SLATE blog

Using SLATE to Deploy Open OnDemand

Open OnDemand is a web application enabling easy access to high-performance computing resources. Open OnDemand, through a plugin system, provides many different ways to interact with these resources. Most simply, OnDemand can launch a shell to remote resources in one’s web browser. Additionally, OnDemand can provide several ways of submitting batch jobs and launching interactive computing sessions. It is also able to serve as a portal to computationally expensive software running on remote HPC nodes. For example, users can launch remote Jupyter Notebooks or Matlab instances.

The SLATE platform provides a simple way to rapidly deploy this application in a containerized environment, complete with integration into an existing LDAP user directory.

How to Use JupyterLab to Submit Jobs to the Open Science Grid with SLATE

Our previous post JupyterLab and HTCondor with SLATE described deployment of an HTCondor pool onto a SLATE-registered Kubernetes cluster with job submission provided by a JupyterLab application. But what if you want just a JupyterLab capable of submitting jobs to the Open Science Grid?

Using Kubernetes Network Policies for SLATE applications

A major priority of SLATE is ensuring that our clusters and applications are secure. In order to better secure the applications there is an ongoing effort to ensure that they all have built in Network Policies. This allows a user or site administrator to more strictly limit who exactly has access to a given application.

Implementing Kubernetes Network Policies for SLATE applications

The SLATE team and collaborators continue to target security as a major focus. The SLATE team is configuring all the offered applications in the SLATE stable catalog with Kubernetes Network Policy hooks in the Helm deployment charts. As an application developer, being able to build this functionality into your application will make many site administrators much more comfortable with your application being used on their clusters.

SLATE Quarterly Updates

Welcome to another round of SLATE project updates! It’s been quite some time since our last update, so this post will be especially long. On the plus side, we have a lot of cool and interesting new things to tell you about!

JupyterLab and HTCondor with SLATE

JupyterLab is a great tool for data analysis, visualization, machine learning and much more. It allows users to run code interactively via its web notebook interface and thus iterate changes quickly. Often users need to scale up their work and thus require submission to a backend cluster from the notebook. We show how this can be done with HTCondor using SLATE.