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feat(batch-predictor): remove CPU limits from SparkApplication resour…
…ce (#615) <!-- Thanks for sending a pull request! Here are some tips for you: 1. Run unit tests and ensure that they are passing 2. If your change introduces any API changes, make sure to update the e2e tests 3. Make sure documentation is updated for your PR! --> # Description Similar to kserve models, this PR removes CPU limits for batch prediction jobs. If limits are set, executors will experience significant throttling because batch jobs tend to consume all available CPU by its nature. # Modifications * SparkApplication resource no longer has `coreLimit` set in driver and executor specs. This field is optional according to [the specs](https://github.com/kubeflow/spark-operator/blob/master/docs/api-docs.md#sparkoperator.k8s.io/v1beta2.SparkPodSpec) and it should be safe to simply remove it. # Tests <!-- Besides the existing / updated automated tests, what specific scenarios should be tested? Consider the backward compatibility of the changes, whether corner cases are covered, etc. Please describe the tests and check the ones that have been completed. Eg: - [x] Deploying new and existing standard models - [ ] Deploying PyFunc models --> # Checklist - [x] Added PR label - [x] Added unit test, integration, and/or e2e tests - [x] Tested locally - [ ] Updated documentation - [ ] Update Swagger spec if the PR introduce API changes - [ ] Regenerated Golang and Python client if the PR introduces API changes # Release Notes <!-- Does this PR introduce a user-facing change? If no, just write "NONE" in the release-note block below. If yes, a release note is required. Enter your extended release note in the block below. If the PR requires additional action from users switching to the new release, include the string "action required". For more information about release notes, see kubernetes' guide here: http://git.k8s.io/community/contributors/guide/release-notes.md --> ```release-note Remove CPU Limits for batch prediction jobs ```
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