Opening in Denver on November 17, Supercomputing 2019 lets you interact with the world’s leading experts in HPC and learn how the latest cutting-edge technology provides computational solutions for the world’s greatest challenges ranging from precision medicine and agricultural technologies to autonomous vehicles in smart cities and renewable energy. At SC19, PNY invites you to visit our Partner’s booths, meet with product experts, and see how NVIDIA® Quadro® RTX™ GPU powered solutions, including RTX Servers and NVIDIA Powered Data Science Workstations, are accelerating deep learning, machine learning, machine vision, and big data analytics.
AI and big data analytics are changing the way enterprises and institutions are making decisions and delivering products and services worldwide. Data scientists (and others) need sophisticated hardware, development, and software platforms to separate actionable intelligence, patterns and trends from daily data tsunamis.
It is imperative to separate the digital wheat from the chaff, and new Quadro® RTX™ GPUs with Tensor Cores, mixed precision compute, and unprecedented GPU memory capacity provide the hardware foundation to meet today’s AI and analytics challenges. Turnkey solutions like the NVIDIA-Powered Data Science Workstation provide data scientists and others with the productivity enhancing tools they need. OmniSci’s GPU accelerated analytics platform overcomes the scalability and performance limitations of legacy analytics tools faced with the scale, velocity and location attributes of today’s big data analytics.
Topics: PNY, NVIDIA, Webinar, NVIDIA Quadro, AI, PNY PRO, NVIDIA Quadro GPUs, Quadro RTX, Data Science Workstation, nvidia quadro rtx 4000, data science, Big Data Analytics, omnisci, turnkey solutions
Data is fundamentally changing the way companies do business, driving demand for data scientists and increasing the complexity in their workflows. To meet these challenges Data scientists (and others) need sophisticated hardware, development, and software platforms which traditionally entailed IT or high-cost Data Science professionals spending many man hours configuring an engineering workstation that would meet their daily demands. With the launch of Quadro RTX, a new class of system was introduced– the NVIDIA-Powered Data Science Workstation – that delivers a fully integrated hardware and software solution for data science. This is just one of the reasons why Digital Engineering (DE) magazine selected the NVIDIA-Powered Data Science Workstation as its Editor’s Pick of the Week.
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
Excitement and anticipation were everywhere at the NVIDIA GPU Technology Conference (GTC) 2019. GTC’s speaker roster reads like a who’s who in AI and deep learning. The expectation was high for NVIDIA CEO Jensen Huang to announce new industry-disruptive technologies during his keynote address. Last year it was the launch of NVIDIA RTX real-time ray tracing technology. This year Jensen took it a step further with the introduction of NVIDIA- RTX-powered Data Science Workstations and expanded NVIDIA RTX Server offerings.
Topics: PNY, NVIDIA, GPUs, GPU, Rendering, GPU Technology Conference, NVIDIA Quadro, workstation, Professional Graphics, NVIDIA GPU, PNYPRO, GTC Sponsor, NVIDIA RTX Technology, ray tracing, GTC, nvidia quadro rtx, Artificial Intelligence, JensenHuang, GTC2019, QuadroRTX8000, RTX Server, Data Science Workstation