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Server infrastructure for GPU inference-as-a-service in large scientific experiments

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fastmachinelearning/SuperSONIC

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logo logo SuperSONIC

The SuperSONIC project implements server infrastructure for inference-as-a-service applications in large high energy physics (HEP) and multi-messenger astrophysics (MMA) experiments. The server infrastructure is designed for deployment at Kubernetes clusters equipped with GPUs.

The main components of SuperSONIC are:

Installation

helm repo add fastml https://fastmachinelearning.org/SuperSONIC
helm repo add prometheus-community https://prometheus-community.github.io/helm-charts
helm repo add grafana https://grafana.github.io/helm-charts
helm repo update
helm install <release-name> fastml/supersonic --values <your-values.yaml> -n <namespace>

To construct the values.yaml file for your application, follow Configuration guide.

The full list of configuration parameters is available in the Configuration reference.

Server diagram

diagram diagram-dark

Grafana dashboard

grafana

Status of deployment

CMS ATLAS IceCube
Purdue Geddes - -
Purdue Anvil - -
NRP Nautilus