Comments (4)
MaximilianPi
commented on June 3, 2024
Hi Doug,
this seems to be caused by the 'parallel' pkg (tuning steps are parallelized in sjSDM_cv). I will upgrade one of my systems to R4.0 and get to the bottom of this.
But for now you can turn off the parallelization in sjSDM_cv by setting n_cores=NULL (and it should hopefully work).
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dougwyu
commented on June 3, 2024
and...it works! (although with n_cores = NULL, R is using six cores)
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MaximilianPi
commented on June 3, 2024
Yes and no, pytorch should always use all of your cpu cores, but sometimes (if you have really small models) it is efficient to parallelize additionally over the pytorch models from within R
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dougwyu
commented on June 3, 2024
ah.
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Related Issues (20)
- sjsdm_cv Gpu not enabled HOT 4
- Question: importance() x anova() HOT 4
- Cannot reference package in RStudio on Ubuntu 22.04 HOT 9
- Categorical enviornmental variables HOT 2
- incorrect mean evaluation metrics in short_summary from sjSDM_cv.R ? HOT 1
- Co-occurrence matrix based on residual associations HOT 1
- PyTorch not loading HOT 6
- Time as "spatial" variable HOT 14
- Adding spatial component reduces R2 of model HOT 3
- generateSpatialEV improve documentation HOT 1
- Error/; Error in py_call_impl(callable, dots$args, dots$keywords) : AssertionError: Size mismatch between tensors HOT 1
- Error in py_call_impl(callable, dots$args, dots$keywords) : torch._C._LinAlgError: linalg.inv: The diagonal element 1 is zero HOT 1
- Different R2 produced for Model and Anova HOT 1
- Easy Model Residuals HOT 1
- Bug: plot.sjSDM doesnt work for species with whitespace HOT 1
- Error: object 'torch' not found in the example Pichler and Hartig- 2020 HOT 2
- Scale of predicted results and questions about tuning HOT 3
- plotInternalStructure: there is no package called ‘ggplodt2’ HOT 1
- Error : torch._C._LinAlgError HOT 3
- new feature gradient plots HOT 5
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