Tetap teratur dengan koleksi
Simpan dan kategorikan konten berdasarkan preferensi Anda.
Membuat versi spesifik dari instance notebook yang dikelola pengguna
Halaman ini menjelaskan cara membuat
instance notebook yang dikelola pengguna berdasarkan versi
Deep Learning VM Image
tertentu.
Alasan Anda perlu membuat versi tertentu
Untuk memastikan bahwa instance notebook yang dikelola pengguna memiliki software
yang kompatibel dengan kode atau aplikasi Anda, Anda dapat membuat
versi spesifik.
Instance notebook yang dikelola pengguna dibuat menggunakan Deep Learning VM Image. Deep Learning VM Image
sering diupdate, dan versi tertentu dari software
dan paket yang diinstal sebelumnya bervariasi dari satu versi ke versi lainnya.
Setelah membuat versi spesifik dari instance notebook yang dikelola pengguna, Anda dapat mengupgradenya. Dengan mengupgrade instance, paket dan software yang telah diinstal sebelumnya akan diupdate. Untuk mengetahui informasi selengkapnya, lihat Mengupgrade lingkungan instance notebook yang dikelola
pengguna.
Sebelum memulai
Sebelum dapat membuat instance notebook yang dikelola pengguna, Anda harus memiliki projectGoogle Cloud dan mengaktifkan Notebooks API untuk project tersebut.
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.
Jika Anda berencana menggunakan GPU dengan instance notebook yang dikelola pengguna, lihat halaman kuota di konsol Google Cloud untuk memastikan bahwa Anda memiliki cukup GPU yang tersedia dalam project Anda. Jika GPU tidak tercantum di halaman kuota, atau Anda memerlukan kuota GPU tambahan, Anda dapat meminta penambahan kuota. Lihat Meminta penambahan kuota di halaman Kuota resource Compute Engine.
Peran yang diperlukan
Jika Anda membuat project, Anda memiliki
peran IAM Pemilik (roles/owner) di project,
yang mencakup semua izin yang diperlukan. Lewati bagian ini dan
mulai buat instance notebook yang dikelola pengguna. Jika Anda tidak
membuat project sendiri, lanjutkan di bagian ini.
Untuk mendapatkan izin yang Anda perlukan guna membuat instance notebook yang dikelola pengguna Vertex AI Workbench, minta administrator Anda untuk memberi Anda peran IAM berikut pada project:
Anda mungkin juga bisa mendapatkan
izin yang diperlukan melalui peran
khusus atau peran
bawaan lainnya.
Menemukan versi tertentu yang Anda inginkan
Untuk membuat instance notebook yang dikelola pengguna berdasarkan versi Deep Learning VM tertentu, Anda harus mengetahui nama image versi Deep Learning VM tertentu yang ingin digunakan.
Setiap rilis Deep Learning VM menyertakan update untuk
banyak image yang berbeda, dan setiap image dalam rilis tersebut memiliki
nama image-nya sendiri.
Untuk menemukan nama image tertentu yang Anda inginkan:
Temukan nomor rilis Deep Learning VM
yang nama image-nya ingin Anda dapatkan.
Nomor rilis disertakan dalam catatan rilis
Deep Learning VM.
Nomor rilis berbentuk M diikuti dengan
nomor rilis, misalnya, M79.
Guna menampilkan daftar nama image untuk rilis Deep Learning VM tertentu, jalankan perintah berikut.
[[["Mudah dipahami","easyToUnderstand","thumb-up"],["Memecahkan masalah saya","solvedMyProblem","thumb-up"],["Lainnya","otherUp","thumb-up"]],[["Sulit dipahami","hardToUnderstand","thumb-down"],["Informasi atau kode contoh salah","incorrectInformationOrSampleCode","thumb-down"],["Informasi/contoh yang saya butuhkan tidak ada","missingTheInformationSamplesINeed","thumb-down"],["Masalah terjemahan","translationIssue","thumb-down"],["Lainnya","otherDown","thumb-down"]],["Terakhir diperbarui pada 2025-08-18 UTC."],[],[],null,["# Create a specific version of a Vertex AI Workbench user-managed notebooks instance\n\nCreate a specific version of a user-managed notebooks instance\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\nThis page describes how you can create\na user-managed notebooks instance based on a specific\n[Deep Learning VM Images](/deep-learning-vm/docs)\nversion.\n\nWhy you might want to create a specific version\n-----------------------------------------------\n\nTo ensure that your user-managed notebooks instance has software\nthat is compatible with your code or application, you might want to create\na specific version.\n\nUser-managed notebooks instances are created by using Deep Learning VM images. Deep Learning VM\nimages are updated frequently, and specific versions of preinstalled software\nand packages vary from version to version.\n\nTo learn more about specific Deep Learning VM versions,\nsee the [Deep Learning VM\nrelease notes](/deep-learning-vm/docs/release-notes).\n\nAfter you create a specific version of a user-managed notebooks instance, you can upgrade it. Upgrading the instance updates the preinstalled software and packages. For more information, see [Upgrade a user-managed\nnotebooks instance's environment](/vertex-ai/docs/workbench/user-managed/upgrade).\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\nFind the specific version that you want\n---------------------------------------\n\nTo create a user-managed notebooks instance based on a specific\nDeep Learning VM version, you must know\nthe image name of the specific Deep Learning VM\nversion that you want to use.\n\nEach release of Deep Learning VM includes updates to\nmany different images, and each image in the release has its own\nimage name.\n\nTo find the specific image name that you want:\n\n1. Find the Deep Learning VM release number\n that you want to get image names for.\n Release numbers are included in the [Deep Learning VM\n release notes](/deep-learning-vm/docs/release-notes).\n Release numbers are in the form of an `M` followed by\n the number of the release, for example, `M79`.\n\n2. To list the image names for a specific Deep Learning VM\n release, run the following command.\n\n ```bash\n gcloud compute images list --project=\"deeplearning-platform-release\" \\\n --format=\"value(name)\" \\\n --filter=\"labels.release=\u003cvar translate=\"no\"\u003eRELEASE_NUMBER\u003c/var\u003e\" \\\n --show-deprecated\n ```\n\n Replace \u003cvar translate=\"no\"\u003eRELEASE_NUMBER\u003c/var\u003e with\n a Deep Learning VM release number, such as `M79`.\n3. Find the image name that you want to use.\n\nCreate a specific version from the command line\n-----------------------------------------------\n\nTo create a specific version of\na user-managed notebooks instance from\nthe command line, complete the following steps:\n\n1. Run the following [`gcloud\n notebooks`](/sdk/gcloud/reference/notebooks/instances/create) command:\n\n ```bash\n gcloud notebooks instances create INSTANCE_NAME \\\n --vm-image-project=\"deeplearning-platform-release\" \\\n --vm-image-name=VM_IMAGE_NAME \\\n --machine-type=MACHINE_TYPE \\\n --location=LOCATION\n ```\n\n Replace the following:\n - \u003cvar translate=\"no\"\u003eINSTANCE_NAME\u003c/var\u003e: the name of your new instance\n - \u003cvar translate=\"no\"\u003eVM_IMAGE_NAME\u003c/var\u003e: the image name that you want to use to create your instance\n - \u003cvar translate=\"no\"\u003eMACHINE_TYPE\u003c/var\u003e: the [machine\n type](/compute/docs/machine-resource) of your instance's VM\n - \u003cvar translate=\"no\"\u003eLOCATION\u003c/var\u003e: the Google Cloud [location](/vertex-ai/docs/general/locations#user-managed-notebooks-locations) where you want your new instance to be\n2. Access your instance from the\n [Google Cloud console](https://console.cloud.google.com/vertex-ai/workbench/user-managed).\n\nWhat's next\n-----------\n\n- Learn more about [upgrading\n user-managed notebooks instances](/vertex-ai/docs/workbench/user-managed/upgrade)\n to ensure that your instance upgrades only when you are ready.\n\n- [Install dependencies](/vertex-ai/docs/workbench/user-managed/dependencies) on\n your new user-managed notebooks instance.\n\n- Learn more about Deep Learning VM instances in the\n [Deep Learning VM\n documentation](/deep-learning-vm/docs).\n\n- Learn about [monitoring the health status](/vertex-ai/docs/workbench/user-managed/monitor-health) of\n your user-managed notebooks instance."]]
