NVIDIA Accelerated Data Science

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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. 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.

                  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

                   

                  Features and Benefits

                  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 50X with more iterations for better model accuracy.

                  Cost-Efficiency

                  Cost-Efficiency

                  Reduce data science compute infrastructure costs and increase data center efficiency.

                  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

                  DATA SCIENCE SOLUTIONS

                  PC

                  Get started in machine learning.

                  Learn More >

                  Workstations

                  A new breed of workstations for data science.

                  Learn More >

                  Data Center

                  Purpose-built AI systems for maximum performance.

                  Learn More >

                  Cloud

                  Accelerated machine learning, anywhere.

                  Learn More >

                  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 by the top enterprise leaders in data science and analytics.

                  Big Data, Analytics, Visualisation

                  Anaconda
                  BlazingDB
                  DataBricks
                  Datalogue
                  FastData
                  Graphistry
                  H20.ai
                  Kinetica
                  MAPR
                  Omni Sci
                  Sqream
                  Uber

                  Enterprise Data Science Platform

                  IBM
                  Oracle
                  SAP
                  Sas

                  Storage

                  DellEMC
                  DDN STORAGE
                  HPE
                  IBM
                  NetApp
                  Pure Storage

                  Deep Learning

                  Chainer
                  PyTorch

                  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 RAPIDS accelerated hardware solutions