Reducing time-to-science with self-service HPC and AI platforms in the Azimuth portal

Matt Pryor, Stack HPC

Matt Pryor, John Garbutt, Matt Anson, StackHPC

Recent years have seen increasing divergence from the traditional HPC model, with researchers keen to take advantage of new and rapidly developing tools such as Jupyter Notebooks, Dask, Apache Spark and Kubeflow while still maintaining the ability to run existing codes in a traditional batch environment, all without sacrificing performance. The explosion of tools and platforms, coupled with the fact that many of these platforms also need to be customised for each use-case, places a heavy burden on the operators of traditional HPC systems where individual platforms are deployed and maintained by the operator on behalf of users. We demonstrate here how the Azimuth portal is able to reduce time-to-science and operational overhead by providing researchers with self-service access to HPC and machine learning platforms via a simple and intuitive user interface. Azimuth builds on work done at JASMIN, with funding from the IRIS collaboration, to present users with a catalogue of customisable platforms that they can deploy into their cloud allocation. Leveraging cloud-native technologies and automation, these platforms can be deployed on virtual machines or in Kubernetes clusters and are able to take advantage of hardware acceleration such as GPUs or RDMA networking without explicit configuration from the user. The Azimuth portal is in use at several IRIS sites, and is providing platforms for projects including the SKA.

One Comment

Leave a Reply

Fill in your details below or click an icon to log in: Logo

You are commenting using your account. Log Out /  Change )

Twitter picture

You are commenting using your Twitter account. Log Out /  Change )

Facebook photo

You are commenting using your Facebook account. Log Out /  Change )

Connecting to %s