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