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          NVIDIA-Accelerated Data Science

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

          亚洲欧美日韩中文影院

          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