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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

The installation is done via a custom Helm plugin which takes care of internal connectivity of the chart components. Standard Helm installation is also supported, but requires a lot more manual configuration.

helm plugin install https://github.com/fastmachinelearning/SuperSONIC/
helm install-supersonic <release-name> -n <namespace> -f <your-values.yaml>

Installer plugin usage:

Usage:
  helm install-supersonic [RELEASE_NAME] [flags]

Flags:
  -h, --help              Show this help message
  -f, --values            Specify values file for custom configuration
  -n, --namespace         Specify Kubernetes namespace for deployment
  --version               Specify chart version (default: latest version)
                          Note: Ignored if --local flag is set
  --local                 Install from local chart path instead of remote repository
  --path                  Local chart path (default: ./helm/supersonic)
                          Only used when --local flag is set
Additional flags will be passed directly to the 'helm install' command

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
UChicago - -