Comments (2)
I think it is normal. I use a 1080Ti and the example training on RSC15 finishes in about 2 hours. On the older Titan X (Maxwell architecture) it takes 4-5 hours. This is with an additional speed-up technique which has not been pushed to the public repo yet*. The K80 is a fairly old GPU, so 10 hours with the public code sounds beliveable to me.
If you want to speed-up training and don't mind losing some accuracy, set batch_size to 64 (instead of 32) and n_epochs to 5 (instead of 10). This will essentially make training 4 times faster. With this setting I get 0.7198 for recall@20 and 0.3074 for MRR@20 (instead of 0.7261 and 0.3124), but training takes only 30 minutes on the 1080Ti.
*The speed-up will be published as soon as we can finalize the license text for the code.
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Thanks for the quick and detailed response, I'll try that.
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Related Issues (20)
- About training time HOT 1
- Is it possible to output the embedding of user/session and item vectors? HOT 1
- NOT RNN MODEL HOT 2
- Additional Negative Sampling: Conditional Statement Logic Error HOT 1
- generate_samples function call in gru4rec.py HOT 2
- BPR loss implementation question
- Fit function in gru4rec.py missing data sort HOT 1
- predict_next_batch not considering other products in the same session HOT 2
- (Question) - How to use all items in a session for prediction? HOT 2
- No hidden state reset in get_metrics HOT 4
- Where is the data file ?
- theano error HOT 2
- Can you make a brief explaination on how you calculate recall ? HOT 2
- Incremental training (retrain) support removed
- ValueError: Input dimension mis-match. (input[2].shape[0] = 2080, input[3].shape[0] = 32)
- cuda error
- GFF code
- Testing Error:: start = offset_sessions[iters] IndexError: index 2 is out of bounds for axis 0 with size 2
- Evaluating baselines
- Non-session based custom dataset HOT 2
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