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        NVIDIA-Accelerated Data Science

        The Only Hardware-to-Software Stack Optimized for Data Science

        亚洲免费精品aⅴ国产

        Join us at GTC 2020 for the latest on Data Science, March 23-26

        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. GPUs substantially reduce infrastructure costs and provide superior performance for end-to-end data science workflows using RAPIDS? open source software libraries. GPU-accelerated data science is available everywhere—on the laptop, in the data center, at the edge, and in the cloud.

        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

         

        Apache Spark 3.0 Is GPU-Accelerated with RAPIDS

        Apache Spark 3.0 is the first release of Spark to offer fully integrated and seamless GPU acceleration for analytics and AI workloads. Tap into the power of Spark 3.0 with GPUs either on-premises or in the cloud, without changing your code. The breakthrough performance of GPUs empowers enterprises and researchers to train bigger models more frequently ultimately unlocking the value of big data with the power of AI.

        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

        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

        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

        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