Calling Python in Rust code
This chapter of the guide documents some ways to interact with Python code from Rust:
- How to call Python functions
- How to execute existing Python code
Calling Python functions
Any Python-native object reference (such as &PyAny
, &PyList
, or &PyCell<MyClass>
) can be used to call Python functions.
PyO3 offers two APIs to make function calls:
call
- call any callable Python object.call_method
- call a method on the Python object.
Both of these APIs take args
and kwargs
arguments (for positional and keyword arguments respectively). There are variants for less complex calls:
call1
andcall_method1
to call only with positionalargs
.call0
andcall_method0
to call with no arguments.
For convenience the Py<T>
smart pointer also exposes these same six API methods, but needs a Python
token as an additional first argument to prove the GIL is held.
The example below shows a calling Python functions behind a PyObject
(aka Py<PyAny>
) reference:
use pyo3::prelude::*; use pyo3::types::{PyDict, PyTuple}; struct SomeObject; impl SomeObject { fn new(py: Python) -> PyObject { PyDict::new(py).to_object(py) } } fn main() { let arg1 = "arg1"; let arg2 = "arg2"; let arg3 = "arg3"; let gil = Python::acquire_gil(); let py = gil.python(); let obj = SomeObject::new(py); // call object without empty arguments obj.call0(py); // call object with PyTuple let args = PyTuple::new(py, &[arg1, arg2, arg3]); obj.call1(py, args); // pass arguments as rust tuple let args = (arg1, arg2, arg3); obj.call1(py, args); }
Creating keyword arguments
For the call
and call_method
APIs, kwargs
can be None
or Some(&PyDict)
. You can use the IntoPyDict
trait to convert other dict-like containers, e.g. HashMap
or BTreeMap
, as well as tuples with up to 10 elements and Vec
s where each element is a two-element tuple.
use pyo3::prelude::*; use pyo3::types::{IntoPyDict, PyDict}; use std::collections::HashMap; struct SomeObject; impl SomeObject { fn new(py: Python) -> PyObject { PyDict::new(py).to_object(py) } } fn main() { let key1 = "key1"; let val1 = 1; let key2 = "key2"; let val2 = 2; let gil = Python::acquire_gil(); let py = gil.python(); let obj = SomeObject::new(py); // call object with PyDict let kwargs = [(key1, val1)].into_py_dict(py); obj.call(py, (), Some(kwargs)); // pass arguments as Vec let kwargs = vec![(key1, val1), (key2, val2)]; obj.call(py, (), Some(kwargs.into_py_dict(py))); // pass arguments as HashMap let mut kwargs = HashMap::<&str, i32>::new(); kwargs.insert(key1, 1); obj.call(py, (), Some(kwargs.into_py_dict(py))); }
Executing existing Python code
If you already have some existing Python code that you need to execute from Rust, the following FAQs can help you select the right PyO3 functionality for your situation:
Want to access Python APIs? Then use PyModule::import
.
Pymodule::import
can
be used to get handle to a Python module from Rust. You can use this to import and use any Python
module available in your environment.
use pyo3::prelude::*; fn main() -> PyResult<()> { Python::with_gil(|py| { let builtins = PyModule::import(py, "builtins")?; let total: i32 = builtins.call1("sum", (vec![1, 2, 3],))?.extract()?; assert_eq!(total, 6); Ok(()) }) }
Want to run just an expression? Then use eval
.
Python::eval
is
a method to execute a Python expression
and return the evaluated value as a &PyAny
object.
use pyo3::prelude::*; use pyo3::types::IntoPyDict; fn main() -> Result<(), ()> { Python::with_gil(|py| { let result = py.eval("[i * 10 for i in range(5)]", None, None).map_err(|e| { e.print_and_set_sys_last_vars(py); })?; let res: Vec<i64> = result.extract().unwrap(); assert_eq!(res, vec![0, 10, 20, 30, 40]); Ok(()) }) }
Want to run statements? Then use run
.
Python::run
is a method to execute one or more
Python statements.
This method returns nothing (like any Python statement), but you can get
access to manipulated objects via the locals
dict.
You can also use the py_run!
macro, which is a shorthand for Python::run
.
Since py_run!
panics on exceptions, we recommend you use this macro only for
quickly testing your Python extensions.
use pyo3::prelude::*; use pyo3::{PyCell, PyObjectProtocol, py_run}; fn main() { #[pyclass] struct UserData { id: u32, name: String, } #[pymethods] impl UserData { fn as_tuple(&self) -> (u32, String) { (self.id, self.name.clone()) } } #[pyproto] impl PyObjectProtocol for UserData { fn __repr__(&self) -> PyResult<String> { Ok(format!("User {}(id: {})", self.name, self.id)) } } Python::with_gil(|py| { let userdata = UserData { id: 34, name: "Yu".to_string(), }; let userdata = PyCell::new(py, userdata).unwrap(); let userdata_as_tuple = (34, "Yu"); py_run!(py, userdata userdata_as_tuple, r#" assert repr(userdata) == "User Yu(id: 34)" assert userdata.as_tuple() == userdata_as_tuple "#); }) }
You have a Python file or code snippet? Then use PyModule::from_code
.
PyModule::from_code
can be used to generate a Python module which can then be used just as if it was imported with
PyModule::import
.
use pyo3::{prelude::*, types::{IntoPyDict, PyModule}}; fn main() -> PyResult<()> { Python::with_gil(|py| { let activators = PyModule::from_code(py, r#" def relu(x): """see https://en.wikipedia.org/wiki/Rectifier_(neural_networks)""" return max(0.0, x) def leaky_relu(x, slope=0.01): return x if x >= 0 else x * slope "#, "activators.py", "activators")?; let relu_result: f64 = activators.call1("relu", (-1.0,))?.extract()?; assert_eq!(relu_result, 0.0); let kwargs = [("slope", 0.2)].into_py_dict(py); let lrelu_result: f64 = activators .call("leaky_relu", (-1.0,), Some(kwargs))? .extract()?; assert_eq!(lrelu_result, -0.2); Ok(()) }) }