Abstract: There are some problems in the deep learning work of large enterprises, such as scattered management and a large number of redundant projects. In order to support the whole process management of large-scale deep learning and efficient reuse of model results, a deep learning platform is proposed based on the two level multi center deployment architecture of State Grid Corporation of China. The system distributed the management work of training, inferencing, data and models into different centers, and they cooperated to complete the closed-loop of deep learning. A private cloud based on Kubernetes was used to support the parallel computing of large number of deep learning applications. The front-end interface adopted operator-based flow arrangement to realize modeling visualization and function expansion. The experimental results show that the system can support the parallel execution of multiple deep learning tasks, and the additional performance overhead is acceptable.