Give Control of the Machine Learning Pipeline Back to the Data Scientist

RAPIDS achieves speedup factors of 50x or more on typical end-to-end data science workflows. RAPIDS uses NVIDIA CUDA for high-performance GPU execution, exposing that GPU parallelism and high memory bandwidth through user-friendly Python interfaces. RAPIDS focuses on common data preparation tasks for analytics and data science, offering a powerful and familiar DataFrame API. This API integrates with a variety of machine learning algorithms without paying typical serialization costs, enabling acceleration for end-to-end pipelines. RAPIDS also includes support for multi-node, multi-GPU deployments, enabling scaling up and out on much larger dataset sizes.

Here is an example that loads data into GPU memory using the RAPIDS CSV reader. The ETL in this example performs a number of operations including extracting months and years from datetime fields, joins of multiple columns between DataFrames, and groupby aggregations for feature engineering. The resulting feature data is then converted and used to train a gradient boosted decision tree model on the GPU using XGBoost.

This workflow runs end-to-end on a single NVIDIA DGX-2 server with 16x Tesla V100 GPUs, 10x faster than 100 AWS r4.2xLarge instances, as the chart below shows. Comparing GPU to CPU performance one-to-one, this equates to well over a 50x speedup.

For a more comprehensive explanation of this example, please Click Here.