We'll do a dive deep into best practices and real world examples of leveraging the power and flexibility of local GPU workstations, such as the DGX Station, to rapidly develop and prototype deep learning applications. This journey will take you from experimenting and iterating fast and often, to obtaining a trained model, to eventually deploying scale-out GPU inference servers in a datacenter. Tools available, that will be explained, are NGC (NVIDIA GPU Cloud), TensorRT, TensorRT Inference Server, and YAIS. NGC is a cloud registry of docker images, TensorRT is an inference optimizing compiler, TensorRT Inference Server is a containerized microservice that maximizes GPU utilization and runs multiple models from different frameworks concurrently on a node, and YAIS is a C++ library for developing compute intensive asynchronous microservices using gRPC. This session will consist of a lecture, live demos, and detailed instructions about different inference compute options, pre- and post-processing considerations, inference serving options, monitoring considerations, and scalable workload orchestration using Kubernetes.
NVIDIA DGX Systems powered by Volta deliver breakthrough performance for today''s most popular deep learning frameworks. Attend this session to hear from DGX product experts and gain insights that will help researchers, developers, and data science practitioners accelerate training and iterate faster than ever. Learn (1) best practices for deploying an end-to-end deep learning practice, (2) how the newest DGX systems including DGX Station address the bottlenecks impacting your data science, and (3) how DGX software including optimized deep learning frameworks give your environment a performance advantage over GPU hardware alone.
Deep learning practitioners have traditionally been forced to spend protracted cycle time cobbling together platforms using consumer-grade components and unsupported open source software. Learn (1) the benefits of rapid experimentation and deep learning framework optimization as a precursor to scalable production training in the data center, (2) the technical challenges that must be overcome for extending deep learning to more practitioners across the enterprise, and (3) how many organizations can benefit from a powerful enterprise-grade solution that's pre-built, simple to manage, and readily accessible to every practitioner.