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Currently the PAV backend collects the subject vectors for each document into memory. This takes up a lot of RAM and limits the number of training documents it can handle. Several improvements could be made in this area:
limit the precision of subject vectors (float32 instead of float64)
use sparse arrays for the subject vectors
when collecting the subject vectors training the PAV backend, store them on disk (e.g. in a temporary file or LMDB database) instead of in RAM
The text was updated successfully, but these errors were encountered:
Currently the PAV backend collects the subject vectors for each document into memory. This takes up a lot of RAM and limits the number of training documents it can handle. Several improvements could be made in this area:
The text was updated successfully, but these errors were encountered: