Troubleshooting

This page contains common issues and solutions for Compiled Graph execution.

Limitations

Compiled Graph is a new feature and has some limitations:

• Invoking Compiled Graph

  • Only the process that compiles the Compiled Graph may call it.

  • A Compiled Graph has a maximum number of in-flight executions. When using the DAG API, if there aren’t enough resources at the time of dag.execute(), Ray will queue the tasks for later execution. Ray Compiled Graph currently doesn’t support queuing past its maximum capacity. Therefore, you may need to consume some results using ray.get() before submitting more executions. As a stopgap, dag.execute() throws a RayCgraphCapacityExceeded exception if the call takes too long. In the future, Compiled Graph may have better error handling and queuing.

• Compiled Graph Execution

  • Ideally, you should try not to execute other tasks on the actor while it is participating in a Compiled Graph. Compiled Graph tasks will be executed on a background thread. Any concurrent tasks submitted to the actor can still execute on the main thread, but you are responsible for synchronization with the Compiled Graph background thread.

  • For now, actors can only execute one Compiled Graph at a time. To execute a different Compiled Graph on the same actor, you must teardown the current Compiled Graph. See Return NumPy arrays for more details.

• Passing and getting Compiled Graph results (CompiledDAGRef)

  • Compiled Graph results can’t be passed to another task or actor. This restriction may be loosened in the future, but for now, it allows for better performance because the backend knows exactly where to push the results.

  • ray.get() can be called at most once on a CompiledDAGRef. An exception will be raised if it is called twice on the same CompiledDAGRef. This is because the underlying memory for the result may need to be reused for a future DAG execution. Restricting ray.get() to once per reference simplifies the tracking of the memory buffers.

  • If the value returned by ray.get() is zero-copy deserialized, then subsequent executions of the same DAG will block until the value goes out of scope in Python. Thus, if you hold onto zero-copy deserialized values returned by ray.get(), and you try to execute the Compiled Graph above its max concurrency, it may deadlock. This case will be detected in the future, but for now you will receive a RayChannelTimeoutError. See Explicitly teardown before reusing the same actors for more details.

• Collective operations

  • For GPU to GPU communication, Compiled Graph only supports peer-to-peer transfers. Collective communication operations are coming soon.

Keep an eye out for additional features in future Ray releases: - Support better queuing of DAG inputs, to enable more concurrent executions of the same DAG. - Support for more collective operations with NCCL. - Support for multiple DAGs executing on the same actor. - General performance improvements.

If you run into additional issues, or have other feedback or questions, file an issue on GitHub. For a full list of known issues, check the compiled-graphs label on Ray GitHub.

Returning NumPy arrays

Ray zero-copy deserializes NumPy arrays when possible. If you execute compiled graph with a NumPy array output multiple times, you could possibly run into issues if a NumPy array output from a previous Compiled Graph execution isn’t deleted before attempting to get the result of a following execution of the same Compiled Graph. This is because the NumPy array stays in the buffer of the Compiled Graph until you or Python delete it. It’s recommended to explicitly delete the NumPy array as Python may not always garbage collect the NumPy array immediately as you may expect.

For example, the following code sample could result in a hang or RayChannelTimeoutError if the NumPy array isn’t deleted:

import ray
import numpy as np
from ray.dag import InputNode


@ray.remote
class NumPyActor:
    def get_arr(self, _):
        numpy_arr = np.ones((5, 1024))
        return numpy_arr


actor = NumPyActor.remote()
with InputNode() as inp:
    dag = actor.get_arr.bind(inp)
cgraph = dag.experimental_compile()
for _ in range(5):
    ref = cgraph.execute(0)
    result = ray.get(ref)
    # Adding this explicit del would fix any issues
    # del result

In the preceding code snippet, Python may not garbage collect the NumPy array in result on each iteration of the loop. Therefore, you should explicitly delete the NumPy array before you try to get the result of subsequent Compiled Graph executions.

Explicitly teardown before reusing the same actors

If you want to reuse the actors of a Compiled Graph, it’s important to explicitly teardown the Compiled Graph before reusing the actors. Without explicitly tearing down the Compiled Graph, the resources created for actors in a Compiled Graph may have conflicts with further usage of those actors.

For example, in the following code, Python could delay garbage collection, which triggers the implicit teardown of the first Compiled Graph. This could lead to a segfault due to the resource conflicts mentioned:

@ray.remote
class SendActor:
    def send(self, x):
        return x


actor = SendActor.remote()
with InputNode() as inp:
    dag = actor.send.bind(inp)
cgraph = dag.experimental_compile()

# Not adding this explicit teardown before reusing `actor` could cause problems
# cgraph.teardown()

with InputNode() as inp:
    dag = actor.send.bind(inp)
cgraph = dag.experimental_compile()