Quickstart

Hello World

This “hello world” example uses Ray Compiled Graph. First, install Ray.

pip install "ray[cgraph]"

# For a ray version before 2.41, use the following instead:
# pip install "ray[adag]"

Next, define a simple actor that echoes its argument.

import ray


@ray.remote
class SimpleActor:
    def echo(self, msg):
        return msg

Then instantiate the actor and use the classic Ray Core APIs remote and ray.get to execute tasks on the actor.

import time

a = SimpleActor.remote()

# warmup
for _ in range(5):
    msg_ref = a.echo.remote("hello")
    ray.get(msg_ref)

start = time.perf_counter()
msg_ref = a.echo.remote("hello")
ray.get(msg_ref)
end = time.perf_counter()
print(f"Execution takes {(end - start) * 1000 * 1000} us")

Execution takes 969.0364822745323 us

Next, create an equivalent program with Ray Compiled Graph. First, define a graph and execute it with classic Ray Core, without any compilation. Then compile the graph to apply optimizations and prevent further changes to it.

First, create a Ray DAG, which is a lazily executed directed acyclic graph of Ray tasks. A Ray DAG differs from the classic Ray Core APIs in three ways:

1. Use the ray.dag.InputNode context manager to indicate which DAG inputs you provide at run time.

2. Use bind() instead of remote() to indicate lazily executed Ray tasks.

3. Use execute() to execute the DAG.

Define a graph and execute it. This code doesn’t compile the graph, and it uses the same execution backend as the preceding example:

import ray.dag

with ray.dag.InputNode() as inp:
    # Note that it uses `bind` instead of `remote`.
    # This returns a ray.dag.DAGNode, instead of the usual ray.ObjectRef.
    dag = a.echo.bind(inp)

# warmup
for _ in range(5):
    msg_ref = dag.execute("hello")
    ray.get(msg_ref)

start = time.perf_counter()
# `dag.execute` runs the DAG and returns an ObjectRef. You can use `ray.get` API.
msg_ref = dag.execute("hello")
ray.get(msg_ref)
end = time.perf_counter()
print(f"Execution takes {(end - start) * 1000 * 1000} us")

Next, compile the dag using the experimental_compile API. The graph uses the same APIs for execution:

dag = dag.experimental_compile()

# warmup
for _ in range(5):
    msg_ref = dag.execute("hello")
    ray.get(msg_ref)

start = time.perf_counter()
# `dag.execute` runs the DAG and returns CompiledDAGRef. Similar to
# ObjectRefs, you can use the ray.get API.
msg_ref = dag.execute("hello")
ray.get(msg_ref)
end = time.perf_counter()
print(f"Execution takes {(end - start) * 1000 * 1000} us")

Execution takes 86.72196418046951 us

The same task graph runs 10x faster. The improvement is large because the echo function is cheap, so system overhead highly affects it. Because of bookkeeping and distributed protocols, the classic Ray Core APIs usually have 1 ms or more of system overhead.

Because the system knows the task graph ahead of time, Ray Compiled Graph can pre-allocate all necessary resources and greatly reduce the system overhead. For example, if the actor a is on the same node as the driver, Ray Compiled Graph uses shared memory instead of RPC to transfer data directly between the driver and the actor.

Currently, the DAG tasks run on a background thread of the involved actors. An actor can only participate in one DAG at a time. Normal tasks can still execute on the actors while the actors participate in a Compiled Graph, but these tasks execute on the main thread.

When you’re done, you can tear down the Compiled Graph by deleting it or by calling dag.teardown() explicitly. After teardown, you can reuse the actors in a new Compiled Graph.

dag.teardown()

Specifying data dependencies

When you create the DAG, pass a ray.dag.DAGNode as an argument to other .bind calls to specify data dependencies. For example, the following code builds on the preceding example to create a DAG that passes the same message from one actor to another:

a = SimpleActor.remote()
b = SimpleActor.remote()

with ray.dag.InputNode() as inp:
    # Note that it uses `bind` instead of `remote`.
    # This returns a ray.dag.DAGNode, instead of the usual ray.ObjectRef.
    dag = a.echo.bind(inp)
    dag = b.echo.bind(dag)

dag = dag.experimental_compile()
print(ray.get(dag.execute("hello")))

hello

The next example passes the same message to both actors, which can then execute in parallel. It uses ray.dag.MultiOutputNode to indicate that this DAG returns multiple outputs. Then, dag.execute() returns multiple CompiledDAGRef objects, one per node:

import ray.dag

a = SimpleActor.remote()
b = SimpleActor.remote()

with ray.dag.InputNode() as inp:
    # Note that it uses `bind` instead of `remote`.
    # This returns a ray.dag.DAGNode, instead of the usual ray.ObjectRef.
    dag = ray.dag.MultiOutputNode([a.echo.bind(inp), b.echo.bind(inp)])

dag = dag.experimental_compile()
print(ray.get(dag.execute("hello")))

Execution takes 86.72196418046951 us

Keep the following execution behavior in mind:

• On the same actor, a Compiled Graph executes in order. If an actor has multiple tasks in the same Compiled Graph, it executes all of them to completion before executing on the next DAG input.

• Across actors in the same Compiled Graph, the execution might be pipelined. An actor might begin executing on the next DAG input while a downstream actor executes on the current one.

• Compiled Graph currently supports only actor tasks. It doesn’t support non-actor tasks.

asyncio support

If your Compiled Graph driver runs in an asyncio event loop, use the async APIs so that executing the Compiled Graph and getting the results don’t block the event loop. First, pass enable_async=True to dag.experimental_compile():

import ray


@ray.remote
class EchoActor:
    def echo(self, msg):
        return msg


actor = EchoActor.remote()
with ray.dag.InputNode() as inp:
    dag = actor.echo.bind(inp)

cdag = dag.experimental_compile(enable_asyncio=True)

Next, use execute_async to invoke the Compiled Graph. Calling await on execute_async returns once the input is submitted, and it returns a future that you can use to get the result. Finally, use await to get the result of the Compiled Graph.

import asyncio


async def async_method(i):
    fut = await cdag.execute_async(i)
    result = await fut
    assert result == i


loop = asyncio.get_event_loop()
loop.run_until_complete(async_method(42))

Execution and failure semantics

Like classic Ray Core, Ray Compiled Graph propagates exceptions to the final output. It handles application and system exceptions as follows:

• Application exceptions: If an application task throws an exception, Compiled Graph wraps the exception in a RayTaskError and raises it when you call ray.get() on the result. The thrown exception inherits from both RayTaskError and the original exception class.

• System exceptions: System exceptions include actor death or unexpected errors such as network errors. For actor death, Compiled Graph raises an ActorDiedError, and for other errors, it raises a RayChannelError.

The graph can still execute after application exceptions. However, the graph shuts down automatically after a system exception. If an actor’s death causes the graph to shut down, the remaining actors stay alive.

The following example explicitly destroys an actor while it participates in a Compiled Graph. The remaining actors are reusable:

from ray.dag import InputNode, MultiOutputNode


@ray.remote
class EchoActor:
    def echo(self, msg):
        return msg


actors = [EchoActor.remote() for _ in range(4)]
with InputNode() as inp:
    outputs = [actor.echo.bind(inp) for actor in actors]
    dag = MultiOutputNode(outputs)

compiled_dag = dag.experimental_compile()
# Kill one of the actors to simulate unexpected actor death.
ray.kill(actors[0])
ref = compiled_dag.execute(1)

live_actors = []
try:
    ray.get(ref)
except ray.exceptions.ActorDiedError:
    # At this point, the Compiled Graph is shutting down.
    for actor in actors:
        try:
            # Check for live actors.
            ray.get(actor.echo.remote("ping"))
            live_actors.append(actor)
        except ray.exceptions.RayActorError:
            pass

# Optionally, use the live actors to create a new Compiled Graph.
assert live_actors == actors[1:]

Execution timeouts

Some errors, such as NCCL network errors, require additional handling to avoid hanging. Ray might detect such errors in the future. As a fallback, you can configure timeouts for compiled_dag.execute() and ray.get().

The default timeout is 10 seconds for both. Set the following two environment variables to change the default timeout:

• RAY_CGRAPH_submit_timeout: Timeout for compiled_dag.execute().

• RAY_CGRAPH_get_timeout: Timeout for ray.get().

ray.get() also has a timeout parameter that sets the timeout for each call.

CPU to GPU communication

With classic Ray Core, passing torch.Tensors between actors can become expensive, especially when transferring between devices, because Ray Core doesn’t know the final destination device. As a result, you might see unnecessary copies across devices other than the source and destination devices.

Ray Compiled Graph natively supports passing torch.Tensors between actors that execute on different devices. Use type hint annotations in the Compiled Graph declaration to indicate the final destination device of a torch.Tensor.

import torch
import ray
import ray.dag


@ray.remote(num_gpus=1)
class GPUActor:
    def process(self, tensor: torch.Tensor):
        assert tensor.device.type == "cuda"
        return tensor.shape


actor = GPUActor.remote()

In Ray Core, if you try to pass a CPU tensor from the driver, the GPU actor receives a CPU tensor:

# This will fail because the driver passes a CPU copy of the tensor,
# and the GPU actor also receives a CPU copy.
ray.get(actor.process.remote(torch.zeros(10)))

With Ray Compiled Graph, you can annotate DAG nodes with type hints to indicate that the value might contain a torch.Tensor:

with ray.dag.InputNode() as inp:
    inp = inp.with_tensor_transport(device="cuda")
    dag = actor.process.bind(inp)

cdag = dag.experimental_compile()
print(ray.get(cdag.execute(torch.zeros(10))))

The Ray Compiled Graph backend copies the torch.Tensor to the GPU that Ray Core assigns to the GPUActor.

You can also copy the tensor yourself, but Compiled Graph has the following advantages:

• Ray Compiled Graph can minimize the number of data copies. For example, passing from one CPU to multiple GPUs requires one copy to a shared memory buffer, and then one host-to-device copy per destination GPU.

• In the future, Ray can further optimize this path through techniques such as memory pinning and zero-copy deserialization when the CPU is the destination.

GPU to GPU communication

Ray Compiled Graph supports NCCL-based transfers of CUDA torch.Tensor objects, avoiding any copies through Ray’s CPU-based shared-memory object store. When you provide type hints, Ray prepares NCCL communicators and operation scheduling ahead of time. This avoids deadlock and supports overlapping compute and communication.

Ray Compiled Graph uses CuPy to support NCCL operations. The CuPy version affects the NCCL version. The Ray team also plans to support custom communicators in the future, for example to support collectives across CPUs or to reuse existing collective groups.

First, create sender and receiver actors. This example requires at least two GPUs.

import torch
import ray
import ray.dag
from ray.experimental.channel.torch_tensor_type import TorchTensorType


# Note that the following example requires at least 2 GPUs.
assert (
    ray.available_resources().get("GPU") >= 2
), "At least 2 GPUs are required to run this example."


@ray.remote(num_gpus=1)
class GPUSender:
    def send(self, shape):
        return torch.zeros(shape, device="cuda")


@ray.remote(num_gpus=1)
class GPUReceiver:
    def recv(self, tensor: torch.Tensor):
        assert tensor.device.type == "cuda"
        return tensor.shape


sender = GPUSender.remote()
receiver = GPUReceiver.remote()

To support GPU-to-GPU communication with NCCL, wrap the DAG node that contains the torch.Tensor that you want to transmit using the with_tensor_transport API hint:

with ray.dag.InputNode() as inp:
    dag = sender.send.bind(inp)
    # Add a type hint that the return value of `send` should use NCCL.
    dag = dag.with_tensor_transport("nccl")
    # NOTE: With ray<2.42, use `with_type_hint()` instead.
    # dag = dag.with_type_hint(TorchTensorType(transport="nccl"))
    dag = receiver.recv.bind(dag)

# Compile API prepares the NCCL communicator across all workers and schedule operations
# accordingly.
dag = dag.experimental_compile()
assert ray.get(dag.execute((10,))) == (10,)

GPU-to-GPU communication currently has limitations that include the following:

• It supports only torch.Tensor and NVIDIA NCCL.

• It supports peer-to-peer transfers. Collective communication operations are coming soon.

• Communication operations run synchronously. Overlapping compute and communication is an experimental feature.