Instructions for using singularity at PDC¶
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.
More information about singularity can be found at https://docs.sylabs.io/guides/latest/user-guide/
Singularity is more secure on a HPC than other similar solutions like docker or shifter. Read a comparison about them at http://geekyap.blogspot.se/2016/11/docker-vs-singularity-vs-shifter-in-hpc.html
important to remember is that if you download images they should be trusted since any container you run will have full access tor your account and your data.
The same goes for images you build yourself that they are built upon trusted images.
Executing software in containers is very efficient as well as you create a sandbox of all applications that you do need. Very little performance loss has been seen.
Installation of singularity¶
Singularity is installed as a nosuid on the cluster, meaning that you are unable to use singularity files, but are able to use singularity sandboxes instead. Otherwise there should be no difference. At PDC you cannot write within your containers while being logged in on the cluster. Operation for creating containers is better performed on your local computer.
How to use singularity on your local computer¶
How to install singularity¶
Singularity can be installed on your computer, with root access, so you can build your own images. Installation instructions are available at… https://docs.sylabs.io/guides/latest/user-guide/quick_start.html#quick-installation-steps
Download images from singularity hub¶
You can find numerous images at https://singularity-hub.org/ Just download them directly to our file system and use them for your analysis. The build command in singularity, besides downloading the image, also converts the image to the latest singularity version.
singularity build --sandbox <sandbox name> shub://<name of image>
You can also use images from docker https://hub.docker.com
singularity build --sandbox <sandbox name> docker://<name of image>
Building your own images¶
Different Linux OS are available for you to download. At this time the best Ubuntu distribution is available as a docker image and can be downloaded
sudo singularity build --sandbox <sandbox name> docker://ubuntu:latest
Which will download this docker image and convert it into a sandbox.
Building your own image from recipes¶
You can also create an image using a recipe from our PDC github. Singularity recipes with MPI support are available at https://github.com/PDC-support/PDC-SoftwareStack/tree/master/other/singularity In order to create these
sudo singularity build --sandbox <sandbox name> <recipe name>
Here is a full example on how to create an image from one of our recipes.
wget https://raw.githubusercontent.com/PDC-support/PDC-SoftwareStack/master/other/singularity/ubuntu-mpich-full.def sudo singularity build --sandbox ubuntu-full ubuntu-mpich-full.def
With this command you will automatically login into your singularity image shell.
singularity shell <sandbox name>
In the shell you can do the usual Linux shell commands.
So now that you do have a sandbox you can start by installing software in the image. By login into the writable sandbox you can use all the commands that are normal in the operating system, and your internetaccess is also available in the container, so you can use wget or other commands to download data and softwares. By default on a writable sandbox you login with the user root, therefore this should be executed on your personal computer where you have root access.
sudo singularity shell -w <sandbox name>
Read mode: You can read/write to file system outside container and read inside container.
write mode: You can read/write inside container. In write mode you are user ROOT, home folder: /root
Installing the essentials in your image¶
Although you have downloaded the latest OS, it still needs some basic software, compilers and libraries. This can either be installed using a recipe like explained above, or you can login into your image using write mode and install everything you need, for example updating a sandbox
sudo singularity shell -w ubuntu_sandbox Singularity> apt-get install update Singularity> exit
Copying data from local file system to singularity sandbox¶
You can copy data to your singularity sandbox in several ways. Either by adding your files into the /root folder in singularity, and then they will automatically be available in the /root folder in singularity.
sudo cp <your file> /root
You can also bind your folder in singularity. In order to do that you must bind a folder in singularity to your local file system. Also the folder in singularity must be created first. The following example…
creates a new folder in the sandbox
binds a local folder to that folder
logins into the container,
copies <files> into your sandbox
sudo singularity exec -w ubuntu_write mkdir <singularity folder> sudo singularity shell -B <local folder>:/root/<singularity folder> -w ubuntu_write/ Singularity> cp <singularity folder>/<files> .
Where to store runtime files¶
As the root folder you are login into when using a writable container is not available using exec from outside the container, if you plan to run software that you compiled yourself, you must put the executable somewhere that is accessible by PATH. /usr/local/bin is a good example. Installed software can then be executed by
singularity exec <sandbox name> <myexe>
You can also add a software path in the containers runscript which is a shell file which will be executed when you run the container. The runscript file should be stored in /.singularity.d/ folder The script is then executed using
singularity run <sandbox name>
help documents on the image should be saved as runscript.help in the folder /.singularity.d This can then be read by
singularity run-help <sandbox name>
Saving your sandbox to PDC cluster¶
When you are ready installing all the software, paths, folders you need to transfer your sandbox to the cluster. To achieve this, it is advisable to first compress it and then transfer it.
sudo tar czf <sandbox name>.tar.gz <sandbox name> scp <sandbox name>.tar.gz <username>@dardel.pdc.kth.se:/cfs/klemming/home/<u>/<username> # On Dardel tar xfp <sandbox name>.tar.gz
Another possibility is to transfer a .SIF file to Dardel and convert it to a sandbox with the build command
scp <name>.sif <username>@dardel.pdc.kth.se:/cfs/klemming/home/<u>/<username> # On Dardel singularity build --sandbox <sandbox name> <name>.sif
Do remember that you do need OpenMPI to execute your software in parallel on HPC systems. We provide images with OpenMPI installed in the PDC Hub, but you can also build your own.
Running singularity images at PDC¶
Singularity works on Dardel by loading the singularity module. There are however restrictions. As explained above you are not able to write into singularity container. You are however able to build singularity sandboxes from containers available online, but not from recipes.
singularity build --sandbox <sandbox name> docker://ubuntu:latest
Also, you are able to run, run-help, exec, shell, as well as other non-write commands, on singularity sandboxes at PDC.
Below are a couple of example on how to run a singularity container on PDC systems. As the Lustre filesystem is not recognised by default it is important to bind said filesystem, which is done in the example scripts below.
Ready made containers at PDC¶
PDC provides some default containers for testing and certain software which are placed at /pdc/software/sing_hub These can also be reached by invoking $PDC_SHUB
As highlighted before these containers are built as a sandbox and can be run with our clusters. In order to get information about what they contain please run
singularity run-help $PDC_SHUB/<sandbox name>
Recipes on how several of these container are bult is available at https://github.com/PDC-support/PDC-SoftwareStack/tree/master/other/singularity
Follow information on how to create Job scripts at this page, which is not covered below. Although here are a couple of examples on how to run jobs on the dardel cluster.
Batch job without MPI¶
In case you would like to run on one node you do not need MPI support in your image and you can send in a job using…
#!/bin/bash -l # The -l above is required to get the full environment with modules # Set the allocation to be charged for this job # not required if you have set a default allocation #SBATCH -A 201X-X-XX # The name of the script is myjob #SBATCH -J myjob # Only 1 hour wall-clock time will be given to this job #SBATCH -t 1:00:00 # Number of nodes #SBATCH --nodes=1 # Using the shared partition as we are not using all cores #SBATCH -p shared # Number of MPI processes per node #SBATCH --ntasks-per-node=24 # Run the executable named myexe ml PDC singularity srun -n 24 singularity exec -B /cfs/klemming <sandbox folder> <myexe>
Batchjob with MPI¶
In case you need to parallelize your software across nodes you should use one of the recipes with Cray MPI support mentioned earlier which do reside in the https://github.com/PDC-support/PDC-SoftwareStack
#!/bin/bash -l # The -l above is required to get the full environment with modules # Set the allocation to be charged for this job # not required if you have set a default allocation #SBATCH -A 201X-X-XX # The name of the script is myjob #SBATCH -J myjob # Only 1 hour wall-clock time will be given to this job #SBATCH -t 1:00:00 # Number of nodes #SBATCH --nodes=2 # Using the shared partition as we are not using all cores #SBATCH -p shared # Number of MPI processes per node #SBATCH --ntasks-per-node=12 # Run the executable named myexe ml PDC singularity srun -n 24 --mpi=pmi2 singularity exec -B /cfs/klemming <sandbox folder> <myexe>
Batch job with GPU support¶
In case you would like to run on one node with AMD GPUs. Be aware that you need a container with software compilated using GPU support. Look at our https://github.com/PDC-support/PDC-SoftwareStack for recipes on how to build. in your image and you can send in a job using…
#!/bin/bash -l # The -l above is required to get the full environment with modules # Set the allocation to be charged for this job # not required if you have set a default allocation #SBATCH -A 201X-X-XX # The name of the script is myjob #SBATCH -J myjob # Only 1 hour wall-clock time will be given to this job #SBATCH -t 1:00:00 # Number of nodes #SBATCH --nodes=1 # Using the GPU partition which is at the moment is under testing #SBATCH -p gpu # Run the executable named myexe ml PDC singularity srun -n 1 singularity exec --rocm -B /cfs/klemming <sandbox folder> <myexe>