How can I use KNN, Random Forest models in Pytorch?
This may seem like a X Y problem, but initially I had huge data and I was not able to train in given resources (RAM problem). So I thought I could use batch feature of Pytorch. But I want to use Methods like KNN, Random Forest, Clustering except Deep Learning. So is it possible or can I use scikit libraries in Pytorch?

Update
Currently, there are some sklearn alternatives utilizing GPU, most prominent being cuML (link here) provided by rapidsai.
Previous answer
I would advise against using PyTorch solely for the purpose of using batches.
Argumentation goes as follows:

scikit-learn has docs about scaling where one can find MiniBatchKMeans and there are other options like partial_fit method or warm_start arguments (as is the case with RandomForest, check this approach).
KNN cannot be easily used without hand-made implementation with disk caching as it stores whole dataset in memory (and you lack RAM). This approach would be horribly inefficient either way, do not try.
You most probably will not be able to create algorithms on-par with those from scikit (at least not solo and not without considerable amount of work). Your best bet is to go with quite battle-tested solutions (even though it's still 0.2x currently). It should be possible to get some speed improvements through numba but that's beside the scope of this question. Maybe you could utilize CUDA for different algorithms but it's even more non-trivial task.

All in all PyTorch is suited for deep learning computations with heavy CUDA usage. If you need neural networks, this framework is one of the best out there, otherwise go with something like sklearn or other frameworks allowing incremental training. You can always bridge those two easily with numpy() and few other  calls in pytorch.
EDIT: I have found KNN implementation possibly suiting your requirements in this github repository
