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Argonne Leadership Computing Facility

Containers on Polaris

Polaris, powered by NVIDIA A100 GPUs, benefits from container-based workloads for seamless compatibility across NVIDIA systems. This guide details the use of containers on Polaris, including custom container creation, large-scale execution, and common pitfalls.

Apptainer Setup

Polaris employs Apptainer (formerly known as Singularity) for container management. To set up Apptainer, run:

module use /soft/spack/gcc/0.6.1/install/modulefiles/Core
module load apptainer
apptainer version #1.2.2

The Apptainer version on Polaris is 1.2.2. Detailed user documentation is available here

Building from Docker or Argonne GitHub Container Registry

Containers on Polaris can be built by writing Dockerfiles on a local machine and then publish the container to DockerHub, or by directly building them on ALCF compute node by writing an Apptainer recipe file. If you prefer to use existing containers, you can pull them from various registries like DockerHub and run them on Polaris.

Since Docker requires root privileges, which users do not have on Polaris, existing Docker containers must be converted to Apptainer. To build a Docker-based container on Polaris, use the following as an example:

qsub -I -A <Project> -q debug -l select=1 -l walltime=01:00:00 -l filesystems=home:eagle -l singularity_fakeroot=true
export HTTP_PROXY=http://proxy.alcf.anl.gov:3128
export HTTPS_PROXY=http://proxy.alcf.anl.gov:3128
export http_proxy=http://proxy.alcf.anl.gov:3128
export https_proxy=http://proxy.alcf.anl.gov:3128
module use /soft/spack/gcc/0.6.1/install/modulefiles/Core
module load apptainer
apptainer build --fakeroot pytorch:22.06-py3.sing docker://nvcr.io/nvidia/pytorch:22.06-py3
You can find the latest prebuilt Nvidia PyTorch containers here. The Tensorflow containers are here (though note that LCF doesn't prebuild the TF-1 containers typically). You can search the full container registry here. For custom containers tailored for Polaris, visit ALCF's GitHub container registry

Note: Currently container build and executions are only supported on the Polaris compute nodes

Recipe-Based Container Building

As mentioned earlier, you can build Apptainer containers from recipe files. Instructions are available here. See available containers for more recipes.

Note: You can also build custom recipes by bootstrapping from prebuilt images. For e.g the first two lines in a recipe to use our custom Tensorflow implementation would be Bootstrap: oras followed by From: ghcr.io/argonne-lcf/tf2-mpich-nvidia-gpu:latest

Running Containers on Polaris

To run a container on Polaris you can use the submission script described here. Below is an explanation of the job submission script.

#!/bin/sh
#PBS -l select=2:system=polaris
#PBS -q debug
#PBS -l place=scatter
#PBS -l walltime=0:30:00
#PBS -l filesystems=home:grand
#PBS -A <project_name>
cd ${PBS_O_WORKDIR}
echo $CONTAINER

We move to current working directory and enable network access at run time by setting the proxy. We also load apptainer.

# SET proxy for internet access
module use /soft/spack/gcc/0.6.1/install/modulefiles/Core
module load apptainer
export HTTP_PROXY=http://proxy.alcf.anl.gov:3128
export HTTPS_PROXY=http://proxy.alcf.anl.gov:3128
export http_proxy=http://proxy.alcf.anl.gov:3128
export https_proxy=http://proxy.alcf.anl.gov:3128

This is important for system (Polaris - Cray) mpich to bind to containers mpich. Set the following environment variables

ADDITIONAL_PATH=/opt/cray/pe/pals/1.2.12/lib
module load cray-mpich-abi
export APPTAINERENV_LD_LIBRARY_PATH="$CRAY_LD_LIBRARY_PATH:$LD_LIBRARY_PATH:$ADDITIONAL_PATH"

Set the number of ranks per node spread as per your scaling requirements

# MPI example w/ 16 MPI ranks per node spread evenly across cores
NODES=`wc -l < $PBS_NODEFILE`
PPN=16
PROCS=$((NODES * PPN))
echo "NUM_OF_NODES= ${NODES} TOTAL_NUM_RANKS= ${PROCS} RANKS_PER_NODE= ${PPN}"

Finally launch your script

echo C++ MPI
mpiexec -hostfile $PBS_NODEFILE -n $PROCS -ppn $PPN apptainer exec -B /opt -B /var/run/palsd/ $CONTAINER /usr/source/mpi_hello_world

echo Python MPI
mpiexec -hostfile $PBS_NODEFILE -n $PROCS -ppn $PPN apptainer exec -B /opt -B /var/run/palsd/ $CONTAINER python3 /usr/source/mpi_hello_world.py

The job can be submitted using:

qsub -v CONTAINER=mpich-4_latest.sif job_submission.sh

Available containers

If you just want to know what containers are available, here you go.

  • For running mpich/MPI containers on Polaris, it can be found here
  • For running databases on Polaris. It can be found here
  • For using shpc - that allows for running containers as modules. It can be found here

The latest containers are updated periodically. If you have trouble using containers, or request a newer or a different container please contact ALCF support at [email protected].

Troubleshooting Common Issues

  1. Permission Denied Error: If you encounter permission errors during the build

    • Check your quota and delete any unnecessary files.
    • Clean-up apptainer cache, ~/.apptainer/cache, and set the apptainer tmp and cache directories as below: 
      export APPTAINER_TMPDIR=/tmp/apptainer-tmpdir
mkdir $APPTAINER_TMPDIR
export APPTAINER_CACHEDIR=/tmp/apptainer-cachedir/
mkdir $APPTAINER_CACHEDIR
    • Make sure you are not on a directory accessed with a symlink, i.e. check if pwd and pwd -P returns the same path.
    • If any of the above doesn't work, try running the build in your home directory.

  2. Mapping to rank 0 on all nodes: Ensure that the container's MPI aligns with the system MPI. Follow the additional steps outlined in the container registry documentation for MPI on Polaris

  3. libmpi.so.40 not found: This can happen if the container's application has an OpenMPI dependency which is not currently supported on Polaris. It can also spring up if the containers base environment is not debian architecture like Ubuntu. Ensure the application has an MPICH implementation as well. Also try removing .conda, .cache, and .local folders from your home directory and rebuild the container.