GPU-Enabled Data Science & Machine Learning

If you're interested in the NVIDIA academic grant program you can learn more here.

Use NVIDIA GPUs to speed up your data science by up to 2000x on Saturn Cloud

Dask on GPUs

Dask integrates with RAPIDS cuDF, XGBoost, and RAPIDS cuML for GPU-accelerated data analytics and machine learning.

Python Integration

Speed up Python with minimal code changes by using the GPU powered versions of many popular Python libraries like PyTorch.

The right size resource

Choose from a selection of instance sizes with different numbers of GPUs, or connect them together.

Accelerate Model Runtime by 2000x with RAPIDS

Native Integration

Out-of-the-box support and easy setup

Greater Model Accuracy

Increase machine learning model accuracy by iterating on models faster

Reducing Training Time

Improve your productivity with the fastest data science capabilities on market

Open Source

Customizable, extensible, interoperable – the open-source software is supported by NVIDIA and built on Apache Arrow.

A forest at sunset

Random Forest on GPUs: 2000x Faster than Apache Spark

We trained a random forest model using 300 million instances: Spark took 37 minutes on a 20-node CPU cluster, whereas RAPIDS took 1 second on a 20-node GPU cluster. That’s over 2000x faster with GPUs.

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A room full of stars

Examples with GPUs

Saturn Cloud has multiple examples with GPUs in Python, R, and Julia.

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A space shuttle

Accelerate common data science workloads on GPUs with RAPIDS

Dive into example notebooks using a GPU with RAPIDS, scaling to a cluster with Dask, then some runtime comparisons.

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