The main idea behind PyTrack is to easily run, reproduce, save and restore model pipelines that allow for easy parameter optimizations without any processing overhead.
Currently all of this is handled through DVC. Altough DVC can do these things very nicely it might make sense to think about the integration of other tools into PyTrack.
An example could be the integration of MLFlow 1363b05 to be able to use the MLFlow UI features. MLFlow also provides more detailed timestep logging.
Another very common tool for pipelines might be Airflow
I would keep the basic idea of PyTrack based on DVC, because of the way it builds the dependency graphs, handles the cache and overhead and can run experiments in parallel on temporary directories. But for this discussion I would motivate everyone to collect missing functionality or integration into common tools here.
This does not mean, that PyTrack will be expanded to use multiple tools! There must be a very clear advantage to add dependencies for multiple tools!
The main idea behind PyTrack is to easily run, reproduce, save and restore model pipelines that allow for easy parameter optimizations without any processing overhead.
Currently all of this is handled through DVC. Altough DVC can do these things very nicely it might make sense to think about the integration of other tools into PyTrack.
An example could be the integration of MLFlow 1363b05 to be able to use the MLFlow UI features. MLFlow also provides more detailed timestep logging.
Another very common tool for pipelines might be Airflow
I would keep the basic idea of PyTrack based on DVC, because of the way it builds the dependency graphs, handles the cache and overhead and can run experiments in parallel on temporary directories. But for this discussion I would motivate everyone to collect missing functionality or integration into common tools here.
This does not mean, that PyTrack will be expanded to use multiple tools! There must be a very clear advantage to add dependencies for multiple tools!