A secure GPU data science cluster ready in minutes at the click of a button.

No drivers to install, no packages to setup, simply login to the automatically launched JupyterLab and start coding on as many GPUs as you wish with RAPIDS.

RAPIDS is an open-source data science framework designed to have a familiar look and feel for data scientists working in Python. Built on a GPU-accelerated version of Apache Arrow, RAPIDS enables end-to-end data science workloads to run on GPUs, yielding dramatic performance improvements at a fraction of the cost.

BlazingSQL is a distributed SQL engine built on the RAPIDS Apache Arrow dataframe. With BlazingSQL, RAPIDS, and Dask, users can seamlessly build and scale Python data science workloads to terabytes and beyond.

BlazingSQL helped build and is a large contributor to the RAPIDS ecosystem to support our vision of creating an open-source SQL engine that provides an ETL bridge to GPU-accelerated data science.

FOR DATA SCIENTISTS

• Secure Jupyterlab deployment for quick interactive analytics within seconds.
• Code in familiar APIs such as SQL, pandas, scikit-learn, NetworkX, and more, but in their GPU-accelerated counterparts.
• Dozens of example notebooks to building end-to-end data science workloads on GPUs.

• Create complex data pipelines at scale leveraging BlazingSQL, RAPIDS, and Dask.
• Go from one to many GPUs for larger pipeline requirements.
• Robust SQL support simplifies accelerating old workloads onto GPUs.