NVIDIA RAPIDS
Provided texts come from the official RAPIDS website.
RAPIDS provides unmatched speed with familiar APIs that match the most popular PyData libraries. Built on state-of-the-art foundations like NVIDIA CUDA and Apache Arrow, it unlocks the speed of GPUs with code you already know.
As always, our app comes with the Jupyter Lab environment, which is ready to use instantly from the browser
How to run it​
Nvidia RAPIDS provides an effortless interface to work with your data directly on the GPU, so you should run it with at least one.
Don't forget to mount your data volume at the start.
The amount of CPU and RAM depends on the type and quantity of the chosen GPU.
It should be at least RAM ⩾ sum(vRAM) + 2GB - but remember, this is only a recommendation, you can always start small and grow with your problem.
$ cgc compute create --name rapids01 -c 10 -m 82 -g 1 -gt A100 -v data_volume nvidia-rapids
Why RAPIDS​
RAPIDS allows fluid, creative interaction with data for everyone from BI users to AI researchers on the cutting edge. GPU acceleration means less time and less cost moving data and training models.
See use cases
Faster Pandas with cuDF​
cuDF is a near drop-in replacement to pandas for most use cases and has greatly improved performance. cuDF replaces Pandas command set. To use it in your existing project just import it instead of Pandas.
import cudf as pd
Faster scikit-learn with cuML​
cuML brings huge speedups to ML modeling with an API that matches scikit-learn.
See benchmarks
Faster NetworkX with cuGRAPH​
cuGraph makes migration from NetworkX easy, accelerates graph analytics, and allows scaling far beyond existing tools. See benchmarks