BlazingSQL Notebooks is a fully managed, high-performance JupyterLab environment.
No setup required. You just login and start writing code, immediately.
Every environment has:
- An attached GPU
- Pre-Installed GPU Data Science Packages (BlazingSQL, RAPIDS, Dask, and many more)
Below, we have listed a series of notebooks that demonstrate the utility of an open-source GPU powered Jupyter environment. Through the pre-installed packages you can execute familiar Python code on high-performance GPUs.
| Notebook Title | Description | Try Now |
|---|---|---|
| Welcome Notebook | An introduction to BlazingSQL Notebooks and the GPU Data Science Ecosystem. |  |
| The DataFrame | Learn how to use BlazingSQL and cuDF to create GPU DataFrames with SQL and Pandas-like APIs. | |
| Data Visualization | Plug in your favorite Python visualization packages, or use GPU accelerated visualization tools to render millions of rows in a flash. | |
| Machine Learning | Learn about cuML, mirrored after the Scikit-Learn API, it offers GPU accelerated machine learning on GPU DataFrames. | |