How does TensorFlow compare to MXNet for large-scale distributed training?
The question is about Tensorflow
TensorFlow and MXNet both support large-scale distributed training but are different in approach. TensorFlow has a rich ecosystem of tools, including TensorFlow Serving; thus, it finds significant applications for enterprise usage and supports synchronous and asynchronous training across multiple GPUs and TPUs. MXNet has the backing of Apache and AWS with optimizations that allow running high efficiency, dynamic computation graphs that enable flexibility and even speed up training in high-performance settings. While TensorFlow is far easier to work with and has wider acceptance, MXNet’s lightweight design can translate into better performance for specialized, very large environments where computational efficiency is key.
