RAPIDS: Difference between revisions
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RAPIDS<ref>RAPIDS Open GPU Data Science : https://rapids.ai/ </ref> is a suite of open source software libraries from NVIDIA mainly for executing data science and analytics pipelines on GPUs. It relies on NVIDIA CUDA primitives for low level compute optimization and provides users with friendly Python APIs, similar to those in Pandas, Scikit-learn, etc. | RAPIDS<ref>RAPIDS Open GPU Data Science : https://rapids.ai/ </ref> is a suite of open source software libraries from NVIDIA mainly for executing data science and analytics pipelines on GPUs. It relies on NVIDIA CUDA primitives for low level compute optimization and provides users with friendly Python APIs, similar to those in Pandas, Scikit-learn, etc. | ||
Since RAPIDS is available as Conda packages which requires to have Anaconda for the installation, however [[Anaconda/en|Anaconda]] is not advised to use on the Compute Canada clusters. Instead, a container solution of using [[Singularity|Singularity]] is recommended. As RAPIDS is also available as Docker container images from NVIDIA, and a Singularity image for RAPIDS can be built based from a Docker image. | |||
This page provides the instructions for working with RAPIDS on Compute Canada clusters based from a Singularity container. | This page provides the instructions for working with RAPIDS on Compute Canada clusters based from a Singularity container. | ||
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=Build a Singularity image for RAPIDS= | =Build a Singularity image for RAPIDS= | ||
==Where to look for a Docker image for RAPIDS== | |||
==Build a RAPIDS Singularity image== | |||
=Work on Clusters with a RAPIDS Singularity image= | =Work on Clusters with a RAPIDS Singularity image= | ||
Revision as of 22:16, 18 December 2020
This is not a complete article: This is a draft, a work in progress that is intended to be published into an article, which may or may not be ready for inclusion in the main wiki. It should not necessarily be considered factual or authoritative.
Overview
RAPIDS[1] is a suite of open source software libraries from NVIDIA mainly for executing data science and analytics pipelines on GPUs. It relies on NVIDIA CUDA primitives for low level compute optimization and provides users with friendly Python APIs, similar to those in Pandas, Scikit-learn, etc.
Since RAPIDS is available as Conda packages which requires to have Anaconda for the installation, however Anaconda is not advised to use on the Compute Canada clusters. Instead, a container solution of using Singularity is recommended. As RAPIDS is also available as Docker container images from NVIDIA, and a Singularity image for RAPIDS can be built based from a Docker image.
This page provides the instructions for working with RAPIDS on Compute Canada clusters based from a Singularity container.
Build a Singularity image for RAPIDS
Where to look for a Docker image for RAPIDS
Build a RAPIDS Singularity image
Work on Clusters with a RAPIDS Singularity image
Work interactively on a GPU node
Submit a RAPIDS job to Slurm scheduler
Helpful Links
- ↑ RAPIDS Open GPU Data Science : https://rapids.ai/