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|logo| ``tractor``: next-gen Python parallelism
|gh_actions|
|docs|
``tractor`` is a `structured concurrent`_, multi-processing_ runtime
built on trio_.
Fundamentally ``tractor`` gives you parallelism via ``trio``-"*actors*":
our nurseries_ let you spawn new Python processes which each run a ``trio``
scheduled runtime - a call to ``trio.run()``.
We believe the system adheres to the `3 axioms`_ of an "`actor model`_"
but likely *does not* look like what *you* probably think an "actor
model" looks like, and that's *intentional*.
The first step to grok ``tractor`` is to get the basics of ``trio`` down.
A great place to start is the `trio docs`_ and this `blog post`_.
Features
--------
- **It's just** a ``trio`` API
- *Infinitely nesteable* process trees
- Builtin IPC streaming APIs with task fan-out broadcasting
- A (first ever?) "native" multi-core debugger UX for Python using `pdb++`_
- Support for a swappable, OS specific, process spawning layer
- A modular transport stack, allowing for custom serialization (eg. with
`msgspec`_), communications protocols, and environment specific IPC
primitives
- Support for spawning process-level-SC, inter-loop one-to-one-task oriented
``asyncio`` actors via "infected ``asyncio``" mode
- `structured chadcurrency`_ from the ground up
Run a func in a process
-----------------------
Use ``trio``'s style of focussing on *tasks as functions*:
.. code:: python
"""
Run with a process monitor from a terminal using::
$TERM -e watch -n 0.1 "pstree -a $$" \
& python examples/parallelism/single_func.py \
&& kill $!
"""
import os
import tractor
import trio
async def burn_cpu():
pid = os.getpid()
# burn a core @ ~ 50kHz
for _ in range(50000):
await trio.sleep(1/50000/50)
return os.getpid()
async def main():
async with tractor.open_nursery() as n:
portal = await n.run_in_actor(burn_cpu)
# burn rubber in the parent too
await burn_cpu()
# wait on result from target function
pid = await portal.result()
# end of nursery block
print(f"Collected subproc {pid}")
if __name__ == '__main__':
trio.run(main)
This runs ``burn_cpu()`` in a new process and reaps it on completion
of the nursery block.
If you only need to run a sync function and retreive a single result, you
might want to check out `trio-parallel`_.
Zombie safe: self-destruct a process tree
-----------------------------------------
``tractor`` tries to protect you from zombies, no matter what.
.. code:: python
"""
Run with a process monitor from a terminal using::
$TERM -e watch -n 0.1 "pstree -a $$" \
& python examples/parallelism/we_are_processes.py \
&& kill $!
"""
from multiprocessing import cpu_count
import os
import tractor
import trio
async def target():
print(
f"Yo, i'm '{tractor.current_actor().name}' "
f"running in pid {os.getpid()}"
)
await trio.sleep_forever()
async def main():
async with tractor.open_nursery() as n:
for i in range(cpu_count()):
await n.run_in_actor(target, name=f'worker_{i}')
print('This process tree will self-destruct in 1 sec...')
await trio.sleep(1)
# raise an error in root actor/process and trigger
# reaping of all minions
raise Exception('Self Destructed')
if __name__ == '__main__':
try:
trio.run(main)
except Exception:
print('Zombies Contained')
If you can create zombie child processes (without using a system signal)
it **is a bug**.
"Native" multi-process debugging
--------------------------------
Using the magic of `pdb++`_ and our internal IPC, we've
been able to create a native feeling debugging experience for
any (sub-)process in your ``tractor`` tree.
.. code:: python
from os import getpid
import tractor
import trio
async def breakpoint_forever():
"Indefinitely re-enter debugger in child actor."
while True:
yield 'yo'
await tractor.breakpoint()
async def name_error():
"Raise a ``NameError``"
getattr(doggypants)
async def main():
"""Test breakpoint in a streaming actor.
"""
async with tractor.open_nursery(
debug_mode=True,
loglevel='error',
) as n:
p0 = await n.start_actor('bp_forever', enable_modules=[__name__])
p1 = await n.start_actor('name_error', enable_modules=[__name__])
# retreive results
stream = await p0.run(breakpoint_forever)
await p1.run(name_error)
if __name__ == '__main__':
trio.run(main)
You can run this with::
>>> python examples/debugging/multi_daemon_subactors.py
And, yes, there's a built-in crash handling mode B)
We're hoping to add a respawn-from-repl system soon!
SC compatible bi-directional streaming
--------------------------------------
Yes, you saw it here first; we provide 2-way streams
with reliable, transitive setup/teardown semantics.
Our nascent api is remniscent of ``trio.Nursery.start()``
style invocation:
.. code:: python
import trio
import tractor
@tractor.context
async def simple_rpc(
ctx: tractor.Context,
data: int,
) -> None:
'''Test a small ping-pong 2-way streaming server.
'''
# signal to parent that we're up much like
# ``trio_typing.TaskStatus.started()``
await ctx.started(data + 1)
async with ctx.open_stream() as stream:
count = 0
async for msg in stream:
assert msg == 'ping'
await stream.send('pong')
count += 1
else:
assert count == 10
async def main() -> None:
async with tractor.open_nursery() as n:
portal = await n.start_actor(
'rpc_server',
enable_modules=[__name__],
)
# XXX: this syntax requires py3.9
async with (
portal.open_context(
simple_rpc,
data=10,
) as (ctx, sent),
ctx.open_stream() as stream,
):
assert sent == 11
count = 0
# receive msgs using async for style
await stream.send('ping')
async for msg in stream:
assert msg == 'pong'
await stream.send('ping')
count += 1
if count >= 9:
break
# explicitly teardown the daemon-actor
await portal.cancel_actor()
if __name__ == '__main__':
trio.run(main)
See original proposal and discussion in `#53`_ as well
as follow up improvements in `#223`_ that we'd love to
hear your thoughts on!
.. _#53: https://github.com/goodboy/tractor/issues/53
.. _#223: https://github.com/goodboy/tractor/issues/223
Worker poolz are easy peasy
---------------------------
The initial ask from most new users is *"how do I make a worker
pool thing?"*.
``tractor`` is built to handle any SC (structured concurrent) process
tree you can imagine; a "worker pool" pattern is a trivial special
case.
We have a `full worker pool re-implementation`_ of the std-lib's
``concurrent.futures.ProcessPoolExecutor`` example for reference.
You can run it like so (from this dir) to see the process tree in
real time::
$TERM -e watch -n 0.1 "pstree -a $$" \
& python examples/parallelism/concurrent_actors_primes.py \
&& kill $!
This uses no extra threads, fancy semaphores or futures; all we need
is ``tractor``'s IPC!
"Infected ``asyncio``" mode
---------------------------
Have a bunch of ``asyncio`` code you want to force to be SC at the process level?
Check out our experimental system for `guest-mode`_ controlled
``asyncio`` actors:
.. code:: python
import asyncio
from statistics import mean
import time
import trio
import tractor
async def aio_echo_server(
to_trio: trio.MemorySendChannel,
from_trio: asyncio.Queue,
) -> None:
# a first message must be sent **from** this ``asyncio``
# task or the ``trio`` side will never unblock from
# ``tractor.to_asyncio.open_channel_from():``
to_trio.send_nowait('start')
# XXX: this uses an ``from_trio: asyncio.Queue`` currently but we
# should probably offer something better.
while True:
# echo the msg back
to_trio.send_nowait(await from_trio.get())
await asyncio.sleep(0)
@tractor.context
async def trio_to_aio_echo_server(
ctx: tractor.Context,
):
# this will block until the ``asyncio`` task sends a "first"
# message.
async with tractor.to_asyncio.open_channel_from(
aio_echo_server,
) as (first, chan):
assert first == 'start'
await ctx.started(first)
async with ctx.open_stream() as stream:
async for msg in stream:
await chan.send(msg)
out = await chan.receive()
# echo back to parent actor-task
await stream.send(out)
async def main():
async with tractor.open_nursery() as n:
p = await n.start_actor(
'aio_server',
enable_modules=[__name__],
infect_asyncio=True,
)
async with p.open_context(
trio_to_aio_echo_server,
) as (ctx, first):
assert first == 'start'
count = 0
async with ctx.open_stream() as stream:
delays = []
send = time.time()
await stream.send(count)
async for msg in stream:
recv = time.time()
delays.append(recv - send)
assert msg == count
count += 1
send = time.time()
await stream.send(count)
if count >= 1e3:
break
print(f'mean round trip rate (Hz): {1/mean(delays)}')
await p.cancel_actor()
if __name__ == '__main__':
trio.run(main)
Yes, we spawn a python process, run ``asyncio``, start ``trio`` on the
``asyncio`` loop, then send commands to the ``trio`` scheduled tasks to
tell ``asyncio`` tasks what to do XD
We need help refining the `asyncio`-side channel API to be more
`trio`-like. Feel free to sling your opinion in `#273`_!
.. _#273: https://github.com/goodboy/tractor/issues/273
Higher level "cluster" APIs
---------------------------
To be extra terse the ``tractor`` devs have started hacking some "higher
level" APIs for managing actor trees/clusters. These interfaces should
generally be condsidered provisional for now but we encourage you to try
them and provide feedback. Here's a new API that let's you quickly
spawn a flat cluster:
.. code:: python
import trio
import tractor
async def sleepy_jane():
uid = tractor.current_actor().uid
print(f'Yo i am actor {uid}')
await trio.sleep_forever()
async def main():
'''
Spawn a flat actor cluster, with one process per
detected core.
'''
portal_map: dict[str, tractor.Portal]
results: dict[str, str]
# look at this hip new syntax!
async with (
tractor.open_actor_cluster(
modules=[__name__]
) as portal_map,
trio.open_nursery() as n,
):
for (name, portal) in portal_map.items():
n.start_soon(portal.run, sleepy_jane)
await trio.sleep(0.5)
# kill the cluster with a cancel
raise KeyboardInterrupt
if __name__ == '__main__':
try:
trio.run(main)
except KeyboardInterrupt:
pass
.. _full worker pool re-implementation: https://github.com/goodboy/tractor/blob/master/examples/parallelism/concurrent_actors_primes.py
Install
-------
From PyPi::
pip install tractor
From git::
pip install git+git://github.com/goodboy/tractor.git
Under the hood
--------------
``tractor`` is an attempt to pair trionic_ `structured concurrency`_ with
distributed Python. You can think of it as a ``trio``
*-across-processes* or simply as an opinionated replacement for the
stdlib's ``multiprocessing`` but built on async programming primitives
from the ground up.
Don't be scared off by this description. ``tractor`` **is just** ``trio``
but with nurseries for process management and cancel-able streaming IPC.
If you understand how to work with ``trio``, ``tractor`` will give you
the parallelism you may have been needing.
Wait, huh?! I thought "actors" have messages, and mailboxes and stuff?!
***********************************************************************
Let's stop and ask how many canon actor model papers have you actually read ;)
From our experience many "actor systems" aren't really "actor models"
since they **don't adhere** to the `3 axioms`_ and pay even less
attention to the problem of *unbounded non-determinism* (which was the
whole point for creation of the model in the first place).
From the author's mouth, **the only thing required** is `adherance to`_
the `3 axioms`_, *and that's it*.
``tractor`` adheres to said base requirements of an "actor model"::
In response to a message, an actor may:
- send a finite number of new messages
- create a finite number of new actors
- designate a new behavior to process subsequent messages
**and** requires *no further api changes* to accomplish this.
If you want do debate this further please feel free to chime in on our
chat or discuss on one of the following issues *after you've read
everything in them*:
- https://github.com/goodboy/tractor/issues/210
- https://github.com/goodboy/tractor/issues/18
Let's clarify our parlance
**************************
Whether or not ``tractor`` has "actors" underneath should be mostly
irrelevant to users other then for referring to the interactions of our
primary runtime primitives: each Python process + ``trio.run()``
+ surrounding IPC machinery. These are our high level, base
*runtime-units-of-abstraction* which both *are* (as much as they can
be in Python) and will be referred to as our *"actors"*.
The main goal of ``tractor`` is is to allow for highly distributed
software that, through the adherence to *structured concurrency*,
results in systems which fail in predictable, recoverable and maybe even
understandable ways; being an "actor model" is just one way to describe
properties of the system.
What's on the TODO:
-------------------
Help us push toward the future of distributed `Python`.
- Erlang-style supervisors via composed context managers (see `#22
<https://github.com/goodboy/tractor/issues/22>`_)
- Typed messaging protocols (ex. via ``msgspec.Struct``, see `#36
<https://github.com/goodboy/tractor/issues/36>`_)
- Typed capability-based (dialog) protocols ( see `#196
<https://github.com/goodboy/tractor/issues/196>`_ with draft work
started in `#311 <https://github.com/goodboy/tractor/pull/311>`_)
- We **recently disabled CI-testing on windows** and need help getting
it running again! (see `#327
<https://github.com/goodboy/tractor/pull/327>`_). **We do have windows
support** (and have for quite a while) but since no active hacker
exists in the user-base to help test on that OS, for now we're not
actively maintaining testing due to the added hassle and general
latency..
Feel like saying hi?
--------------------
This project is very much coupled to the ongoing development of
``trio`` (i.e. ``tractor`` gets most of its ideas from that brilliant
community). If you want to help, have suggestions or just want to
say hi, please feel free to reach us in our `matrix channel`_. If
matrix seems too hip, we're also mostly all in the the `trio gitter
channel`_!
.. _structured concurrent: https://trio.discourse.group/t/concise-definition-of-structured-concurrency/228
.. _multi-processing: https://en.wikipedia.org/wiki/Multiprocessing
.. _trio: https://github.com/python-trio/trio
.. _nurseries: https://vorpus.org/blog/notes-on-structured-concurrency-or-go-statement-considered-harmful/#nurseries-a-structured-replacement-for-go-statements
.. _actor model: https://en.wikipedia.org/wiki/Actor_model
.. _trionic: https://trio.readthedocs.io/en/latest/design.html#high-level-design-principles
.. _async sandwich: https://trio.readthedocs.io/en/latest/tutorial.html#async-sandwich
.. _3 axioms: https://www.youtube.com/watch?v=7erJ1DV_Tlo&t=162s
.. .. _3 axioms: https://en.wikipedia.org/wiki/Actor_model#Fundamental_concepts
.. _adherance to: https://www.youtube.com/watch?v=7erJ1DV_Tlo&t=1821s
.. _trio gitter channel: https://gitter.im/python-trio/general
.. _matrix channel: https://matrix.to/#/!tractor:matrix.org
.. _pdb++: https://github.com/pdbpp/pdbpp
.. _guest mode: https://trio.readthedocs.io/en/stable/reference-lowlevel.html?highlight=guest%20mode#using-guest-mode-to-run-trio-on-top-of-other-event-loops
.. _messages: https://en.wikipedia.org/wiki/Message_passing
.. _trio docs: https://trio.readthedocs.io/en/latest/
.. _blog post: https://vorpus.org/blog/notes-on-structured-concurrency-or-go-statement-considered-harmful/
.. _structured concurrency: https://en.wikipedia.org/wiki/Structured_concurrency
.. _structured chadcurrency: https://en.wikipedia.org/wiki/Structured_concurrency
.. _structured concurrency: https://en.wikipedia.org/wiki/Structured_concurrency
.. _unrequirements: https://en.wikipedia.org/wiki/Actor_model#Direct_communication_and_asynchrony
.. _async generators: https://www.python.org/dev/peps/pep-0525/
.. _trio-parallel: https://github.com/richardsheridan/trio-parallel
.. _msgspec: https://jcristharif.com/msgspec/
.. _guest-mode: https://trio.readthedocs.io/en/stable/reference-lowlevel.html?highlight=guest%20mode#using-guest-mode-to-run-trio-on-top-of-other-event-loops
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:width: 250
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