Vertex AI 永久性资源指的是长时间运行的集群,您可以创建此类集群来运行自定义训练作业。在一个训练作业完成后,永久性资源会保留下来,您仍可用其来运行其他训练作业,直到您将其删除。您可以使用永久性资源来确保计算资源的可用性,并且可以缩短作业启动时间,因为省去了创建计算资源这一步骤。永久性资源支持自定义训练作业支持的所有虚拟机和 GPU。本页面介绍何时使用永久性资源,并提供有关结算和配额的信息。
[[["易于理解","easyToUnderstand","thumb-up"],["解决了我的问题","solvedMyProblem","thumb-up"],["其他","otherUp","thumb-up"]],[["很难理解","hardToUnderstand","thumb-down"],["信息或示例代码不正确","incorrectInformationOrSampleCode","thumb-down"],["没有我需要的信息/示例","missingTheInformationSamplesINeed","thumb-down"],["翻译问题","translationIssue","thumb-down"],["其他","otherDown","thumb-down"]],["最后更新时间 (UTC):2025-09-04。"],[],[],null,["# Overview of persistent resources\n\nA Vertex AI persistent resource is a long-running cluster that you can\ncreate to run custom training jobs. After a training job completes, the\npersistent resource remains available to run other training jobs until you\ndelete it. You can use a persistent resource to ensure compute resource\navailability and to reduce the job startup time that's otherwise needed for\ncompute resource creation. Persistent resources support all VMs and GPUs that\nare supported by custom training jobs. This page explains when to use a\npersistent resource and gives you information about billing and quota.\n\nWhen to use a persistent resource\n---------------------------------\n\nWe recommend using persistent resources in the following scenarios:\n\n- You want to ensure capacity availability for critical ML workloads or during peak seasons. Unlike custom jobs, where the training service releases the resource after job completion, persistent resource remains available until it's deleted.\n- You're submitting the same job multiple times and can benefit from data and image caching by running the jobs on the same persistent resource.\n- You run many short-lived training jobs where the actual training time is shorter than the job startup time.\n\nFor more context on when to and why use a persistent resource, see the blog post\n[Bringing capacity assurance and faster startup times to Vertex AI Training](/blog/products/ai-machine-learning/vertex-ai-persistent-resources-and-capacity-assurance).\n\nBilling details\n---------------\n\nYou are billed for the entire duration that a persistent resource is in a\nrunning state, regardless of whether there is a job running on the persistent\nresource. For each instance in the persistent resource pool, you are billed by\ncore hour. All jobs running on a persistent resource are not separately charged.\nYou are billed only for the persistent resource.\n\nIf you set up auto scaling for your persistent resource, you only pay\nfor the provisioned instances. For example, if `min-replica-count` is set to `4`,\n`4` instances are always provisioned and this is the minimum amount you're billed\nfor. When your workload increases, the resource pool might scale up to `6` to\naccommodate the increased demand. Then, you're billed for the `6` provisioned instances\nuntil your resource pool scales down again. To avoid paying for idle nodes,\nuse auto scaling for your persistent resource, or delete it when you no longer\nneed it. To learn more about pricing, see the [Custom-trained models](/vertex-ai/pricing#custom-trained_models)\nsection in the Vertex AI pricing page.\n\nQuotas\n------\n\nPersistent resources use your training quota, so verify you have sufficient\nquota for persistent resource creation. To learn more about quotas, see [Training quotas and limits](/vertex-ai/docs/quotas#training).\n\nWhat's next\n-----------\n\n- [Create and use a persistent resource](/vertex-ai/docs/training/persistent-resource-create).\n- [Run training jobs on a persistent resource](/vertex-ai/docs/training/persistent-resource-train).\n- [Get information about a persistent resource](/vertex-ai/docs/training/persistent-resource-get).\n- [Reboot a persistent resource](/vertex-ai/docs/training/persistent-resource-reboot).\n- [Delete a persistent resource](/vertex-ai/docs/training/persistent-resource-delete)."]]