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Singularity Container Workflow: Part 2 – Workflow Readiness

By Adam Hughes

Aug 10, 2022 | Blog, How To Guides

Singularity Container Services Workflow Readiness

This is part two of the Singularity Container Services (SCS) demonstration.

Singularity is a standalone command line tool that integrates with SCS to augment your container workflows.

The first part covered the background of SCS, this second part will include installation readiness and a container workflow in Windows with the following components:

  • ^Windows Subsystem Linux (WSL2) with Ubuntu 20.04
  • ^SingularityCE (Community Edition) the open source container runtime
  • ^Sylabs’ Singularity Container Services
  • ^Tensorflow and a sample data set
  • ^NVIDIA GPU

The Singularity open source container project was created in 2016 and has received improvements along the way, thanks to the dedication of the open source community and the creation of SIF, the Singularity Image Format. SIF continues to grow, as shown by the adoption of Redhat in Podman, support of SIF through various registries, such as Amazon ECR. Nvidia NGC, Azure Batch-Shipyard and other container runtimes. Here are a few benefits of the SIF container format: 

  • ^SIF has everything built into single file, rather than set of layers
  • ^Layers work well in microservices containers, where storage can be de-duplicated.
  • ^Layers have higher startup cost, which is important in an HPC environment.
  • ^SIF is immutable by design. This is key to knowing your application will run months or years into the future.

    Demonstration

    This content of this video is based upon a blog post titled, CUDA GPU Containers on Windows with WSL2,  and will include several steps such as

    • ^Update the linux environment
    • ^Install the latest Nvidia drivers and container tools
    • ^Install and test SingularityCE on WSL2

      By now WSL2 should be installed along with a distribution, in our case we are using Ubuntu 20.0.4. We have followed the Microsoft blog post titled Install Windows on Linux with WSL. The next step is to start the Ubuntu environment for the first time and install SingularityCE, described on GitHub What is SingularityCE.

      Let’s begin by getting the WSL2 environment ready

      Open up your ‘Ubuntu’ app to drop into a WSL2 session. 

      For the WSL2 default of Ubuntu 20.04 you can just use the commands below:

      First we’ll update the installation of Ubuntu in WSL2. 

        $ sudo apt update && sudo apt upgrade

        Singularity natively supports running application containers that use NVIDIA’s CUDA GPU compute framework, lets install the NVIDIA utilities.

        Fetch and add the signing key and the repository file

        $ curl -s -L https://nvidia.github.io/libnvidia-container/gpgkey | sudo apt-key add -
$ curl -s -L https://nvidia.github.io/libnvidia-container/ubuntu20.04/libnvidia-container.list | sudo tee /etc/apt/sources.list.d/libnvidia-container.list

        Get the NVIDIA metadata from the new repositories 

        $ sudo apt update

        Install the needed packages

        $ sudo apt install libnvidia-container-tools

        Install Singularity

        In this demonstration we will be installing open source SingularityCE (community edition). If you are a SingularityPRO customer and would like to follow along, your customer-specific installation instructions can be found in the Sylabs SingularityPRO welcome email or via the SingularityPRO Administration guide.

        SingularityCE

        In this demo we will be using open source SingularityCE, the packages are available at the GitHub releases page or you can quickly install the 3.10.2 package with:

        $ wget  https://github.com/sylabs/singularity/releases/download/v3.10.2/singularity-ce_3.10.2-focal_amd64.deb
$ sudo apt install ./singularity-ce_3.10.2-focal_amd64.deb

        *The latest SingularityCE version will be noted in this link for the changelog 

        Quick Test the Singularity Installation

        We will test the installation with a quick Singularity version check, followed by performing an “exec” operation on a base container stored in the Singularity Container Services Library, Alpine in this scenario

         
        $ singularity --version
singularity-ce version 3.10.2-focal
$ singularity exec library://alpine cat /etc/alpine-release
INFO:    Downloading library image
2.7MiB / 2.7MiB [===================================================================================] 100 % 1.4 MiB/s 0s
3.15.5

        Add NVIDIA GPU Configuration

        Because the nvidia-container-cli utility functionality is a new, and still experimental feature, the pre-built packages are not set up by default. We will add the functionality by editing the singularity configuration file

        $ sudo nano /etc/singularity/singularity.conf

        Find the line that begins “nvidia-container-cli path =” uncomment add in the path the NVIDIA 

        nvidia-container-cli path = /usr/bin/nvidia-container-cli

        Finally we will run an NVIDIA command to detect GPU availability

        nvidia-container-cli path = /usr/bin/nvidia-container-cli $ nvidia-smi -L
GPU 0: NVIDIA GeForce GTX 950 (UUID: GPU-f2ec52a2-0f12-a0c6-1eb5-7aad5c2f7ed5)

        We are now at the point where Windows has the Nvidia drivers installed, Ubuntu distribution of 20.04.4 is running inside WSL2, and SingularityCE is installed with the NVIDIA libraries packages.

        Coming Next Week

        In the next video we’ll create a Singularity Container Services account, configure SingularityCE to use Singularity Container servers and then play with some containers. We are so excited, we can’t contain ourselves…

        Thank you for joining us!

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