@ancient-wolf-19325 I have a long running GPU ml training task that takes more than 5 hours, but the pod terminates with no error messages in the container apart from "terminated in the background, manually". How do I debug this?

It seems likely that your Flyte task pod is being terminated externally (e.g., preemption, node drain, or resource eviction), which results in the "terminated in the background, manually" message, and no explicit error in your container logs. This is a common scenario for long-running GPU jobs, especially on cloud-managed Kubernetes clusters with spot/preemptible nodes or aggressive resource management. To debug, you should check the Kubernetes pod events and node status for clues about why the pod was killed, as Flyte may not surface the root cause directly in the task logs or UI. Use:

kubectl describe pod <PodName> -n <namespace>

and review the Events section for reasons like node preemption, OOMKilled, or node shutdown. Also, check the Flyte workflow and task execution details for retry or failure information. For more persistent issues, ensure your resource requests/limits are appropriate and consider using non-preemptible nodes for critical long-running jobs. My understanding is that Flyte's error reporting in these cases is best-effort and may not always be actionable without Kubernetes-level investigation, as discussed in the troubleshooting guide and related issues (source1, source2, source3). Would you like more detail on how to interpret specific Kubernetes events or how to configure Flyte for more robust long-running GPU jobs? Sources: - Debugging executions (Flyte GitHub) - source1 - source3 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.

@ancient-wolf-19325 PermissionError: The difference between the request time and the current time is too large. getting this error when my folder is too large

The Flyte documentation I have ingested does not mention how to resolve a "PermissionError: The difference between the request time and the current time is too large" when uploading or handling large folders. This error typically indicates a clock skew issue between your local machine and the remote storage service (such as AWS S3), which can become more apparent during lengthy upload operations, especially with large files or folders. You might want to provide more details about your environment (e.g., cloud provider, storage backend, and how you are uploading the folder), and ensure your system clock is synchronized with a reliable time source (like using NTP). If this doesn't help, please reach out to the Flyte support team for further assistance. 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.

@ancient-wolf-19325 It's s3 on aws and using s3fs

It could be possible that this error is due to your local system clock being out of sync with AWS S3, which is strict about request timestamps. This is a common issue when using s3fs, especially for large uploads that take a long time, as S3 will reject requests if the time difference is too large (source). The Flyte documentation does not provide a direct solution, but synchronizing your machine's clock (e.g., with NTP) is generally recommended. Would you like more detail on how to check and synchronize your system clock, or on s3fs configuration for large uploads? Sources: - https://github.com/flyteorg/flyte/issues/6438 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.

@ancient-wolf-19325 yes give me details on sync my system lock and for s3fs config

The Flyte documentation I have ingested does not mention how to synchronize your system clock or configure s3fs specifically to avoid "The difference between the request time and the current time is too large" errors. To help, you can synchronize your system clock by enabling and starting the NTP (Network Time Protocol) service on your machine; for example, on Linux, you can run

sudo timedatectl set-ntp true

or install and start the

ntpd

or

chrony

service, and on Windows, ensure "Set time automatically" is enabled in Date & Time settings. For s3fs, while there are no Flyte-specific settings, you can try increasing the number of retries and timeout settings (e.g., using

--retries

and

--stat-cache-ttl

options if using the s3fs-fuse tool, or adjusting

retries

and

timeout

parameters in the Python s3fs library). If you are running inside a container, make sure the container's clock is also synchronized with the host or a time server. If this doesn't help, please reach out to the Flyte support team for further assistance. 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.