Graph Neural Networks (GNNs) play a very important role today. It does analyze not only the graph data itself, but also the data connectivity of the graph. The quality of a GNN is thus altered by the result of extracted graph structure information. The extraction could be enhanced by GNN model design or directly from the training dataset with a GNN-decoupled method. In this paper, we propose RankedDrop, a new sampling method to improve the extraction of graph structure information. This approach is based on droppingout technique, and it adopts a spatial-aware selection of edges to drop. It takes into account structure information of the graph to control the dropping-out, and its random selection of edges to be dropped is under the control of a probability generated with respect to graph’s topological importance. Our experiments point out that RankedDrop provides high-quality and robust training results compared to the leading solutions. Furthermore, RankedDrop could be a framework plugin and combined with GNN model improvements to maximize GNN quality. Furthermore, RankedDrop could be a plugin for AI frameworks like MindSpore and combined with GNN model improvements to maximize GNN quality.

Ensembles of Deep Neural Networks (DNNs) has achieved qualitative predictions but they are computing and memory intensive. Therefore, the demand is growing to make them answer a heavy workload of requests with available computational resources. Unlike recent initiatives on inference servers and inference frameworks, which focus on the prediction of single DNNs, we propose a new software layer to serve with flexibility and efficiency ensembles of DNNs.Our inference system is designed with several technical innovations. First, we propose a novel procedure to found a good allocation matrix between devices (CPUs or GPUs) and DNN instances. It runs successively a worst-fit to allocate DNNs into the memory devices and a greedy algorithm to optimize allocation settings and speed up the ensemble. Second, we design the inference system based on multiple processes to run asynchronously: batching, prediction, and the combination rule with an efficient internal communication scheme to avoid overhead.Experiments show the flexibility and efficiency under extreme scenarios: It successes to serve an ensemble of 12 heavy DNNs into 4 GPUs and at the opposite, one single DNN multi-threaded into 16 GPUs. It also outperforms the simple baseline consisting of optimizing the batch size of DNNs by a speedup up to 2.7X on the image classification task.