This is an alpha family and friends service, so please do not expect this to never be down or run into errors. It worked fine in the settings that we tried, though.

What model is behind the API? It is a new TabPFN which we allow to handle up to 10K data points with up to 500 features for both regression and classification. You can control all pre-processing, the amount of ensembling etc.

We created a colab tutorial to get started quickly.

pip install tabpfn-client

Import and login

from tabpfn_client import init, TabPFNClassifier
init()

Now you can use our model just like any other sklearn estimator

tabpfn = TabPFNClassifier()
tabpfn.fit(X_train, y_train)
tabpfn.predict(X_test)
# or you can also use tabpfn.predict_proba(X_test)

To login using your access token, skipping the interactive flow, use:

from tabpfn_client import config

# Retrieve Token
with open(config.g_tabpfn_config.user_auth_handler.CACHED_TOKEN_FILE, 'r') as file:
    token = file.read()
print(f"TOKEN: {token}")

from tabpfn_client import config

# Set Token
service_client = config.ServiceClient()
config.g_tabpfn_config.user_auth_handler = config.UserAuthenticationClient(service_client=service_client)
user_auth = config.g_tabpfn_config.user_auth_handler.set_token(token)

To encourage better coding practices, ruff has been added to the pre-commit hooks. This will ensure that the code is formatted properly before being committed. To enable pre-commit (if you haven't), run the following command:

pre-commit install

Additionally, it is recommended that developers install the ruff extension in their preferred editor. For installation instructions, refer to the Ruff Integrations Documentation.

if [ -d "dist" ]; then rm -rf dist/*; fi
python3 -m pip install --upgrade build; python3 -m build
python3 -m twine upload --repository pypi dist/*