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Set Up Python Environment for ustats

Source: R/setup.R
setup_ustats.Rd

Installs and configures the Python environment required to run ustat(), including u_stats, numpy, and torch.

Usage

setup_ustats(
  method = c("auto", "virtualenv", "conda", "system"),
  envname = "r-ustats",
  gpu = FALSE,
  restart = FALSE,
  persist = FALSE
)

Arguments

method

Installation method for Python:

  • "auto" (default): use existing Python or install Miniconda

  • "virtualenv": create a virtual environment

  • "conda": create a conda environment

  • "system": use system Python

envname

Name of the virtualenv/conda environment (default: "r-ustats")

gpu

Logical; if FALSE (default), install the CPU-only build of PyTorch from the official PyTorch wheel index (https://download.pytorch.org/whl/cpu). The CPU build is much smaller (roughly 200 MB instead of more than 2 GB with bundled CUDA libraries on Linux) and is sufficient for machines without an NVIDIA GPU. Set gpu = TRUE to install the default PyPI build of PyTorch, which includes CUDA support on Linux; for GPU builds on Windows, or for a specific CUDA version, see https://pytorch.org/get-started/locally/.

restart

Logical; whether to restart the R session after setup

persist

Logical; if TRUE, print the RETICULATE_PYTHON configuration line that you can add to your .Rprofile yourself to make the environment persist across sessions. The function never writes to your files (default: FALSE)

Value

Invisibly returns TRUE if setup completed and the environment verifies, FALSE otherwise.

Details

Most users do not need to call this function. With reticulate (>= 1.41), the Python dependencies declared by this package are provisioned automatically in a cached environment the first time Python is used (e.g. on the first call to ustat()). Call setup_ustats() only if you prefer a persistent, dedicated environment, or if you want to control how PyTorch is installed (see the gpu argument).

Note: PyTorch is strongly recommended. The NumPy backend is slower and may be numerically less stable for higher-order U-statistics.

Examples

if (FALSE) { # \dontrun{
setup_ustats()                # CPU-only PyTorch (small, default)
setup_ustats(gpu = TRUE)      # default PyPI PyTorch (CUDA on Linux)
setup_ustats(method = "conda", envname = "ustats-env")
} # }