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, passed to the Python library, allowing data past the limits the model was pre-trained for. It covers the number of training set samples and predictors, and the much lower sample limit that applies when the fit runs on a CPU. The limit on the number of classes always applies. See the Data limits by version section of
tab_pfn().- 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.
- ...
Additional named arguments passed directly to the TabPFN Python constructor. Use this to supply options not covered by the named parameters above (e.g. arguments added in newer versions of the Python package).
Value
A list with extra class "control_tab_pfn" that has named elements
for each of the argument values.