Dimensionality reduction without select components I would like to use dimensionality reduction algorithm in my pipeline.
I have 2k features and I'm using xgboost.
My model is rebuilding each day (there are new records that should be involve to training set).

I'm looking for method for dimensionality reduction with out setting n_components.
I know that in PCA it shouldn't be set. But I'm looking for method that find something like clusters on my data and then I will use it to train my model. Of course the same flow I'll be using for prediction. 

Do you have idea how should I do my data processing for this case?
It would be helpful to know a bit better what you're trying to achieve and why the selection of a specific number of eigenvalues bothers you.
From the generic information you gave it seems you're aiming at training a model on a compressed/dense representation of several features, in which case I would suggest to train an autoencoder (or something similar), on top of which you could then train whatever classifier you need.
Otherwise if the problem relies only on the amount of features you have you could try with some feature selection strategies