Comments (2)
I would make the argument here that, since the user has explicitly chosen with a non-default argument setting save_workflow = TRUE
to store that object, we ought not to warn at all, as we don't with save_pred = TRUE
from tune.
I mostly agree. People might not realize that their workflow is huge (for some value of huge) but that should be relatively rare. If we keep it, we should bump the threshold up by a log unit or two.
The problem that sparked this was that simulated annealing needs the workflow and, when it calls tune_grid()
, it sets save_workflow = TRUE
even if the opposite was done in control_sim_anneal()
.
from tune.
Related Issues (20)
- route appropriate `split_args` through `last_fit()` HOT 1
- `.catch_and_log()` requires passing arguments through `...` by position rather than name
- refactor `predict_model()` so that it takes `data` rather than a `split` HOT 1
- `.gitignore` `revdep` HOT 2
- better error when passing a preprocessor with a workflow HOT 1
- remove step_ns and similar from docs HOT 1
- Make Bayesian optimization more fault tolerant
- profile compute resources for different parallel backends
- add a function to debias resampling estimates
- use tidy style in "Introduction to tune" vignette HOT 1
- use tidy style in "Optimizations and parallel processing" vignette HOT 1
- use tidy style in "Acquisition functions" vignette HOT 1
- summarize control options with `verbose = TRUE`
- Use cli errors
- Getting started vignette uses deprecated functions HOT 1
- delayed logging when using parallel processing with future HOT 3
- reduce the object size by trimming the split elements HOT 1
- better error message for multivariate models
- workers error with custom metrics when using socket clusters
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from tune.