New RunLLM response generated: >>>*# Question* When using the training operator and PyTroch elastic with Flyte, how does Flyte decide which pod's error to propagate at the task level? Say I have a 2 node PyTorch elastic training. And say both pods have a failure reported by PyTorch elastic. Which one propagates at the Flyte task level? # Answer In Flyte, when using PyTorch elastic training with the training operator, the error propagation at the task level can be controlled by specifying which worker's output should be considered. By default, if multiple workers report errors, it might seem random which error gets propagated. However, you can control this behavior by raising an

IgnoreOutputs

exception for all workers except the one you want to propagate the error from. According to Flyte discussion, you can achieve this by checking the rank of the worker and raising an

IgnoreOutputs

exception for all workers except the rank 0 worker. Here is an example of how you can implement this:

import os
from flytekit import task
from flytekit.exceptions import IgnoreOutputs

@task
def pytorch_task():
    if os.environ.get("RANK") != "0":
        raise IgnoreOutputs("Ignoring outputs from non-rank 0 workers")
    # Your training logic here

This ensures that only the rank 0 worker's output (or error) is propagated to the subsequent tasks in the Flyte workflow. Would you like more detailed guidance on implementing this in your Flyte setup? Sources: - Flyte discussion Slack Link https://flyte-org.slack.com/archives/CP2HDHKE1/p1721687448.769399 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.