You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
Since orca.automl is used for customized model for any area, while most of the metrics in orca.automl.metrics is designed and optimized for time series tasks.
The built-in metrics in orca.automl can be a simple map between string and sklearn.metrics
This will help Chronos to be self-contained.
The text was updated successfully, but these errors were encountered:
Since in ·orca.automl·, AutoXGBoost also uses metrics, it might not be convenient to directly move metrics from orca to chronos.Otherwise, orca need depend on chronos.
For chronos, in the cases of
Single node acceleration with nano: we could directly use TorchMetrics in pytorch-lightning, which might also be more efficient regarding both performance and memory.
Distributed training or distributed tuning, both of which will depend on orca and could choose to use metrics in orca.automl.
[TBD]However, we might move metrics to nano, since it is for single node only...
orca.automl
is used for customized model for any area, while most of the metrics inorca.automl.metrics
is designed and optimized for time series tasks.orca.automl
can be a simple map between string andsklearn.metrics
Chronos
to be self-contained.The text was updated successfully, but these errors were encountered: