Controlling TabPFN execution

Usage

control_tab_pfn(
  n_preprocessing_jobs = 1L,
  device = "auto",
  ignore_pretraining_limits = FALSE,
  inference_precision = "auto",
  fit_mode = "fit_preprocessors",
  memory_saving_mode = "auto",
  random_state = sample.int(10^6, 1)
)

Arguments

n_preprocessing_jobs: An integer for the number of worker processes. A value of -1L indicates all possible resources.
device: A character value for the device used for torch (e.g., "cpu", "cuda", "mps", etc.). Th default is "auto".
ignore_pretraining_limits: A logical to bypass the default data limits on:the number of training set samples (10,000) and, the number of predictors (500). There is an unchangeable limit to the number of classes (10).
inference_precision: A character value for the trade off between speed and reproducibility. This can be a torch dtype, "autocast" (for torch's mixed-precision autocast), or "auto".
fit_mode: A character value to control how the are preprocessed and/or cached. Values are "fit_preprocessors" (the default), "low_memory", "fit_with_cache", and "batched".
memory_saving_mode: A character string to help with out-of-memory errors. Values are either a logical or "auto".
random_state: An integer to set the random number stream.

References

https://github.com/PriorLabs/TabPFN/blob/main/src/tabpfn/classifier.py, https://github.com/PriorLabs/TabPFN/blob/main/src/tabpfn/regressor.py

Examples

control_tab_pfn()
#> control object for `tab_pfn()`
#> 
#> non-default arguments:
#> • `random_state`: 731199