Comments (3)
Hi. Thanks for trying out the package.
All the code in the package is implemented for single-core execution, so the server-swamping behavior is surprising. Are you sure this occurs within a single call to umap()?
It would be useful if you could post a small example that shows this behavior. In particular, are you loading any other packages, or running custom code related to parallelization?
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Thanks for the reply. I am running umap within a Snakemake pipeline, where I am calling it multiple times with different parameters and random seeds.
I tried to isolate a small example to post here, but found that the problem no longer occurred in the smaller example. So it seems likely that the problem is somewhere in the way I have set up the Snakemake pipeline, and not in umap itself.
If I find out more, I will post here again. But at this point it looks like the problem is probably somewhere in my Snakemake setup. Sorry for the confusion!
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Ok, no worries. I'll close this for now, but we can revisit if something else turns up.
As an aside: you mentioned testing several configurations and parameters. If you find certain parameters might benefit from different defaults, please do get in touch; that would be quite valuable. There is always the possibility to change the object umap.defaults
, or to provide additional configuration objects for specific applications, e.g. in bioinformatics.
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Related Issues (20)
- predict() on a umap object with n_components=1 gets two errors -- Looks like missing drop=F HOT 2
- Failed creating initial embedding; using random embedding instead HOT 3
- Intel MKL FATAL ERROR HOT 3
- Differences with Python version? HOT 4
- Add support for umap-learn 0.5 HOT 4
- Sparse Matrix support HOT 4
- is there any spark version implementations? HOT 2
- missing value where TRUE/FALSE needed HOT 3
- Problem with using custom metric HOT 2
- umap() produces matrix instead of S3 object HOT 2
- method = "python" does not work HOT 1
- when random_state is set automatically in config, it is not sufficient for reproducibility HOT 1
- Citing the package HOT 1
- Type error in optimize_embedding HOT 3
- transforming new data to an embedding HOT 3
- Error with n_components=1 HOT 3
- Allow for supervised/semi-supervised dimension reduction with labels HOT 1
- min_dist not updating with Python backend HOT 3
- predict() generates different predictions if called with multiple points at once versus called with each point individually HOT 7
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