Example jobs

Single node jobs


Here we render one frame from the classroom demo (see here). The required Blender files are downloaded automatically before the job starts, and the output file is saved to cloud storage.

prominence create --cpus 8 \
                  --memory 8 \
                  --artifact https://download.blender.org/demo/test/classroom.zip \
                  --output frame_001.png \
                  ikester/blender:latest \
                  "/usr/local/blender/blender -b classroom/classroom.blend -o frame_### -f 1"


Here we run a variation of the windAroundBuildings tutorial from OpenFOAM v6 from here:

prominence create --cpus 8 \
                  --memory 8 \
                  --artifact https://github.com/alahiff/of-on-aws-tests/archive/master.zip \
                  --workdir of-on-aws-tests-master/windAroundBuildings_02 \
                  --env OMPI_MCA_orte_tmpdir_base=/tmp openfoam/openfoam6-paraview56 \
                  "/bin/bash -c \"source /opt/openfoam6/etc/bashrc; ./Allrun; cat log.simpleFoam\""

We use one of the official OpenFOAM Docker images and obtain the example code from GitHub. The main script executed (“Allrun”) itself runs Open MPI within the container.

Tensorflow (CPU)

Here we train Tensorflow to classify the MNIST dataset. The output file containing the saved model (saved_model.pb) will be made available on cloud storage. This example demonstrates running an external code (in this case obtained from GitHub) using a generic container image.

prominence create --cpus 8 \
                  --memory 8 \
                  --artifact https://github.com/tensorflow/models/archive/v1.11.tar.gz \
                  --env PYTHONPATH="\$PYTHONPATH:models-1.11" \
                  --output "mnist_saved_model/*/saved_model.pb" \
                  alahiff/tensorflow:1.11.0 \
                  "python models-1.11/official/mnist/mnist.py --export_dir mnist_saved_model"

The Docker image used in this example is just the tensorflow/tensorflow:1.11.0 image but with the Python requests module installed, as it is needed in this example.


Here we run one of the LAMMPS benchmark problems using Intel’s Singularity image:

prominence create --cpus 2 \
                  --memory 2 \
                  --artifact https://lammps.sandia.gov/inputs/in.lj.txt \
                  --runtime singularity \
                  shub://intel/HPC-containers-from-Intel:lammps \
                  "/lammps/lmp_intel_cpu_intelmpi -in in.lj.txt"

See here for more information about this container image.

MPI jobs

Open MPI hello world

Here we run a basic “Hello World” job using Open MPI and the udocker container runtime:

prominence create --openmpi --runtime=udocker --cpus 2 --nodes 2 alahiff/openmpi-hello-world:latest /mpi_hello_world