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Singularity

  • It allows you to create and run containers that package up pieces of software in a way that is portable and reproducible.
  • You can build a container using SingularityCE on your laptop, and then run it on many of the largest HPC clusters in the world, local university or company clusters, a single server, in the cloud, or on a workstation down the hall. Your container is a single file, and you don't have to worry about how to install all the software you need on each different operating system.
  • Installed version: 3.11.3

Usage

Full SBATCH Singularity example:

#!/bin/bash
#SBATCH -p all-a100 -t 00:30:00
# REFERENCE: https://sylabs.io/guides/3.11/user-guide/gpu.html
#https://www.tensorflow.org/install/docker
#2.10.1 release w/ GPU support: cuda 11.2 
#To be downloaded from login nodes (internet access)
#singularity pull tensorflow.sif docker://tensorflow/tensorflow:2.10.1-gpu
#wget https://github.com/tensorflow/benchmarks/archive/039fa9550c9efd25e09db2e73f360b67378436fe.zip
#mv benchmarks-039fa9550c9efd25e09db2e73f360b67378436fe/ benchmarks
CONTAINER_URI='tensorflow.sif'
#--nv option will setup the container's environment to use an NVIDIA GPU and the basic CUDA libraries to run a CUDA enabled application.
# instance tensorflow started
singularity instance start --nv $CONTAINER_URI tensorflow
singularity exec instance://tensorflow python \
        $(pwd)/benchmarks/scripts/tf_cnn_benchmarks/tf_cnn_benchmarks.py \
        --num_gpus=4 --model resnet50 --batch_size 32
singularity exec instance://tensorflow python \
        $(pwd)/benchmarks/scripts/tf_cnn_benchmarks/tf_cnn_benchmarks.py \
        --num_gpus=4 --model inception3 --batch_size 32
#shutdown instance
singularity instance stop -t 30 tensorflow

To get full examples of MPI integration using Singularity please go to our Containerization Tab.