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管理和请求 Azure 资源的配额Manage and request quotas for Azure resources

与其他 Azure 服务一样,与 Azure 机器学习关联的某些资源存在限制。As with other Azure services, there are limits on certain resources associated with Azure Machine Learning. 这些限制范围为可以创建的工作区数的上限,以限制用于模型定型或推理/计分的实际基础计算。These limits range from a cap on the number of workspaces you can create to limits on the actual underlying compute that gets used for model training or inference/scoring.

本文提供关于为你的订阅中各种 Azure 资源预配置的限制的更多详细信息,同时还包括为每种资源类型请求配额增加的可用链接。This article gives more details on the pre-configured limits on various Azure resources for your subscription and also contains handy links to request quota enhancements for each type of resource. 这些限制用于防止由于欺诈导致的预算超支,并且符合 Azure 容量限制。These limits are put in place to prevent budget over-runs due to fraud, and to honor Azure capacity constraints.

在为生产工作负荷设计和扩展 Azure 机器学习资源时,请记住这些配额。Keep these quotas in mind as you design and scale up your Azure Machine Learning resources for production workloads. 例如,如果你的群集无法访问指定的节点数,则你可能已达到订阅的 Azure 机器学习计算核心数限制。For example, if your cluster doesn't reach the target number of nodes you specified, then you might have reached an Azure Machine Learning Compute cores limit for your subscription. 如果想将限制或配额提高到默认值限制以上,可以免费打开联机客户支持请求。If you want to raise the limit or quota above the Default Limit, open an online customer support request at no charge. 由于 Azure 容量限制,无法将限制提高到超过下表中显示的最大限制值。The limits can't be raised above the Maximum Limit value shown in the following tables due to Azure Capacity constraints. 如果没有“最大限制”列,则资源没有可调整的限制。If there is no Maximum Limit column, then the resource doesn't have adjustable limits.

特殊注意事项Special considerations

  • 配额是一种信用限制,不附带容量保证。A quota is a credit limit, not a capacity guarantee. 如果有大规模容量需求,请与 Azure 支持部门联系。If you have large-scale capacity needs, contact Azure support.

  • 你的配额在你的订阅中的所有服务(包括 Azure 机器学习)之间共享。Your quota is shared across all the services in your subscriptions including Azure Machine Learning. 唯一的例外是 Azure 机器学习计算,它的配额独立于核心计算配额。The only exception is Azure Machine Learning compute which has a separate quota from the core compute quota. 在评估容量需求时,请务必计算所有服务的配额使用情况。Be sure to calculate the quota usage across all services when evaluating your capacity needs.

  • 默认限制根据产品/服务类别类型(例如免费试用、即用即付)和系列(例如 Dv2、F、G 等)而有所不同。Default limits vary by offer Category Type, such as Free Trial, Pay-As-You-Go, and series, such as Dv2, F, G, and so on.

默认资源配额Default resource quotas

下面是 Azure 订阅中各种资源类型的配额限制的细分条目。Here is a breakdown of the quota limits by various resource types within your Azure subscription.

重要

限制随时会变化。Limits are subject to change. 始终可以在所有 Azure 的服务级别配额文档中找到最新的限制。The latest can always be found at the service-level quota document for all of Azure.

虚拟机Virtual machines

可以在 Azure 订阅上跨服务或以独立方式预配虚拟机的数量有限。There is a limit on the number of virtual machines you can provision on an Azure subscription across your services or in a standalone manner. 基于总核心数和每个系列的区域级别也同样有此限制。This limit is at the region level both on the total cores and also on a per family basis.

必须强调的是,虚拟机核心数既有区域总数限制,也有区域按大小系列(Dv2、F 等)限制,这两种限制单独实施。It is important to emphasize that virtual machine cores have a regional total limit and a regional per size series (Dv2, F, etc.) limit that are separately enforced. 例如,某个订阅的美国东部 VM 核心总数限制为 30,A 系列核心数限制为 30,D 系列核心数限制为 30。For example, consider a subscription with a US East total VM core limit of 30, an A series core limit of 30, and a D series core limit of 30. 该订阅可以部署 30 个 A1 VM,或者 30 个 D1 VM,或者同时部署这二者,但其总数不能超过 30 个核心(例如,10 个 A1 VM 和 20 个 D1 VM)。This subscription would be allowed to deploy 30 A1 VMs, or 30 D1 VMs, or a combination of the two not to exceed a total of 30 cores (for example, 10 A1 VMs and 20 D1 VMs).

ResourceResource 默认限制Default limit 最大限制Maximum limit
每个订阅的 VM 数VMs per subscription 每个区域 25,000 个125,0001 per region. 每个区域 25,000 个。25,000 per region.
每个订阅的 VM 核心总数VM total cores per subscription 每个区域 20 个1201 per region. 联系支持人员。Contact support.
VM 系列(例如 Dv2 和 F)、每个订阅的核心数VM per series, such as Dv2 and F, cores per subscription 每个区域 20 个1201 per region. 联系支持人员。Contact support.
每个订阅的协同管理员数Coadministrators per subscription 不受限制。Unlimited. 不受限制。Unlimited.
每个订阅在每个区域中的存储帐户数Storage accounts per region per subscription 250250 250250
每个订阅的资源组数Resource groups per subscription 980980 980980
每个订阅的可用性集数Availability sets per subscription 每个区域 2,000 个。2,000 per region. 每个区域 2,000 个。2,000 per region.
Azure 资源管理器 API 请求大小Azure Resource Manager API request size 4,194,304 字节。4,194,304 bytes. 4,194,304 字节。4,194,304 bytes.
每个订阅的标记数2Tags per subscription2 不受限制。Unlimited. 不受限制。Unlimited.
每个订阅的唯一标记计算2Unique tag calculations per subscription2 10,00010,000 10,00010,000
每个订阅的云服务数Cloud services per subscription 不适用3N/A3 不适用3N/A3
每个订阅的地缘组数Affinity groups per subscription 不适用3N/A3 不适用3N/A3
每个位置的订阅级部署数Subscription-level deployments per location 80048004 800800

1默认限制根据产品类别类型(例如免费试用版和即用即付,以及按序列(如 Dv2、F 和 G)而有所不同。例如,企业协议订阅的默认值为350。1Default limits vary by offer category type, such as Free Trial and Pay-As-You-Go, and by series, such as Dv2, F, and G. For example, the default for Enterprise Agreement subscriptions is 350.

2每个订阅可以应用无限数量的标记。2You can apply an unlimited number of tags per subscription. 每个资源或资源组的标记数限制为 50。The number of tags per resource or resource group is limited to 50. 当标记数少于或等于 10,000 时,资源管理器仅返回订阅中唯一标记名和值的列表Resource Manager returns a list of unique tag name and values in the subscription only when the number of tags is 10,000 or less. 即使数目超过 10,000,也仍可按标记查找资源。You still can find a resource by tag when the number exceeds 10,000.

3使用 Azure 资源组和资源管理器时不再需要这些功能。3These features are no longer required with Azure resource groups and Resource Manager.

4如果达到部署数限制 800,则会从历史记录中删除不再需要的部署。4If you reach the limit of 800 deployments, delete deployments from the history that are no longer needed. 若要删除订阅级别部署,请使用 Remove-AzDeploymentaz deployment deleteTo delete subscription level deployments, use Remove-AzDeployment or az deployment delete.

备注

虚拟机核心数存在区域总数限制。Virtual machine cores have a regional total limit. 区域大小系列(例如 Dv2 和 F)也存在限制。这些限制是单独实施的。They also have a limit for regional per-size series, such as Dv2 and F. These limits are separately enforced. 例如,某个订阅的美国东部 VM 核心总数限制为 30,A 系列核心数限制为 30,D 系列核心数限制为 30。For example, consider a subscription with a US East total VM core limit of 30, an A series core limit of 30, and a D series core limit of 30. 此订阅可以部署 30 个 A1 VM、30 个 D1 VM,或两者的组合,但总共不能超过 30 个核心。This subscription can deploy 30 A1 VMs, or 30 D1 VMs, or a combination of the two not to exceed a total of 30 cores. 例如,10 个 A1 VM 和 20 个 D1 VM 就是一种组合。An example of a combination is 10 A1 VMs and 20 D1 VMs.

有关配额限制更详细的最新列表,请在此处查看适用于 Azure 的配额文章。For a more detailed and up-to-date list of quota limits, check the Azure-wide quota article here.

Azure 机器学习计算Azure Machine Learning Compute

在 Azure 机器学习计算中,订阅中每个区域所允许的核心数和唯一计算资源数都有默认配额限制。For Azure Machine Learning Compute, there is a default quota limit on both the number of cores and number of unique compute resources allowed per region in a subscription. 该配额独立于上述的 VM 核心配额,并且核心限制目前没有在这两种资源类型之间共享。This quota is separate from the VM core quota above and the core limits are not shared currently between the two resource types.

可用资源:Available resources:

  • 每个区域的专用核心数的默认限制为 24-300,具体取决于你的订阅产品/服务类型。Dedicated cores per region have a default limit of 24 - 300 depending on your subscription offer type. 可增加每个订阅的专用核心数。The number of dedicated cores per subscription can be increased. 请联系 Azure 支持以讨论增加选项。Contact Azure support to discuss increase options.

  • 每个区域的低优先级核心数的默认限制为 24-300,具体取决于你的订阅产品/服务类型。Low-priority cores per region have a default limit of 24 - 300 depending on your subscription offer type. 可增加每个订阅的低优先级核心数。The number of low-priority cores per subscription can be increased. 请联系 Azure 支持以讨论增加选项。Contact Azure support to discuss increase options.

  • 每个区域的群集数默认限制为 100,最大限制为 200。Clusters per region have a default limit of 100 and a maximum limit of 200. 如果请求增加的配额超出此限制,请与 Azure 支持部门联系。Contact Azure support if you want to request an increase beyond this limit.

  • 下面是“其他严格限制”,不能超出这些限制。There are other strict limits which cannot be exceeded once hit.

资源Resource 最大限制Maximum limit
每个资源组的最大工作区数Maximum workspaces per resource group 800800
单个 Azure 机器学习计算 (AmlCompute) 资源中的最大节数点Maximum nodes in a single Azure Machine Learning Compute (AmlCompute) resource 100 个节点100 nodes
每个节点的最大 GPU MPI 进程数Maximum GPU MPI processes per node 1-41-4
每个节点的最大 GPU 辅助角色数Maximum GPU workers per node 1-41-4
最长作业生存期Maximum job lifetime 90天190 days1
低优先级节点上的最大作业生存期Maximum job lifetime on a Low Priority Node 1天21 day2
每个节点的最大参数服务器数Maximum parameter servers per node 11

1 最长生存期是指运行从开始到结束的时间。1 The maximum lifetime refers to the time that a run start and when it finishes. 已完成的运行会无限期保存;最长生存期内未完成的运行的数据不可访问。Completed runs persist indefinitely; data for runs not completed within the maximum lifetime is not accessible. 对于低优先级节点上的2个作业,在存在容量约束时可能会被预先清空。2 Jobs on a Low Priority node could be pre-empted any time there is a capacity constraint. 建议在作业中实施检查点操作。It is recommended to implement checkpointing in your job.

Azure 机器学习管道Azure Machine Learning Pipelines

对于 Azure 机器学习管道,管道中的步骤数和订阅中每个区域基于计划的已发布管道的运行数有限制。For Azure Machine Learning Pipelines, there is a quota limit on the number of steps in a pipeline and on the number of schedule-based runs of published pipelines per region in a subscription.

  • 管道中允许的最大步骤数为30000Maximum number of steps allowed in a pipeline is 30,000
  • 基于计划的运行和 blob 请求的最大数目每月每个订阅的已发布管道的已触发计划数为100000Maximum number of the sum of schedule-based runs and blob pulls for blog-triggered schedules of published pipelines per subscription per month is 100,000

备注

如果想要增加此限制,请联系 Microsoft 支持If you want to increase this limit, contact Microsoft Support.

容器实例Container instances

可以在给定时间段内(以小时为范围)或在你的整个订阅中启动的容器实例数量也有限制。There is also a limit on the number of container instances that you can spin up in a given time period (scoped hourly) or across your entire subscription.

ResourceResource 默认限制Default limit
每个订阅的容器组数Container groups per subscription 10011001
每个容器组的容器数Number of containers per container group 6060
每个容器组的卷数Number of volumes per container group 2020
每个 IP 的端口数Ports per IP 55
容器实例日志大小 - 正在运行的实例Container instance log size - running instance 4 MB4 MB
容器实例日志大小 - 已停止的实例Container instance log size - stopped instance 16 KB 或 1,000 行16 KB or 1,000 lines
每小时创建容器次数Container creates per hour 30013001
每 5 分钟创建容器次数Container creates per 5 minutes 10011001
每小时删除容器次数Container deletes per hour 30013001
每 5 分钟删除容器次数Container deletes per 5 minutes 10011001

1要请求提高上限,请创建一个 Azure 支持请求1To request a limit increase, create an Azure Support request.

有关配额限制更详细的最新列表,请在此处查看适用于 Azure 的配额文章。For a more detailed and up-to-date list of quota limits, check the Azure-wide quota article here.

存储Storage

给定订阅中每个区域的存储帐户数量也有限制。There is a limit on the number of storage accounts per region as well in a given subscription. 默认限制数量为 200,包括标准和高级存储帐户。The default limit is 200 and includes both Standard and Premium Storage accounts. 如果在某特定区域中需要的存储帐户多于 200 个,请通过 Azure 支持提出请求。If you require more than 200 storage accounts in a given region, make a request through Azure Support. Azure 存储团队将评审业务案例,对于特定区域最多可以批准 250 个存储帐户。The Azure Storage team will review your business case and may approve up to 250 storage accounts for a given region.

查找你的配额Find your quotas

通过 Azure 门户可以轻松查看各种资源的配额,例如虚拟机、存储和网络。Viewing your quota for various resources, such as Virtual Machines, Storage, Network, is easy through the Azure portal.

  1. 在左窗格上,选择“所有服务”,然后在“一般”类别下选择“订阅”。On the left pane, select All services and then select Subscriptions under the General category.

  2. 从订阅列表中选择要查找其配额的订阅。From the list of subscriptions, select the subscription whose quota you are looking for.

    有一个警告提示,专门用于查看 Azure 机器学习计算配额。There is a caveat, specifically for viewing the Azure Machine Learning Compute quota. 如上所述,此配置独立于订阅上的计算配额。As mentioned above, that quota is separate from the compute quota on your subscription.

  3. 在左侧窗格中,选择 "机器学习服务",然后从显示的列表中选择任何工作区On the left pane, select Machine Learning service and then select any workspace from the list shown

  4. 在下一个边栏选项卡中,在“支持 + 故障排除部分”下,选择“使用情况 + 配额”以查看当前配额限制和使用情况。On the next blade, under the Support + troubleshooting section select Usage + quotas to view your current quota limits and usage.

  5. 选择订阅以查看配额限制。Select a subscription to view the quota limits. 请记住筛选到所需的区域。Remember to filter to the region you are interested in.

请求增加配额Request quota increases

如果想将限制或配额提高到默认值限制以上,可以免费打开联机客户支持请求If you want to raise the limit or quota above the default limit, open an online customer support request at no charge.

无法将限制提高到表中所示的最大限制值。The limits can't be raised above the maximum limit value shown in the tables. 如果没有最大限制,则资源没有可调整的限制。If there is no maximum limit, then the resource doesn't have adjustable limits. 这篇文章详细介绍了配额增加过程。This article covers the quota increase process in more detail.

请求配额增加时,需要选择要请求提高配额的服务,这可能是机器学习服务配额、容器实例或存储配额的服务。When requesting a quota increase, you need to select the service you are requesting to raise the quota against, which could be services such as Machine Learning service quota, Container instances or Storage quota. 此外,对于 Azure 机器学习计算,只需单击“请求配额”按钮,然后按照上述步骤查看配额。In addition for Azure Machine Learning Compute, you can simply click on the Request Quota button while viewing the quota following the steps above.

备注

免费试用版订阅不符合增加限制或配额的条件。Free Trial subscriptions are not eligible for limit or quota increases. 如果有免费试用版订阅,可将其升级到即用即付订阅。If you have a Free Trial subscription, you can upgrade to a Pay-As-You-Go subscription. 有关详细信息,请参阅将 Azure 免费试用版订阅升级到即用即付订阅免费试用版订阅常见问题解答For more information, see Upgrade Azure Free Trial to Pay-As-You-Go and Free Trial subscription FAQ.