Klik nama dan ID versi model yang ingin Anda deploy untuk membuka
halaman detailnya.
Pilih tab Deploy & Uji.
Jika model Anda sudah di-deploy ke endpoint, model tersebut akan tercantum di
bagian Deploy model Anda.
Klik Deploy to endpoint.
Untuk men-deploy model ke endpoint baru:
Pilih
radio_button_checkedCreate new endpoint
Berikan nama untuk endpoint baru.
Untuk membuat endpoint publik khusus (tidak dibagikan), centang kotak Aktifkan DNS khusus.
Klik Lanjutkan.
Untuk men-deploy model ke endpoint yang ada:
Pilih
radio_button_checkedTambahkan ke endpoint yang ada.
Pilih endpoint dari menu drop-down.
Klik Lanjutkan.
Anda dapat men-deploy beberapa model ke satu endpoint, atau men-deploy
model yang sama ke beberapa endpoint.
Jika Anda men-deploy model ke endpoint yang sudah ada dan satu atau beberapa
beberapa model telah di-deploy ke endpoint tersebut, Anda harus memperbarui persentase Pemisahan traffic
untuk model yang sedang di-deploy dan yang telah di-deploy sehingga jumlah semua
persentasenya menjadi 100%.
Jika Anda men-deploy model ke endpoint baru, terima nilai 100 untuk
Pemisahan traffic. Jika tidak, sesuaikan nilai pemisahan traffic untuk
semua model di endpoint sehingga jumlahnya menjadi 100.
Masukkan Jumlah minimum node komputasi yang ingin Anda berikan untuk
model Anda.
Ini adalah jumlah node yang harus selalu tersedia untuk model.
Anda akan dikenai biaya untuk node yang digunakan, baik untuk menangani beban inferensi maupun untuk
node standby (minimum) meskipun tanpa traffic inferensi. Lihat
halaman harga.
Jumlah node komputasi dapat meningkat jika diperlukan untuk menangani traffic inferensi, tetapi tidak akan pernah melebihi jumlah maksimum node.
Untuk menggunakan penskalaan otomatis, masukkan Jumlah maksimum node komputasi yang ingin Anda tingkatkan skalanya menggunakan Vertex AI.
Jika Anda mengaktifkan penggunaan akselerator saat mengimpor
atau membuat model, opsi ini akan ditampilkan.
Untuk mengetahui jumlah akselerator, lihat tabel
GPU untuk memeriksa jumlah
GPU valid yang dapat Anda gunakan dengan setiap jenis mesin CPU. Jumlah akselerator
mengacu pada jumlah akselerator per node, bukan total
jumlah akselerator dalam deployment Anda.
Jika Anda ingin menggunakan akun layanan
kustom untuk deployment, pilih
akun layanan di kotak drop-down Akun layanan.
[[["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 2025-08-19 UTC."],[],[],null,["# Deploy a model by using the Google Cloud console\n\nIn the Google Cloud console, you can create a\n[public endpoint](/vertex-ai/docs/predictions/choose-endpoint-type)\nand deploy a model to it.\n\nModels can be deployed from the\nOnline prediction page or the Model Registry\npage.\n\nDeploy a model from the Online prediction page\n----------------------------------------------\n\nIn the Online prediction page, you can create an endpoint and deploy\none or more models to it as follows:\n\n1. In the Google Cloud console, in the Vertex AI section, go\n to the **Online prediction** page.\n\n [Go to the Online prediction page](https://console.cloud.google.com/vertex-ai/online-prediction/endpoints)\n2. Click add **Create**.\n\n3. In the **New endpoint** pane:\n\n 1. Enter the **Endpoint name**.\n\n 2. Select **Standard** for the access type.\n\n 3. To create a dedicated (not shared) public endpoint, select the\n **Enable dedicated DNS** checkbox.\n\n 4. Click **Continue**.\n\n4. In the **Model settings** pane:\n\n 1. Select your model from the drop-down list.\n\n 2. Choose the model version from the drop-down list.\n\n 3. Enter the **Traffic split** percentage for the model.\n\n 4. Click **Done**.\n\n 5. Repeat these steps for any additional models to be deployed.\n\nDeploy a model from the Model Registry page\n-------------------------------------------\n\nIn the Model Registry page, you can deploy a model to one\nor more new or existing endpoints as follows:\n\n1. In the Google Cloud console, in the Vertex AI section, go\n to the **Models** page.\n\n [Go to the Models page](https://console.cloud.google.com/vertex-ai/models)\n2. Click the name and version ID of the model you want to deploy to open\n its details page.\n\n3. Select the **Deploy \\& Test** tab.\n\n If your model is already deployed to any endpoints, they are listed in the\n **Deploy your model** section.\n4. Click **Deploy to endpoint**.\n\n5. To deploy your model to a new endpoint:\n\n 1. Select radio_button_checked**Create new endpoint**\n 2. Provide a name for the new endpoint.\n 3. To create a dedicated (not shared) public endpoint, select the **Enable dedicated DNS** checkbox.\n 4. Click **Continue**.\n\n To deploy your model to an existing endpoint:\n 1. Select radio_button_checked**Add to existing endpoint**.\n 2. Select the endpoint from the drop-down list.\n 3. Click **Continue**.\n\n You can deploy multiple models to an endpoint, or you can deploy the\n same model to multiple endpoints.\n6. If you deploy your model to an existing endpoint that has one or more\n models deployed to it, you must update the **Traffic split** percentage\n for the model you are deploying and the already deployed models so that all\n of the percentages add up to 100%.\n\n7.\n If you're deploying your model to a new endpoint, accept 100 for the\n **Traffic split**. Otherwise, adjust the traffic split values for\n all models on the endpoint so they add up to 100.\n\n8. Enter the **Minimum number of compute nodes** you want to provide for\n your model.\n\n This is the number of nodes that need to be available to the model at all times.\n\n You are charged for the nodes used, whether to handle inference load or for\n standby (minimum) nodes, even without inference traffic. See the\n [pricing page](/vertex-ai/pricing).\n\n The number of compute nodes can increase if needed to handle inference\n traffic, but it will never go higher than the maximum number of nodes.\n9. To use autoscaling, enter the **Maximum number of compute nodes** you\n want Vertex AI to scale up to.\n\n10. Select your **Machine type**.\n\n Larger machine resources increase your inference performance and\n increase costs.\n [Compare the available machine types](/vertex-ai/docs/predictions/configure-compute#machine_type_comparison).\n11. Select an **Accelerator type** and an **Accelerator count**.\n\n If you enabled accelerator use when you [imported](/vertex-ai/docs/model-registry/import-model)\n or created the model, this option displays.\n\n For the accelerator count, refer to the [GPU\n table](/vertex-ai/docs/predictions/configure-compute#gpus) to check for valid numbers\n of GPUs that you can use with each CPU machine type. The accelerator\n count refers to the number of accelerators per node, not the total\n number of accelerators in your deployment.\n12. If you want to use a [custom service\n account](/vertex-ai/docs/general/custom-service-account) for the deployment, select\n a service account in the **Service account** drop-down box.\n\n13.\n Learn how to [change the\n default settings for inference logging](/vertex-ai/docs/predictions/online-prediction-logging#enabling-and-disabling).\n\n14.\n Click **Done** for your model, and when all the **Traffic split**\n percentages are correct, click **Continue**.\n\n The region where your model deploys is displayed. This\n must be the region where you created your model.\n\n \u003cbr /\u003e\n\n15.\n Click **Deploy** to deploy your model to the endpoint.\n\nWhat's next\n-----------\n\n- Learn how to [get an online inference](/vertex-ai/docs/predictions/get-online-predictions).\n- Learn how to [change the\n default settings for inference logging](/vertex-ai/docs/predictions/online-prediction-logging#enabling-and-disabling)."]]
