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[[["容易理解","easyToUnderstand","thumb-up"],["確實解決了我的問題","solvedMyProblem","thumb-up"],["其他","otherUp","thumb-up"]],[["難以理解","hardToUnderstand","thumb-down"],["資訊或程式碼範例有誤","incorrectInformationOrSampleCode","thumb-down"],["缺少我需要的資訊/範例","missingTheInformationSamplesINeed","thumb-down"],["翻譯問題","translationIssue","thumb-down"],["其他","otherDown","thumb-down"]],["上次更新時間：2025-09-04 (世界標準時間)。"],[],[],null,["# Create a Vertex AI Workbench user-managed notebooks instance with a custom container\n\nCreate a user-managed notebooks instance with a custom container\n================================================================\n\n\n| Vertex AI Workbench user-managed notebooks is\n| [deprecated](/vertex-ai/docs/deprecations). On\n| April 14, 2025, support for\n| user-managed notebooks will end and the ability to create user-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 user-managed notebooks instances to Vertex AI Workbench instances](/vertex-ai/docs/workbench/user-managed/migrate-to-instances).\n\n\u003cbr /\u003e\n\nYou can create a user-managed notebooks instance based on a custom\ncontainer. Using a custom container lets you customize a\nuser-managed notebooks environment for your specific needs.\nThe container must be accessible to your\nGoogle Cloud service account and expose a service on port 8080.\nWe recommend creating a container derived from a\n[Deep Learning Containers\nimage](/deep-learning-containers/docs/choosing-container#choose_a_container_image_type),\nbecause those images are already configured to be compatible\nwith user-managed notebooks.\n\nHow custom container kernels are updated\n----------------------------------------\n\nVertex AI Workbench pulls the latest container image for your kernel:\n\n- When you create your instance.\n\n- When you upgrade your instance.\n\n- When you start your instance.\n\nThe custom container kernel doesn't persist when your instance is stopped,\nso each time your instance is started, Vertex AI Workbench pulls\nthe latest version of the container image.\n\nIf your instance is running when a new version of a container is released,\nyour instance's kernel isn't updated until you stop and start your instance.\n\nBefore you begin\n----------------\n\nBefore you can create a user-managed notebooks instance, you must have a Google Cloud project and enable the Notebooks API for that project.\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 API.\n\n\n [Enable the API](https://console.cloud.google.com/flows/enableapi?apiid=notebooks.googleapis.com&redirect=https://console.cloud.google.com)\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 API.\n\n\n [Enable the API](https://console.cloud.google.com/flows/enableapi?apiid=notebooks.googleapis.com&redirect=https://console.cloud.google.com)\n\n1. If you plan to use GPUs with your user-managed notebooks instance, [check the quotas page in the\n Google Cloud console](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, you can request a quota increase. See [Requesting an increase in\n quota](/compute/quotas#requesting_additional_quota) on the Compute Engine [Resource quotas](/compute/quotas) page.\n\n\u003cbr /\u003e\n\n### Required roles\n\nIf you created the project, you have the\nOwner (`roles/owner`) IAM role on the project,\nwhich includes all required permissions. Skip this section and\nstart creating your user-managed notebooks instance. If you didn't\ncreate the project yourself, continue in this section.\n\n\nTo get the permissions that\nyou need to create a Vertex AI Workbench user-managed notebooks instance,\n\nask your administrator to grant you the\nfollowing IAM roles on the project:\n\n- Notebooks Admin ([`roles/notebooks.admin`](/vertex-ai/docs/workbench/user-managed/iam#notebooks.admin))\n- Service Account User ([`roles/iam.serviceAccountUser`](/iam/docs/understanding-roles#iam.serviceAccountUser))\n\n\nFor more information about granting roles, see [Manage access to projects, folders, and organizations](/iam/docs/granting-changing-revoking-access).\n\n\nYou might also be able to get\nthe required permissions through [custom\nroles](/iam/docs/creating-custom-roles) or other [predefined\nroles](/iam/docs/roles-overview#predefined).\n\n### Make sure your custom container is ready\n\nMake sure you have a custom container that is accessible to your\nGoogle Cloud service account. For information about how to create a\ncustom container from a\n[Deep Learning Containers image](/deep-learning-containers/docs/choosing-container#choose_a_container_image_type), see\n[Creating a derivative container](/deep-learning-containers/docs/derivative-container).\n\nCreate an instance with a custom container\n------------------------------------------\n\nTo create a user-managed notebooks instance\nwith a custom container, complete the following steps:\n\n1. In the Google Cloud console, go to the **User-managed notebooks** page.\n Or go to [notebook.new](https://notebook.new)\n (https://notebook.new) and skip the next step.\n\n [Go to User-managed notebooks](https://console.cloud.google.com/vertex-ai/workbench/user-managed)\n2. Click add_box **Create new**.\n\n3. Click **Advanced options**.\n\n4. On the **Create instance** page,\n in the **Details** section,\n provide the following information for your new instance:\n\n - **Name**: a name for your new instance\n - **Region** and **Zone** : Select a region and zone for the new instance. For best network performance, select the region that is geographically closest to you. See the available [user-managed notebooks\n locations](/vertex-ai/docs/general/locations#user-managed-notebooks-locations).\n5. In the **Environment** section, in the **Environment** field,\n select **Custom container**.\n\n6. In the **Docker container image** field, add a Docker container image\n in one of the following ways:\n\n - Enter a Docker container image path. For example, to use a TensorFlow 2.12 container image with accelerators from [Deep Learning Containers](/deep-learning-containers/docs/choosing-container#deciding), enter `us-docker.pkg.dev/deeplearning-platform-release/gcr.io/tf-cpu.2-12.py310`.\n - Click **Select** to add a Docker container image from Artifact Registry. Then on the **Artifact Registry** tab where your container image is stored, change the project to the project that includes your container image, and select your container image.\n7. Make the rest of your selections, or leave them on their default\n setting. For more information about these settings, see [Create a\n user-managed notebooks instance with specific properties](/vertex-ai/docs/workbench/user-managed/create-new#create-with-options).\n\n8. Click **Create**. Vertex AI Workbench creates\n a user-managed notebooks instance for you, based\n on your custom container.\n\nWhat's next\n-----------\n\n- Read about how to [push container images to\n Artifact Registry](/artifact-registry/docs/docker/pushing-and-pulling). If the container images you push to Artifact Registry are derived from a [Deep Learning Containers\n image](/deep-learning-containers/docs/choosing-container#choose_a_container_image_type), you can use these container images when creating user-managed notebooks instances.\n- Learn more about modifying your custom containers by reading [Best practices for writing\n Dockerfiles](https://docs.docker.com/develop/develop-images/dockerfile_best-practices/)."]]
