mindspore.dataset.engine.cache_client 源代码

# Copyright 2019-2021 Huawei Technologies Co., Ltd
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# Licensed under the Apache License, Version 2.0 (the "License");
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# http://www.apache.org/licenses/LICENSE-2.0
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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


[文档]class DatasetCache: r""" A client to interface with tensor caching service. For details, please check `Tutorial <https://www.mindspore.cn/tutorials/experts/en/r2.3.0rc2/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 subprocess >>> import mindspore.dataset as ds >>> >>> # Create a cache instance with command line `cache_admin --start` and create a session with `cache_admin -g` >>> # After creating cache with a valid session, get session id with command `cache_admin --list_sessions` >>> command = "cache_admin --list_sessions | tail -1 | awk -F ' ' '{{print $1;}}'" >>> session_id = subprocess.getoutput(command).split('\n')[-1] >>> some_cache = ds.DatasetCache(session_id=int(session_id), size=0) >>> >>> dataset_dir = "/path/to/image_folder_dataset_directory" >>> dataset = 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)
[文档] def get_stat(self): r""" 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). Examples: >>> import os >>> import subprocess >>> import mindspore.dataset as ds >>> >>> # In example above, we created cache with a valid session id >>> command = "cache_admin --list_sessions | tail -1 | awk -F ' ' '{{print $1;}}'" >>> id = subprocess.getoutput(command).split('\n')[-1] >>> some_cache = ds.DatasetCache(session_id=int(id), size=0) >>> >>> # run the dataset pipeline to trigger cache >>> dataset = ds.ImageFolderDataset("/path/to/image_folder_dataset_directory", cache=some_cache) >>> data = list(dataset) >>> >>> # get status of cache >>> stat = some_cache.get_stat() >>> # Average cache size >>> cache_sz = stat.avg_cache_sz >>> # Number of rows cached in memory >>> num_mem_cached = stat.num_mem_cached >>> # Number of rows spilled to disk >>> num_disk_cached = stat.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