trio-parallel: CPU parallelism for Trio

Do you have CPU-bound work that just keeps slowing down your Trio event loop no matter what you try? Do you need to get all those cores humming at once? This is the library for you!

The aim of trio-parallel is to use the lightest-weight, lowest-overhead, lowest-latency method to achieve CPU parallelism of arbitrary Python code with a dead-simple API.



MIT -or- Apache License 2.0














Test coverage


Code style


Latest Pypi version
Supported Python versions
Supported Python interpreters


import multiprocessing
import trio
import trio_parallel
import time

def hard_work(n, x):
    t = time.perf_counter() + n
    y = x
    while time.perf_counter() < t:
        x = not x
    print(y, "transformed into", x)
    return x

async def too_slow():
    await trio_parallel.run_sync(hard_work, 20, False, cancellable=True)

async def amain():
    t0 = time.perf_counter()
    async with trio.open_nursery() as nursery:
        nursery.start_soon(trio_parallel.run_sync, hard_work, 2, True)
        nursery.start_soon(trio_parallel.run_sync, hard_work, 1, False)
        result = await trio_parallel.run_sync(hard_work, 1.5, None)
    print("got", result, "in", time.perf_counter() - t0, "seconds")
    # prints

if __name__ == "__main__":

Additional examples and the full API are available in the documentation


  • Bypasses the GIL for CPU-bound work

  • Minimal API complexity

    • looks and feels like Trio threads

  • Minimal internal complexity

    • No reliance on multiprocessing.Pool, ProcessPoolExecutor, or any background threads

  • Cross-platform

  • print just works

  • Automatic, opportunistic use of cloudpickle

  • Automatic LIFO caching of subprocesses

  • Cancel seriously misbehaving code

    • currently via SIGKILL/TerminateProcess

  • Convert segfaults and other scary things to catchable errors


How does trio-parallel run Python code in parallel?

Currently, this project is based on multiprocessing subprocesses and has all the usual multiprocessing caveats (freeze_support, pickleable objects only). The case for basing these workers on multiprocessing is that it keeps a lot of complexity outside of the project while offering a set of quirks that users are likely already familiar with. The pickling limitations can be partially alleviated by installing cloudpickle.

Can I have my workers talk to each other?

This is currently possible through the use of multiprocessing.Manager, but we don’t and will not officially support it.

This package focuses on providing a flat hierarchy of worker subprocesses to run synchronous, CPU-bound functions. If you are looking to create a nested hierarchy of processes communicating asynchronously with each other, while preserving the power, safety, and convenience of structured concurrency, look into tractor. Or, if you are looking for a more customized solution, try using trio.run_process to spawn additional Trio runs and have them talk to each other over sockets.

Can I let my workers outlive the main Trio process?

The worker processes are started with the daemon flag for lifetime management, so this use case is not supported.

How should I map a function over a collection of arguments?

This is fully possible but we leave the implementation of that up to you. Think of us as a loky for your joblib, but natively async and Trionic. Some example parallelism patterns can be found in the documentation. Also, look into trimeter?


If you notice any bugs, need any help, or want to contribute any code, GitHub issues and pull requests are very welcome! Please read the code of conduct.