- class torchdata.datapipes.iter.InMemoryCacheHolder(source_dp: IterDataPipe[T_co], size: Optional[int] = None)¶
Stores elements from the source DataPipe in memory, up to a size limit if specified (functional name:
in_memory_cache). This cache is FIFO - once the cache is full, further elements will not be added to the cache until the previous ones are yielded and popped off from the cache.
source_dp – source DataPipe from which elements are read and stored in memory
size – The maximum size (in megabytes) that this DataPipe can hold in memory. This defaults to unlimited.
>>> from torchdata.datapipes.iter import IterableWrapper >>> source_dp = IterableWrapper(range(10)) >>> cache_dp = source_dp.in_memory_cache(size=5) >>> list(cache_dp) [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]