I am trying to train a doc2vec based on user browsing history (urls tagged to user_id). I use chainer deep learning framework.
There are more than 20 millions (user_id and urls) of embeddings to initialize which doesn’t fit in a GPU internal memory (maximum available 12 GB). Training on CPU is very slow.
I am giving an attempt using code written in chainer given here https://github.com/monthly-hack/chainer-doc2vec
Please advise options to try if any.