Comments (3)
Hi, sorry for being quite late on this...
What's the number of distinct items in your dataset?
Input to hidden and hidden to output matrices are the ones that use the most memory, so I'd start checking those.
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@mquad In order to avoid out of memory, I downsample the dataset. And now the data has 27854 distinct number of items, 22068 distinct number of users and 4,867,558 number of samples.
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Nice. Extremely large item sets will likely benefit of an additional embedding layer in input to reduce the memory consumption. Also, if you like, check out the main GRU4Rec repo (https://github.com/hidasib/GRU4Rec) for some updates on speed and mem. consumption that will likely apply to this code base as well.
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Related Issues (15)
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