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NVIDIA-Accelerated Data Science
The only hardware-to-software stack optimized for 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. With NVIDIA AI software, including RAPIDS™ open-source software libraries, GPUs substantially reduce infrastructure costs and provide superior performance for end-to-end data science workflows. GPU-accelerated data science is available everywhere—on the laptop, in the data center, at the edge, and in the cloud.
Features and Benefits
Maximize Productivity
Reduce time spent waiting to get the most valuable insights and accelerate ROI.
Accomplish More
Accelerate machine learning training up to 215X faster and perform more iterations, increase experimentation and carry out deeper exploration.
Cost-Efficiency
Reduce data science infrastructure costs and increase data center efficiency.
150X
Faster Pandas with cuDF
* Benchmark on Groupy advanced operation (5GB) DuckDB Data Benchmark
HW: Intel Xeon Platinum 8480CL CPU and NVIDIA Grace Hopper™ GPU
SW: pandas v1.5 and cudf.pandas v23.10
5X
Faster Spark with the RAPIDS Accelerator for Spark
* NDS 2.0 benchmarks were run with parquet decimal data @ SF3K with UCX off
CPU-only: 8x n1-standard-32
GPU: 8x g2-standard-16, 8x L4 24GB
SW: Spark RAPIDS 24.02
48X
Faster NetworkX with cuGraph
* Benchmark on PageRank with synthetic dataset having ~16,384 vertices and ~524,288 edges
HW: Intel Xeon Platinum 8480CL CPU and NVIDIA H100 80GB (1x GPU)
SW: NetworkX v3.2 and cuGraph v23.10
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
NVIDIA GPU Solutions for Data Science
Explore unparalleled acceleration across a variety of different NVIDIA GPU solutions.
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.
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
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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.
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