Abstract: Techniques for facilitating inter-cloud federated learning (FL) are provided. In one set of embodiments, these techniques comprise an FL lifecycle manager that enables users to centrally manage the lifecycles of FL components across different cloud platforms. The lifecycle management operations enabled by the FL lifecycle manager can include deploying/installing FL components on the cloud platforms, updating the components, and uninstalling the components. In a further set of embodiments, these techniques comprise an FL job manager that enables users to centrally manage the execution of FL training runs (i.e., FL jobs) on FL components that have been deployed via the FL lifecycle manager. For example, the FL job manager can enable users to define the parameters and configuration of an FL job, initiate the job, monitor the job's status, take actions on the running job, and collect the job's results.

Abstract: A working method and device for a deep learning training task. GPUs are allocated to multiple deep learning training tasks according to the remaining resources of the GPUs in a single server node or multiple server nodes to achieve the effect of considering multiple deep learning training tasks while ensuring the utilization rate of the GPUs. The method comprises : obtaining a deep learning training task parameter input by a user, determining the type of the deep learning training task from the task parameter, the type of the deep learning training task type comprising : single model and multi-model; selecting GPUs by different policies according to different deep learning training task types; and selecting, according to the position of the GPU, a CPU having a shortest communication distance from the GPU for working.