演讲者／陈宇恒 概要 • 我们是谁 • 智慧城市中机器视觉应用 • 我们是如何构建城市级AI+智慧城市系统 • 大规模深度学习实战系统的几点经验 l商汤科技联合创始人，架构师 lC++/Go/Rust/Ruby开发者 l多个开源项目贡献者 lNIPS国际会议论文作者 @chyh1990 2017.6 2016.3 2015.11 2014.6 2013.3 2011年中 2017

Tensor Cores, which provide an 8X increase in computational throughput over FP32 arithmetic. Comprehensive guidance and examples demonstrating AMP for PyTorch can be found in the documentation. For more throughput over FP32 arithmetic. PyTorch Release 20.02 PyTorch RN-08516-001_v23.07 | 269 Comprehensive guidance and examples demonstrating AMP for PyTorch can be found in the documentation. For more Tensor Cores, which provide an 8X increase in computational throughput over FP32 arithmetic. Comprehensive guidance and examples demonstrating AMP for PyTorch can be found in the documentation. For more