Using Python inside targets

This vignette is the reference for running Python scripts as targets steps with reticulate: every tar_target_py() argument, how to choose the Python environment, and the common use cases. For a gentler tour start with vignette("get_started"); for Julia see vignette("julia").

The two constructors

tar_target_py(): bare name (and unquoted pattern), for direct use in _targets.R.
tar_target_py_raw(): string name, for use inside targets factories.

Both return a single targets target and forward every targets::tar_target_raw() argument, so anything an ordinary target supports (pattern, format, iteration, deployment, resources, cue, memory, error, priority, description, …) works exactly as usual.

Arguments

Scripts

Argument	Meaning
`script`	Path to the Python script to run (required).
`pre_script`	Optional R script run before the Python script. Assign a named list `to_py` to push objects into Python’s `__main__`.
`post_script`	Optional R script run after the Python script. `py` and `py_get()` are available; its last expression is the value (object mode) or file paths (file mode).

Each of the three accepts either a literal path string (untracked: editing the file does not invalidate the target) or a tar_target_path("name") reference to an upstream format = "file" target (tracked: the step re-runs when the file changes). See “Tracking scripts” below.

Wiring in data and reading it back

Argument	Meaning
`inputs`	Named character vector mapping the name seen in the step to an upstream target, e.g. `c(x = "prepared_x")`. Each becomes a real dependency (and is sliced under dynamic branching).
`output`	`"object"` (default) returns a converted R object; `"file"` returns a character vector of paths and defaults `format` to `"file"`.
`retrieve`	Character vector of Python variable names to return when there is no post-script (object mode). One name returns that object; several return a named list.
`files`	Character vector of paths to return when there is no post-script (file mode).

Choosing the interpreter / environment

Argument	Meaning
`python`	Path to a specific interpreter executable.
`env` + `env_manager`	Point at an existing environment built by a known tool.
`python_version`	A version string (e.g. `"3.12"`); reticulate provisions it via an ephemeral uv environment.

These are alternatives: normally set only one. If several are given, the first in this precedence wins: python > env/env_manager > python_version > nothing. They are covered in detail in the next section.

The three-script model

 upstream targets ─► pre_script (R) ─► script (.py) ─► post_script (R) ─► target value
                     builds `to_py`     computes         reads `py$name` /
                                        `result`         `py_get("name")`

script: the Python file. Runs in the __main__ module.
pre_script: the inputs are already bound by name. Assign a named list to_py; each element is pushed as a top-level variable into __main__ using reticulate’s conversion rules.
post_script: py (the reticulate __main__ proxy) and py_get(name) (an explicit reticulate::py_to_r() shortcut) are available. Read a variable with py$result or py_get("result").

Object output

py/compute.py:

# `x` was pushed from R by the pre-script.
seq = list(x) if hasattr(x, "__iter__") else [x]
result = {"sum": float(sum(seq)), "n": len(seq), "mean": float(sum(seq)) / len(seq)}

R/pre_push.R:

to_py <- list(x = x)   # `x` came from inputs = c(x = "prepared_x")

R/post_result.R (last expression becomes the value):

res <- py_get("result")
data.frame(sum = res$sum, n = res$n, mean = res$mean)

_targets.R:

list(
  tar_target(prepared_x, c(1, 2, 3, 4)),

  # (a) return a Python variable directly with `retrieve` (no post-script)
  tar_target_py(
    name = py_direct,
    script = "py/compute.py",
    inputs = c(x = "prepared_x"),
    pre_script = "R/pre_push.R",
    retrieve = "result"
  ),

  # (b) reshape the result in a post-script
  tar_target_py(
    name = py_prepost,
    script = "py/compute.py",
    inputs = c(x = "prepared_x"),
    pre_script = "R/pre_push.R",
    post_script = "R/post_result.R"
  )
)

File output

Set output = "file" when the script writes to disk; return the path(s) from the post-script (or via files when there is no post-script). The target format becomes "file" automatically.

tar_target_py(
  name = py_file,
  script = "py/write_csv.py",       # writes a CSV, stores its path in `out_path`
  inputs = c(x = "prepared_x"),
  pre_script = "R/pre_push.R",
  post_script = "R/post_files.R",   # returns py_get("out_path")
  output = "file"
)

Dynamic branching

inputs are real dependencies, so pattern branches them:

list(
  tar_target(chunks, list(10, 20, 30)),
  tar_target_py(
    name = py_branch,
    script = "py/compute.py",
    inputs = c(x = "chunks"),
    pre_script = "R/pre_push.R",
    retrieve = "result",
    pattern = map(chunks)           # 3 branches: x = 10, 20, 30
  )
)

Run the branches in parallel by setting a crew controller (see vignette("get_started"), the “Parallelism and isolation with crew” section).

Choosing the Python environment

The four selection arguments and when to reach for each:

python: a path to a specific interpreter executable (e.g. ".venv/Scripts/python.exe"). Use when you already have the exact interpreter and want no ambiguity.
env + env_manager: an existing environment built by a known tool (env_manager says how to read env; see the table). Recommended for reproducible pipelines: install the packages once, every run reuses them.
python_version (e.g. "3.12"), not an environment, just an interpreter version: reticulate provisions it through an ephemeral, uv-managed environment (py_require()), resolving packages on the fly. Use when only the version matters.
nothing set: reticulate’s default discovery (the computer’s Python, or an ephemeral uv-managed one on recent reticulate). Fine for exploration; pin an environment for reproducibility.

The key contrast is reproducible vs throwaway: env/python reuse an environment you built and pinned; python_version lets reticulate build a fresh one keyed only on a version. One more difference: python and env/env_manager override an ambient RETICULATE_PYTHON (e.g. an RStudio project Python inherited by crew workers), while python_version and the default respect it.

`env_manager`	what `env` is	reticulate call
`"system"` (default)	none (optionally set `python`)	`use_python(python)` or default
`"venv"`	a `python -m venv` directory path	`use_virtualenv(env)`
`"virtualenv"`	same as venv	`use_virtualenv(env)`
`"conda"`	conda environment name	`use_condaenv(env)`
`"uv"`	a uv-created venv directory path	`use_virtualenv(env)`
`"poetry"`	a poetry project directory path	`poetry env info -p`, then `use_virtualenv()`

"virtualenv", "venv", and "uv" all point at a standard Python virtualenv and behave identically: this includes one created by renv::use_python() (use env_manager = "venv" with the path under renv/python/virtualenvs/). For these managers, a relative env that is an existing directory is expanded to an absolute path, so env = ".venv" works; a bare name that is not a directory is passed through unchanged, so a genuine named environment still resolves. Setting env while env_manager = "system" is an error.

Use case 1: global / system default

Set nothing; reticulate uses the computer’s default Python (on recent versions, an ephemeral uv-managed one provisioned on first use). Convenient for exploration; pin a real environment for reproducibility.

tar_target_py(
  name = probe,
  script = "py/probe.py",
  retrieve = "result"
  # no env / env_manager / python -> computer default Python
)

Use case 2: a pinned uv virtualenv (recommended)

Create it once, then point to it. Reproducible: the exact package set is reused every run.

uv venv .venv
uv pip install --python .venv/Scripts/python.exe numpy pandas scikit-learn

tar_target_py(
  name = fit,
  script = "py/fit.py",
  env_manager = "uv",
  env = ".venv",
  retrieve = "model"
)

Use case 3: a poetry project

Give the project directory (the folder with pyproject.toml); its virtualenv is resolved for you with poetry env info -p.

tar_target_py(
  name = embed,
  script = "py/embed.py",
  env_manager = "poetry",
  env = "path/to/poetry_project",
  retrieve = "vectors"
)

If the poetry on PATH is not the one you want, select a specific executable with options(tarpolyglot.poetry = "/path/to/poetry").

Use case 4: a plain venv or a conda env

# python -m venv .venv
tar_target_py(
  name = score,
  script = "py/score.py",
  env_manager = "venv",
  env = ".venv",
  retrieve = "result"
)

# a named conda environment
tar_target_py(
  name = classify,
  script = "py/classify.py",
  env_manager = "conda",
  env = "my_conda_env",
  retrieve = "labels"
)

Use case 5: pin just the version

To fix the interpreter version without naming an environment, use python_version (leaves the environment at the default, uv-managed one):

tar_target_py(
  name = probe_ver,
  script = "py/probe.py",
  python_version = "3.12",           # reticulate provisions 3.12 via uv
  retrieve = "result"
)

One interpreter per session. reticulate binds a single Python per R session, so all Python targets that run in the same session share one environment. To use different environments in one pipeline, run those targets on separate crew workers (see vignette("get_started")).