Troubleshooting

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

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()