New RunLLM response generated: >>>*# Question* Are there users that run multi-node distributed training on Flyte that is fault-tolerance? (By using Intratask checkpoints with elastic torch?) I spoke to some scientists that do large scale distributed training, and all they want is a way to spin up spot instances and have it keep training even when some GPU go down. # Answer Yes, Flyte supports multi-node distributed training with fault tolerance using intratask checkpoints and elastic training with

torchrun

. Flyte's intratask checkpointing allows tasks to save their state periodically, enabling recovery from failures without restarting the entire task. This is particularly useful for long-running tasks and can be combined with elastic training to handle dynamic resource allocation and worker failures gracefully. For example, you can configure an elastic training task with intratask checkpoints as follows:

from flytekit import task, workflow
from flytekitplugins.kfpytorch import Elastic

@task(
    task_config=Elastic(
        nnodes=2,
        nproc_per_node=4,
    ),
    retries=3  # Set retries to enable fault tolerance
)
def train_model():
    # Training logic with checkpointing
    ...

@workflow
def training_workflow():
    train_model()

This setup allows the training to continue even if some GPUs go down, leveraging spot instances for cost efficiency. Sources: - Flyte Intratask Checkpoints - Flyte PyTorch Elastic Training Slack Link https://flyte-org.slack.com/archives/CP2HDHKE1/p1726173253.532799 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.