mindspore.dataset.DatasetCache
- class mindspore.dataset.DatasetCache(session_id, size=0, spilling=False, hostname=None, port=None, num_connections=None, prefetch_size=None)[source]
A client to interface with tensor caching service.
For details, please check Tutorial .
- Parameters
session_id (int) – A user assigned session id for the current pipeline.
size (int, optional) – Size of the memory set aside for the row caching. Default:
0, which means unlimited, note that it might bring in the risk of running out of memory on the machine.spilling (bool, optional) – Whether or not spilling to disk if out of memory. Default:
False.hostname (str, optional) – Host name. Default:
None, use default hostname ‘127.0.0.1’.port (int, optional) – Port to connect to server. Default:
None, use default port 50052.num_connections (int, optional) – Number of tcp/ip connections. Default:
None, use default value 12.prefetch_size (int, optional) – The size of the cache queue between operations. Default:
None, use default value 20.
Examples
>>> import mindspore.dataset as ds >>> >>> # Create a cache instance, in which session_id is generated from command line `cache_admin -g` >>> # In the following code, suppose the session_id is 780643335 >>> some_cache = ds.DatasetCache(session_id=780643335, size=0) >>> >>> dataset_dir = "/path/to/image_folder_dataset_directory" >>> ds1 = ds.ImageFolderDataset(dataset_dir, cache=some_cache)