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Implante os recursos personalizados de previsão no cluster de previsão
criado pelo operador de infraestrutura (IO). O operador cria cargas de trabalho de previsão no mesmo cluster.
Para criar o cluster de previsão, trabalhe com o IO para associar seu projeto de previsão e alocar os pools de nós necessários para previsões on-line na Google Distributed Cloud (GDC) isolada por air-gap.
Para criar um cluster de previsão, siga estas etapas:
Identifique o projeto na sua organização que você quer associar ao novo cluster para previsões on-line.
O tipo de máquina escolhido depende do tamanho e da complexidade do modelo de previsão e determina os recursos de computação e unidade de processamento gráfico (GPU) que o IO fornece ao cluster.
Siga as recomendações de seleção de nós ao escolher o tipo de máquina para seus nós.
Se necessário, comunique-se com o IO até que ele termine de criar o cluster de previsão associado ao seu projeto e atribuir os pools de nós adequados no cluster.
Depois de concluir o provisionamento do cluster, ele estará pronto para
previsões on-line.
Recomendações de seleção de nós
Quando o IO cria pools de nós em um cluster, ele atribui um dos tipos de máquina disponíveis no Distributed Cloud para fornecer um conjunto predefinido de recursos para os nós de trabalho. Dependendo do tamanho e da complexidade do modelo, você precisa de diferentes
desempenhos de computação e, consequentemente, de uma quantidade específica de CPU, memória e
GPU. Você precisa fornecer esses detalhes na sua comunicação com o IO quando quiser criar um cluster de previsão.
Ao determinar com o IO o tipo de máquina para os pools de nós necessários
no cluster de previsão, siga estas práticas:
O Distributed Cloud adiciona sobrecarga de computação aos nós para componentes obrigatórios do sistema. Portanto, escolha um tipo de máquina maior para os pools de nós do que o que você pretende usar no pool de recursos dos modelos.
Escolha a solução que oferece a memória e os recursos de computação mínimos necessários para seus requisitos. Por exemplo, se o modelo
exigir oito vCPUs, escolha o tipo de máquina n2-highcpu-8-gdc, a
menor solução com oito vCPUs e 8 GB de memória no
Distributed Cloud.
À medida que você avança, considere soluções de maior desempenho apenas se as menores não forem adequadas para suas necessidades e para o tamanho e a complexidade do modelo. É fundamental aderir ao princípio de privilégio mínimo, usando apenas os recursos necessários para executar seu fluxo de trabalho específico. Essa abordagem responsável garante o uso adequado dos recursos no ambiente da Distributed Cloud.
Escolha apenas soluções com GPUs se você precisar delas para seu modelo.
Se o modelo exigir GPUs, considere o tipo de máquina a2-highgpu-1g-gdc, a menor solução que oferece GPUs.
Modelo de caso de cluster Prediction
Use o modelo a seguir para enviar um e-mail à sua IO. O e-mail abre um caso
para criar o cluster de previsão necessário para previsões on-line.
Good day,
I need to create a prediction cluster and associate it with a project in my organization to use online predictions.
Please use the following information for the creation of the cluster:
- **Cluster name:** vtx-ai-prediction
- **Name of the organization:** [Specify your organization's name.]
- **Project name:** [Specify the name of your project to associate with the prediction cluster.]
- **Machine type for the node pool:** [Specify the machine type you chose from the list of available machine types for the cluster nodes based on node selection recommendations. Please note that the IO can respond with a different suggestion based on your needs.]
- **Compute resources:** [Optionally, if you know how many compute resources your workloads need, describe them in this field.]
- **Memory resources:** [Optionally, if you know how many memory resources your workloads need, describe them in this field.]
- **GPU resources:** [Optionally, if you know how many GPU resources your workloads need, describe them in this field.]
**Note for IO:** Review the instructions to create the prediction cluster in the following section of the documentation: Operator > Configure the deployment > Create the Prediction cluster
Thank you,
[Your name]
[[["Fácil de entender","easyToUnderstand","thumb-up"],["Meu problema foi resolvido","solvedMyProblem","thumb-up"],["Outro","otherUp","thumb-up"]],[["Difícil de entender","hardToUnderstand","thumb-down"],["Informações incorretas ou exemplo de código","incorrectInformationOrSampleCode","thumb-down"],["Não contém as informações/amostras de que eu preciso","missingTheInformationSamplesINeed","thumb-down"],["Problema na tradução","translationIssue","thumb-down"],["Outro","otherDown","thumb-down"]],["Última atualização 2025-09-04 UTC."],[[["\u003cp\u003eOnline Prediction is a Preview feature not intended for production environments and lacks service-level agreements or technical support commitments from Google.\u003c/p\u003e\n"],["\u003cp\u003eTo use online predictions in Google Distributed Cloud (GDC) air-gapped, you must work with the Infrastructure Operator (IO) to create a dedicated prediction cluster and associate it with your project, noting only one prediction cluster can exist per organization.\u003c/p\u003e\n"],["\u003cp\u003eWhen creating a prediction cluster, you need to select a suitable machine type for the cluster nodes based on your model's size and complexity, and then communicate these details to the IO.\u003c/p\u003e\n"],["\u003cp\u003eWhen selecting a machine type, it is recommended to start with the smallest solution that meets the minimum computing and memory needs of the model.\u003c/p\u003e\n"],["\u003cp\u003eA specific template is provided to use when sending an email to the IO, containing the cluster name, the organization's name, the associated project name, machine type for the node pool, compute, memory and GPU resources.\u003c/p\u003e\n"]]],[],null,["# Create the prediction cluster\n\n| **Preview:** Online Prediction is a Preview feature that is available as-is and is not recommended for production environments. Google provides no service-level agreements (SLA) or technical support commitments for Preview features. For more information, see GDC's [feature stages](/distributed-cloud/hosted/docs/latest/gdch/resources/feature-stages).\n\nYou must deploy your prediction custom resources in the prediction cluster\nthat the Infrastructure Operator (IO) creates for you. The operator creates\nprediction workloads in this same cluster.\n\nTo create the prediction cluster, work with the IO to associate your prediction\nproject and allocate the node pools needed for online predictions in\nGoogle Distributed Cloud (GDC) air-gapped.\n| **Important:** Only one prediction cluster can exist in each organization. However, the IO can attach and associate multiple projects to the cluster to separate and organize the endpoints.\n\nTo create a prediction cluster, perform the following steps:\n\n1. Identify the project in your organization that you want to associate with\n the new cluster for online predictions.\n\n To create a project, see\n [Set up a project for Vertex AI](/distributed-cloud/hosted/docs/latest/gdch/application/ao-user/vertex-ai-set-up-project).\n You need your project ID when making API calls.\n2. From [the list of available machine types](/distributed-cloud/hosted/docs/latest/gdch/platform/pa-user/cluster-node-machines#available-machine-types)\n in Distributed Cloud, choose the machine type for the nodes that\n your workloads need in the cluster.\n\n The machine type you choose depends on your prediction model size and\n complexity and determines the compute and graphic processing unit (GPU)\n resources your IO provides to the cluster.\n Follow [node selection recommendations](#node-selection-recommendations)\n when selecting the machine type for your nodes.\n3. Email the IO using the [prediction cluster case template](#case-template) to\n open a case and address your request to create the cluster.\n\n4. If necessary, communicate with the IO until they finish creating the\n prediction cluster associated with your project and assigning the\n appropriate node pools within the cluster.\n\nAfter completing cluster provisioning, the prediction cluster is ready for\nonline predictions.\n\nNode selection recommendations\n------------------------------\n\nWhen the IO creates node pools in a cluster, they assign one of the\n[available machine types](/distributed-cloud/hosted/docs/latest/gdch/platform/pa-user/cluster-node-machines#available-machine-types)\nin Distributed Cloud to provide a predefined set of resources for the\nworker nodes. Depending on the model size and complexity, you require different\ncomputing performances and, consequently, a specific amount of CPU, memory, and\nGPU. You must provide these details in your communication with the IO when you\nwant to create a prediction cluster.\n| **Important:** Distributed Cloud uses virtualized GPUs in the cluster, which means you get a one-seventh slice of the GPU you have for each requested accelerator count. For example, if you ask for an accelerator count of three in the [resource pool](/distributed-cloud/hosted/docs/latest/gdch/application/ao-user/vertex-ai-deploy-model#resource-pool), you get three-sevenths of a GPU.\n\nWhen you determine with the IO the machine type for node pools that you require\nin the prediction cluster, you must adhere to the following practices:\n\n- Distributed Cloud adds computing overhead to the nodes for mandatory system components. Therefore, you must choose a larger machine type for your node pools than the one you intend to use in the [resource pool](/distributed-cloud/hosted/docs/latest/gdch/application/ao-user/vertex-ai-deploy-model#resource-pool) for your models.\n- Choose the solution that provides the minimum memory and computing resources necessary for your requirements. For example, if your model requires eight vCPUs, choose the `n2-highcpu-8-gdc` machine type, the smallest solution with eight vCPUs and 8 GB of memory in Distributed Cloud.\n- As you progress, consider higher performance solutions only if smaller solutions are not adequate for your needs and the size and complexity of the model. It's crucial to adhere to the principle of least privilege, using only the resources you need to execute your specific workflow. This responsible approach ensures considerate use of resources in the Distributed Cloud environment.\n- Only choose solutions that have GPUs if you require them for your model.\n- If your model requires GPUs, consider the `a2-highgpu-1g-gdc` machine type, the smallest solution providing GPUs.\n\nPrediction cluster case template\n--------------------------------\n\nUse the following template to send an email to your IO. The email opens a case\nto create the prediction cluster that you need for online predictions. \n\n Good day,\n\n I need to create a prediction cluster and associate it with a project in my organization to use online predictions.\n\n Please use the following information for the creation of the cluster:\n\n - **Cluster name:** vtx-ai-prediction\n - **Name of the organization:** [Specify your organization's name.]\n - **Project name:** [Specify the name of your project to associate with the prediction cluster.]\n - **Machine type for the node pool:** [Specify the machine type you chose from the list of available machine types for the cluster nodes based on node selection recommendations. Please note that the IO can respond with a different suggestion based on your needs.]\n - **Compute resources:** [Optionally, if you know how many compute resources your workloads need, describe them in this field.]\n - **Memory resources:** [Optionally, if you know how many memory resources your workloads need, describe them in this field.]\n - **GPU resources:** [Optionally, if you know how many GPU resources your workloads need, describe them in this field.]\n\n **Note for IO:** Review the instructions to create the prediction cluster in the following section of the documentation: Operator \u003e Configure the deployment \u003e Create the Prediction cluster\n\n Thank you,\n [Your name]"]]
