Install the TabPFN Python environment — install

Sets up a persistent Python virtual environment containing the tabpfn Python library so that tab_pfn() and friends can use it. By default the environment is named "r-tabpfn", which reticulate automatically discovers and prefers over an ephemeral environment (see the Environment discovery section).

Usage

install_tabpfn(
  version = "default",
  envname = "r-tabpfn",
  check_latest = TRUE,
  extra_packages = NULL,
  python_version = NULL,
  method = c("auto", "virtualenv", "conda"),
  new_env = identical(envname, "r-tabpfn"),
  restart_session = TRUE,
  ...
)

Arguments

version: The tabpfn version to install. Use "default" (or NULL) to install the latest release, a bare version string such as "2.0.9" to pin an exact version, or a full pip specification such as ">=2.0".
envname: The name of the Python virtual environment to create or use. The default, "r-tabpfn", is auto-discovered by reticulate.
check_latest: A logical. When TRUE (the default) and no explicit version is given, an existing environment is compared against the latest release on PyPI and you are asked whether to upgrade. Set to FALSE to skip this check (useful when intentionally staying on an older version).
extra_packages: An optional character vector of additional Python packages to install alongside tabpfn. You can also use it to set the version of one of tabpfn's own dependencies, such as holding torch at an older release. See the Pinning dependencies section.
python_version: An optional Python version to use for the environment.
method: The installation method, passed to reticulate::py_install().
new_env: A logical. When TRUE, an existing environment named envname is removed and rebuilt from scratch. Defaults to TRUE only for the canonical "r-tabpfn" environment.
restart_session: A logical. When TRUE (the default) and running in RStudio, the R session is restarted after installation so the new environment takes effect.
...: Additional arguments passed to reticulate::py_install().

Value

Invisibly returns the environment name.

Environment discovery

Because the package calls reticulate::import("tabpfn"), reticulate will automatically use a virtual environment named "r-tabpfn" if one exists, preferring it over the ephemeral environment that is otherwise created on demand. Environments selected via RETICULATE_PYTHON, VIRTUAL_ENV, or a project-local .venv take precedence over "r-tabpfn".

Pinning dependencies

The Python tabpfn library pulls in other Python packages, among them torch, numpy, pandas, and scikit-learn. A new release of any one of them can break code that worked the week before, even though your R version and your tabpfn version did not change. The error usually points at tabpfn rather than at the package that changed, so pinning the suspect dependency is a quick way to confirm the cause and to keep working until a fix lands upstream.

To pin a dependency in the persistent environment that install_tabpfn() builds, pass it and the version you want in extra_packages:


  install_tabpfn(
    version = "2.0.9",
    extra_packages = "torch==2.13.0",
    new_env = TRUE
  )

extra_packages is used only when an install runs. If envname already exists and already has the version you asked for, this function returns early and ignores the pin. Pass new_env = TRUE to delete the environment and build it again with the pin.

To pin a dependency in the ephemeral environment instead, call reticulate::py_require() before you fit a model:


  library(tabpfn)
  reticulate::py_require("torch==2.13.0")

Examples

if (FALSE) { # \dontrun{
# Install the latest release into "r-tabpfn"
install_tabpfn()

# Pin a specific version
install_tabpfn(version = "2.0.9")

# Pin a dependency as well
install_tabpfn(version = "2.0.9", extra_packages = "torch==2.13.0")
} # }