Distributed Parallel Computing in Python With GPU Acceleration

Saturn Cloud’s integration with NVIDIA enables data science teams to speed up training time on models using Dask on GPUs

Dask on GPUs

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

Python Integration

Speed up your Python data science toolchain with minimal code changes and no new tools to learn.

Scale Out on GPUs

Get support for multi-node, multi-GPU deployments, enabling vastly accelerated processing and training on much larger dataset sizes.

Get 50x Speedup Factors on End-to-End Data Science Workflows With RAPIDS

Hassle-Free Integration

Accelerate your Python data science toolchain with minimal code changes and no new tools to learn.

Top Model Accuracy

Increase machine learning model accuracy by iterating on models faster and deploying them more frequently.

Reduced Training Time

Drastically improve your productivity with near-interactive data science.

Open Source

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

Dask and Saturn Cloud: Distributed for Deep Learning on GPUs

Deep Learning on GPUs

GPUs provide a real performance boost to deep learning model training and inference. Learn how the Dask, Saturn Cloud stack makes justifying GPU usage for deep learning model training a lot easier.

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NVIDIA

Create a Jupyter Instance With an Nvidia GPU

Spin up a Jupyter instance with a Nvidia GPU

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