서버리스 Dataproc Spark에서 노트북 파일을 실행하려면 다음 요구사항을 참조하세요.
서버리스 Dataproc 세션은 관리형 노트북 인스턴스와 동일한 리전에서 실행되어야 합니다.
OS 로그인 필요(constraints/compute.requireOsLogin) 제약조건은 프로젝트에 대해 사용 설정되지 않아야 합니다. 조직의 OS 로그인 관리를 참조하세요.
서버리스 Dataproc에서 노트북 파일을 실행하려면 특정 권한이 있는 서비스 계정을 제공해야 합니다. 이러한 권한을 기본 서비스 계정에 부여하거나 커스텀 서비스 계정을 제공할 수 있습니다.
이 페이지의 권한 섹션을 참조하세요.
서버리스 Dataproc Spark 세션은 Virtual Private Cloud(VPC) 네트워크를 사용하여 워크로드를 실행합니다.
VPC 서브네트워크는 특정 요구사항을 충족해야 합니다.
Spark를 위한 서버리스 Dataproc 네트워크 구성의 요구사항을 참조하세요.
권한
서비스 계정에 Dataproc Serverless에서 노트북 파일을 실행하는 데 필요한 권한이 있는지 확인하려면 관리자에게 프로젝트에 대한 Dataproc 편집자(roles/dataproc.editor) IAM 역할을 서비스 계정에 부여해 달라고 요청하세요.
이 사전 정의된 역할에는 Dataproc Serverless에서 노트북 파일을 실행하는 데 필요한 권한이 포함되어 있습니다. 필요한 정확한 권한을 보려면 필수 권한 섹션을 펼치세요.
필수 권한
서버리스 Dataproc에서 노트북 파일을 실행하려면 다음 권한이 필요합니다.
dataproc.agents.create
dataproc.agents.delete
dataproc.agents.get
dataproc.agents.update
dataproc.session.create
dataproc.sessions.get
dataproc.sessions.list
dataproc.sessions.terminate
dataproc.sessions.delete
dataproc.tasks.lease
dataproc.tasks.listInvalidatedLeases
dataproc.tasks.reportStatus
관리자는 커스텀 역할이나 다른 사전 정의된 역할을 사용하여 서비스 계정에 이러한 권한을 부여할 수도 있습니다.
시작하기 전에
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.
[[["이해하기 쉬움","easyToUnderstand","thumb-up"],["문제가 해결됨","solvedMyProblem","thumb-up"],["기타","otherUp","thumb-up"]],[["이해하기 어려움","hardToUnderstand","thumb-down"],["잘못된 정보 또는 샘플 코드","incorrectInformationOrSampleCode","thumb-down"],["필요한 정보/샘플이 없음","missingTheInformationSamplesINeed","thumb-down"],["번역 문제","translationIssue","thumb-down"],["기타","otherDown","thumb-down"]],["최종 업데이트: 2025-07-09(UTC)"],[],[],null,["# Use Dataproc Serverless Spark with managed notebooks\n====================================================\n\n\n| Vertex AI Workbench managed notebooks is\n| [deprecated](/vertex-ai/docs/deprecations). On\n| April 14, 2025, support for\n| managed notebooks will end and the ability to create managed notebooks instances\n| will be removed. Existing instances will continue to function\n| but patches, updates, and upgrades won't be available. To continue using\n| Vertex AI Workbench, we recommend that you\n| [migrate\n| your managed notebooks instances to Vertex AI Workbench instances](/vertex-ai/docs/workbench/managed/migrate-to-instances).\n\n\u003cbr /\u003e\n\n|\n| **Preview**\n|\n|\n| This feature is subject to the \"Pre-GA Offerings Terms\" in the General Service Terms section\n| of the [Service Specific Terms](/terms/service-terms#1).\n|\n| Pre-GA features are available \"as is\" and might have limited support.\n|\n| For more information, see the\n| [launch stage descriptions](/products#product-launch-stages).\n\nThis page shows you how to run a notebook file on serverless Spark\nin a Vertex AI Workbench managed notebooks instance\nby using [Dataproc Serverless](/dataproc-serverless/docs).\n\nYour managed notebooks instance\ncan submit a notebook file's code to run on\nthe Dataproc Serverless service. The service runs\nthe code on a managed compute infrastructure that automatically\nscales resources as needed. Therefore,\nyou don't need to provision and manage your own cluster.\n\n[Dataproc Serverless charges](/dataproc-serverless/pricing)\napply only to the time when the workload is executing.\n\nRequirements\n------------\n\nTo run a notebook file on Dataproc Serverless Spark,\nsee the following requirements.\n\n- Your Dataproc Serverless session must run in the same\n region as your managed notebooks instance.\n\n- The Require OS Login (`constraints/compute.requireOsLogin`) constraint\n must not be enabled for your project. See [Manage OS Login in\n an organization](https://cloud.google.com/compute/docs/oslogin/manage-oslogin-in-an-org).\n\n- To run a notebook file on Dataproc Serverless,\n you must provide a [service account](/iam/docs/service-accounts)\n that has specific permissions. You can grant these permissions\n to the default service account or provide a custom service account.\n See the [Permissions section of this page](#permissions).\n\n- Your Dataproc Serverless Spark session uses\n a Virtual Private Cloud (VPC) network to execute workloads.\n The VPC subnetwork must meet specific requirements.\n See the requirements in [Dataproc Serverless for\n Spark network configuration](/dataproc-serverless/docs/concepts/network).\n\nPermissions\n-----------\n\n\nTo ensure that the service account has the necessary\npermissions to run a notebook file on Dataproc Serverless,\n\nask your administrator to grant the service account the\n\n\n[Dataproc Editor](/iam/docs/roles-permissions/dataproc#dataproc.editor) (`roles/dataproc.editor`)\nIAM role on your project.\n\n\n| **Important:** You must grant this role to the service account, *not* to your user account. Failure to grant the role to the correct principal might result in permission errors.\nFor more information about granting roles, see [Manage access to projects, folders, and organizations](/iam/docs/granting-changing-revoking-access).\n\n\u003cbr /\u003e\n\n\nThis predefined role contains\n\nthe permissions required to run a notebook file on Dataproc Serverless. To see the exact permissions that are\nrequired, expand the **Required permissions** section:\n\n\n#### Required permissions\n\nThe following permissions are required to run a notebook file on Dataproc Serverless:\n\n- ` dataproc.agents.create `\n- ` dataproc.agents.delete `\n- ` dataproc.agents.get `\n- ` dataproc.agents.update `\n- ` dataproc.session.create `\n- ` dataproc.sessions.get `\n- ` dataproc.sessions.list `\n- ` dataproc.sessions.terminate `\n- ` dataproc.sessions.delete `\n- ` dataproc.tasks.lease `\n- ` dataproc.tasks.listInvalidatedLeases `\n- ` dataproc.tasks.reportStatus`\n\n\nYour administrator might also be able to give the service account\nthese permissions\nwith [custom roles](/iam/docs/creating-custom-roles) or\nother [predefined roles](/iam/docs/roles-overview#predefined).\n\nBefore you begin\n----------------\n\n- Sign in to your Google Cloud account. If you're new to Google Cloud, [create an account](https://console.cloud.google.com/freetrial) to evaluate how our products perform in real-world scenarios. New customers also get $300 in free credits to run, test, and deploy workloads.\n- In the Google Cloud console, on the project selector page,\n select or create a Google Cloud project.\n\n | **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.\n\n [Go to project selector](https://console.cloud.google.com/projectselector2/home/dashboard)\n-\n [Verify that billing is enabled for your Google Cloud project](/billing/docs/how-to/verify-billing-enabled#confirm_billing_is_enabled_on_a_project).\n\n-\n\n\n Enable the Notebooks, Vertex AI, and Dataproc APIs.\n\n\n [Enable the APIs](https://console.cloud.google.com/flows/enableapi?apiid=notebooks.googleapis.com,aiplatform.googleapis.com,dataproc)\n\n- In the Google Cloud console, on the project selector page,\n select or create a Google Cloud project.\n\n | **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.\n\n [Go to project selector](https://console.cloud.google.com/projectselector2/home/dashboard)\n-\n [Verify that billing is enabled for your Google Cloud project](/billing/docs/how-to/verify-billing-enabled#confirm_billing_is_enabled_on_a_project).\n\n-\n\n\n Enable the Notebooks, Vertex AI, and Dataproc APIs.\n\n\n [Enable the APIs](https://console.cloud.google.com/flows/enableapi?apiid=notebooks.googleapis.com,aiplatform.googleapis.com,dataproc)\n\n1. If you haven't already, [create\n a managed notebooks instance](/vertex-ai/docs/workbench/managed/create-instance#create).\n2. If you haven't already, configure a VPC network that meets the requirements listed in [Dataproc Serverless\n for Spark network configuration](/dataproc-serverless/docs/concepts/network).\n\nOpen JupyterLab\n---------------\n\n1. In the Google Cloud console, go to the **Managed notebooks** page.\n\n [Go to Managed notebooks](https://console.cloud.google.com/vertex-ai/workbench/managed)\n2. Next to your managed notebooks instance's name,\n click **Open JupyterLab**.\n\nStart a Dataproc Serverless Spark session\n-----------------------------------------\n\nTo start a Dataproc Serverless Spark session,\ncomplete the following steps.\n\n1. In your managed notebooks instance's JupyterLab interface,\n select the **Launcher** tab, and then select **Serverless Spark** .\n If the **Launcher** tab is not open,\n select **File \\\u003e New Launcher** to open it.\n\n The **Create Serverless Spark session** dialog appears.\n2. In the **Session name** field, enter a name for your session.\n\n3. In the **Execution configuration** section, enter\n the **Service account** that you want to use. If you don't enter\n a service account, your session will use the [Compute Engine default\n service account](/compute/docs/access/service-accounts#default_service_account).\n\n4. In the **Network configuration** section, select the\n **Network** and **Subnetwork** of a network that meets the requirements\n listed in [Dataproc Serverless for\n Spark network configuration](/dataproc-serverless/docs/concepts/network).\n\n5. Click **Create**.\n\n A new notebook file opens.\n The Dataproc Serverless Spark session that you created is\n the kernel that runs your notebook file's code.\n\nRun your code on Dataproc Serverless Spark and other kernels\n------------------------------------------------------------\n\n1. Add code to your new notebook file, and run the code.\n\n2. To run code on a different kernel,\n [change the kernel](/vertex-ai/docs/workbench/managed/create-managed-notebooks-instance-console-quickstart#change-kernel).\n\n3. When you want to run the code on\n your Dataproc Serverless Spark session again,\n change the kernel back to\n the Dataproc Serverless Spark kernel.\n\nTerminate your Dataproc Serverless Spark session\n------------------------------------------------\n\nYou can terminate a Dataproc Serverless Spark session\nin the JupyterLab interface or in the Google Cloud console.\nThe code in your notebook file is preserved. \n\n### JupyterLab\n\n1. In JupyterLab, close the notebook file that was created when you\n created your Dataproc Serverless Spark session.\n\n2. In the dialog that appears, click **Terminate session**.\n\n### Google Cloud console\n\n1. In the Google Cloud console, go to the **Dataproc sessions** page.\n\n [Go to Dataproc sessions](https://console.cloud.google.com/dataproc/interactive)\n2. Select the session that you want to terminate,\n and then click **Terminate**.\n\nDelete your Dataproc Serverless Spark session\n---------------------------------------------\n\nYou can delete a Dataproc Serverless Spark session\nby using the Google Cloud console.\nThe code in your notebook file is preserved.\n\n1. In the Google Cloud console, go to the **Dataproc sessions** page.\n\n [Go to Dataproc sessions](https://console.cloud.google.com/dataproc/interactive)\n2. Select the session that you want to delete,\n and then click **Delete**.\n\nWhat's next\n-----------\n\n- Learn more about [Dataproc Serverless](/dataproc-serverless/docs/overview)."]]
