In this recipe, you will learn how to create your own container-based environment that
you can use at home and on the supercomputer. It is compatible with windows as well as Linux and MacOS, however only if your host is of X86 architecture (new Macs might pose a problem).
It is composed of the following steps:
you can use at home and on the supercomputer. In this picture it is outlined:
You will create a docker-based environment on your machine, that can be use to serve
a jupyter server. Then you transfer the docker image to the supercomputer and make it available
within Jupyter-JSC. Of course, this works without Jupyter, but is much less fun.
In General, the usage of singularity containers on the HPC is highly recommended. If the containers from the NVIDIA container registry
[link](https://catalog.ngc.nvidia.com/) are used, you can be quite sure that you are as fast as it gets.
The workflow is compatible with windows as well as Linux and MacOS, however only if your host is of X86 architecture (new Macs might pose a problem).
We however did not get a chance to try.
You will perform the following steps:
0. Install docker
1. Create a docker container that contains the environment
2. Run the docker container to serve a local jupyter server and to execute programs
3. Export the docker container for singularity. (Does this work on WSL?)
4. Recreate the environment on the supercomputers
3. Export the docker image, transfer it to the supercomputer and convert it to a singularity container
4. Run things in the singularity container.
Finally, we say a few words about recommended workflows and give a few details that we have omitted before.
# Install docker on your home machine
This step depends on the OS you use. Please follow the instructions on the [docker web page](https://docs.docker.com/get-docker/). After that you should be able to
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@@ -65,6 +79,7 @@ cd singularity_docker_jupyter
```
In this step, you will export the docker image you have created as a singularity container. It requires the following steps:
**1.** Save the docker image in a tarball
```bash
docker save singularity_docker_jupyter -o singularity_docker_jupyter.tar
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@@ -88,9 +103,9 @@ This will create a file `singularity_docker_jupyter.sif` in your local directory
If you local machine is a Linux machine, you also have the option to create the singularity image `singularity_docker_jupyter.sif` on your local machine.
# Use the container with singularity on the supercomputer
We have created an example submission script, that you can submit to slurm. It is also possible to
The typical usage of a singularity container is `singularity run image.sif command --with-args`. We demonstrate the usage
in an example submission script `example_submission_script.sh`, that you can submit to slurm. It is also possible to
execute it directly on the login node. Here is the output:
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@@ -107,6 +122,11 @@ If you look at the file `jupyter-jsc/kernel.sh` you will see that it only is it
is executed in the singularity container.
# Workflow
Your first shot at creating an environment will never be your last one. Creating an environment is an iterative task, and you'll soon find a library or a tool that is required in
your workflow. An option that speeds up your productivity might be using a virtual environment that is stored outside of the container. This makes it much easier to add libraries.
In another recipe, we have covered, how to do this. In General, we discourage the use of `docker commit`, even though this might feel like a very easy way to develop an environment step by step. However, you give
up the great reproducibility you achieve with docker files.
# Details about the docker container is started
The script `run_jupyter.sh` does a few things that are untypical when using docker. Here are the most important points.
