Frequently Asked Questions and troubleshooting

Sorry that you're having trouble using PyO3. If you can't find the answer to your problem in the list below, you can also reach out for help on GitHub Discussions and on Discord.

I'm experiencing deadlocks using PyO3 with `std::sync::OnceLock`, `std::sync::LazyLock`, `lazy_static`, and `once_cell`!

OnceLock, LazyLock, and their thirdparty predecessors use blocking to ensure only one thread ever initializes them. Because the Python GIL is an additional lock this can lead to deadlocks in the following way:

A thread (thread A) which has acquired the Python GIL starts initialization of a OnceLock value.
The initialization code calls some Python API which temporarily releases the GIL e.g. Python::import.
Another thread (thread B) acquires the Python GIL and attempts to access the same OnceLock value.
Thread B is blocked, because it waits for OnceLock's initialization to lock to release.
Thread A is blocked, because it waits to re-acquire the GIL which thread B still holds.
Deadlock.

PyO3 provides a struct GILOnceCell which implements a single-initialization API based on these types that relies on the GIL for locking. If the GIL is released or there is no GIL, then this type allows the initialization function to race but ensures that the data is only ever initialized once. If you need to ensure that the initialization function is called once and only once, you can make use of the OnceExt and OnceLockExt extension traits that enable using the standard library types for this purpose but provide new methods for these types that avoid the risk of deadlocking with the Python GIL. This means they can be used in place of other choices when you are experiencing the deadlock described above. See the documentation for GILOnceCell and OnceExt for further details and an example how to use them.

I can't run `cargo test`; or I can't build in a Cargo workspace: I'm having linker issues like "Symbol not found" or "Undefined reference to _PyExc_SystemError"!

Currently, #340 causes cargo test to fail with linking errors when the extension-module feature is activated. Linking errors can also happen when building in a cargo workspace where a different crate also uses PyO3 (see #2521). For now, there are three ways we can work around these issues.

Make the extension-module feature optional. Build with maturin develop --features "extension-module"

[dependencies.pyo3]
version = "0.23.2"

[features]
extension-module = ["pyo3/extension-module"]

Make the extension-module feature optional and default. Run tests with cargo test --no-default-features:

[dependencies.pyo3]
version = "0.23.2"

[features]
extension-module = ["pyo3/extension-module"]
default = ["extension-module"]

If you are using a pyproject.toml file to control maturin settings, add the following section:

[tool.maturin]
features = ["pyo3/extension-module"]
# Or for maturin 0.12:
# cargo-extra-args = ["--features", "pyo3/extension-module"]

I can't run `cargo test`: my crate cannot be found for tests in `tests/` directory!

The Rust book suggests to put integration tests inside a tests/ directory.

For a PyO3 extension-module project where the crate-type is set to "cdylib" in your Cargo.toml, the compiler won't be able to find your crate and will display errors such as E0432 or E0463:

error[E0432]: unresolved import `my_crate`
 --> tests/test_my_crate.rs:1:5
  |
1 | use my_crate;
  |     ^^^^^^^^^^^^ no external crate `my_crate`

The best solution is to make your crate types include both rlib and cdylib:

# Cargo.toml
[lib]
crate-type = ["cdylib", "rlib"]

Ctrl-C doesn't do anything while my Rust code is executing!

This is because Ctrl-C raises a SIGINT signal, which is handled by the calling Python process by simply setting a flag to action upon later. This flag isn't checked while Rust code called from Python is executing, only once control returns to the Python interpreter.

You can give the Python interpreter a chance to process the signal properly by calling Python::check_signals. It's good practice to call this function regularly if you have a long-running Rust function so that your users can cancel it.

`#[pyo3(get)]` clones my field!

You may have a nested struct similar to this:

use pyo3::prelude::*;
#[pyclass]
#[derive(Clone)]
struct Inner {/* fields omitted */}

#[pyclass]
struct Outer {
    #[pyo3(get)]
    inner: Inner,
}

#[pymethods]
impl Outer {
    #[new]
    fn __new__() -> Self {
        Self { inner: Inner {} }
    }
}

When Python code accesses Outer's field, PyO3 will return a new object on every access (note that their addresses are different):

outer = Outer()

a = outer.inner
b = outer.inner

assert a is b, f"a: {a}\nb: {b}"

AssertionError: a: <builtins.Inner object at 0x00000238FFB9C7B0>
b: <builtins.Inner object at 0x00000238FFB9C830>

This can be especially confusing if the field is mutable, as getting the field and then mutating it won't persist - you'll just get a fresh clone of the original on the next access. Unfortunately Python and Rust don't agree about ownership - if PyO3 gave out references to (possibly) temporary Rust objects to Python code, Python code could then keep that reference alive indefinitely. Therefore returning Rust objects requires cloning.

If you don't want that cloning to happen, a workaround is to allocate the field on the Python heap and store a reference to that, by using Py<...>:

use pyo3::prelude::*;
#[pyclass]
struct Inner {/* fields omitted */}

#[pyclass]
struct Outer {
    inner: Py<Inner>,
}

#[pymethods]
impl Outer {
    #[new]
    fn __new__(py: Python<'_>) -> PyResult<Self> {
        Ok(Self {
            inner: Py::new(py, Inner {})?,
        })
    }

    #[getter]
    fn inner(&self, py: Python<'_>) -> Py<Inner> {
        self.inner.clone_ref(py)
    }
}

This time a and b are the same object:

outer = Outer()

a = outer.inner
b = outer.inner

assert a is b, f"a: {a}\nb: {b}"
print(f"a: {a}\nb: {b}")

a: <builtins.Inner object at 0x0000020044FCC670>
b: <builtins.Inner object at 0x0000020044FCC670>

The downside to this approach is that any Rust code working on the Outer struct now has to acquire the GIL to do anything with its field.

I want to use the `pyo3` crate re-exported from dependency but the proc-macros fail!

All PyO3 proc-macros (#[pyclass], #[pyfunction], #[derive(FromPyObject)] and so on) expect the pyo3 crate to be available under that name in your crate root, which is the normal situation when pyo3 is a direct dependency of your crate.

However, when the dependency is renamed, or your crate only indirectly depends on pyo3, you need to let the macro code know where to find the crate. This is done with the crate attribute:

use pyo3::prelude::*;
pub extern crate pyo3;
mod reexported { pub use ::pyo3; }
#[allow(dead_code)]
#[pyclass]
#[pyo3(crate = "reexported::pyo3")]
struct MyClass;

I'm trying to call Python from Rust but I get `STATUS_DLL_NOT_FOUND` or `STATUS_ENTRYPOINT_NOT_FOUND`!

This happens on Windows when linking to the python DLL fails or the wrong one is linked. The Python DLL on Windows will usually be called something like:

python3X.dll for Python 3.X, e.g. python310.dll for Python 3.10
python3.dll when using PyO3's abi3 feature

The DLL needs to be locatable using the Windows DLL search order. Some ways to achieve this are:

Put the Python DLL in the same folder as your build artifacts
Add the directory containing the Python DLL to your PATH environment variable, for example C:\Users\<You>\AppData\Local\Programs\Python\Python310
If this happens when you are distributing your program, consider using PyOxidizer to package it with your binary.

If the wrong DLL is linked it is possible that this happened because another program added itself and its own Python DLLs to PATH. Rearrange your PATH variables to give the correct DLL priority.

Note: Changes to PATH (or any other environment variable) are not visible to existing shells. Restart it for changes to take effect.

For advanced troubleshooting, Dependency Walker can be used to diagnose linking errors.

PyO3 user guide