Learn more about using the most popular computer vision and natural language processing models with state-of-the-art accuracy in MXNet, accelerated for NVIDIA Tensor Cores, to reduce training time. The session will explore the MXNet Gluon CV and NLP toolkits with a demo showing how to achieve out-of-the-box acceleration on Tensor Cores. We'll also review and demo a new tool for MXNet, automated mixed-precision, which shows that with only a few lines of code, any MXNet Gluon model can be accelerated on NVIDIA Tensor Cores. In addition, we'lldiscuss the MXNet ResNet-50 MLPerf submission on NVIDIA DGX systems and share how MXNet was enhanced with additions such as Horovod and small batch to set a new benchmark record. Beyond training, we'll also cover improvements to the existing experimental MXNet-TRT integration going further than FP32 and ResNets.