# 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](https://github.com/PyO3/pyo3/discussions) and on [Discord](https://discord.gg/33kcChzH7f).

## 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 interpreter can introduce additional locks (the Python GIL and GC can both require all other threads to pause) this can lead to deadlocks in the following way:

1. A thread (thread A) which is attached to the Python interpreter starts initialization of a `OnceLock` value.
2. The initialization code calls some Python API which temporarily detaches from the interpreter e.g. `Python::import`.
3. Another thread (thread B) attaches to the Python interpreter and attempts to access the same `OnceLock` value.
4. Thread B is blocked, because it waits for `OnceLock`'s initialization to lock to release.
5. On GIL-enabled Python, thread A is now also blocked, because it waits to re-attach to the interpreter (by taking the GIL which thread B still holds).
6. Deadlock.

PyO3 provides a struct [`PyOnceLock`] which implements a single-initialization API based on these types that avoids deadlocks.
You can also make use of the [`OnceExt`] and [`OnceLockExt`] extension traits that enable using the standard library types for this purpose by providing new methods for these types that avoid the risk of deadlocking with the Python interpreter.
This means they can be used in place of other choices when you are experiencing the deadlock described above.
See the documentation for [`PyOnceLock`] and [`OnceExt`] for further details and an example how to use them.

[`PyOnceLock`]: {{#PYO3_DOCS_URL}}/pyo3/sync/struct.PyOnceLock.html
[`OnceExt`]: {{#PYO3_DOCS_URL}}/pyo3/sync/trait.OnceExt.html
[`OnceLockExt`]: {{#PYO3_DOCS_URL}}/pyo3/sync/trait.OnceLockExt.html

## 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"

The `extension-module` feature (now deprecated) disables linking to `libpython`.
This breaks binaries and tests which need to load symbols from `libpython` to execute.

Remove the `extension-module` feature and upgrade to `maturin >= 1.9.4` or `setuptools-rust 1.12`.

If building manually, see the [`PYO3_BUILD_EXTENSION_MODULE` environment variable](./building-and-distribution.md#the-pyo3_build_extension_module-environment-variable).

## 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](https://doc.rust-lang.org/book/ch11-03-test-organization.html#integration-tests).

For a PyO3 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`:

```text
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`:

```toml
# 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:

```rust,no_run
# use pyo3::prelude::*;
#[pyclass(from_py_object)]
#[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):

```python
outer = Outer()

a = outer.inner
b = outer.inner

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

```text
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<...>`]({{#PYO3_DOCS_URL}}/pyo3/struct.Py.html):

```rust,no_run
# 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:

```python
outer = Outer()

a = outer.inner
b = outer.inner

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

```text
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 potentially has to attach to the Python interpreter to do anything with the `inner` field. (If `Inner` is `#[pyclass(frozen)]` and implements `Sync`, then `Py::get` may be used to access the `Inner` contents from `Py<Inner>` without needing to attach to the interpreter.)

## 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:

```rust,no_run
# 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
- `python3t.dll` when using PyO3's `abi3t` feature

The DLL needs to be locatable using the [Windows DLL search order](https://learn.microsoft.com/en-us/windows/win32/dlls/dynamic-link-library-search-order#standard-search-order-for-unpackaged-apps).
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](https://github.com/indygreg/PyOxidizer) to package it with your binary.
- The legacy MSI Python installer for Python 3.15 installs `python3t.dll` into an `abi3t_compat` subfolder of the main Python installation.
  Add that folder to your PATH as well if you are using `abi3t` features on Python 3.15.

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](https://www.dependencywalker.com/) can be used to diagnose linking errors.
