Introduction

The RAPIDS suite of software libraries gives you the freedom to execute end-to-end data science and analytics pipelines entirely on GPUs. It relies on NVIDIA® CUDA® primitives for low-level compute optimization, but exposes that GPU parallelism and high-bandwidth memory speed through user-friendly Python interfaces.

RAPIDS also focuses on common data preparation tasks for analytics and data science. This includes a familiar DataFrame API that integrates with a variety of machine learning algorithms for end-to-end pipeline accelerations without paying typical serialization costs. RAPIDS also includes support for multi-node, multi-GPU deployments, enabling vastly accelerated processing and training on much larger dataset sizes.


Benefits of Using Rapids

image source: rapid.ai

Performance at Scale

image source: rapid.ai


prerequisites

PreReq Version
Ubuntu 16.04 / 18.04
CUDA 9.2 +
NVIDIA driver 396.44 +

 


Installation using Conda

RAPIDS is currently available in Conda package manager (documentation say it will be available in PIP package manager soon).

Type the following command in the terminal:

$ conda install -c nvidia -c rapidsai -c numba -c conda-forge -c defaults cudf=0.3.0

Note: This Conda installation only applies to Linux and Python versions 3.5/3.6.

You can create and activate a development environment using the Conda commands:

# create the conda environment (assuming in base `cudf` directory)
$ conda env create --name cudf_dev --file conda/environments/dev_py35.yml
# activate the environment
$ source activate cudf_dev
# when not using default arrow version 0.10.0, run
$ conda install -c nvidia -c rapidsai -c numba -c conda-forge -c defaults pyarrow=$ARROW_VERSION