HPE, IBM, Oracle, Open-Source Community, Startups
Integrate RAPIDS, Giving Giant Performance Boost to End-to-End
Predictive Data Analytics
GTC Europe—NVIDIA today announced a
GPU-acceleration platform for data science and machine learning,
with broad adoption from industry leaders, that enables even the
largest companies to analyze massive amounts of data and make
accurate business predictions at unprecedented speed.
RAPIDS™ open-source software gives data scientists a giant
performance boost as they address highly complex business
challenges, such as predicting credit card fraud, forecasting
retail inventory and understanding customer buying behavior.
Reflecting the growing consensus about the GPU’s importance in data
analytics, an array of companies is supporting RAPIDS — from
pioneers in the open-source community, such as Databricks and
Anaconda, to tech leaders like Hewlett Packard Enterprise, IBM
and Oracle.
Analysts estimate the server market for data science and machine
learning at $20 billion annually, which — together with scientific
analysis and deep learning — pushes up the value of the high
performance computing market to approximately $36 billion.
“Data analytics and machine learning are the largest segments of
the high performance computing market that have not been
accelerated — until now,” said Jensen Huang, founder and CEO of
NVIDIA, who revealed RAPIDS in his keynote address at the GPU
Technology Conference. “The world’s largest industries run
algorithms written by machine learning on a sea of servers to sense
complex patterns in their market and environment, and make fast,
accurate predictions that directly impact their bottom line.
“Building on CUDA and its global ecosystem, and working closely
with the open-source community, we have created the RAPIDS
GPU-acceleration platform. It integrates seamlessly into the
world’s most popular data science libraries and workflows to speed
up machine learning. We are turbocharging machine learning like we
have done with deep learning,” he said.
RAPIDS offers a suite of open-source libraries for
GPU-accelerated analytics, machine learning and, soon, data
visualization. It has been developed over the past two years by
NVIDIA engineers in close collaboration with key open-source
contributors.
For the first time, it gives scientists the tools they need to
run the entire data science pipeline on GPUs. Initial RAPIDS
benchmarking, using the XGBoost machine learning algorithm for
training on an NVIDIA DGX-2™ system, shows 50x speedups compared
with CPU-only systems. This allows data scientists to reduce
typical training times from days to hours, or from hours to
minutes, depending on the size of their dataset.
Close Collaboration with Open-Source
CommunityRAPIDS builds on popular open-source projects —
including Apache Arrow, pandas and scikit-learn — by adding GPU
acceleration to the most popular Python data science toolchain. To
bring additional machine learning libraries and capabilities to
RAPIDS, NVIDIA is collaborating with such open-source ecosystem
contributors as Anaconda, BlazingDB, Databricks, Quansight and
scikit-learn, as well as Wes McKinney, head of Ursa Labs and
creator of Apache Arrow and pandas, the fastest-growing Python data
science library.
“RAPIDS, a GPU-accelerated data science platform, is a
next-generation computational ecosystem powered by Apache Arrow,”
McKinney said. “NVIDIA’s collaboration with Ursa Labs will
accelerate the pace of innovation in the core Arrow libraries and
help bring about major performance boosts in analytics and feature
engineering workloads.”
To facilitate broad adoption, NVIDIA is integrating RAPIDS into
Apache Spark, the leading open-source framework for analytics and
data science.
“At Databricks, we are excited about RAPIDS’ potential to
accelerate Apache Spark workloads,” said Matei Zaharia, co-founder
and chief technologist of Databricks, and founder of Apache Spark.
“We have multiple ongoing projects to integrate Spark better with
native accelerators, including Apache Arrow support and GPU
scheduling with Project Hydrogen. We believe that RAPIDS is an
exciting new opportunity to scale our customers’ data science and
AI workloads.”
Broad Ecosystem Support and
AdoptionTech-leading enterprises across a broad range of
industries are early adopters of NVIDIA’s GPU-acceleration platform
and RAPIDS.
“NVIDIA’s GPU-acceleration platform with RAPIDS software has
immensely improved how we use data — enabling the most complex
models to run at scale and deliver even more accurate forecasting,”
said Jeremy King, executive vice president and chief technology
officer at Walmart. “RAPIDS has its roots in deep collaboration
between NVIDIA’s and Walmart’s engineers, and we plan to build on
this relationship.”
Additionally, some of the world’s leading technology companies
are supporting RAPIDS through new systems, data science platforms
and software solutions:
“HPE is committed to advancing the way customers live and work.
Artificial intelligence, analytics and machine learning technology
can play a critical role in uncovering insights that can help
customers achieve breakthrough results and improve the world we
live in. HPE is unique in the market in that we provide complete AI
and data analytics solutions from strategic advisory to
purpose-built GPU accelerator technology, operational support and a
strong partner ecosystem to tailor the right solution for each
customer. We are excited to partner with NVIDIA on RAPIDS to
accelerate the application of data science and machine learning to
help our customers drive faster and more insightful outcomes.” —
Antonio Neri, CEO, Hewlett Packard Enterprise
“IBM has built the world’s leading platform for enterprise AI,
regardless of deployment model. We look forward to extending our
successful partnership with NVIDIA, leveraging RAPIDS to provide
new machine learning tools for our clients.” — Arvind Krishna,
senior vice president of Hybrid Cloud and director of IBM
Research
“The compute world today requires powerful processing to handle
complex workloads like data science and analytics — it’s a job for
NVIDIA GPUs. RAPIDS is accelerating the speed at which this
processing and machine learning training can be done. We are
excited to support this new suite of open-source software natively
on Oracle Cloud Infrastructure and look forward to working with
NVIDIA to support RAPIDS across our platform, including the Oracle
Data Science Cloud, to further accelerate our customers’ end to-end
data science workflows. RAPIDS software runs seamlessly on the
Oracle Cloud, allowing customers to support their HPC, AI and data
science needs, all while taking advantage of the portfolio of GPU
instances available on Oracle Cloud Infrastructure.” — Clay
Magouyrk, senior vice president of Software Development, Oracle
Cloud Infrastructure
Support from other leading innovators — including Cisco, Dell
EMC, Lenovo, NERSC, NetApp, Pure Storage, SAP and SAS, as well as a
wide range of data science pioneers — is appended to this press
release.
AvailabilityAccess to the RAPIDS open-source
suite of libraries is immediately available at
http://www.rapids.ai, where the code is being released under the
Apache license. Containerized versions of RAPIDS will be available
this week on the NVIDIA GPU Cloud container registry.
About NVIDIA NVIDIA‘s (NASDAQ: NVDA) invention
of the GPU in 1999 sparked the growth of the PC gaming market,
redefined modern computer graphics and revolutionized parallel
computing. More recently, GPU deep learning ignited modern AI — the
next era of computing — with the GPU acting as the brain of
computers, robots and self-driving cars that can perceive and
understand the world. More information at
http://nvidianews.nvidia.com/.
Additional Supporting Quotations
Anaconda - Scott Collison, CEO“NVIDIA has made
the training and deployment of complex AI models scalable and
economically viable. Today’s RAPIDS announcement by NVIDIA extends
the same benefits to earlier data transformation stages of the data
science lifecycle. Anaconda is proud to have helped NVIDIA develop
these new capabilities, which will be available to the community of
7 million users of the Anaconda Distribution through our public
package repository. We’ll also be including them in Anaconda
Enterprise, which, combined with NVIDIA DGX, delivers a
high-performance, proven solution for business. Anaconda Enterprise
on NVIDIA DGX will enable IT organizations of all sizes to
accelerate data science and AI workflows.”
BlazingDB - Rodrigo Aramburu, CEO"We are
thrilled to be early contributors to the RAPIDS open-source
software from NVIDIA, and have built BlazingSQL, a free to use
version of our distributed GPU SQL engine, on RAPIDS. Our
partnership with NVIDIA has provided immense value to us as a
startup as we collaborated with the RAPIDS team, joined as key
contributors to cuDF, and will continue to support the RAPIDS
software as we build our vision of integrating Data Lakes with AI,
all using SQL.”
Cisco - Kaustabh Das, vice president of Product
Management, Data Center Group“Cisco and NVIDIA are
collaborating on AI/ML software stacks on NVIDIA GPU-optimized
Cisco UCS platforms to simplify and accelerate AI/ML workload
deployment. We are excited to learn that, with RAPIDS, NVIDIA is
expanding their GPU applicability with accelerated software stacks
to address traditional machine learning and big data analytics. We
look forward to the possibilities for our GPU-accelerated server
portfolio, including the recently launched Cisco UCS C480 ML M5
Rack Server, a best in class, purpose-built server with eight
NVIDIA V100 GPUs and NVIDIA NVLink interconnect.”
Dell EMC - Ravi Pendekanti, senior vice president of
Product Management and Marketing, Servers & Infrastructure
Systems“Dell EMC is committed to providing our customers
with world-class IT infrastructures that enable them to gain real,
competitive business advantage. We work with our ecosystem partners
to ensure our customers have the latest data science tools
available to help them transform data insights into business
outcomes. Our goal is to combine the new GPU-accelerated
open-source data science software from NVIDIA with our portfolio of
NVLink-enabled Dell EMC PowerEdge servers to significantly
accelerate the fields of machine learning and big data
analytics.”
FASTDATA.io - Alen Capalik, founder and CEO"The
RAPIDS open-source project launched by NVIDIA is going to
revolutionize the data science pipeline. At FASTDATA.io, we're
excited that our Plasma Engine — the first software to fully
leverage NVIDIA GPUs for real-time processing of infinite data in
motion — will play a part in that revolution."
Georgia Tech - David Bader, professor“Georgia
Tech is excited to contribute to RAPIDS, an open-source playground
for NVIDIA GPU-accelerated analytics. In this age of massive data,
our contribution to the RAPIDS graph libraries will help data
scientists gain meaningful knowledge from ever-growing
datasets.”
Graphistry - Leo Meyerovich, co-founder and
CEO“Graphistry, one of the first GPU cloud startups, has
been quietly bringing new levels of visibility to sensitive F500
and federal teams that must comb through records in finance,
cybersecurity, operations, and sales. As an early contributor to
RAPIDS and a force behind Apache Arrow, Graphistry has taken a big
bet on RAPIDS. The firm is already known for having redefined the
visual compute fabric to be a real-time blending of browser and
cloud GPUs, and is working with the RAPIDS team to add next-level
tabular analytics to its existing graph GPU visual analytics
core."
H2O.ai - Sri Ambati, founder and CEO“Machine
learning is transforming businesses and NVIDIA GPUs are speeding
them up. With the support of the open source communities and
customers, H2O.ai made machine learning on GPUs mainstream and won
recognition as a leader in data science and machine learning
platforms by Gartner. NVIDIA's support of the GPU machine learning
community with RAPIDS, its open-source data science libraries, is a
timely effort to grow the GPU data science ecosystem and an
endorsement of our common mission to bring AI to the data center.
Thanks to our partnership, H2O Driverless AI powered by NVIDIA GPUs
has been on an exponential adoption curve — making AI faster,
cheaper and easier."
INRIA (scikit-learn) - Gael Varoquaux, director of
Scikit-Learn Operations "NVIDIA is demonstrating real
progress in accelerating data science with new productivity tools
such as RAPIDS. Combining very fast computation in a high-language
is a game changer for data-analytics teams. We are excited that
NVIDIA has chosen to make RAPIDS compatible with scikit-learn. We
believe that it can benefit our community and look forward to
collaborating with NVIDIA."
Kinetica - Nima Negahban, co-founder and
CTO“The RAPIDS suite of open-source libraries is a
significant improvement in enabling data scientists to leverage the
power of the GPU across their model development toolchain. RAPIDS
can dramatically simplify and optimize training and improve model
accuracy, without any significant logical redesign effort on the
part of the data scientist. We’re excited to partner with NVIDIA in
this journey to democratize AI — with NVIDIA driving model
development and training and Kinetica driving operationalization
and deployment of those models, enabling enterprises to gain
maximum insight from their data.”
Lenovo - Kirk Skaugen, president of Data Center
Group“Enterprise customers and academia continue to be
challenged in working with and analyzing massive amounts of data as
they develop and test new strategies. The new RAPIDS open-source
software promises to accelerate workflows by running them
end-to-end on NVIDIA GPUs. We believe this innovation and
collaboration will make a significant impact for customers.”
MapR - John Schroeder, CEO“RAPIDS is a
breakthrough announcement for data science and, more importantly,
the ability to directly impact an organization with data science.
MapR is supporting this effort by focusing on complementary data
management and deployment activities to accompany the end-to-end
RAPIDS data science training and model workflow.”
NERSC - Rollin Thomas, Python data analytics
lead "NERSC supports more than 7,000 researchers at
universities, national labs and in industry. They increasingly want
productive, high-performance ways of interacting with their data
from complex science simulations or experimental and observational
facilities like particle accelerators and telescopes. We look
forward to working with NVIDIA to put new high-performance Python
data analytics tools like RAPIDS in the hands of our users to
accelerate their pace of discovery across many scientific
disciplines."
NetApp - Octavian Tanase, senior vice president of
ONTAP“Organizations must take advantage of new artificial
intelligence capabilities to drive competitive advantage and
accelerate digital transformation. The combination of RAPIDS
powered by NVIDIA GPUs and NetApp’s AFF A800 cloud-connected
all-flash storage will help customers confidently tap into growing
data resources with virtually unlimited scalability and performance
needed to feed, train and operate data-hungry AI applications.”
NumFOCUS - Andy Terrel, president of the board of
directors"NVIDIA’s support of NumFOCUS represents an
investment to the community. As two leaders in data science, we
feel our work together will bring better tools to science and
business alike."
OmniSci - Todd Mostak, CEO and co-founder“Data
scientists use OmniSci on NVIDIA GPUs to accelerate data
exploration and feature engineering when creating machine learning
models. Now our users can interactively query and visualize data at
scale in OmniSci, and then pipe the results into RAPIDS’
open-source libraries, enabling powerful end-to-end data science
workflows. Together, NVIDIA and OmniSci make it much faster to
build and iterate on models, resulting in increased accuracy and
quicker time to deployment.”
Pure Storage - Matt Burr, general manager of
FlashBlade"Our customers look to data for insights that
separate them from the competition and deliver ever-increasing
value for their end users. RAPIDS amplifies the impact of NVIDIA
GPU acceleration and Pure Storage FlashBlade for data science and
machine learning workflows to help more data scientists speed their
training pipelines while maintaining optimal low-latency
performance for faster time to results.”
Quansight - Travis Oliphant, NumPy and SciPy creator,
co-founder and director of Anaconda, founder and CEO of
Quansight "NVIDIA has long been a leader in accelerated
tools for advanced analytics and has consistently offered freely
available high-speed libraries for use by developers in the
data-science community. I am thrilled to see their expanded
open-source framework for data-science and their commitment to an
end-to-end software and hardware solution. These innovations will
enable a dramatic speed-up of the entire data-science workflow and
unleash innovation across the broader open-source ecosystem."
SAP - Juergen Mueller, chief innovation
officer“SAP has worked with NVIDIA closely over the past
several years to take advantage of GPU acceleration for many SAP
Leonardo Machine Learning-enabled solutions. We are furthering that
collaboration now to explore the possibilities offered by RAPIDS,
which promises to hypercharge data science pipelines on GPUs. This
is an important step to accelerate data science and machine
learning for data scientists as we bring intelligence to
enterprises with SAP Leonardo and SAP HANA.”
SAS - Saratendu Sethi, head of Artificial Intelligence
and Machine Learning“We are working closely with NVIDIA to
contribute to the new GPU-accelerated data science library. We look
forward to future SAS Viya offerings to take advantage of RAPIDS so
that our customers can gain valuable insight from their data even
faster.”
SQream - Ami Gal, CEO“The work NVIDIA has done
on RAPIDS presents an exciting opportunity for dramatically
speeding up the data science pipeline. By combining SQream DB’s
capability of piping in very large amounts of data into the RAPIDS
data science platform, we expect that data scientists will be able
to run models faster and on more data than ever before.”
University of California, Davis - John Owens, professor
and Gunrock project lead“We are delighted to be part of
the RAPIDS community and look forward to working with NVIDIA and
its partners in building the highest-performance, most
comprehensive ecosystem for data analytics.”
For further information, contact:Kristin
BrysonPR Director for Data Center AI, HPC and Accelerated
ComputingNVIDIA Corporation+1-203-241-9190kbryson@nvidia.com
Certain statements in this press release including, but not
limited to, statements as to: the benefits, impact, performance,
and availability of the RAPIDS GPU-acceleration platform; the sizes
of the server market for data science and machine learning and of
the high performance computing market; the benefits and impact of
NVIDIA’s collaboration with Ursa Labs; and Walmart’s relationship
plans with NVIDIA are forward-looking statements that are subject
to risks and uncertainties that could cause results to be
materially different than expectations. Important factors that
could cause actual results to differ materially include: global
economic conditions; our reliance on third parties to manufacture,
assemble, package and test our products; the impact of
technological development and competition; development of new
products and technologies or enhancements to our existing product
and technologies; market acceptance of our products or our
partners’ products; design, manufacturing or software defects;
changes in consumer preferences or demands; changes in industry
standards and interfaces; unexpected loss of performance of our
products or technologies when integrated into systems; as well as
other factors detailed from time to time in the most recent reports
NVIDIA files with the Securities and Exchange Commission, or SEC,
including, but not limited to, its annual report on Form 10-K and
quarterly reports on Form 10-Q. Copies of reports filed with the
SEC are posted on the company’s website and are available from
NVIDIA without charge. These forward-looking statements are not
guarantees of future performance and speak only as of the date
hereof, and, except as required by law, NVIDIA disclaims any
obligation to update these forward-looking statements to reflect
future events or circumstances.
© 2018 NVIDIA Corporation. All rights reserved. NVIDIA, the
NVIDIA logo, DGX and RAPIDS are trademarks and/or registered
trademarks of NVIDIA Corporation in the U.S. and other countries.
Other company and product names may be trademarks of the respective
companies with which they are associated. Features, pricing,
availability and specifications are subject to change without
notice.
NVIDIA (NASDAQ:NVDA)
Historical Stock Chart
From Apr 2024 to May 2024
NVIDIA (NASDAQ:NVDA)
Historical Stock Chart
From May 2023 to May 2024