AsyncIO / Concurrency for Actors

Within a single actor process, it is possible to execute concurrent threads.

Ray offers two types of concurrency within an actor:

Keep in mind that the Python’s Global Interpreter Lock (GIL) will only allow one thread of Python code running at once.

This means if you are just parallelizing Python code, you won’t get true parallelism. If you calls Numpy, Cython, Tensorflow, or PyTorch code, these libraries will release the GIL when calling into C/C++ functions.

Neither the Threaded Actors nor AsyncIO for Actors model will allow you to bypass the GIL.

AsyncIO for Actors

Since Python 3.5, it is possible to write concurrent code using the async/await syntax. Ray natively integrates with asyncio. You can use ray alongside with popular async frameworks like aiohttp, aioredis, etc.

You can try it about by running the following snippet in ipython or a shell that supports top level await:

import ray
import asyncio
ray.init()

@ray.remote
class AsyncActor:
    # multiple invocation of this method can be running in
    # the event loop at the same time
    async def run_concurrent(self):
        print("started")
        await asyncio.sleep(2) # concurrent workload here
        print("finished")

actor = AsyncActor.remote()

# regular ray.get
ray.get([actor.run_concurrent.remote() for _ in range(4)])

# async ray.get
await actor.run_concurrent.remote()

ObjectRefs as asyncio.Futures

ObjectRefs can be translated to asyncio.Futures. This feature make it possible to await on ray futures in existing concurrent applications.

Instead of:

@ray.remote
def some_task():
    return 1

ray.get(some_task.remote())
ray.wait([some_task.remote()])

you can do:

@ray.remote
def some_task():
    return 1

await some_task.remote()
await asyncio.wait([some_task.remote()])

Please refer to asyncio doc for more asyncio patterns including timeouts and asyncio.gather.

Defining an Async Actor

By using async method definitions, Ray will automatically detect whether an actor support async calls or not.

import asyncio

@ray.remote
class AsyncActor:
    async def run_task(self):
        print("started")
        await asyncio.sleep(1) # Network, I/O task here
        print("ended")

actor = AsyncActor.remote()
# All 50 tasks should start at once. After 1 second they should all finish.
# they should finish at the same time
ray.get([actor.run_task.remote() for _ in range(50)])

Under the hood, Ray runs all of the methods inside a single python event loop. Please note that running blocking ray.get or ray.wait inside async actor method is not allowed, because ray.get will block the execution of the event loop.

In async actors, only one task can be running at any point in time (though tasks can be multi-plexed). There will be only one thread in AsyncActor! See Threaded Actors if you want a threadpool.

Setting concurrency in Async Actors

You can set the number of “concurrent” task running at once using the max_concurrency flag. By default, 1000 tasks can be running concurrently.

import asyncio

@ray.remote
class AsyncActor:
    async def run_task(self):
        print("started")
        await asyncio.sleep(1) # Network, I/O task here
        print("ended")

actor = AsyncActor.options(max_concurrency=10).remote()

# Only 10 tasks will be running concurrently. Once 10 finish, the next 10 should run.
ray.get([actor.run_task.remote() for _ in range(50)])

Threaded Actors

Sometimes, asyncio is not an ideal solution for your actor. For example, you may have one method that performs some computation heavy task while blocking the event loop, not giving up control via await. This would hurt the performance of an Async Actor because Async Actors can only execute 1 task at a time and rely on await to context switch.

Instead, you can use the max_concurrency Actor options without any async methods, allowng you to achieve threaded concurrency (like a thread pool).

Warning

When there is at least one async def method in actor definition, Ray will recognize the actor as AsyncActor instead of ThreadedActor.

@ray.remote
class ThreadedActor:
    def task_1(self): print("I'm running in a thread!")
    def task_2(self): print("I'm running in another thread!")

a = ThreadedActor.options(max_concurrency=2).remote()
ray.get([a.task_1.remote(), a.task_2.remote()])

Each invocation of the threaded actor will be running in a thread pool. The size of the threadpool is limited by the max_concurrency value.