New RunLLM response generated: >>>*# Question* What is the "flyte way" to handle workflows with a lot of data? We have several workflows that handles 10s of GBs, and they're failing due to the output size -

is too large [28775519] bytes, max allowed [10485760] bytes

For now, we're passing a Flytefile instead of the actual data to overcome this issue, and I also understand another approach could be increasing the

max-output-size-bytes

parameter, but this is only temporary, as data in the future could succeed this threshold. So - What is the proper way to handle large I/O? # Answer The "Flyte way" to handle workflows with large data is to offload the data to external storage systems like S3 or GCS and pass references (e.g., FlyteFile) instead of the actual data. This approach avoids hitting the size limits for task inputs and outputs. Increasing the

max-output-size-bytes

parameter is a temporary solution and not recommended for long-term scalability. Flyte is also working on features to automatically offload large metadata and data, which will help manage large I/O more efficiently. For example, the literal offloading feature allows Flyte to offload large literals to external storage, reducing the size of data passed between tasks. Would you like more details on how to implement these solutions? Sources: - Flyte Discussion on Handling Large Data - Flyte PR on Literal Offloading Slack Link https://.slack.com/archives/CP2HDHKE1/p1727101652.867419 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.