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Debes implementar tus recursos personalizados de predicción en el clúster de predicción que crea el operador de infraestructura (IO) para ti. El operador crea cargas de trabajo de predicción en este mismo clúster.
Para crear el clúster de predicción, trabaja con el IO para asociar tu proyecto de predicción y asignar los grupos de nodos necesarios para las predicciones en línea en Google Distributed Cloud (GDC) aislado.
Para crear un clúster de predicción, realiza los siguientes pasos:
Identifica el proyecto de tu organización que deseas asociar con el nuevo clúster para las predicciones en línea.
El tipo de máquina que elijas dependerá del tamaño y la complejidad de tu modelo de predicción, y determinará los recursos de procesamiento y de unidad de procesamiento gráfico (GPU) que tu IO proporciona al clúster.
Sigue las recomendaciones para la selección de nodos cuando elijas el tipo de máquina para tus nodos.
Si es necesario, comunícate con el IO hasta que termine de crear el clúster de predicción asociado a tu proyecto y de asignar los grupos de nodos adecuados dentro del clúster.
Después de completar el aprovisionamiento del clúster, el clúster de predicción estará listo para las predicciones en línea.
Recomendaciones de selección de nodos
Cuando el IO crea grupos de nodos en un clúster, asigna uno de los tipos de máquinas disponibles en Distributed Cloud para proporcionar un conjunto predefinido de recursos para los nodos trabajadores. Según el tamaño y la complejidad del modelo, necesitarás diferentes rendimientos de procesamiento y, en consecuencia, una cantidad específica de CPU, memoria y GPU. Debes proporcionar estos detalles en tu comunicación con el IO cuando quieras crear un clúster de predicción.
Cuando determines con el IO el tipo de máquina para los grupos de nodos que necesitas en el clúster de predicción, debes seguir las siguientes prácticas:
Distributed Cloud agrega una sobrecarga de procesamiento a los nodos para los componentes obligatorios del sistema. Por lo tanto, debes elegir un tipo de máquina más grande para tus grupos de nodos que el que piensas usar en el grupo de recursos para tus modelos.
Elige la solución que proporcione la memoria y los recursos de procesamiento mínimos necesarios para tus requisitos. Por ejemplo, si tu modelo requiere ocho CPU virtuales, elige el tipo de máquina n2-highcpu-8-gdc, la solución más pequeña con ocho CPU virtuales y 8 GB de memoria en Distributed Cloud.
A medida que avanzas, considera soluciones de mayor rendimiento solo si las soluciones más pequeñas no son adecuadas para tus necesidades y el tamaño y la complejidad del modelo. Es fundamental cumplir con el principio de privilegio mínimo y usar solo los recursos que necesitas para ejecutar tu flujo de trabajo específico. Este enfoque responsable garantiza un uso considerado de los recursos en el entorno de Distributed Cloud.
Elige solo soluciones que tengan GPUs si las necesitas para tu modelo.
Si tu modelo requiere GPUs, considera el tipo de máquina a2-highgpu-1g-gdc, la solución más pequeña que proporciona GPUs.
Plantilla de caso de clúster de predicción
Usa la siguiente plantilla para enviar un correo electrónico a tu IO. El correo electrónico abre un caso para crear el clúster de predicción que necesitas para las predicciones en línea.
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 comprender","easyToUnderstand","thumb-up"],["Resolvió mi problema","solvedMyProblem","thumb-up"],["Otro","otherUp","thumb-up"]],[["Difícil de entender","hardToUnderstand","thumb-down"],["Información o código de muestra incorrectos","incorrectInformationOrSampleCode","thumb-down"],["Faltan la información o los ejemplos que necesito","missingTheInformationSamplesINeed","thumb-down"],["Problema de traducción","translationIssue","thumb-down"],["Otro","otherDown","thumb-down"]],["Última actualización: 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]"]]