Running existing containers

Puhti supports running Singularity containers. The easiest option is to build containers from existing Docker images.

Building containers from existing Docker images

Singularity containers are typically built from Docker images, and the easiest way to get started is to use a pre-built Docker image. For example Nvidia has a library of container images for different GPU-enabled applications in their Nvidia GPU cloud (NGC).

Here is an example how to build a Singularity image from Nvidia's PyTorch Docker image on a Puhti compute node:

# We'll reserve 100GB NVME for fast temporary disk space
srun -A <project> --gres=nvme:100 -t 1:00:00 --mem=16G --pty $SHELL

# Let's use the NVME space for temporary storage

# This is just to avoid some annoying warnings

# Do the actual conversion
# NOTE: the Docker image is downloaded straight from NGC
singularity build pytorch_19.10-py3.sif docker://

Note that the Singularity image .sif files can easily be several GB in size, so they should not be stored in your home directory, but for example in the project appliction directory projappl.

Run batch job using a container

To run a slurm batch job using a container you just need to add singularity exec to the place in your slurm script where you are executing the command.

For example, to a run a GPU job with the PyTorch image created above you could use the following script:

#SBATCH --account=<project> 
#SBATCH --cpus-per-task=10 
#SBATCH --partition=gpu 
#SBATCH --gres=gpu:v100:1 
#SBATCH --time=10
#SBATCH --mem=16G  # Total amount of memory reserved for job

srun singularity exec --nv --bind /projappl:/projappl --bind /scratch:/scratch \
    /path/to/pytorch_19.10-py3.sif \
    python3 myprog <options>

Note that we use --bind to map /projappl and /scratch so that they are visible in the Singularity environment. The --nv flag is only needed for GPU jobs.