RAPIDS: Difference between revisions

RAPIDS (view source)

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-[https://rapids.ai/ RAPIDS] is a suite of open source software libraries from NVIDIA mainly for executing data science and analytics pipelines in Python 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.
+[https://rapids.ai/ RAPIDS] is a suite of open source software libraries from NVIDIA mainly for executing data science and analytics pipelines in Python 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.
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-While RAPIDS can be installed using [[Anaconda/en|Anaconda]], we do not recommend the use of Anaconda on Compute Canada clusters. We propose instead that users obtain a Docker image from NVIDIA, which can then be converted into a [[Singularity|Singularity]] image for use on the clusters.
+While RAPIDS can be installed using [[Anaconda/en|Anaconda]], we do not recommend the use of Anaconda on Compute Canada clusters. We propose instead that you obtain a Docker image from NVIDIA, which can then be converted into a [[Singularity|Singularity]] image for use on our clusters.
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 This page provides the instructions for working with RAPIDS on Compute Canada clusters based from a Singularity container.
-=Build a Singularity image for RAPIDS= <!--T:5-->
+=Building a Singularity image for RAPIDS= <!--T:5-->
-To build a Singularity image for RAPIDS the first thing to do is to find and select a Docker image for RAPIDS from NVIDIA.
+To build a Singularity image for RAPIDS the first thing to do is to find and select a Docker image for RAPIDS provided by NVIDIA.
-==Find a Docker image for RAPIDS== <!--T:6-->
+==Finding a Docker image for RAPIDS== <!--T:6-->
-There are three types of RAPIDS Docker images, i.e. ''base'', ''runtime'', and ''devel'' types, and they are available at two major sites. For each type of Docker images, multiple images are provided with different combinations of RAPIDS versions and CUDA versions either on Ubuntu or on CentOS. You can find the Docker pull command of a selected image via the '''Tags''' tab on each given site:
+There are three types of RAPIDS Docker images: ''base'', ''runtime'', and ''devel''. For each type, multiple images are provided for different combinations of RAPIDS and CUDA versions, either on Ubuntu or on CentOS. You can find the Docker <tt>pull</tt> command for a selected image under the '''Tags''' tab on each site.
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-* [https://ngc.nvidia.com/catalog/containers/nvidia:rapidsai:rapidsai NVIDIA GPU Cloud (NGC)]: this site provides two types of RAPIDS images, i.e. ''base'' type and ''runtime'' type.
+* [https://ngc.nvidia.com/catalog/containers/nvidia:rapidsai:rapidsai NVIDIA GPU Cloud (NGC)]
-** ''base'' - contains a RAPIDS environment ready to use. Use this type of image if you want to submit a job to the Slurm scheduler.
+** '''base''' contains a RAPIDS environment ready to use. Use this type of image if you want to submit a job to the Slurm scheduler.
-** ''runtime'' - extends the base image by adding a Jupyter notebook server and example notebooks. Use this type of image if you want to interactively work with RAPIDS through notebooks and examples.
+** '''runtime''' extends the base image by adding a Jupyter notebook server and example notebooks. Use this type of image if you want to interactively work with RAPIDS through notebooks and examples.
-* [https://hub.docker.com/r/rapidsai/rapidsai-dev Docker Hub]: this site provides RAPIDS images in ''devel'' type.
+* [https://hub.docker.com/r/rapidsai/rapidsai-dev Docker Hub]
-** ''devel'' - contains the full RAPIDS source tree, the compiler toolchain, the debugging tools, the headers and the static libraries for RAPIDS development. Use this type of image if you want to implement any customized operations with low-level access to cuda-based processes.
+** '''devel''' contains the full RAPIDS source tree, the compiler toolchain, the debugging tools, the headers and the static libraries for RAPIDS development. Use this type of image if you want to implement customized operations with low-level access to cuda-based processes.
-==Build a RAPIDS Singularity image== <!--T:8-->
+==Building a RAPIDS Singularity image== <!--T:8-->
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 You are then back to the host shell.
-==Work interactively on a GPU node== <!--T:22-->
+==Working interactively on a GPU node== <!--T:22-->
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