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prihoda avatar prihoda commented on August 18, 2024

Hi @youngDouble,

so the log output is hanging at "Epoch 77"? Since you are running with nohup, are you sure that the process is still running? It could have crashed, for example on unsufficient memory. Can you try running the training again?

I would also suggest running with less epochs, e.g. 100, since the accuracy seems quite high already on epoch 77. Also, if you want to try reserving part of training data for validation, you can set "validation_size" in your JSON file. For example, you can use "validation_size": 0.2 for validating your model on 20% of your data. Then, both training and validation metrics will be shown during training.

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prihoda avatar prihoda commented on August 18, 2024

Also, please see the updated README section on training: https://github.com/Merck/deepbgc#train-deepbgc-on-your-own-data

You can upgrade DeepBGC to version 0.1.10 to be able to provide your trained model for detection and classification like so:

deepbgc pipeline \
    mySequence.fa \
    --detector path/to/myDetector.pkl \
    --classifier path/to/myClassifier.pkl 

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youngDouble avatar youngDouble commented on August 18, 2024

Thank you for your reply, as you said, it is a problem with insufficient memory. After adjusting the parameters(validation_size and num_epochs), it can work.
It seems that the training process uses a lot of memory (more than 256G), I have delivered the task to the cluster.

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