PNY realizes and appreciates that scientists, researchers, and engineers are solving the world’s most important data science and big data analytics challenges with AI and high-performance computing (HPC). Businesses, even entire industries, harness the power of AI to extract new insights from massive data sets, both on-premises and in the cloud. New NVIDIA® Ampere architecture-based GPUs, designed for the age of elastic computing, deliver the next giant leap by providing unmatched acceleration at every scale, enabling innovators to push the boundaries of human knowledge forward.
Topics: PNY, NVLink, AI, NVIDIA GPU, PNYPRO, RT Cores, Tensor Cores, GTC, hpc, data science, AI and Big Data Analytics, AI Denoising, Ampere, NVIDIA RTX, NVIDIA Automatic Mixed Precision, TF32, Multi-Instance GPU, A100
Get Amped for Latest Platform Breakthroughs in AI, Deep Learning, Autonomous Vehicles, Robotics and Professional Graphics
NVIDIA will release its GTC 2020 keynote address, featuring founder and CEO Jensen Huang, on YouTube on May 14, at 6 a.m. Pacific time.
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
This is going to be a long blog post, but by the end, you will have an Ubuntu environment connected to the NVIDIA GPU Cloud platform, pulling a TensorFlow container and ready to start benchmarking GPU performance.
Let's split this into four phases:
1) Install Ubuntu 18.04 LTS and NVIDIA Graphics Driver
2) Install Docker CE and NVIDIA Docker v 2.0
3) Setup NVIDIA GPU Cloud and pull down GPU optimized docker containers
4) Run the TensorFlow benchmark
It's time to get started!
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