이 페이지에서는 명령줄을 사용하지 않고 Google Cloud Console의 Cloud Marketplace에서 Deep Learning VM Image 인스턴스를 만드는 방법을 보여줍니다.
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.
GPU에서 GPU 유형 및 GPU 수를 선택합니다.
GPU를 사용하지 않으려면 GPU 삭제 버튼을 클릭하고 7단계로 건너뜁니다. GPU 자세히 알아보기
GPU 유형을 선택합니다.
영역에 따라 일부 GPU 유형이 지원되지 않을 수 있습니다.
지원되는 조합 찾기
GPU 수를 선택합니다.
각 GPU는 서로 다른 수의 GPU를 지원합니다.
지원되는 조합 찾기
머신러닝 프레임워크를 선택합니다.
GPU를 사용하는 경우 NVIDIA 드라이버가 필요합니다.
드라이버는 직접 설치하거나 처음 시작 시 NVIDIA GPU 드라이버가 자동으로 설치되도록 선택할 수 있습니다.
SSH 대신 URL을 통해 JupyterLab에 액세스 사용 설정(베타)을 선택할 수 있습니다. 이 베타 기능을 사용 설정하면 URL을 사용하여 JupyterLab 인스턴스에 액세스할 수 있습니다. Google Cloud 프로젝트의 편집자 또는 소유자 역할이 있는 사용자 누구나 이 URL에 액세스할 수 있습니다.
현재 이 기능은 미국, 유럽연합, 아시아에서만 작동합니다.
부팅 디스크 유형과 부팅 디스크 크기를 선택합니다.
원하는 네트워크 설정을 선택합니다.
배포를 클릭합니다.
NVIDIA 드라이버 설치를 선택한 경우 설치가 완료될 때까지 3~5분 정도 기다려 주세요.
VM 배포가 완료되면 페이지는 인스턴스 액세스에 관한 안내가 업데이트됩니다.
다음 단계
Google Cloud Console 또는 명령줄을 통해 새 딥 러닝 VM 인스턴스에 연결하는 방법은 인스턴스에 연결을 참조하세요. 인스턴스 이름은 지정된 배포 이름 뒤에 -vm이 붙습니다.
[[["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: 2025-09-04(UTC)"],[[["\u003cp\u003eThis guide outlines how to create a Deep Learning VM instance directly from the Google Cloud Marketplace within the console, eliminating the need for command-line operations.\u003c/p\u003e\n"],["\u003cp\u003eBefore creating the instance, you must select a specific Deep Learning VM image based on your preferred framework and processor type, and check that enough GPU quota is available if you are planning to use GPUs.\u003c/p\u003e\n"],["\u003cp\u003eThe instance creation process involves selecting a deployment name, zone, machine type, optional GPU settings, and machine learning framework, then it includes selecting a boot disk and networking settings before deployment.\u003c/p\u003e\n"],["\u003cp\u003eIf you are planning to use GPUs, you will need to install the NVIDIA drivers, which can be done automatically on the first startup, and you also have the choice of enabling JupyterLab access via URL.\u003c/p\u003e\n"],["\u003cp\u003eAfter deployment, you will be provided instructions to access the instance, and the instance name is created by appending \u003ccode\u003e-vm\u003c/code\u003e to the deployment name that was chosen during setup.\u003c/p\u003e\n"]]],[],null,["# Create a Deep Learning VM instance from Cloud Marketplace\n\nThis page shows you how to create a Deep Learning VM Images instance\nfrom Cloud Marketplace within the\nGoogle Cloud console without using the command line.\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- 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\n1. [Choose a specific Deep Learning VM\n image to use](/deep-learning-vm/docs/images). Your choice depends on your preferred framework and processor type.\n2. If you are using GPUs with your Deep Learning VM, check the [quotas page](https://console.cloud.google.com/quotas) to ensure that you have enough GPUs available in your project. If GPUs are not listed on the quotas page or you require additional GPU quota, [request a\n quota increase](/compute/quotas#requesting_additional_quota).\n\nCreating an instance\n--------------------\n\n1. Go to the Deep Learning VM Cloud Marketplace page in\n the Google Cloud console.\n\n [Go to the Deep Learning VM Cloud Marketplace page](https://console.cloud.google.com/marketplace/details/click-to-deploy-images/deeplearning)\n2. Click **Get started**.\n\n3. Enter a **Deployment name** , which will be the root of your VM name.\n Compute Engine appends `-vm` to this name when naming your instance.\n\n4. Select a **Zone**.\n\n5. Under **Machine type** , select the specifications that you\n want for your VM.\n [Learn more about machine types.](/compute/docs/machine-types)\n\n6. Under **GPUs** , select the **GPU type** and **Number of GPUs** .\n If you don't want to use GPUs,\n click the **Delete GPU** button\n and skip to step 7. [Learn more about GPUs.](/gpu)\n\n 1. Select a **GPU type** . Not all GPU types are available in all zones. [Find a combination that is supported.](/compute/docs/gpus)\n 2. Select the **Number of GPUs** . Each GPU supports different numbers of GPUs. [Find a combination that is supported.](/compute/docs/gpus)\n7. Select a machine learning **Framework**.\n\n8. If you're using GPUs, an NVIDIA driver is required.\n You can install the driver\n yourself, or select **Install NVIDIA GPU driver automatically\n on first startup**.\n\n9. You have the option to select **Enable access to JupyterLab via URL\n instead of SSH (Beta)**. Enabling this Beta feature lets you\n access your JupyterLab\n instance using a URL. Anyone who is in the Editor or Owner role in your\n Google Cloud project can access this URL.\n Currently, this feature only works in\n the United States, the European Union, and Asia.\n\n10. Select a boot disk type and boot disk size.\n\n11. Select the networking settings that you want.\n\n12. Click **Deploy**.\n\nIf you choose to install NVIDIA drivers, allow 3-5 minutes for installation\nto complete.\n\nAfter the VM is deployed, the page updates with instructions for\naccessing the instance.\n\nWhat's next\n-----------\n\nFor instructions on connecting to your new Deep Learning VM instance\nthrough the Google Cloud console or command line, read [Connecting to\nInstances](/compute/docs/instances/connecting-to-instance). Your instance name\nis the **Deployment name** you specified with `-vm` appended."]]
