Dear author of NeuralLog, we try to use the recommended default hyper-parameters in the paper to reproduce the time efficiency of NeuralLog on the BGL dataset.
The paper set the window size is 20 and step is 1, which makes the training data very huge.
It takes more than two hours to encode the log and train the model. While the paper "Log-based Anomaly Detection Without Log Parsing" claimed it takes 20 minutes to do so.
Could you give us some suggestions to reproduce the experiments? Our machine is 12900k and RTX3090. According to the Github repository, we use batch_size = 256 and epoch = 4.
Thanks for your kindness.
Dear author of NeuralLog, we try to use the recommended default hyper-parameters in the paper to reproduce the time efficiency of NeuralLog on the BGL dataset.
The paper set the window size is 20 and step is 1, which makes the training data very huge.
It takes more than two hours to encode the log and train the model. While the paper "Log-based Anomaly Detection Without Log Parsing" claimed it takes 20 minutes to do so.
Could you give us some suggestions to reproduce the experiments? Our machine is 12900k and RTX3090. According to the Github repository, we use batch_size = 256 and epoch = 4.
Thanks for your kindness.