Comments (8)
Optimox
commented on July 24, 2024
Does the command torch.cuda.is_available() returns True in your environment ?
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SH-2093
commented on July 24, 2024
Does the command
torch.cuda.is_available()returns True in your environment ?
yes, the cuda environment is prepared. I have no idea about how to pinpoint the issue.
this is the tabnet related part in the code,
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Optimox
commented on July 24, 2024
Why did you close this issue ? Did you solve your problem?
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SH-2093
commented on July 24, 2024
Why did you close this issue ? Did you solve your problem?
sorry, i didn't solve it. close the issue by mistake click.
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Optimox
commented on July 24, 2024
what is the size of your dataset? what batch size are you using? the smaller the dataset and the smaller the batch the smaller the impact of your GPU on computational speed.
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SH-2093
commented on July 24, 2024
what is the size of your dataset? what batch size are you using? the smaller the dataset and the smaller the batch the smaller the impact of your GPU on computational speed.
The size of the training dataset is approximately 600,000 samples, with each sample containing 256 features. the batch size is the default value (1024). In actually, I did not perform the operation of transferring the dataset to the GPU , according to the input parameter of the fit function in document is a numpy-type variable. I am not sure if this is the cause of the problem.
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Optimox
commented on July 24, 2024
no everything should be done internally, you may want to increase the batch size to see if you see an imrpovement in speed with gpu against cpu. It's diffocult to help you as the cpu vs gpu version has been here for a long time and is working for sure, so I don't know what is going wrong with your setting.
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SH-2093
commented on July 24, 2024
no everything should be done internally, you may want to increase the batch size to see if you see an imrpovement in speed with gpu against cpu. It's diffocult to help you as the cpu vs gpu version has been here for a long time and is working for sure, so I don't know what is going wrong with your setting.
thank u very much for the help. I will comment here if i make any progress.
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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
- 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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