 Azure 解决方案的相关资源。A resource group holds related resources for an Azure solution. 本示例使用 docs-aml 。In this example, we use docs-aml.
    位置Location 选择最靠近用户和数据资源的位置。Select the location closest to your users and the data resources. 这是用于创建工作区的位置。This location is where the workspace is created.

    创建工作区

  6. 若要开始创建过程,请选择“查看 + 创建” 。To start the creation process, select Review + Create.

    创建

  7. 查看工作区配置。Review your workspace configuration. 如果配置正确,请选择“创建” 。If it is correct, select Create. 创建工作区可能需要一些时间。It can take a few moments to create the workspace.

    创建

  8. 若要检查部署状态,请选择工具栏上的“通知”图标(铃铛) 。To check on the status of the deployment, select the Notifications icon, bell, on the toolbar.

  9. 完成创建后,会显示部署成功消息。When the process is finished, a deployment success message appears. 通知部分也会出现该消息。It's also present in the notifications section. 若要查看新工作区,请选择“转到资源” 。To view the new workspace, select Go to resource.

    工作区创建状态

查看工作区View a workspace

  1. 选择门户左上角的“所有服务” 。In top left corner of the portal, select All services.

  2. 在中所有服务筛选器字段中,键入机器学习服务In the All services filter field, type machine learning service.

  3. 选择机器学习服务工作区Select Machine Learning service workspaces.

    Azure 机器学习服务工作区中搜索

  4. 浏览筛选出的工作区列表。Look through the list of workspaces found. 筛选依据可包括订阅、资源组和位置。You can filter based on subscription, resource groups, and locations.

  5. 选择工作区以显示其属性。Select a workspace to display its properties. 工作区属性Workspace properties

创建工作区Delete a workspace

单击要删除的工作区顶部的“删除”按钮。Use the Delete button at the top of the workspace you wish to delete.

“删除”按钮

清理资源Clean up resources

重要

已创建的资源可以用作其他 Azure 机器学习服务教程和操作方法文章的先决条件。The resources you created can be used as prerequisites to other Azure Machine Learning service tutorials and how-to articles.

如果不打算使用已创建的资源,请删除它们,以免产生任何费用:If you don't plan to use the resources you created, delete them, so you don't incur any charges:

  1. 在 Azure 门户中,选择最左侧的“资源组” 。In the Azure portal, select Resource groups on the far left.

    在 Azure 门户中删除

  2. 从列表中选择已创建的资源组。From the list, select the resource group you created.

  3. 选择“删除资源组” 。Select Delete resource group.

  4. 输入资源组名称。Enter the resource group name. 然后选择“删除” 。Then select Delete.

后续步骤Next steps

学习整个教程,了解如何通过 Azure 机器学习服务使用工作区来生成、定型和部署模型。Follow the full-length tutorial to learn how to use a workspace to build, train, and deploy models with Azure Machine Learning service.