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.
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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Introduction to GPUs
Understand what GPUs are, how they work, and how to get them on Saturn Cloud. We also included several tutorials you can try out right away.
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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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