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→Where to look for a Docker image for RAPIDS
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==Where to look for a Docker image for RAPIDS== | ==Where to look for a Docker image for RAPIDS== | ||
There are two major sites | |||
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 in Ubuntu or CentOS base. You can find the Docker pull command of a selected image via the '''Tag''' tab on each give site: | |||
* [https://ngc.nvidia.com/catalog/containers/nvidia:rapidsai:rapidsai NVIDIA GPU Cloud (NGC)]: | * [https://ngc.nvidia.com/catalog/containers/nvidia:rapidsai:rapidsai NVIDIA GPU Cloud (NGC)]: there are two types of RAPIDS images at this site, i.e. base type and runtime type. | ||
** base type - contains a RAPIDS environment ready to use. Use this type of image if you want to submit a job to the Slurm scheduler. | ** base type - contains a RAPIDS environment ready to use. Use this type of image if you want to submit a job to the Slurm scheduler. | ||
** runtime type - extends the base image by adding a Jupyter notebook server and example notebooks. Use this type of images if you want to interactively work with RAPIDS through notebooks and examples. | ** runtime type - extends the base image by adding a Jupyter notebook server and example notebooks. Use this type of images 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 Docker 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 images 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 images if you want to implement any customized operations with low-level access to cuda-based processes. | ||