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

Maximize Productivity

Reduce time spent waiting to get the most valuable insights and accelerate ROI.

Ease of Use

Accelerate your entire Python toolchain with open-source, hassle-free software integration and minimal code changes.

Accomplish More

Accelerate machine learning training up to 215X faster and perform more iterations, increase experimentation and carry out deeper exploration.

Improve Accuracy

Fastest model iteration for better results and performance

Cost-Efficiency

Reduce data science infrastructure costs and increase data center 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

CPU: Core i9 | End-to-end time = Data Prep + Conversion + Training + Validation

Learn how to get started today with GPU-accelerated XGBoost

Get Started

NVIDIA GPU SOLUTIONS FOR DATA SCIENCE

Explore unparalleled acceleration across a variety of different NVIDIA GPU solutions.

PC

Get started in machine learning.

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Workstations

A new breed of workstations for data science.

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Data Center

AI systems for enterprise production.

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Cloud

Versatile accelerated machine learning.

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GPU-ACCELERATED BUSINESS IN ACTION

Maximize performance, productivity and ROI for machine learning workflows.

Interactive Infographic

Solution Brief

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.

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Watch More Webinars

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.

NVIDIA Accelerated Data Science

GPU-ACCELERATE YOUR DATA SCIENCE WORKFLOWS

Features and Benefits

Maximize Productivity

Ease of Use

Accomplish More

Improve Accuracy

Cost-Efficiency

Total Cost of Ownership

XGBOOST TRAINING ON NVIDIA GPUs

Data Prep

XGBoost

End-to-end

NVIDIA GPU SOLUTIONS FOR DATA SCIENCE

PC

Workstations

Data Center

Cloud

GPU-ACCELERATED BUSINESS IN ACTION

Rapids: SUITE OF DATA SCIENCE LIBRARIES

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!?

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.

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!?

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.

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!?

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.

Partner Ecosystem

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

NVIDIA Accelerated Data Science

GPU-ACCELERATE YOUR DATA SCIENCE WORKFLOWS

Features and Benefits

Maximize Productivity

Ease of Use

Accomplish More

Improve Accuracy

Cost-Efficiency

Total Cost of Ownership

XGBOOST TRAINING ON NVIDIA GPUs

Data Prep

XGBoost

End-to-end

NVIDIA GPU SOLUTIONS FOR DATA SCIENCE

PC

Workstations

Data Center

Cloud

GPU-ACCELERATED BUSINESS IN ACTION

Rapids: SUITE OF DATA SCIENCE LIBRARIES

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!?

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.

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!?

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.

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!?

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.

Partner Ecosystem

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

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