New RunLLM response generated: >>>*# Question* hey flyte users, am curious what patterns / libraries people use for things like dataset / output caching. Flytekit does have a nice cache feature, and I appreciate that many users probably just read/write to S3 or similar blob storage. But suppose you have a flytekit task that: • Reads from a large dataset, which could potentially be cached locally on the machine somewhere (e.g. Huggingface datasets can do this). That path might be a local volume-mount. • The Task might want to write intermediate data locally somewhere (e.g. another volume-mounted location), provide some path to this data, then let the next task in the workflow worry about reading the data, even if the task is on some other machine. E.g. perhaps this data gets written to a local NFS export, and other machines in the cluster can read from this export. ◦ (This usage pattern is similar to a shuffle where machines may directly move data between each other instead of through e.g. a main S3 bucket) How do folks handle this case today? Or do folks tend to "design to avoid it" ? e.g. just always write to S3, and then maybe later delete old stuff (intermediate data) from S3. Or in the case of Ceph, there could be some architecting / configuration for local storage. Lastly, maybe it's common to just have a very powerful central SAN that can service all Flyte workers very well. # Answer Handling large datasets and intermediate data efficiently in Flyte can be approached in several ways, depending on your infrastructure and specific use case. Here are some patterns and suggestions based on the Flyte community's discussions: 1. Local Caching and Volume Mounts: - Local Caching: Flyte supports local caching of task outputs using the

@task(cache=True, cache_version="1.0")

decorator. This can cache results based on input parameters, reducing redundant computations (source). - Volume Mounts: You can use Kubernetes Persistent Volumes (PVs) and Persistent Volume Claims (PVCs) to mount shared storage in your Flyte tasks. This allows tasks to read/write data to a common storage location that can be accessed by other tasks (source). 2. Using S3 and Blob Storage: - S3 Integration: Many users leverage S3 or similar blob storage for storing large datasets and intermediate results. Flyte's caching mechanism can also handle S3 paths, ensuring that data is not re-uploaded/downloaded unnecessarily if it hasn't changed (source). - S3FS and Mountable File Systems: For more efficient data access, you can use S3FS or similar tools to mount S3 buckets directly onto your compute nodes. This can help avoid the overhead of copying data locally (source).