We can see above that the training is successfully using our GPU via the cuda_blas library.
That’s all there is to it! Singularity containers for CUDA applications can now be developed, tested, and used on a Windows laptop or desktop. All of the standard Singularity features work well under WSL2, so it’s a really powerful development environment. When you need to run on more powerful GPU nodes, just take your SIF file to your HPC environment.
If you don’t want to have to remember to use the
--nvccli flags for each GPU container you run in WSL2 you can set
always use nv and
use nvidia-container-cli to
yes in your
In future versions of SingularityCE and SingularityPRO we’ll be aiming to make the
--nvccli method of GPU setup the default, simplifying this process further.
Let us know via the Singularity community spaces if you have questions, comments, or hit any trouble.