The RAPIDS data science framework includes a collection of libraries for executing end-to-end data science pipelines completely in the GPU. It is designed to have a familiar look and feel to data scientists working in Python. Here’s a code snippet where we read in a CSV file and output some descriptive statistics:
gdf = cudf.read_csv('path/to/file.csv')
for column in gdf.columns:
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GPU Powered Data Science
RAPIDS uses optimized NVIDIA CUDA® primitives and high-bandwidth GPU memory to accelerate data preparation and machine learning. The goal of RAPIDS is not only to accelerate the individual parts of the typical data science workflow, but to accelerate the complete end-to-end workflow.
We suggest that you take a look at the sample workflow in our Docker container (described below), which illustrates just how straightforward a basic XGBoost model training and testing workflow looks in RAPIDS.
GPU: NVIDIA Pascal™ or better with compute capability 6.0+
Supported OS: Ubuntu 16.04/18.04 or CentOS 7 with gcc 5.4 & 7.3
Docker Prereqs: Docker CE v18+ and NVIDIA-docker v2+
CUDA: 9.2 with driver v396.37+ or 10.0 with driver v410.48+
CUDA 10.1 is not supported in v0.8, support will be added soon