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GTC ON-DEMAND

Presentation
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Abstract:
Well show how MXNet-AMP and MXNet-TRT integration helps improve the training and inference performance within the MXNet deep learning framework by better utilizing the Tensor Cores inside NVIDIA GPUs. Well demonstrate how to do so through a very small change to your deep learning code.
Well show how MXNet-AMP and MXNet-TRT integration helps improve the training and inference performance within the MXNet deep learning framework by better utilizing the Tensor Cores inside NVIDIA GPUs. Well demonstrate how to do so through a very small change to your deep learning code.  Back
 
Topics:
AI & Deep Learning Research, AI Application, Deployment & Inference
Type:
Talk
Event:
GTC Washington D.C.
Year:
2019
Session ID:
DC91256
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Abstract:
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.
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.  Back
 
Topics:
Deep Learning & AI Frameworks, Tools & Libraries
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S91003
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