在 Dataproc 集群上安装并运行 Jupyter 笔记本

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目标

本教程介绍如何在新集群上安装 Dataproc Jupyter 组件，然后使用 Dataproc 组件网关从本地浏览器连接到在集群上运行的 Jupyter 笔记本界面。

注意：运行本教程将产生 Google Cloud 费用 - 请参阅 Dataproc 价格。

费用

在本文档中，您将使用 Google Cloud的以下收费组件：

• Dataproc
• Cloud Storage

您可使用价格计算器根据您的预计使用情况来估算费用。

新 Google Cloud 用户可能有资格申请免费试用。

准备工作

如果您尚未创建项目和 Cloud Storage 存储桶，请先创建这些资源。 Google Cloud

1. 设置项目

   Sign in to your Google Cloud account. If you're new to Google Cloud, create an account to evaluate how our products perform in real-world scenarios. New customers also get $300 in free credits to run, test, and deploy workloads.

   In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

   Note: If you don't plan to keep the resources that you create in this procedure, create a project instead of selecting an existing project. After you finish these steps, you can delete the project, removing all resources associated with the project.

   Go to project selector

   Make sure that billing is enabled for your Google Cloud project.

   Enable the Dataproc, Compute Engine, and Cloud Storage APIs.

   Enable the APIs

   Install the Google Cloud CLI.

   If you're using an external identity provider (IdP), you must first sign in to the gcloud CLI with your federated identity.

   To initialize the gcloud CLI, run the following command:

   gcloud init

   In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

   Note: If you don't plan to keep the resources that you create in this procedure, create a project instead of selecting an existing project. After you finish these steps, you can delete the project, removing all resources associated with the project.

   Go to project selector

   Make sure that billing is enabled for your Google Cloud project.

   Enable the Dataproc, Compute Engine, and Cloud Storage APIs.

   Enable the APIs

   Install the Google Cloud CLI.

   If you're using an external identity provider (IdP), you must first sign in to the gcloud CLI with your federated identity.

   To initialize the gcloud CLI, run the following command:

   gcloud init

2. 在您的项目中创建 Cloud Storage 存储分区，以存储您在本教程中创建的任何笔记本。

   1. In the Google Cloud console, go to the Cloud Storage Buckets page.

      Go to Buckets

   2. Click Create.
   3. On the Create a bucket page, enter your bucket information. To go to the next step, click Continue.
      1. In the Get started section, do the following:
         • Enter a globally unique name that meets the bucket naming requirements.
         • To add a bucket label, expand the Labels section (), click Add label, and specify a key and a value for your label.
      2. In the Choose where to store your data section, do the following:
         1. Select a Location type.
         2. Choose a location where your bucket's data is permanently stored from the Location type drop-down menu.
            • If you select the dual-region location type, you can also choose to enable turbo replication by using the relevant checkbox.
         3. To set up cross-bucket replication, select Add cross-bucket replication via Storage Transfer Service and follow these steps:

            Set up cross-bucket replication

            1. In the Bucket menu, select a bucket.

            2. In the Replication settings section, click Configure to configure settings for the replication job.

               The Configure cross-bucket replication pane appears.

               • To filter objects to replicate by object name prefix, enter a prefix that you want to include or exclude objects from, then click Add a prefix.
               • To set a storage class for the replicated objects, select a storage class from the Storage class menu. If you skip this step, the replicated objects will use the destination bucket's storage class by default.
               • Click Done.
      3. In the Choose how to store your data section, do the following:
         1. Select a default storage class for the bucket or Autoclass for automatic storage class management of your bucket's data.
         2. To enable hierarchical namespace, in the Optimize storage for data-intensive workloads section, select Enable hierarchical namespace on this bucket.
            Note: You cannot enable hierarchical namespace in existing buckets.
      4. In the Choose how to control access to objects section, select whether or not your bucket enforces public access prevention, and select an access control method for your bucket's objects.
         Note: You cannot change the Prevent public access setting if this setting is enforced at an organization policy.
      5. In the Choose how to protect object data section, do the following:
         • Select any of the options under Data protection that you want to set for your bucket.
           • To enable soft delete, click the Soft delete policy (For data recovery) checkbox, and specify the number of days you want to retain objects after deletion.
           • To set Object Versioning, click the Object versioning (For version control) checkbox, and specify the maximum number of versions per object and the number of days after which the noncurrent versions expire.
           • To enable the retention policy on objects and buckets, click the Retention (For compliance) checkbox, and then do the following:
             • To enable Object Retention Lock, click the Enable object retention checkbox.
             • To enable Bucket Lock, click the Set bucket retention policy checkbox, and choose a unit of time and a length of time for your retention period.
         • To choose how your object data will be encrypted, expand the Data encryption section (), and select a Data encryption method.
   4. Click Create.
   您的笔记本将存储在 Cloud Storage 中的 gs://bucket-name/notebooks/jupyter 下。

创建集群并安装 Jupyter 组件

使用已安装的 Jupyter 组件创建集群。

注意：创建集群时，指定您在准备工作第 2 步中创建的存储分区的名称（仅指定名称作为存储分区 staging bucket），如需设置暂存存储分区，请参阅 Dataproc 暂存存储分区和临时存储分区。您的笔记本将存储在 Cloud Storage 中的 gs://bucket-name/notebooks/jupyter 下。

打开 Jupyter 和 JupyterLab 界面

在 Google Cloud 控制台中点击 Google Cloud 控制台组件网关链接，以打开集群主节点上运行的 Jupyter 笔记本或 JupyterLab 界面。

Jupyter 实例显示的顶级目录是一个虚拟目录，可让您查看 Cloud Storage 存储分区或本地文件系统的内容。您可以通过点击集群中 Cloud Storage 的 GCS 链接或集群中主节点的本地文件系统的本地磁盘来选择位置。

1. 点击 GCS 链接。Jupyter 笔记本网页界面会显示存储在 Cloud Storage 存储分区中的笔记本，包括您在本教程中创建的所有笔记本。

   

清理

完成本教程后，您可以清理您创建的资源，让它们停止使用配额，以免产生费用。以下部分介绍如何删除或关闭这些资源。

删除项目

为了避免产生费用，最简单的方法是删除您为本教程创建的项目。

要删除项目，请执行以下操作：

注意：删除项目会产生以下影响：

• 项目中的所有内容都会被删除。如果您将现有项目用于本文档中的任务，则删除该项目后，还将删除您已在该项目中完成的任何其他工作。
• 自定义项目 ID 丢失。创建此项目时，您可能创建了要在将来使用的自定义项目 ID。要保留使用该项目 ID 的网址（例如 appspot.com 网址），请删除项目内的所选资源，而不是删除整个项目。

如果您打算探索多个架构、教程或快速入门，则重复使用项目可以帮助您避免超出项目配额限制。

1. In the Google Cloud console, go to the Manage resources page.

   Go to Manage resources

2. In the project list, select the project that you want to delete, and then click Delete.
3. In the dialog, type the project ID, and then click Shut down to delete the project.

删除集群

• 如需删除您的集群，请输入以下命令：

  gcloud dataproc clusters delete cluster-name \
      --region=${REGION}
  

删除存储分区

• 如需删除您在准备工作第 2 步中创建的 Cloud Storage 存储分区，包括存储在存储分区中的笔记本，请运行以下命令：

  gcloud storage rm gs://${BUCKET_NAME} --recursive
  

后续步骤

• 请参阅 Jupyter/IPython 笔记本快速入门指南