class DatasetCache:
"""
A client to interface with tensor caching service
"""
def __init__(self,
session_id=None,
size=0,
spilling=False,
hostname=None,
port=None,
num_connections=None,
prefetch_size=None):
check_uint32(session_id, "session_id")
check_uint64(size, "size")
type_check(spilling, (bool, ), "spilling")
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
if os.getenv('MS_ENABLE_CACHE') != 'TRUE':
# temporary disable cache feature in the current release
self.cache_client = None
else:
from mindspore._c_dataengine import CacheClient
self.cache_client = CacheClient(session_id, size, spilling,
hostname, port, num_connections,
prefetch_size)
def GetStat(self):
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
class DatasetCache:
"""
A client to interface with tensor caching service
"""
def __init__(self,
session_id=None,
size=0,
spilling=False,
hostname=None,
port=None,
prefetch_size=20):
check_uint32(session_id, "session_id")
check_uint64(size, "size")
type_check(spilling, (bool, ), "spilling")
check_uint32(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.cache_client = CacheClient(session_id, size, spilling, hostname,
port, prefetch_size)
def GetStat(self):
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.cache_client = self.cache_client
return new_cache
class DatasetCache:
"""
A client to interface with tensor caching service.
For details, please check `Chinese tutorial <https://www.mindspore.cn/tutorial/training/zh-CN/master/advanced_use/enable_cache.html>`_,
`Chinese programming guide <https://www.mindspore.cn/doc/programming_guide/zh-CN/master/cache.html?highlight=datasetcache>`_.
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="127.0.0.1").
port (int, optional): Port to connect to server (default=50052).
num_connections (int, optional): Number of tcp/ip connections (default=12).
prefetch_size (int, optional): Prefetch size (default=20).
"""
def __init__(self,
session_id,
size=0,
spilling=False,
hostname=None,
port=None,
num_connections=None,
prefetch_size=None):
check_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_uint32(num_connections, "num_connections")
if prefetch_size is not None:
check_uint32(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)
def GetStat(self):
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
class DatasetCache:
"""
A client to interface with tensor caching service.
For details, please check `Tutorial <https://www.mindspore.cn/tutorial/training/en/master/advanced_use/
enable_cache.html>`_, `Programming guide <https://www.mindspore.cn/doc/programming_guide/en/master/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="127.0.0.1").
port (int, optional): Port to connect to server (default=50052).
num_connections (int, optional): Number of tcp/ip connections (default=12).
prefetch_size (int, optional): Prefetch size (default=20).
Examples:
>>> import mindspore.dataset as ds
>>>
>>> # create a cache instance, in which session_id is generated from command line `cache_admin -g`
>>> some_cache = ds.DatasetCache(session_id=session_id, size=0)
>>>
>>> dataset_dir = "path/to/imagefolder_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)
def get_stat(self):
"""Get the statistics from a cache."""
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