Home > Storage > PowerScale (Isilon) > Industry Solutions and Verticals > Analytics > PowerScale Deep Learning Infrastructure with NVIDIA DGX A100 Systems for Autonomous Driving > NVIDIA A100 GPU
The NVIDIA A100 Tensor Core GPU delivers unprecedented acceleration for AI, data analytics and high-performance computing (HPC) to tackle the world’s toughest computing challenges. With third-generation NVIDIA Tensor Cores providing a huge performance boost, the A100 GPU can efficiently scale up to the thousands or, with Multi-Instance GPU, be allocated as seven smaller, dedicated instances to accelerate workloads of all sizes.
The NVIDIA A100, built on the latest Ampere architecture, supports both training and inference workloads. In the case of AV development, DNN training and validation (RePlay) can be supported by one, unified infrastructure.
DL models are very complex and large, and a DL framework is an interface, library or a tool which allows developers to tackle DL tasks easily and quickly, without requiring in-depth understanding of all the details of the underlying algorithms. These frameworks provide a clear and concise way for defining models using a collection of pre-built and pre-optimized components. Popular DL frameworks include TensorFlow, Keras, PyTorch, and Caffe.
Key characteristics of a well-designed DL framework include:
Training with large datasets and DL networks can be accelerated by using multiple GPUs and/or more servers, but only if the underlying infrastructure is architected correctly.
In the market, there are some popular platforms and toolkits to allow developers to test distributed execution of different DL platforms on GPU clusters including MPI-based Uber Horovod and the Microsoft Distributed Machine Learning Toolkit (DMTK), available on the Microsoft website. Horovod is a distributed training framework for TensorFlow, Keras, PyTorch, and MXNet. The goal of Horovod is to make distributed DL fast and easy to use. These platforms are designed to make large-scale parallel distributed DL jobs easy and better.