Comments (2)
Hi Brando - I'm working on improving my commenting/documentation across the board and will make sure to make GPU support obvious.
I think that the wrapped models that I put in the example will work with GPU since they use:
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
and associated
.to(device)
on both the models and data in training this should work out of the box if your machine has GPU available. But right at this moment I haven't tested as I recently refactored to improve the >2 view case.
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I'm now able to test the gpu methods locally and satisfied that they work as expected - therefore closing.
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Related Issues (20)
- Pass down parameters to BaseSearchCV in cca_zoo.model_selection.GridSearchCV
- the value of the model.fit_transform() is nan HOT 7
- Could you provide a 3-group CCA method? HOT 2
- What happened to the scale argument? HOT 19
- Cannot simulate data HOT 2
- TerminatedWorkerError when using GridSearchCV HOT 29
- Do you have a CCA method incorporated with Multivariate Granger causality? HOT 3
- cca_zoo.model_selection.GridSearchCV's param_grid parameter should accept any type of iterator HOT 3
- Implement CCA-classes that can account for sample groups HOT 2
- predict() method for models? HOT 6
- SPLS is slower than it used to be.
- Installation error HOT 14
- The algorithm failed to converge because the input matrix is ill-conditioned or has too many repeated eigenvalues (error code: 1). HOT 2
- Is the loss function calculated on the raw data? HOT 22
- Is averaged pairwise correlation the first principle to construct MCCA indices? HOT 6
- Unnecessary overhead when calculating CCALoss HOT 5
- SCCA_PMD Inner Loop Convergence Warning in cca_zoo
- SVD Not Converging HOT 2
- torch._C._LinAlgError: torch.linalg.solve: The solver failed because the input matrix is singular. HOT 6
- Inverse transforms
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