@ancient-wolf-19325 what is the maximum recommended size of a dataframe before I should consider using FlyteFile?

It seems likely that Flyte does not specify a strict maximum size for a dataframe before recommending the use of

FlyteFile

. However, based on discussions in the Flyte community, handling large dataframes can lead to performance issues, such as excessive memory usage or task failures due to size limits (source). Would you like more detail on how to manage large dataframes in Flyte? Sources: - https://discuss.flyte.org/t/8235184/hi-team-i-need-some-help-i-have-a-flyte-task-that-needs-to-r/ 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.

@ancient-wolf-19325 could you provide more detail on how to manage large dataframes in Flyte? What defines "large"?

To manage large dataframes in Flyte, consider the following strategies: 1. Use FlyteFile or FlyteDirectory: For very large dataframes, it is often more efficient to store them as files (e.g., CSV, Parquet) and use

FlyteFile

or

FlyteDirectory

to pass references to these files between tasks. This approach offloads the data to a storage backend like S3, reducing memory usage and avoiding size limits on task inputs/outputs (source). 2. Chunking: Break down the dataframe into smaller chunks and process them in parallel using Flyte's map tasks or dynamic workflows. This can help distribute the workload and manage memory usage more effectively (source). 3. Use DataFrame Accelerators: Consider using tools like Modin, which can accelerate Pandas operations by utilizing all available cores, making it easier to handle larger datasets (source). 4. Optimize Data Formats: Use efficient data formats like Parquet, which are optimized for both storage and processing, to reduce the size of the data being handled. 5. Monitor and Adjust Resource Limits: Ensure that your Kubernetes cluster and Flyte tasks have appropriate resource limits set to handle the data size. This includes CPU, memory, and storage configurations. As for what defines "large," it can vary based on the specific use case and available resources. Generally, if a dataframe is causing memory issues or task failures, it may be considered large. In Flyte, tasks have a default output size limit (e.g., 10MB), which can be adjusted if needed (source). Would you like more information on any of these strategies? Sources: - https://discuss.flyte.org/t/15640381/hey-i-hope-it-is-a-quick-one-slightly-smiling-face-we-are-us/ - https://discuss.flyte.org/t/8155588/hi-team-i-m-exploring-flyte-s-map-task-for-a-workflow-i-m-wo/ - https://github.com/flyteorg/flytesnacks/blob/master/examples/modin_plugin/README.md - https://discuss.flyte.org/t/11123391/do-you-have-any-examples-of-class-based-tasks-workflows-inst/ 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.