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In this article we’ll cover lessons learned implementing a Keycloak authentication solution into Django Rest Framework (DRF) using the mozilla-django-oidc library. Note that this article assumes some familiarity with Django.

Requirements

These can be summarized as follows:

  1. Replace the current authentication solution with a Keycloak-based solution so that users can be authenticated and enable single sign-on between applications using different authentication providers.
  2. The solution should cover both Django (session authentication) and DRF (token authentication).
  3. It should be able to handle a dedicated Keycloak client and expandable to allow roles-based authorisation.
  4. OpenID connect preferred
  5. Any libraries used should be currently supported and widely used
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Docking is often used in virtual screening to attempt to identify potential drug leads. To be effective you typically need to screen a large number of candidate molecules and that means you need to parallelise the process across multiple servers. This post describes how this can easily be done using AWS Parallel Cluster and Nextflow.

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After a long break I complete a series of posts on building smaller container images, this time showing how to build lightweight images for RDKit using buildah. In the previous post I introduced using buildah. In short, it showed how to package what you want into a container images without needing to include the build infrastructure (make etc.) or package managers (yum etc.).

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[Actions] are a welcome addition to the GitHub service. They add a free, built-in CI/CD capability similar to [GitLab-CI], [Travis] and others.

What we’ll see in this blog post is a simple pattern to build a container image and push it to [Docker Hub]. What happens is based on whether we’re on a branch, responding to a pull-request, or responding to changes on main.

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In my last post I described the basic details of how the fragment network is composed and how it allows queries for similar molecules that are “chemically intuitive”. Here I show how queries can be executed through the REST API and how this has turned out be be extremely useful in the virtual screening work we are doing on the SARS-Cov-2 main protease in collaboration with the Diamond Light Source.

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Here we’re going to explore repetitive project content and one method you can employ to automate its generation.

After creating a few Ansible-based Kubernetes projects the boilerplate begins to emerge on two fronts - a number of mandatory Ansible files and the Kubernetes object definitions. What’s most frustrating is that, for the most part, Kubernetes objects are often detailed (verbose) yet irritatingly repetitive and predictable.

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In this article we’ll see how to deploy container images from a [GitLab] private registry into Kubernetes.

Public container images, in registries like [Docker Hub], can be deployed easily without needing to provide any credentials. Kubernetes Deployments (and other objects like StatefulSets) simply need the image, i.e. informaticsmatters/neo4j:3.5.20. However, images resident on a private registry will require you to deploy an ImagePullSecret that Kubernetes uses to pull the image.

[Kubernetes documentation] describes such secrets with a section explaining how they can be created from the command-line.

Here we provide a brief cheat-sheet that explains how to create a pull-secret using [GitLab] and then use that in a Deployment.

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In my last post I described how the fragment network can be used as a key part of a virtual screening project by providing allowing your initial hits to be expanded out to a large number of candidates to screen. In this post we describe how the fragment network works and why is more ‘chemically intuitive’ that traditional fingerprint based similarity search.

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We’ve previously mentioned the fragment network and our Fragnet Search application that provides a user friendly way to search and explore the data in the fragment network. But we’ve not really explained the basis of the fragment network and how it can be utilised in a drug discovery program. This is the first is a series of posts that covers this topic.

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In this brief article we’ll see how to setup a Kubernetes nginx ingress to redirect HTTP traffic from example.com to www.example.com.

Prerequisites here are a cluster with an nginx ingress controller and a route to the cluster. This relies on your domain routing example.com and www.example.com to your cluster, usually through some form of load-balancer. We’re not going to cover these aspect of the solution, just the ingress definition you need.

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In this article we’ll see how simple it is to install Kubernetes onto some Ubuntu hosts using [Pharos].

Pharos is a Certified Kubernetes with all batteries included. It is powered by the latest upstream version of Kubernetes kernel and include tools for cluster lifecycle management.

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We’ve been very busy recently helping out on the fragment screening program at the Diamond Light Source. They generated fragment hits of the SARS-Cov-2 main protease back in early March. See here for details.

With that data we’ve been running virtual screening using compounds expanded out from the fragment screening hits using the API of Fragnet Search. Details of the initial virtual screening workflow that was executed on usegalaxy.eu are described here and I, along with Simon Bray from the Freiburg Galaxy team, will be presenting this in session 3 of the Galaxy-ELIXIR webinar series on COVID-19 on 14 May 2020, 17pm CEST. Come along and hear the details. You will need to register first.

We’ll be describing various aspects of the work in more details in this blog at a later stage.

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We now have a better approach for building Centos based RDKit Docker images now that Centos 8 is available.

We previously described our approach to building container images for RDKit (see here for example). Key to our approach is keeping the image size relatively small.

Up till now building Centos based images has been tricky as since the March 2019 release of RDKit a very recent version of the boost libraries were required (due to the switch to modern C++), but the ones that come with Centos 7 are too old, as well as RDKit now requiring Python 3 which traditionally has not played nicely with Centos 7. We did find a workaround for all this, but it was not nice.

Now that Centos 8 is available this problem is solved and we’ve been able to simplify the process. The approach closely follows the one we use for Debian based images. We’ve pushed Python3 and Java images for the October 2019 RDKit releases and a build from the master branch and from now on will do our best to generate these as well as the Debian based images for future releases. We might also provide Centos based cartridge and Tomcat images, but generating those is more complex so stick with Debian based images for those for now.

See the GitHub repo for all the details (and the various branches you’ll find there).

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We’ve recently demonstrated our Fragnet Search application in a SaferWorldbyDesign webinar.

In it we demonstrate using the web application and programmatically accessing the REST API behind it.

Take a look at the recording on YouTube.

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We’ve been working with the fragment network as part of our work with the fragment screening program at the Diamond Light Source for a couple of years now. The fragment network was conceived by Astex and provides a chemically intuitive way to identify similar compounds without the limitations of traditional fingerprint based similarity searching.

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One of our more interesting GitHub projects is the Docking Validation project. We use this to establish and document best practices in virtual screening tools (such as docking) and approaches to semi-automating and scaling these procedures.

We just completed a new ‘experiment’ that is related to our work at the Diamond Light Source’s XChem project which has done some amazing work on fragment based screening using XRay crystallography.

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You’ve probably created a machine image at some point. A base image for AWS that builds upon someone’s work by adding a particular version of Java or Python or a new utility. Did you create the image on AWS using an EC2 instance, login, run some yum or apt-get and then save it? Great, and if someone wants the source code for that image or you want to build a similar image on a different region or provider? Well, [Packer] is an IaC tool for automating the construction of machine images.

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The Python Jenkins module is a convenient wrapper for the Jenkins REST API that gives you control of a Jenkins server in a pythonic way. Here we’ll see how to grab all the jobs from a Jenkins server and also how these jobs can be re-created from the captured material.

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In this post we look at using buildah to generate container images that only contain what we want, no extra fluff. We show how this can let us generate truly small images that will load faster and be more secure, and do this without the need for the Docker daemon to be running.

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Here we’re going to be looking at the the idea of applying automation tools to the wider product development process. Tools that help you do this are part of a collection known as “Infrastructure as Code”, which refers to the the provisioning of compute instances (physical machines and their operating systems) and software applications using revision-controlled machine-readable text files.

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This series of posts describes how we can generate smaller Docker images. In the first post we outlined a common problem with container images - that they frequently contain artefacts that were needed to build the software or to install it into the container. We’ll show one approach that can be used to avoid this extra bloat, and so generate smaller and more secure containers.

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I’ve been in meetings, often driven by the root-cause-analysis of a software fault found in the field, where the topic of code coverage has cropped up. I’m sure many of us have been in similar meetings. On occasions I’ve also been asked to justify some of my apparently poor line coverage figures, where the percentage has fallen short of what was perceived by the inquisitor as acceptable.

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This is the first in a series of blog posts about building better Docker images.

Docker Inc is widely acknowledged for transitioning containers from geekdom to the real world inhabited by us developers, and did this by providing easy to use tools for building, sharing and running containers. Key to this is docker build command and the Dockerfile.

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Welcome to the Informatics Matters blog.

This is the first post of what will become a regular stream of information about our activities at Informatics Matters in providing solutions for scientific computing, including bioinformatics, genomics, cheminformatics and computational chemistry.

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