NVIDIA Accelerated Data Science

GPU-Accelerate your Data Analytics Workflows

Data science workflows have traditionally been slow and cumbersome when it comes to loading, filtering and manipulating data, as well as machine learning training itself. Explore GPU-accelerated machine learning and data analytics libraries, deployed on NVIDIA GPU, for maximized productivity, performance, and ROI.

DATA SCIENCE 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

Purpose-built AI systems for maximum performance.

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Cloud

Accelerated machine learning, anywhere.

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RAPIDS, a GPU-accelerated data science platform, is a next-generation computational ecosystem powered by Apache Arrow. NVIDIA's 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. NVIDIA's 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. NVIDIA's 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

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.

Rapids: New software libraries for data science

RAPIDS is built on more than 15 years of NVIDIA® CUDA® development and machine learning expertise. It’s powerful new software for executing end-to-end data science training pipelines completely in the GPU, reducing training time from days to minutes.

NVIDIA RAPIDS Flow
End-to-End Faster Speeds on RAPIDS

Get started with Rapids today

RAPIDS libraries are open-source, written in Python, and built on Apache Arrow. The software is being developed in partnership with open-source communities globally. Download RAPIDS to experience acceleration of your machine learning and data science workflows.

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

Explore RAPIDS accelerated hardware solutions