NVIDIA 加速数据科学

针对数据科学优化的先进硬件到软件堆栈

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GPU 加速数据科学工作流程

传统上,数据科学工作流程缓慢且繁琐,依靠 CPU 来加载、过滤和操作数据,以及训练和部署模型。凭借 RAPIDS™ 开源软件库,GPU 可显著降低基础设施成本,并为端到端数据科学工作流程提供出色性能。GPU 加速的数据科学在笔记本电脑、数据中心、边缘和云端均可使用。

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 是采用 RAPIDS 的 GPU 加速技术

Version 3.0 是可为分析和 AI 工作负载提供完全集成和无缝的 GPU 加速的先进 Spark 版本。借助 GPU 在本地或云端利用 Spark 3.0 的强大功能,而无需更改您的代码。凭借 GPU 的突破性性能,企业和研究人员能够更频繁地训练更大的模型,最终利用 AI 的强大功能充分挖掘大数据的价值。

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

合作伙伴生态系统

RAPIDS 面向数据科学和分析领域的高层企业领导者,并逐渐被他们接纳。

大数据、分析、可视化

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

企业数据科学平台

IBM
Oracle
SAP
Sas

存储

DellEMC
HPE
IBM
NetApp
Pure Storage

深度学习

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 GPU-Accelerated Hardware Solutions