An accessible infrastructure for artificial intelligence using a Docker-based JupyterLab in Galaxy.

ABSTRACT:

Background

Artificial intelligence (AI) programs that train on large datasets require powerful compute infrastructure consisting of several CPU cores and GPUs. JupyterLab provides an excellent framework for developing AI programs, but it needs to be hosted on such an infrastructure to enable faster training of AI programs using parallel computing.

Findings

An open-source, docker-based, and GPU-enabled JupyterLab infrastructure is developed that runs on the public compute infrastructure of Galaxy Europe consisting of thousands of CPU cores, many GPUs, and several petabytes of storage to rapidly prototype and develop end-to-end AI projects. Using a JupyterLab notebook, long-running AI model training programs can also be executed remotely to create trained models, represented i

SUBMITTER: Kumar A 

PROVIDER: S-EPMC10132306 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature