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并关联业务部门使用的集群或者命名空间。 单个集群的成本由业务命名空间成本、未被分配的空闲成本、集群管理成本（CCE集群Master成本+系统命名空间成本）组成。其中未被分配空闲成本以及集群管理成本，被定义为公共成本。当部门按照命名空间进行设置时，需要关联业务命名空间，并设置公共成本的分摊比例。
and CLOUD_SDK_SK in the local environment ak = os.environ["CLOUD_SDK_AK"] sk = os.environ["CLOUD_SDK_SK"] credentials = BasicCredentials(ak
and CLOUD_SDK_SK in the local environment ak = os.environ["CLOUD_SDK_AK"] sk = os.environ["CLOUD_SDK_SK"] projectId = "{project_id}"
and CLOUD_SDK_SK in the local environment ak = os.environ["CLOUD_SDK_AK"] sk = os.environ["CLOUD_SDK_SK"] projectId = "{project_id}"
略。 单击上方“创建日志采集策略”，勾选“采集插件日志（NGINX Ingress控制器容器标准输出）”，单击确定。 图3 创建日志策略 系统将自动创建名为default-nginx-ingress的日志采集策略。创建完成后，您可前往“日志中心”页面，选择“插件日志”页签，即可查
and CLOUD_SDK_SK in the local environment ak = os.environ["CLOUD_SDK_AK"] sk = os.environ["CLOUD_SDK_SK"] credentials = BasicCredentials(ak
and CLOUD_SDK_SK in the local environment ak = os.environ["CLOUD_SDK_AK"] sk = os.environ["CLOUD_SDK_SK"] projectId = "{project_id}"
and CLOUD_SDK_SK in the local environment ak = os.environ["CLOUD_SDK_AK"] sk = os.environ["CLOUD_SDK_SK"] credentials = BasicCredentials(ak
and CLOUD_SDK_SK in the local environment ak = os.environ["CLOUD_SDK_AK"] sk = os.environ["CLOUD_SDK_SK"] projectId = "{project_id}"
and CLOUD_SDK_SK in the local environment ak = os.environ["CLOUD_SDK_AK"] sk = os.environ["CLOUD_SDK_SK"] credentials = BasicCredentials(ak
and CLOUD_SDK_SK in the local environment ak = os.environ["CLOUD_SDK_AK"] sk = os.environ["CLOUD_SDK_SK"] projectId = "{project_id}"
and CLOUD_SDK_SK in the local environment ak = os.environ["CLOUD_SDK_AK"] sk = os.environ["CLOUD_SDK_SK"] projectId = "{project_id}"
and CLOUD_SDK_SK in the local environment ak = os.environ["CLOUD_SDK_AK"] sk = os.environ["CLOUD_SDK_SK"] projectId = "{project_id}"
and CLOUD_SDK_SK in the local environment ak = os.environ["CLOUD_SDK_AK"] sk = os.environ["CLOUD_SDK_SK"] credentials = BasicCredentials(ak
and CLOUD_SDK_SK in the local environment ak = os.environ["CLOUD_SDK_AK"] sk = os.environ["CLOUD_SDK_SK"] projectId = "{project_id}"
and CLOUD_SDK_SK in the local environment ak = os.environ["CLOUD_SDK_AK"] sk = os.environ["CLOUD_SDK_SK"] projectId = "{project_id}"
and CLOUD_SDK_SK in the local environment ak = os.environ["CLOUD_SDK_AK"] sk = os.environ["CLOUD_SDK_SK"] projectId = "{project_id}"
and CLOUD_SDK_SK in the local environment ak = os.environ["CLOUD_SDK_AK"] sk = os.environ["CLOUD_SDK_SK"] projectId = "{project_id}"
模型训练环节 Kubeflow诞生于2017年，Kubeflow项目是基于容器和Kubernetes构建，旨在为数据科学家、机器学习工程师、系统运维人员提供面向机器学习业务的敏捷部署、开发、训练、发布和管理平台。它利用了云原生技术的优势，让用户更快速、方便地部署、使用和管理当前最流行的机器学习软件。
and CLOUD_SDK_SK in the local environment ak = os.environ["CLOUD_SDK_AK"] sk = os.environ["CLOUD_SDK_SK"] credentials = BasicCredentials(ak
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