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Devi eseguire il deployment delle risorse personalizzate di previsione nel cluster di previsione
che l'operatore dell'infrastruttura (IO) crea per te. L'operatore crea
carichi di lavoro di previsione nello stesso cluster.
Per creare il cluster di previsione, collabora con l'IO per associare il tuo progetto di previsione e allocare i pool di nodi necessari per le previsioni online in Google Distributed Cloud (GDC) air-gapped.
Per creare un cluster di previsione:
Identifica il progetto della tua organizzazione che vuoi associare
al nuovo cluster per le previsioni online.
Dall'elenco dei tipi di macchina disponibili
in Distributed Cloud, scegli il tipo di macchina per i nodi di cui
i tuoi carichi di lavoro hanno bisogno nel cluster.
Il tipo di macchina che scegli dipende dalle dimensioni e dalla complessità del modello di previsione e determina le risorse di calcolo e dell'unità di elaborazione grafica (GPU) che il nodo di input/output fornisce al cluster.
Segui i suggerimenti per la selezione dei nodi
quando selezioni il tipo di macchina per i nodi.
Se necessario, comunica con l'IO finché non termina la creazione del
cluster di previsione associato al tuo progetto e l'assegnazione dei
pool di nodi appropriati all'interno del cluster.
Al termine del provisioning del cluster, il cluster di previsione è pronto per le
previsioni online.
Consigli per la selezione dei nodi
Quando l'IO crea node pool in un cluster, assegna uno dei
tipi di macchine disponibili
in Distributed Cloud per fornire un insieme predefinito di risorse per i
nodi worker. A seconda delle dimensioni e della complessità del modello, sono necessarie prestazioni di calcolo diverse e, di conseguenza, una quantità specifica di CPU, memoria e GPU. Devi fornire questi dettagli nella comunicazione con l'IO quando
vuoi creare un cluster di previsione.
Quando determini con l'IO il tipo di macchina per i pool di nodi che ti servono
nel cluster di previsione, devi rispettare le seguenti pratiche:
Distributed Cloud aggiunge l'overhead di calcolo ai nodi per i componenti di sistema obbligatori. Pertanto, devi scegliere un tipo di macchina più grande
per i tuoi pool di nodi rispetto a quello che intendi utilizzare nel pool di risorse per i tuoi modelli.
Scegli la soluzione che fornisce le risorse di memoria e di calcolo minime necessarie per i tuoi requisiti. Ad esempio, se il tuo modello
richiede otto vCPU, scegli il tipo di macchina n2-highcpu-8-gdc, la
soluzione più piccola con otto vCPU e 8 GB di memoria in
Distributed Cloud.
Man mano che avanzi, prendi in considerazione soluzioni con prestazioni più elevate solo se quelle più piccole non sono adeguate alle tue esigenze e alle dimensioni e alla complessità del modello. È fondamentale rispettare il principio del privilegio minimo, utilizzando
solo le risorse necessarie per eseguire il flusso di lavoro specifico. Questo approccio responsabile garantisce un utilizzo ponderato delle risorse nell'ambiente Distributed Cloud.
Scegli solo soluzioni con GPU se sono necessarie per il tuo modello.
Se il modello richiede GPU, prendi in considerazione il tipo di macchina a2-highgpu-1g-gdc,
la soluzione più piccola che fornisce GPU.
Modello di caso del cluster di previsione
Utilizza il seguente modello per inviare un'email al tuo IO. L'email apre una richiesta
per creare il cluster di previsione necessario per le previsioni online.
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]
[[["Facile da capire","easyToUnderstand","thumb-up"],["Il problema è stato risolto","solvedMyProblem","thumb-up"],["Altra","otherUp","thumb-up"]],[["Difficile da capire","hardToUnderstand","thumb-down"],["Informazioni o codice di esempio errati","incorrectInformationOrSampleCode","thumb-down"],["Mancano le informazioni o gli esempi di cui ho bisogno","missingTheInformationSamplesINeed","thumb-down"],["Problema di traduzione","translationIssue","thumb-down"],["Altra","otherDown","thumb-down"]],["Ultimo aggiornamento 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]"]]
