MXNet is a multi-language machine learning library designed to facilitate the development of deep neural networks. It combines declarative symbolic expression with imperative tensor computation, offering auto-differentiation and efficient memory usage. MXNet supports various host languages, including C++, Python, R, Go, and Julia, and can run on heterogeneous systems from mobile devices to distributed GPU clusters. The library's API design and system implementation are detailed, emphasizing the unified handling of symbolic expressions and tensor operations. MXNet's performance is competitive with other popular ML libraries like Torch7, Caffe, and TensorFlow, and it demonstrates scalability in distributed settings. Experimental results show promising performance and memory efficiency, making MXNet a valuable tool for large-scale deep learning applications.MXNet is a multi-language machine learning library designed to facilitate the development of deep neural networks. It combines declarative symbolic expression with imperative tensor computation, offering auto-differentiation and efficient memory usage. MXNet supports various host languages, including C++, Python, R, Go, and Julia, and can run on heterogeneous systems from mobile devices to distributed GPU clusters. The library's API design and system implementation are detailed, emphasizing the unified handling of symbolic expressions and tensor operations. MXNet's performance is competitive with other popular ML libraries like Torch7, Caffe, and TensorFlow, and it demonstrates scalability in distributed settings. Experimental results show promising performance and memory efficiency, making MXNet a valuable tool for large-scale deep learning applications.