README.adoc

Open Data Hub with Easybuild and Lmod, or thousands of libraries and applications without installation!

TL;DR: container images on a data science platform like Open Data Hub are hard to manage if you want to provide users with many different libraries or applications, moreover at various versions.
This repo shows how you can use EasyBuild, Lmod and a shared PVC to bring thousands of those instantly in the notebook environments with a single container image! (Therefore the name of the project, "There can be only one…​")

Intro

The problem

If you manage shared data science environments based on Kubernetes, like Open Data Hub on OpenShift, you may face a container image management problem. That is especially true with interactive environments like Jupyter-as-a-Service. Your users will want to use various Jupyter images that include this or this library or application, at this or this version. Of course it’s clearly impossible to create behemoth images including each and every library you’re asked for, or to create and manage hundreds (thousands?) of variations of handpicked selection of libraries and apps to fit a specific need. Granted, you could let the users pip or conda install everything from scratch every time they launch the environment. But it’s not that practical, not all packages are available through this mean, especially your own private applications. Moreover, in an enterprise environment, the as-a-service container won’t run as root, so no package can be yum or apt-get installed.

Figure 1. Your options

The solution

In this repo you will find tools and instructions for a different approach to this problem, thanks to the HPC community who has solved it a long time ago! This is kind of a portage of their solution to Kubernetes.

The idea is that instead of building the needed applications and libraries into the container images, you can store all of them in a read-only shared library on a ROX volume (read-only, but by many pods simultaneously), and mount this volume inside the Pods at spawn time.
Then, leveraging Linux Environment Modules with Lmod, it gets easy to dynamically "load" what you need, when you need it, instantly…​ Pretty neat, uh?

Figure 2. Dynamic loading

And with Easybuild, you have access to a few thousands of ready to use (or more accurately to compile, we’ll come to that) software packages, plus the full machinery to easily create you owns.

What does it look like?

With the help of a great Jupyter extension to easily load those shared modules, it’s really easy! Have a look:

Figure 3. Dynamic loading

ℹ️	In this example I did not load the module from the start on purpose to trigger the error, so I had to restart the notebook kernel. It was just to show you how easy and fast it is! More below about modules loading and creating collection of softwares,…​

Demo environment installation

Here are the instructions to create and populate a shared libray with common modules and applications (PyTorch, Tensorflow, R, RStudio, VS Code,…​).

Prerequisites

• A deployment of Open Data Hub in your OpenShift cluster. Only the JupyterHub part is necessary to show the usage of the shared library with notebooks.

• Open Data Hub is deployed into the opendatahub namespace, and Jupyter Notebooks are created in this same namespace. If your configuration is different you will have to adapt the configuration files and deployment instructions.

• The ability to create RWX (Read-Write Many) PVCs. In this deployment, we use the ocs-storagecluster-cephfs storage class provided by OpenShift Data Foundation.

Deployment

Switch to the right project

oc project opendatahub

Create the PVC that will host the library

oc apply -f deploy/01_easybuild-data_pvc.yaml

Initialize the PVC with data

oc apply -f deploy/02_easybuild-data-init_job.yaml

ℹ️	This part can take some time as the package is about 15GB. It has to be downloaded then injected into the PVC.

Create the ImageStream for the module-enabled notebook image

oc apply -f deploy/03_s2i-generic-datascience-lmod-notebook_imagestream.yaml

Create the ConfigMap to enable the notebook image in JupyterHub

oc apply -f deploy/04_jupyter-lmod-profile_configmap.yaml

Optional

This step is only if you want to spawn an easybuild environment to build other modules or create your own ones.

Deploy the EasyBuild container

oc apply -f deploy/05_easybuild_deployment.yaml

Usage

Using the "Modular" notebook image

From you JupyterHub spawner, you now have a new notebook image available, Standard Data Science - Modular.

Figure 4. JupyterHub Launcher

In this JupyterLab instance you have access to a new extension, "Softwares". Here are basic instructions on how to use it.

The list of available modules is splitted in two sections. The "featured" modules, and the full list:

Figure 5. Module list

You can use the filter box to search for a specific module (just enter a few letters). Filtering happens simultaneously on both lists:

Figure 6. Filtered list

If you click on a module name, a pop-up will give you more information: description, dependencies,…​

Figure 7. Module info

To load a module, hover on it and click on the "Load" button:

Figure 8. Module loading

The module and all its dependencies is automatically loaded (torchvision in this example):

Figure 9. Module loaded

To unload a module, hover it the "loaded" list, and click "Unload":

Figure 10. Unload Module

The module and its dependencies will be automatically unloaded.

ℹ️	Lmod keeps track of the loaded dependencies for each module. If two different modules share dependencies, a module unloading won’t affect the other one, its dependencies will still be there. There are only unloaded when no module needs them anymore!

Advanced functions

Collections

If you want to create a specific environment with a set of different modules that you want to use, no need to recreate this from scracth every time! You can simply load those modules, then create a "Collection". Next time, just restore this collection in two clicks.

To create a collection, load the modules you want, click on the icon and give a name to the collection.

Figure 11. Create Collection

Figure 12. Name and Save Collection

When you want to bring back this environment, just click on the Restore icon, and select and load your collection.

Figure 13. Restore Collection

Figure 14. Restore Collection

Imports

You can also directly work from your notebooks and your scripts to load the modules that you need. To know which modules you have to use, you can directly export the relevant Python code!

Click on the "Generate Python code" icon:

Figure 15. Generate Code

You can then copy paste the full code in you first notebook cell or in your script:

Figure 16. Python Code

ℹ️	Of course, for this to work in your notebook or your script, the container image or environment you are using must be "lmod-enabled", and the library with the relevant modules must be accessible/mounted into this environment.