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ARTs:

Services
Benefit hypothesis:

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PSI-low is currently the only environment containing specialised SKA hardware. The computing platform needs to be refined continuously to inform future setups such as sITF.

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PSI-low is currently the only environment containing specialised SKA hardware. The computing platform needs to be refined continuously to inform future setups such as sITF.
Acceptance criteria:
- HW specific nodes can communicate as part of the k8s network
- nodes can be allocated via k8s for dedicated use
- tests allocating hardware can be run from the gitlab ci pipelines
Feature Points:
1.5
Initial Size:
1.5
WSJF:
0
Agile Teams:

Team_VULCAN
Due Sprint:
Sprint 3
Story Point Burn-up:
Overdue:
Outcomes:

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This functionality has been presented in OMC ART System Demo 14.5

The code for deployment is available under https://gitlab.com/ska-telescope/sdi/ska-cicd-deploy-psi-low/

The code for the allocation plugin is documented here https://developer.skao.int/projects/ska-ser-k8s-spookd/en/latest/index.html

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This functionality has been presented in OMC ART System Demo 14.5 The code for deployment is available under https://gitlab.com/ska-telescope/sdi/ska-cicd-deploy-psi-low/ The code for the allocation plugin is documented here https://developer.skao.int/projects/ska-ser-k8s-spookd/en/latest/index.html
Resolved PI.Sprint:
14.6

Feature Checklist:

Stories Completed, Integrated, Solution Intent Updated, Outcomes Reviewed, Demonstrated, Satisfies Acceptance Criteria, Accepted by FO

PI13 didn't achieve feature completion on this as several blockers were encountered and fixes arriving late in the PI.
To complete the issue,

the PST and Perentie nodes need to be added to the k8s network
the resource allocation plugin fully tested and integrated into CI, for both Alveo and GPU capabilities
A plugin or similar needs to be developed to do IP allocation, for e.g. the TPM, so only one DeviceServer can access it at a time.

mentioned in: Page Loading...; Page Loading...; Page Loading...; Page Loading...; Page Loading...

Story Point Burn-up: (100.00%)

Feature Estimate: 1.5

Complete reproducible CI deployments to PSI-LOW platform