Special considerations for CV dataloaders#

Adding support for using worker_cache with a new dataloader#

ML Training is often bottenecked at the dataloader stage. In the Cerebras Wafer-Scale Cluster, in order to improve dataloading speeds, we can avoid network dataset streaming via the create_worker_cache function. This enables caching of a dataset to local SSD, which has significantly faster read speeds versus network.

To enable worker_cache for a new dataloader, we need to ensure that data directory is added to the worker_cache on the worker node. The utility function create_worker_cache allows users to cache the directory on the worker node. It looks at the src directory, and caches this directory on the worker_cache if it doesn’t exist and there is enough space on the cache (shouldn’t exceed 80% after the directory is cached). It returns the path to the directory on the worker_cache. Users just need to replace the returned dir with the original data_dir in the dataloader.


create_worker_cache should be called only for the worker task.


Below is an example of adding worker cache support for InriaAerialDataset

import cerebras_pytorch as cstorch
import cerebras_pytorch.distributed as dist

if use_worker_cache and dist.is_streamer():
    if not cstorch.use_cs():
        raise RuntimeError(
            "use_worker_cache not supported for non-appliance runs"
        self.root = create_worker_cache(self.root)

The above snippet calls create_worker_cache on the VisionDataset’s self.root and overwrites it:

  1. if use_worker_cache is True (controlled by the yaml config),

  2. and dataloader is being called by a worker task (determined with dist.is_streamer).

The updated self.root (containing the SSD path) will be used by the dataloader.