Now that we have readied the WSL2 environment with Singularity and the relevant CUDA libraries, it’s time to run the sample Keras workflow.
Singularity on BioHPC – Guide
Containers are a method of isolating software so it runs in an environment that is separate from, and can be different to, the operating system of the computer you use. For example, BioHPC compute nodes and clients currently run Red Hat Enterprise Linux 7. You may want to try out the very latest version of tensorflow, but most install instructions concentrate on Ubuntu Linux. In fact, the tensorflow developers provide ready made packages for Ubuntu, and even a container with those packages pre-installed. Using containerization you can run Tensorflow in a Ubuntu container, on top of the BioHPC node’s Red Hat Linux.
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Signing the Container The Singularity 3.0 family introduced the ability to create (and manage) PGP keys to sign and verify containers. This provides a trusted method for Singularity users to share containers and ensures a bit-for-bit reproduction of the original...
Create an Account & Authentication Token Now that we have SingularityCE installed in WSL2, and NVIDIA GPU support is enabled, we will create a Singularity Container Services account and configure the local Singularity client, followed by building a remote...