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[Backend][Plugin] Kubeflow operators - Pytorch #338
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This is code complete, pending documentation. |
@igorvalko is working on this |
All related commits A good way to test the entire Plugin in isolation is using |
* Default kubeconfig should be empty Signed-off-by: Ketan Umare <[email protected]> * fixed generate Signed-off-by: Ketan Umare <[email protected]>
…mapTaskListItem component (flyteorg#338) * chore: add component for mapped task support * test: add test coverage for isMapTaskType and MapTaskStatusInfo * chore: ignore *.stories.tsx files when collecting coverage Signed-off-by: Nastya Rusina <[email protected]>
Signed-off-by: Daniel Rammer <[email protected]>
* Default kubeconfig should be empty Signed-off-by: Ketan Umare <[email protected]> * fixed generate Signed-off-by: Ketan Umare <[email protected]>
Signed-off-by: Daniel Rammer <[email protected]>
## Overview Follow up to #293, in which each secret mounted as file uses a separate init container. This change utilizes a single init container. ## Test Plan 1. Deploy to dogfood. 2. Create multiple secrets using unionai's `-f` flag and file. 3. Run a workflow using the secrets created in previous step. 4. Inspect pod spec. 5. Verify that secret values were read correctly. ## Rollout Plan (if applicable) Run `managed-cluster-<env>-sync-all`. ## Upstream Changes Should this change be upstreamed to OSS (flyteorg/flyte)? If not, please uncheck this box, which is used for auditing. Note, it is the responsibility of each developer to actually upstream their changes. See [this guide](https://unionai.atlassian.net/wiki/spaces/ENG/pages/447610883/Flyte+-+Union+Cloud+Development+Runbook/#When-are-versions-updated%3F). - [ ] To be upstreamed to OSS ## Issue ref COR-811 ## Checklist * [x] Added tests * [ ] Ran a deploy dry run and shared the terraform plan * [ ] Added logging and metrics * [ ] Updated [dashboards](https://unionai.grafana.net/dashboards) and [alerts](https://unionai.grafana.net/alerting/list) * [ ] Updated documentation
Why would this plugin be helpful to the Flyte community
Allow users to executed distributed pytorch tasks
Type of Plugin
Can you help us with the implementation?
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