As Brian Albright points out in his article for Digital Engineering on Superworkstations, the introduction of new multi-core CPUs, ultra-fast GPUs and terabytes of memory has made it possible for designers to now handle real-time simulation, rendering, virtual reality applications and complex data science on a desktop engineering workstation. The only draw back is the budget. In his article Albright interviews PNY, NVIDIA and other workstation experts to determine where it makes sense to invest your IT budget based on your workflow.
As a PC enthusiast, I love pitting hardware solutions against each other to determine their relative performance when completing a particular task. This process is also known as “Benchmarking.” Benchmarking results are usually considered the best tool to evaluate the merits of competing systems when making a purchase decision.
In this 3-part blog series, we’ll discuss how to build a system, with an emphasis on benchmarking GPU performance for Deep Learning using Ubuntu 18.04, NVIDIA GPU Cloud (NGC) and TensorFlow.
Topics: Deep Learning, NVIDIA GPU, NVIDIA GPUs, NVIDIA Quadro GPUs, CUDA, NVIDIA RTX Technology, Pro Tip, Tensor Cores, Quadro RTX, NVIDIA Turing Architecture, nvidia quadro rtx, Artificial Intelligence, NVIDIATuring, GeForce RTX, Data Science Workstation, NGC, data science, RAPIDS, GPU-accelerated machine learning, NVIDIA CUDA, analytics, CUDA-X, Linux, Tensorflow, benchmark
According to Data Science Central, a leading online resource for data practitioners, forecasts predict the big data market will approach $203 billion by 2020. Data science is powering the engine of modern enterprise – every industry from retail to financial services to healthcare is deriving insight from data to improve competitiveness and operational efficiency. Retailers are improving forecasting to reduce the cost of excess inventory. Financial services institutions are detecting fraudulent transactions. Healthcare providers are predicting the risk of disease more quickly. Even modest improvements in the accuracy of predictive machine learning models can translate into billions on the bottom line. The NVIDIA accelerated Data Science Workstation (DWS) solution with RAPIDS enables enterprises and data scientists to tap into GPU-accelerated machine learning (ML) and deep learning (DL) with faster model iteration, better prediction accuracy, and lowest data science total cost of ownership (TCO).
Topics: PNY, NVIDIA Quadro, Deep Learning, AI, PNYPRO, NVIDIA RTX Technology, Quadro RTX, nvidia quadro rtx, Data Science Workstation, NGC, hpc, graph analytics, data preparation, data science, RAPIDS, GPU-accelerated machine learning, NVIDIA CUDA, analytics, model training, CUDA-X