Comments (3)
Hi, without a code example it is difficult to know what is going on. The code is not optimized to be run multiple times on parallel. I think the best way to speed up training is to play with batch size, num workers etc so that you have a good gpu utilization and then simply do your hyperparameter search sequentially.
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Hi, thanks for your reply. I monitor the memory usage of the .fit() processing, and I found the create_dataloaders() in utils.py change the X_train into np.float32. I guess this will take up more memory if the X_train is big. I'm not sure if it would cause the OOM problem.
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Yes usually models are trained using float32, we could try to use mixed precision (float16) but that would still not be meant for multiple trainings in parallel.
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Related Issues (20)
- Current version on conda-forge is 4.0 while 4.1 is already released HOT 8
- Minimal working example for TabNetRegressor/Classifier HOT 4
- Transfer learning, capability to change structure of model HOT 1
- Generate Embeddings for Tabular Data HOT 1
- TabNet overfits (help wanted, not a bug) HOT 9
- TabNetRegressor vs other networks HOT 1
- spike in memory when training ends HOT 8
- Severe overfitting HOT 18
- Support for complex-valued datasets HOT 4
- Different classification variables in the test set and train set HOT 1
- Struggling to get model to fit - Help Wanted HOT 7
- Optimizing TabNet for Disease Classification with Continuous Audio Features HOT 1
- Interpreting Sparsity on Global Importance HOT 5
- ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all() HOT 1
- Validation loss HOT 1
- Lightweight Fine-tunning or few-shot learning for limited labeled data HOT 1
- Maybe `drop_last` should be set as False in default? HOT 1
- Incompatiblity of current round() method with pytorch tensors when performing early stopping HOT 1
- Retraining a saved model on different dataset HOT 3
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