Sherif Akoush
Sherif Akoush
> I think the overarching theme is that we should not shy away from callbacks in public Python APIs but embrace them (alibi-detect already does this extensively), at the same...
We might want also to consider: - If we are creating a schema for `init_kwargs`, `explain_kwargs`, we might want something like `ExplainerConfigValidator` exposed so that clients (e.g. `mlserver`) can validate...
Tthere are cases e.g. [sklearn mnist ](https://scikit-learn.org/stable/auto_examples/classification/plot_digits_classification.html#classification)that do not even have a channel dimension. Basically the image is flattened for grayscale images to 2D: `(samples, features)`.
I feel in general that we would need to start thinking about how a user can inject `predict_fn`, `pre`, `post` transformation to explainers as opposed to trying to have everything...
Yes this will be a very useful feature.
As a side note, kserve supports `TorchServe` which offers explainability via `captum`. So this issue also affects SC (v2) feature parity with kserve at this level.
It is unlikely this is going to be addressed in the next release as it stands. Do you have a particular usecase that requires it that you could share?
@RezaYazdaniAminabadi Hi, Is this likely to get fixed at some point? I think that managing the past kv cache externally is required for some cases such as guidance (as referenced...
pydantic v2 migration is currently blocked
@dependabot recreate