Rapidsai

Description

The RAPIDS suite of open source software libraries and APIs gives you the ability to execute end-to-end data science and analytics pipelines entirely on GPUs. Licensed under Apache 2.0, RAPIDS is incubated by NVIDIA® based on extensive hardware and data science science experience. RAPIDS utilizes NVIDIA CUDA® primitives for low-level compute optimization, and exposes 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.

Environment Modules

Run module spider rapidsai to find out what environment modules are available for this application.

System Variables

HPC_RAPIDSAI_DIR - installation directory

More Information

Rapids-singlecell

Rapids-singlecell offers enhanced single-cell data analysis as a near drop-in replacement predominantly for scanpy, while also incorporating select functionalities from squidpy and decoupler. Utilizing GPU computing with CuPy and Nvidia’s RAPIDS, it emphasizes high computational efficiency. As part of the scverse ecosystem, rapids-singlecell continuously aims to maintain compatibility, adapting and growing through community collaboration.

Visit rapids-singlecell documentation (https://rapids-singlecell.readthedocs.io/en/latest/)

Rapids-singlecell on HiPerGator Tutorial

The rapids-singlecell package has already been included in the rapidsai/24.02 and rapidsai/24.04 modules, along with the corresponding Jupyter kernel.
The tutorial is located at: /data/ai/examples/rapids_singlecell