We can see above that the training is successfully using our GPU via the cuda_blas library.
Summary
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 --nv
and --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 singularity.conf
file.
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.