Issue with Ray Remote Functions Tutorial Example

Oct 13, 2025 by miodeya.com 54 views

Troubleshooting Ray Core Tasks Tutorial: Error Type: WORKER_DIED

Introduction

The official Ray tutorial for remote functions is designed to provide a comprehensive guide for users to learn about Ray's core tasks feature. However, some users have reported issues with the tutorial example, where tasks fail silently when the program exits before they complete. In this article, we will delve into the issue, explore the possible causes, and provide a solution to ensure background tasks complete without explicitly calling ray.get().

Issue with Ray Remote Functions Tutorial Example

The issue arises when running the tutorial code, which is designed to demonstrate the use of Ray remote functions. The code consists of two functions: my_function and slow_function. The my_function is a simple remote function that returns an integer, while the slow_function is a remote function that simulates a time-consuming task using time.sleep(10).

When running the code, the dashboard shows that my_function runs successfully, but all four slow_function tasks fail with an error message Error Type: WORKER_DIED. This behavior is unexpected, as the tutorial code suggests that remote tasks will run in the background without blocking the main program.

Problem Description

The problem can be summarized as follows:

The tutorial code is designed to demonstrate the use of Ray remote functions.
The code consists of two functions: my_function and slow_function.
The my_function is a simple remote function that returns an integer.
The slow_function is a remote function that simulates a time-consuming task using time.sleep(10).
When running the code, the dashboard shows that my_function runs successfully, but all four slow_function tasks fail with an error message Error Type: WORKER_DIED.

Code Analysis

The tutorial code is as follows:

import ray
import time


# A regular Python function.
def normal_function():
    return 1


# By adding the `@ray.remote` decorator, a regular Python function
# becomes a Ray remote function.
@ray.remote
def my_function():
    return 1


# To invoke this remote function, use the `remote` method.
# This will immediately return an object ref (a future) and then create
# a task that will be executed on a worker process.
obj_ref = my_function.remote()

# The result can be retrieved with ``ray.get``.
assert ray.get(obj_ref) == 1


@ray.remote
def slow_function():
    time.sleep(10)
    return 1


# Ray tasks are executed in parallel.
# All computation is performed in the background, driven by Ray's internal event loop.
for _ in range(4):
    # This doesn't block.
    slow_function.remote()

The code uses the @ray.remote decorator to define two remote functions: my_function and slow_function. The my_function is a simple remote function that returns an integer, while the slow_function is a remote function that simulates a time-consuming task using time.sleep(10).

Important Observation

The code can be modified to use ray.get() to collect the results from the slow functions, which makes the dashboard show all tasks running successfully:

# Modified version that works
refs = [slow_function.remote() for _ in range(4)]
ray.get(refs)  # Wait for all tasks to complete

This observation suggests that the issue is related to the way Ray handles remote tasks, and that using ray.get() is a workaround to ensure background tasks complete without blocking the main program.

Questions

Is this the expected behavior?
Is there a way to ensure background tasks complete without explicitly calling ray.get()?

Conclusion

The issue with the Ray remote functions tutorial example is related to the way Ray handles remote tasks. The tutorial code suggests that remote tasks will run in the background without blocking the main program, but in reality, the tasks fail silently when the program exits before they complete. The workaround is to use ray.get() to collect the results from the slow functions, which ensures that background tasks complete without blocking the main program.

Recommendations

Based on the analysis, we recommend the following:

Update the tutorial code to use ray.get() to collect the results from the slow functions.
Provide a clear explanation of the expected behavior and the workaround in the tutorial documentation.
Consider adding a warning or a note to the tutorial code to inform users about the potential issue with background tasks.

Future Work

To improve the Ray core tasks feature, we suggest the following:

Investigate the root cause of the issue and provide a more robust solution to ensure background tasks complete without blocking the main program.
Update the Ray documentation to provide a clear explanation of the expected behavior and the workaround for remote tasks.
Consider adding a feature to Ray that allows users to specify a timeout for background tasks, which would prevent the tasks from running indefinitely and causing issues with the program.
Q&A: Troubleshooting Ray Core Tasks Tutorial Error Type: WORKER_DIED

Introduction

In our previous article, we explored the issue with the Ray remote functions tutorial example, where tasks fail silently when the program exits before they complete. We also provided a solution to ensure background tasks complete without explicitly calling ray.get(). In this article, we will answer some frequently asked questions (FAQs) related to the issue and provide additional guidance on troubleshooting Ray core tasks.

Q: What is the expected behavior of Ray remote tasks?

A: The expected behavior of Ray remote tasks is that they will run in the background without blocking the main program. However, in reality, the tasks may fail silently when the program exits before they complete.

Q: Why do my Ray remote tasks fail with an Error Type: WORKER_DIED error?

A: The Error Type: WORKER_DIED error is typically caused by the program exiting before the remote tasks complete. This can happen when the program is terminated abruptly or when the tasks take longer to complete than expected.

Q: How can I ensure that my Ray remote tasks complete without blocking the main program?

A: You can use ray.get() to collect the results from the remote tasks. This will ensure that the tasks complete without blocking the main program.

Q: What is the difference between ray.get() and ray.wait()?

A: ray.get() is used to collect the results from a remote task, while ray.wait() is used to wait for a remote task to complete without collecting the results.

Q: Can I use ray.wait() to ensure that my Ray remote tasks complete without blocking the main program?

A: Yes, you can use ray.wait() to wait for a remote task to complete without blocking the main program. However, you will need to use ray.get() to collect the results from the task if you need to access the results.

Q: How can I troubleshoot Ray core tasks issues?

A: To troubleshoot Ray core tasks issues, you can try the following:

Check the Ray logs for any error messages or warnings.
Use the ray.get() function to collect the results from remote tasks and ensure that they complete without blocking the main program.
Use the ray.wait() function to wait for remote tasks to complete without blocking the main program.
Consider using a timeout for remote tasks to prevent them from running indefinitely.

Q: What are some common pitfalls to avoid when using Ray core tasks?

A: Some common pitfalls to avoid when using Ray core tasks include:

Not using ray.get() or ray.wait() to ensure that remote tasks complete without blocking the main program.
Not checking the Ray logs for error messages or warnings.
Not using a timeout for remote tasks to prevent them from running indefinitely.

Conclusion

In this article, we answered some frequently asked questions (FAQs) related to the issue with the Ray remote functions tutorial example and provided additional guidance on troubleshooting Ray core tasks. We hope that this article has been helpful in resolving any issues you may have encountered with Ray core tasks. If you have any further questions or concerns, please don't hesitate to reach out.

Additional Resources

For more information on Ray core tasks, please refer to the following resources:

Ray documentation: https://docs.ray.io/en/latest/
Ray GitHub repository: https://github.com/ray-project/ray
Ray community forum: https://discuss.ray.io/