Accessing Singularity

Singularity enables users to have full control of their environment. Singularity containers can be used to package entire scientific workflows, software and libraries, and even data. This means that you don’t have to ask your cluster admin to install anything for you - you can put it in a Singularity container and run. Did you already invest in Docker? The Singularity software can import your Docker images without having Docker installed or being a superuser. Need to share your code? Put it in a Singularity container and your collaborator won’t have to go through the pain of installing missing dependencies. Do you need to run a different operating system entirely? You can “swap out” the operating system on your host for a different one within a Singularity container. As the user, you are in control of the extent to which your container interacts with its host. There can be seamless integration, or little to no communication at all.  They have extensive documentation at their website.

Image Removed

Here are some of the use cases we support using Singularity:

• You already use Docker and want to run your jobs on HPC
• You want to preserve your environment so that a system change will not affect your work
• You need newer or different libraries than are offered on HPC systems
• Someone else developed the workflow using a different version of linux
• You prefer to use a Linux distribution other than CentOS, perhaps Ubuntu 
• You want a container with a database server like MariaDB. 

Singularity Commands

On Puma, Singularity is installed in the operating system, so the commands will work without any extra effort.

On Ocelote and ElGato, you will first need to module load singularity

$ singularity --help

Linux container platform optimized for High Performance Computing (HPC) 

Usage: 
   singularity [global options...]

Description:

  Singularity containers provide an application virtualization layer enabling 
 of compute via both application and environment portability. With one is 
 capable of building a root file system that runs on any Linux system where 
 Singularity is installed.



Options:
  -d, --debug              print debugging information (highest verbosity)
  -h, --help               help for singularity
  -q, --quiet              suppress normal output
  -s, --silent             only print errors
  -t, --tokenfile string   path to the file holding your sylabs
                           authentication token (default
                           "/home/u13/chrisreidy/.singularity/sylabs-token")
  -v, --verbose            print additional information
      --version            version for singularity

Available Commands:
  build          Build a new Singularity container
  capability  Manage Linux capabilities on containers
  exec          Execute a command within container
  help          Help about any command
  inspect     Display metadata for container if available
  instance   Manage containers running in the background
  keys          Manage OpenPGP key stores
  pull           Pull a container from a URI
  push         Push a container to a Library URI
  run            Launch a runscript within container
  run-help    Display help for container if available
  search      Search the library
  shell        Run a Bourne shell within container
  sign         Attach cryptographic signatures to container
  test          Run defined tests for this particular container
  verify       Verify cryptographic signatures on container
  version     Show application version

Examples:

  $ singularity help <command>

      Additional help for any Singularity subcommand can be seen by appending
      the subcommand name to the above command.

Singularity Changes in Version 3.x

For existing users two of the biggest changes are; that the file type of images now is ".sif" for Singularity Image Format for cryptographically signed and verifiable container images; and that your container may not run outside your home directory unless you include binding to other directories like /xdisk

Singularity Hub

Singularity Hub lets you build and keep containers at their Hub.  You maintain your recipes there and each time you need to pull one, it gets built there and then you retrieve the container.

This is very convenient for the scenario where you do not have access to root authority to build the container. The build takes place through the Hub.

This also lets you share containers
https://github.com/singularityhub/singularityhub.github.io/wiki/Build-A-Container#naming-recipes

Singularity Remote Builder (root access)

An earlier limitation of Singularity was the requirement for access to a root account to build a container.  You will not have root access on a HPC cluster.  Singularity 3.0 introduced the ability to build a container in the cloud negating the root restriction.
https://cloud.sylabs.io/builder

Here is an example:

on HPC

Singularity is installed on the operating systems of all HPC compute nodes, so can be easily accessed either from an interactive session or batch script without worrying about software modules. 

Building a Container

Local Builds

Building a container locally requires root authority which users do not have on HPC. This means you must use a Mac or Linux workstation where you have sudo privileges and Singularity installed. The Sylabs website has instructions that can help users get started on building their own containers. Additionally, Nvidia provides an HPC Container Maker which lets you build a recipe without having to know all the syntax. You will just include the blocks you need (e.g., Cuda or Infiniband) and it will create the recipe that you can use for a build on your local workstation.

Remote Builds

To bypass the issue of needing root privileges to build your container, Singularity Hub lets you build and keep containers in the cloud as well as share them with other users. You maintain your recipes there and each time you need to pull one, it gets built remotely and is retrieved to your workstation. This conveniently allows you to build containers directly from HPC. 

As an example, if you want to build a container in your account, first go to https://cloud.sylabs.io, generate an access token (API key), and save it to your clipboard. Next, log in to an interactive terminal session and find your recipe file. In this example, we'll use the recipe:

BootStrap: docker
From: nersc/ubuntu-mpi:14.04

%runscript
    echo "This is what happens when you run the container..."

Then, assuming the recipe is stored in our home directory, we can build it remotely using:

$ singularity remote login   # paste in your API key at the prompt
$ singularity build --remote ~/nersc.sif ~/nersc.recipe

This will produce a .sif file in your home directory that is ready for use.

Singularity, Nvidia and GPU's

Pulling Images

The general form to pull and convert a NGC image to a local Singularity image file is:

$ singularity build <local_image> docker://nvcr.io/<registry>/<app:tag>

This Singularity build command will download the app:tag NGC Docker image, convert it to Singularity format, and save it to the local filename local_image. For example, to pull the namd NGC container tagged with version 2.12-171025 to a local file named namd.simg saved to your home directory:

$ singularity build ~/namd.simg docker://nvcr.io/hpc/namd:2.12-171025

Running

Directory access:

Singularity containers are themselves ostensibly read only. In order to provide application input and output host directories are generally bound to the container, this is accomplished through the Singularity -B flag. The format of this flag is -B <host_src_dir>:<container_dst_dir>. Once a host directory, host_src_dir, is bound into the container you may interact with this directory from within the container, located at container_dst_dir, the same as you would outside the container.

You may also make use of the --pwd <container_dir> flag, which will be used to set the present working directory of the command to be run within the container.

Ocelote does not support filesystem overlay and as such the container_dst_dir must exist within the image for a bind to be successful. To get around the inability to bind arbitrary directories $HOME and /tmp are mounted in automatically and may be used for application I/O.

GPU support:

All NGC containers are optimized for NVIDIA GPU acceleration so you will always want to add the --nv flag to enable NVIDIA GPU support within the container.

Standard run command:

The Singularity command below represents the canonical form that will be used on the Ocelote cluster.

$ singularity exec --nv --pwd <work_dir> <image.simg> <cmd>   # <work_dir> should be set to either $HOME or /tmp

Containers Available on HPC

We support the use of HPC and ML/DL containers available on NVIDIA GPU Cloud (NGC). Many of the popular HPC applications including NAMD, LAMMPS and GROMACS containers are optimized for performance and available to run in Singularity on Ocelote or Puma. The containers and respective README files can be found in /contrib/singularity/nvidia. 

Tutorials

• The Sylabs GitHub site has files and instructions for creating sample containers.
• Our Github repository has Singularity examples available that can be run on HPC.

Simple Example

The lolcow image is often used as the standard "hello world!" introduction to containers and is described in Singularity's documentation. To follow their example, first start by logging into an interactive terminal session and pull the image:

$  singularity pull docker://godlovedc/lolcow
INFO:    Converting OCI blobs to SIF format
INFO:    Starting build...
Getting image source signatures
[...]
Writing manifest to image destination
Storing signatures
INFO:    Creating SIF file...
INFO:    Build complete: /home/uxx/netid/.singularity/cache/oci-tmp/a692b57abc43035b197b10390ea2c12855d21649f2ea2cc28094d18b93360eeb/lolcow_latest.sif

This will pull the image from Docker Hub and save it in your home in a hidden directory .singularity. Next, run the image simply using singularity run

$ singularity run lolcow_latest.sif
 ______________________________________
/ Perilous to all of us are the devices \
| of an art deeper than we ourselves    |
| possess.                              |
|                                       |
| -- Gandalf the Grey [J.R.R. Tolkien,  |
\ "Lord of the Rings"]                  /
 ---------------------------------------
        \   ^__^
         \  (oo)\_______
            (__)\       )\/\
                ||----w |
                ||     ||

Running Singularity in a Batch Job

Running a job with Singularity is as easy as running other jobs, simply include your resource requests, and include any commands necessary to execute your workflow. For more detailed information on creating and running jobs, see our SLURM documentation or Puma Quick Start. An example script might look like:

#!/bin/bash
#SBATCH --job-name singularity-job
#SBATCH --account=your_pi
#SBATCH --partition=standard
#SBATCH --nodes=1
#SBATCH --ntasks=1
#SBATCH --time=01:00:00

date
singularity exec --nv dockerTF.img python TFlow_example.py
date

Example Recipe Files

CentOS with Tensorflow

BootStrap: yum
OSVersion: 7
MirrorURL: http://mirror.centos.org/centos-%{OSVERSION}/%{OSVERSION}/os/$basearch/
Include: yum
# best to build up container using kickstart mentality.
# ie, to add more packages to image,
# re-run bootstrap command again.
# bootstrap on existing image will build on top of it, not overwriting it/restarting from scratch
# singularity .def file is like kickstart file
# unix commands can be run, but if there is any error, the bootstrap process ends
%setup
   # commands to be executed on host outside container during bootstrap
%post
     # commands to be executed inside container during bootstrap
     # add python and install some packages
    yum -y install vim wget python3 epel-release
     # install tensorflow
    pip3 install --upgrade pip
    pip3 install tensorflow-gpu==2.0.0-rc1
     # create bind points for storage.
    mkdir /xdisk
    mkdir /groups
    exit 0
# %runscript
   # commands to be executed when the container runs
# %test
   # commands to be executed within container at close of bootstrap process
   python --version

To build and test a container from the recipe from an interactive session on a GPU node:

$ singularity build centosTflow.sif centosTflow.def # Remember, you will need to either build this on a workstation where you have root privileges or will need to user a --remote build
$ singularity exec --nv centosTFlow.simg python3 TFlow_example.py

As a tensorflow example, you could use the following script:

#Linear Regression Example with TensorFlow v2 library 
 
from __future__ import absolute_import, division, print_function
#
import tensorflow as tf
import numpy as np
rng = np.random
#
# Parameters.
learning_rate = 0.01
training_steps = 1000
display_step = 50
#
# Training Data.
X = np.array([3.3,4.4,5.5,6.71,6.93,4.168,9.779,6.182,7.59,2.167,
              7.042,10.791,5.313,7.997,5.654,9.27,3.1])
Y = np.array([1.7,2.76,2.09,3.19,1.694,1.573,3.366,2.596,2.53,1.221,
              2.827,3.465,1.65,2.904,2.42,2.94,1.3])
n_samples = X.shape[0]
#
# Weight and Bias, initialized randomly.
W = tf.Variable(rng.randn(), name="weight")
b = tf.Variable(rng.randn(), name="bias")
 
# Linear regression (Wx + b).
def linear_regression(x):
    return W * x + b
 
# Mean square error.
def mean_square(y_pred, y_true):
    return tf.reduce_sum(tf.pow(y_pred-y_true, 2)) / (2 * n_samples)
 
# Stochastic Gradient Descent Optimizer.
optimizer = tf.optimizers.SGD(learning_rate)
#
# Optimization process. 
def run_optimization():
# Wrap computation inside a GradientTape for automatic differentiation.
    with tf.GradientTape() as g:
        pred = linear_regression(X)
        loss = mean_square(pred, Y)
 
    # Compute gradients.
    gradients = g.gradient(loss, [W, b])    
 
    # Update W and b following gradients.
    optimizer.apply_gradients(zip(gradients, [W, b]))
#
# Run training for the given number of steps.
for step in range(1, training_steps + 1):
    # Run the optimization to update W and b values.
    run_optimization()    
 
    if step % display_step == 0:
        pred = linear_regression(X)
        loss = mean_square(pred, Y)
        print("step: %i, loss: %f, W: %f, b: %f" % (step, loss, W.numpy(), b.numpy()))

MPI

Singularity supports MPI pretty well since, by default, the network is the same inside and outside the container. The more complicated bit is making sure that the container has the right set of MPI libraries. MPI is an open specification, but there are several different implementations (OpenMPI, MVAPICH2, and Intel MPI to name three) with some non-overlapping feature sets. If the host and container are running different MPI implementations, or even different versions of the same implementation, hilarity may ensue. 

The general rule is that you want the version MPI inside the container to be the same version or newer than the host. You may be thinking that this is not good for the portability of your container and you are right. Containerizing MPI applications is not terribly difficult with Singularity, but it comes at the cost of additional requirements for the host system. 

In this example, the infiniband pieces are installed and then the MVAPICH version of MPI. When the job is run, the script will need to load the correct module with the matching version of MVAPICH.

BootStrap: debootstrap
OSVersion: xenial
MirrorURL: http://us.archive.ubuntu.com/ubuntu/


%runscript
    echo "This is what happens when you run the container..."

• The Sylabs GitHub site has files and instructions for creating sample containers.
• We have Singularity tutorials on our Singularity Tutorials page.
• Our Github repository has Singularity examples available that can be run on HPC.
• There are example builds of Singularity containers for python and machine learning on our Singularity Python Methods page.

Singularity, Nvidia and GPU's

"


%post
    echo "Hello from inside the container"
    sed -i 's/$/ universe/' /etc/apt/sources.list
    apt update
    apt -y --allow-unauthenticated install vim build-essential wget gfortran bison libibverbs-dev libibmad-dev libibumad-dev librdmacm-dev libmlx5-dev libmlx4-dev
    wget http://mvapich.cse.ohio-state.edu/download/mvapich/mv2/mvapich2-2.1.tar.gz
    tar xvf mvapich2-2.1.tar.gz
    cd mvapich2-2.1
    ./configure --prefix=/usr/local
    make -j4
    make install
    /usr/local/bin/mpicc examples/hellow.c -o /usr/bin/hellow