NVIDIA Accelerated Data Science

                          GPU-ACCELERATE YOUR DATA SCIENCE WORKFLOWS

                          Data science workflows have traditionally been slow and cumbersome, relying on CPUs to load, filter and manipulate data and train and deploy models. NVIDIA accelerated data science solutions are built on NVIDIA CUDA-X AI and feature RAPIDS for data processing and machine learning and a variety of other data science software to maximize productivity, performance and ROI with the power of NVIDIA GPUs.

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                          Features and Benefits

                          Ease of Use

                          Maximize Productivity

                          Reduce time spent waiting to get the most valuable insights and accelerate ROI.

                          Ease of Use

                          Ease of Use

                          Accelerate your entire Python toolchain with open-source, hassle-free software integration and minimal code changes.

                          Accomplish More

                          Accomplish More

                          Accelerate machine learning training up to 215X faster and perform more iterations, increase experimentation and carry out deeper exploration.

                          Accomplish More

                          Improve Accuracy

                          Fastest model iteration for better results and performance

                          Cost-Efficiency

                          Cost-Efficiency

                          Reduce data science infrastructure costs and increase data center efficiency.

                          Cost-Efficiency

                          Total Cost of Ownership

                          Dramatically reduce data center infrastructure costs

                           

                          XGBOOST TRAINING ON NVIDIA GPUs

                          GPU-accelerated XGBoost brings game-changing performance to the world’s leading machine learning algorithm in both single node and distributed deployments. With significantly faster training speed over CPUs, data science teams can tackle larger data sets, iterate faster, and tune models to maximize prediction accuracy and business value.

                          Data Prep

                          XGBoost

                          End-to-end

                          Learn how to get started today with GPU-accelerated XGBoost

                          NVIDIA GPU SOLUTIONS FOR DATA SCIENCE

                          Explore unparalleled acceleration across a variety of different NVIDIA GPU solutions.

                          PC

                          Get started in machine learning.

                          Workstations

                          A new breed of workstations for data science.

                          Data Center

                          AI systems for enterprise production.

                          Cloud

                          Versatile accelerated machine learning.

                          GPU-ACCELERATED BUSINESS IN ACTION

                          Maximize performance, productivity and ROI for machine learning workflows.

                          Rapids: SUITE OF DATA SCIENCE LIBRARIES

                          RAPIDS, built on NVIDIA CUDA-X AI, leverages more than 15 years of NVIDIA? CUDA? development and machine learning expertise. It’s powerful software for executing end-to-end data science training pipelines completely in NVIDIA GPUs, reducing training time from days to minutes.

                          NVIDIA RAPIDS Flow
                          End-to-End Faster Speeds on RAPIDS

                          RAPIDS, a GPU-accelerated data science platform, is a next-generation computational ecosystem powered by Apache Arrow. The NVIDIA 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.

                          - Wes McKinney, Head of Ursa Labs and Creator of Apache Arrow and Pandas

                          At Databricks, we are excited about RAPIDS’ potential to accelerate Apache Spark workloads. 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.

                          - Matei Zaharia, co-founder and CTO of Databricks, and the original creator of Apache Spark

                          I got 24x speedup using RAPIDS XGBOOST and can now replace hundreds of CPU nodes, running my biggest ML workload on a single node with 8 GPUs. You made XGBOOST too fast!?

                          - Streaming Media Company

                          My previous bottleneck was I/O. …10 minutes to pull in data for 10 stores (about 1 million rows). With RAPIDS, we can pull in data for about 6000 stores (millions of rows) in less than 3 minutes. That scale could have easily taken us 4 days on legacy infrastructure … just plain awesome.

                          - A mid-market specialty retailer with 6000 stores

                          RAPIDS, a GPU-accelerated data science platform, is a next-generation computational ecosystem powered by Apache Arrow. The NVIDIA 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.

                          - Wes McKinney, Head of Ursa Labs and Creator of Apache Arrow and Pandas

                          At Databricks, we are excited about RAPIDS’ potential to accelerate Apache Spark workloads. 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.

                          - Matei Zaharia, co-founder and CTO of Databricks, and the original creator of Apache Spark

                          I got 24x speedup using RAPIDS XGBOOST and can now replace hundreds of CPU nodes, running my biggest ML workload on a single node with 8 GPUs. You made XGBOOST too fast!?

                          - Streaming Media Company

                          My previous bottleneck was I/O. …10 minutes to pull in data for 10 stores (about 1 million rows). With RAPIDS, we can pull in data for about 6000 stores (millions of rows) in less than 3 minutes. That scale could have easily taken us 4 days on legacy infrastructure … just plain awesome.

                          - A mid-market specialty retailer with 6000 stores

                          RAPIDS, a GPU-accelerated data science platform, is a next-generation computational ecosystem powered by Apache Arrow. The NVIDIA 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.

                          - Wes McKinney, Head of Ursa Labs and Creator of Apache Arrow and Pandas

                          At Databricks, we are excited about RAPIDS’ potential to accelerate Apache Spark workloads. 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.

                          - Matei Zaharia, co-founder and CTO of Databricks, and founder of Apache Spark

                          I got 24x speedup using RAPIDS XGBOOST and can now replace hundreds of CPU nodes, running my biggest ML workload on a single node with 8 GPUs. You made XGBOOST too fast!?

                          - Streaming Media Company

                          My previous bottleneck was I/O. …10 minutes to pull in data for 10 stores (about 1 million rows). With RAPIDS, we can pull in data for about 6000 stores (millions of rows) in less than 3 minutes. That scale could have easily taken us 4 days on legacy infrastructure … just plain awesome.

                          - A mid-market specialty retailer with 6000 stores

                          Partner Ecosystem

                          RAPIDS is open to all and being adopted globally in data science and analytics. Our partners together are transforming the traditional big data analytics ecosystem with GPU-accelerated analytics, machine learning, and deep learning advancements.

                           

                          ANACONDA
                          BlazingDB
                          Chainer
                          Datalogue
                          DataBricks
                          DellEMC
                          FastData
                          Graphistry
                          H20.ai
                          HPE
                          IBM
                          Kinetica
                          MAPR
                          NetApp
                          Omni Sci
                          Oracle
                          Pure Storage
                          PyTorch
                          SAP
                          Sas
                          Sqream
                          ZILLIZ
                          ANACONDA
                          BlazingDB
                          Chainer
                          Datalogue
                          DataBricks
                          DellEMC
                          FastData
                          Graphistry
                          H20.ai
                          HPE
                          IBM
                          Kinetica
                          MAPR
                          NetApp
                          Omni Sci
                          Oracle
                          Pure Storage
                          PyTorch
                          SAP
                          Sas
                          Sqream
                          ZILLIZ

                          WEBINARS

                          Transforming AI Development on NVIDIA-Powered Data Science Workstations

                          Improving Machine Learning Performance and Productivity with XGBoost

                          RAPIDS for GPU-Accelerated Data Science in Healthcare

                          End-to-End Data Science Acceleration with RAPIDS and DGX-2

                          Explore GPU-Accelerated Hardware Solutions