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- Overdue by 6 month(s)•Due by March 15, 2025•0/1 issues closed
## Road map for the release 0.7 - Power model training pipeline - Train power model from various machines - SPECPower: [plus] large amount (>900), [minus] offline dataset with simple metric (CPU utilization) - AWS self-hosted instance: [plus] limited number of profiles (40), [minus] on-release kepler metrics (CPU, cache, and so on) - Model selection logic with machine similarity report - Power model continuous integration and delivery framework - Regression testing ### Local XGBoost estimator - [x] Migrate xgboost to the main pipeline abstract (removal of XGBoostRegressionStandalonePipeline) - [x] Train the model and export weight - [x] Test xgboost kepler integration with provided weight ### Page cache - [ ] Collect data with page cache hit - [ ] Update feature group and retrain the model ### Model accuracy - [x] Add MAPE and change error key to MAPE - [ ] Improve training with feature relevance knowledge (such as not using memory feature for core model training) ### Integration and Deployment - [x] Training CI - Tekton - [ ] Platform validation CI - [ ] Operator integration
Overdue by 1 year(s)•Due by April 30, 2024•9/14 issues closed