When saving weights or different types of models in an output channel of a trainer, how do I differentiate between these in a resolver to prevent picking up the wrong model in the next run of a TFX pipeline run?
@Robert_Crowe grateful for any pointers
See this example to use a Resolver: tfx/penguin_pipeline_local.py at v1.12.0 · tensorflow/tfx · GitHub
There are certain resolve policies we have defined, eg. takes in the latest artifact.
However, we don’t have a resolve policy to resolve based on the model weights .
- implement the custom resolving policy.
- alter the way to write the pipeline and use conditional expression to control the pipeline logic to be decided by parameters related to your models.
I think 2 might be easier to pursue.