Abstract
Deep learning is the key core technology that has driven artificial intelligence to achieve breakthroughs in recent years, and a deep learning framework is a pivotal tool for realizing machine learning. However, the existing deep learning framework is difficult to meet the requirements of easy development and efficient execution at the same time. In this paper, a new deep learning framework called MindSpore developed by Huawei Technologies Co., Ltd. is introduced. Mindspore's features such as automatic parallelism, automatic differentiation, and end-side cloud collaborative training greatly improve the training efficiency of deep learning models and have a positive effect on model coding and debugging. Based on this, this enables MindSpore to achieve the three major goals of easy development, efficient execution, and full-scene coverage. We shall implement a deep learning model for human pose estimation and tracking based on the MindSpore in the field of computer vision, and experimental results show that MindSpore has the advantages of easy programming, high accuracy as well as high efficiency.
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