# Copyright 2019-2021 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""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