Add example configs #166
Add example configs #166
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ordabayevy
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examples/cli_workflow/README.md
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| ### data | ||
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| Configure the `DistributedAnnDataCollection`. Here we validate `obs` columns that are used by the transforms and the model (`total_mrna_umis`): |
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Say a word about what it means to "validate` an obs column.
| > attr: model.var_names_g | ||
| > convert_fn: numpy.ndarray.tolist | ||
| ``` | ||
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Maybe add a note below that cellarium-ml does not perform any validation on the content of data loaded from the checkpoints, or whether it is consistent with the rest of the configuration. For example, the mean and std were calculate in a prior stop from data that was subject to normalize total and log1p transform. If the user inadvertently forgets to perform the same transforms here before z-scoring, the workflow will run w/o any error though will produce wrong results.
| > n_components: 50 | ||
| > perform_mean_correction: true | ||
| ``` | ||
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Again, add a sticky note that since we have z-scored the data, mean correction is not strictly necessary but its presence may help mitigate roundoff errors. It is a good exercise for the user to ascertain that the learned mean by IPCA is close to 0/
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I setperform_mean_correction: true by mistake. It should be false in this case because of the z-score!
examples/cli_workflow/README.md
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| ### train | ||
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| Change the number of devices, change strategy to `ddp_find_unused_parameters_true` (because trained PCA model contains parameters that are fixed during training), set the number of epochs, and set the path for logs and weights: |
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perhaps give a reference to PL doc related to this?
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Resolves #114