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TabPFNClassifier.save_fit_state

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Save a fitted classifier, light wrapper around save_fitted_tabpfn_model.
Parameters
Returns

TabPFNClassifier.load_from_fit_state

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Restore a fitted clf, light wrapper around load_fitted_tabpfn_model.
Parameters
Returns

TabPFNClassifier.to

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Move the estimator to the given device(s). If “auto”: devices are selected based on availability in the following order of priority: all available CUDA GPUs, “mps”, “cpu”. To manually select a single device: specify a PyTorch device string e.g. “cuda:1”. See PyTorch’s documentation for information about supported devices. To use several GPUs: specify a list of PyTorch GPU device strings, e.g. [“cuda:0”, “cuda:1”]. This can dramatically speed up inference for larger datasets, by executing the estimators in parallel on the GPUs. Multiple GPUs are only used when fit_mode="fit_preprocessors" or fit_mode="low_memory". In other cases, only the first GPU is used.
Parameters
Note The specified device is only used once the model is initialized. This occurs during the first .fit() call. Returns