Comments (5)
This looks like a setup problem with your GPU, are you able to run other models using your GPU?
Or maybe try to reduce your batch size.
It does not seem directly related to tabnet but to pytorch/your gpu.
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I have never seem this problem either... Maybe something related to the CUDA version/driver version? I'm running on CUDA 10.1.
Perhaps this can help: https://docs.nvidia.com/deploy/cuda-compatibility/index.html.
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@eduardocarvp I experienced the same issue , your gpu is running out of memory , restart the process and it will work fine .It worked for me
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It worked after restarted my kernel indeed. I have this issue because of the large size of my dataset (about 25GB). What I'm still unable to do right now is to run my model multiple times as I'm trying different hyper parameters without restarting my kernel each time. I'm on a google cloud instance and it takes about 20 min just to read this large dataset. Basically I would need to find a way to free up gpu memory from previous models as if I was fitting for the first time every time. Have tried gc.collect() but didn't work so far.
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@amartin211 you could have a look here : https://discuss.pytorch.org/t/how-can-we-release-gpu-memory-cache/14530
torch.cuda.empty_cache()
would be more effevtive to clean the GPU I guess
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Related Issues (20)
- 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
- OOM problem when I search hyperparameters with Tabnet HOT 3
- 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
- change device seems not work HOT 8
- when I try to train with pretrained model loaded, kernel dies HOT 2
- Feature
- Multi-label classification - how to start HOT 3
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