Source code for mindspore.dataset.engine.cache_client

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"""Cache client
"""

import copy
from mindspore._c_dataengine import CacheClient

from ..core.validator_helpers import type_check, check_pos_int32, check_pos_uint32, check_uint64, check_positive, \
    check_value


[docs]class DatasetCache: """ A client to interface with tensor caching service. For details, please check `Tutorial <https://www.mindspore.cn/ tutorials/experts/en/r1.9/dataset/cache.html>`_. Args: 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) """ def __init__(self, session_id, size=0, spilling=False, hostname=None, port=None, num_connections=None, prefetch_size=None): check_pos_uint32(session_id, "session_id") type_check(size, (int,), "size") if size != 0: check_positive(size, "size") check_uint64(size, "size") type_check(spilling, (bool,), "spilling") if hostname is not None: type_check(hostname, (str,), "hostname") if port is not None: type_check(port, (int,), "port") check_value(port, (1025, 65535), "port") if num_connections is not None: check_pos_int32(num_connections, "num_connections") if prefetch_size is not None: check_pos_int32(prefetch_size, "prefetch_size") self.session_id = session_id self.size = size self.spilling = spilling self.hostname = hostname self.port = port self.prefetch_size = prefetch_size self.num_connections = num_connections self.cache_client = CacheClient(session_id, size, spilling, hostname, port, num_connections, prefetch_size)
[docs] def get_stat(self): """ Get the statistics from a cache. After data pipeline, three types of statistics can be obtained, including average number of cache hits (avg_cache_sz), number of caches in memory (num_mem_cached) and number of caches in disk (num_disk_cached). """ return self.cache_client.GetStat()
def __deepcopy__(self, memodict): if id(self) in memodict: return memodict[id(self)] cls = self.__class__ new_cache = cls.__new__(cls) memodict[id(self)] = new_cache new_cache.session_id = copy.deepcopy(self.session_id, memodict) new_cache.spilling = copy.deepcopy(self.spilling, memodict) new_cache.size = copy.deepcopy(self.size, memodict) new_cache.hostname = copy.deepcopy(self.hostname, memodict) new_cache.port = copy.deepcopy(self.port, memodict) new_cache.prefetch_size = copy.deepcopy(self.prefetch_size, memodict) new_cache.num_connections = copy.deepcopy(self.num_connections, memodict) new_cache.cache_client = self.cache_client return new_cache