Add catboost classifier support

[krishanbhasin-gc]

Reviving #529.

From the original PR description

add catboost support:

• add a new runtime by heavily copying lightgbm runtime
• add test that builds a model, sends an inference request, validates the prediction
• add example in docs
• list in index/readme

I have only resolved merge conflicts and done a first pass of reshaping some of the configuration files to match the new poetry approach.

---

Looking ahead to adding support for the Regressor and Ranker models, given catboost/catboost#2504 we can't use the same approach as XGBoost (using a try-catch:

MLServer/runtimes/xgboost/mlserver_xgboost/xgboost.py

Lines 24 to 34 in d41f24e

	def _load_sklearn_interface(model_uri: str) -> XGBModel:
	try:
	regressor = xgb.XGBRegressor()
	regressor.load_model(model_uri)
	return regressor
	except TypeError:
	# If there was an error, it's likely due to the model being a
	# classifier
	classifier = xgb.XGBClassifier()
	classifier.load_model(model_uri)
	return classifier

).

It might be worth creating 3 separate MLModel child classes, one for each of Classifier, Regressor, and Ranker instead. Could this fit the design approach of the project?

[adriangonz]

Hey @krishanbhasin-gc ,

Apologies for the delay reviewing this one.

This is great work! Thanks a lot for jumping on top of this one! 🚀 From my point of view, this looks great as-is. Once the tests are all good, we should be able to merge this one.

On what you mention around classifiers vs regressors, I think we should land this one as a first iteration and then open an issue to add support for extra types of CatBoost models. It's a shame the framework itself doesn't give us any way to differentiate between artefacts - in that case, we may need to add some extra config field to the model-settings.json to select between model types.

[adriangonz]

Actually, tests are already all green - so we should be good to go with this one. Great work @krishanbhasin-gc ! 👍