-
Notifications
You must be signed in to change notification settings - Fork 95
New issue
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
chunk_size value #27
Comments
Hi @dsaeedeh, can you please provide more context about your question? Which script/function of ProteinBERT are you using exactly? |
Hi, model.fit takes X and Y with size of batch_size * batches_per_epoch samples. It means that we only need to import this number of samples into the memory each time. So, can we reduce chunk_size from 100,000 samples to this number ? |
What dataset are you training on? Are you using the same seq_len throughout the entire pretraining (without switching to episodes to different protein lengths)? The idea of a larger chunk_size is to make the process more efficient and run faster by making fewer storage reads, but sure you can make it smaller if you want. |
My dataset is the same as yours but with a different annotation vector. I am using a fixed seq_len throughout the entire pre-training. Thanks for your reply. I agree with you however, in case of memory usage I think smaller chunk_size would be more efficient. |
I figured out the model.fit takes batch_size * batches_per_epoch samples. However, we import 100,000 samples each time we need new data (chunk_size). Can we reduce this number to batch_size * batches_per_epoch samples so that the memory usage decreases? (in case of fixed batch_size=64)
The text was updated successfully, but these errors were encountered: