NVIDIA-Accelerated Data Science

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

                  欧美黄色

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                  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