diff --git a/src/mindspore2022/mindspore/python/mindspore/dataset/engine/datasets_user_defined.py b/src/mindspore2022/mindspore/python/mindspore/dataset/engine/datasets_user_defined.py index a5cc35e5..900150b3 100644 --- a/src/mindspore2022/mindspore/python/mindspore/dataset/engine/datasets_user_defined.py +++ b/src/mindspore2022/mindspore/python/mindspore/dataset/engine/datasets_user_defined.py @@ -452,11 +452,14 @@ class _GeneratorWorkerMp(multiprocessing.Process): """ def __init__(self, dataset, eof, max_rowsize, queue_size, ppid): + # 初始化一个多进程队列，用于存储索引 self.idx_queue = multiprocessing.Queue(queue_size) + # 如果启用了共享内存，则初始化一个共享队列，否则初始化一个多进程队列 if get_enable_shared_mem(): self.res_queue = _SharedQueue(queue_size, max_rowsize=max_rowsize) else: self.res_queue = multiprocessing.Queue(queue_size) + # 设置队列的_joincancelled属性为True，表示在进程退出时，队列不会阻塞 self.idx_queue._joincancelled = True # pylint: disable=W0212 self.res_queue._joincancelled = True # pylint: disable=W0212 super().__init__(target=_generator_worker_loop, args=(dataset, self.idx_queue, self.res_queue, eof, True, ppid)) @@ -465,6 +468,7 @@ class _GeneratorWorkerMp(multiprocessing.Process): """ Put function for worker index queue. Never block. Raise queue.Full on failure. """ + # 将item放入idx_queue队列中，不阻塞，如果失败则抛出queue.Full异常 self.idx_queue.put_nowait(item) def get(self): @@ -476,12 +480,19 @@ class _GeneratorWorkerMp(multiprocessing.Process): return self.res_queue.get(timeout=30) def queue_empty(self): + # 检查idx_queue是否为空 if not self.idx_queue.empty(): + # 如果不为空，记录警告日志 logger.warning("idx_queue is not empty.") + # 返回False return False + # 检查res_queue是否为空 if not self.res_queue.empty(): + # 如果不为空，记录警告日志 logger.warning("res_queue is not empty.") + # 返回False return False + # 如果两个队列都为空，返回True return True def __del__(self): @@ -632,14 +643,17 @@ class GeneratorDataset(MappableDataset, UnionBaseDataset): def __init__(self, source, column_names=None, column_types=None, schema=None, num_samples=None, num_parallel_workers=1, shuffle=None, sampler=None, num_shards=None, shard_id=None, python_multiprocessing=True, max_rowsize=6): + # 调用父类的初始化方法 super().__init__(num_parallel_workers=num_parallel_workers, sampler=sampler, num_samples=num_samples, shuffle=shuffle, num_shards=num_shards, shard_id=shard_id) + # 如果source是zip类型，则将其转换为列表 if isinstance(source, builtins.zip): # Although zip is iteratable, it does not have the feature of repeated iteration, so pass it to the array. self.source = [item for item in source] else: self.source = source self.prepared_source = None # source to be sent to C++ + # 如果self.operator_mixed属性为True，则将num_parallel_workers设置为1 if hasattr(self, 'operator_mixed') and getattr(self, 'operator_mixed') is True: self.num_parallel_workers = 1 logger.warning( @@ -650,56 +664,78 @@ class GeneratorDataset(MappableDataset, UnionBaseDataset): self.python_multiprocessing = python_multiprocessing + # 将column_names转换为列表 self.column_names = to_list(column_names) + # 如果column_types不为空，则将其转换为detypelist类型 if column_types is not None: self.column_types = mstypelist_to_detypelist(column_types) else: self.column_types = [] self.schema = schema + # 如果schema不为空，则将其转换为Schema类型 if schema is not None: + # 如果schema不为空，则将其赋值给self.schema self.schema = schema + # 如果schema不是Schema类型，则将其转换为Schema类型 if not isinstance(schema, Schema): self.schema = Schema(schema) # Move get dataset_size by len from parse to here, because self.source will # lose attribution of '__len__' after deepcopy. self.source_len = -1 # unknown + # 如果self.source有__len__属性，则获取self.source的长度 if hasattr(self.source, "__len__"): self.source_len = len(self.source) + # 设置最大行大小 self.max_rowsize = max_rowsize + # 设置采样函数为None self.sample_fn = None def __deepcopy__(self, memodict): + # 深度复制当前对象，并传入一个字典memodict，用于存储已经复制的对象 if id(self) in memodict: + # 如果当前对象的id已经在memodict中，则直接返回该对象 return memodict[id(self)] + # 否则，调用__safe_deepcopy__方法进行深度复制，并传入memodict和exclude参数 new_op = self.__safe_deepcopy__(memodict, exclude=("source", "__transfer_dataset__")) sample_fn = None + # 如果新对象的sampler属性不为空，并且self.source对象具有__getitem__方法 if new_op.sampler is not None and hasattr(self.source, "__getitem__"): # The reason why there is a try catch here is because when the new op is being constructed with shared # memory enabled, there will be an exception thrown if there is not enough shared memory available + # 如果self.source_len为-1，则抛出RuntimeError异常，因为尝试构造一个随机访问的数据集，需要__len__方法 if self.source_len == -1: raise RuntimeError("Attempt to construct a random access dataset, '__len__' method is required!") try: + # 如果新对象的num_parallel_workers大于1，则调用__validate_memory_usage方法进行内存使用验证 if new_op.num_parallel_workers > 1: self.__validate_memory_usage() + # 创建一个SamplerFn对象，用于并行采样 sample_fn = SamplerFn(self.source, new_op.num_parallel_workers, self.python_multiprocessing, self.max_rowsize) + # 将新对象的prepared_source属性设置为_cpp_sampler_fn_mp函数，用于并行采样 new_op.prepared_source = (lambda sample_ids: _cpp_sampler_fn_mp(sample_ids, sample_fn)) else: + # 否则，将新对象的prepared_source属性设置为_cpp_sampler_fn函数，用于单线程采样 new_op.prepared_source = (lambda sample_ids: _cpp_sampler_fn(sample_ids, self.source)) + # 将新对象的sample_fn属性设置为sample_fn new_op.sample_fn = sample_fn except RuntimeError as e: + # 如果抛出RuntimeError异常，则抛出Exception异常，并传入异常信息 raise Exception(str(e)) else: try: + # 否则，将新对象的sampler属性设置为None，sample_fn属性设置为sample_fn new_op.sampler = None new_op.sample_fn = sample_fn + # 将新对象的source_len属性设置为min(new_op.source_len, new_op.num_samples)，如果new_op.num_samples不为0，否则设置为new_op.source_len new_op.source_len = min(new_op.source_len, new_op.num_samples) if new_op.num_samples != 0 else new_op.source_len + # 遍历self.source对象 iter(self.source) except TypeError: # Use generator function if input callable @@ -711,19 +747,26 @@ class GeneratorDataset(MappableDataset, UnionBaseDataset): return new_op + # 判断是否被洗牌 def is_shuffled(self): return self.sampler.is_shuffled() + # 判断是否被分片 def is_sharded(self): return self.sampler.is_sharded() + # 解析 def parse(self, children=None): + # 如果schema为空，则返回GeneratorNode对象 if self.schema is None: return cde.GeneratorNode(self.prepared_source, self.column_names, self.column_types, self.source_len, self.sampler, self.num_parallel_workers) + # 获取schema schema = self.schema + # 如果schema是Schema类型，则获取cpp_schema if isinstance(schema, Schema): schema = self.schema.cpp_schema + # 返回GeneratorNode对象 return cde.GeneratorNode(self.prepared_source, schema, self.source_len, self.sampler, self.num_parallel_workers) @@ -735,24 +778,37 @@ class GeneratorDataset(MappableDataset, UnionBaseDataset): # if use num_parallel_workers is to large when python_multiprocessing=True which would cause # OOM error get the num_shards valid_num_shards = 1 + # 判断self.sampler是否为samplers.DistributedSampler类型 if isinstance(self.sampler, samplers.DistributedSampler): + # 如果是，则将self.sampler的num_shards赋值给valid_num_shards valid_num_shards = self.sampler.num_shards + # 否则，判断self.num_shards是否为None elif self.num_shards is not None: + # 如果不是，则将self.num_shards赋值给valid_num_shards valid_num_shards = self.num_shards # get process memory usage + # 获取当前进程 process = psutil.Process(os.getpid()) + # 获取当前进程的内存信息 process_memory = process.memory_info().rss + # 获取系统内存的空闲量 sys_memory_free = psutil.virtual_memory().free + # 计算可能使用的总内存量 total_memory_maybe_used = process_memory * self.num_parallel_workers * valid_num_shards + # 如果总内存可能使用的内存量除以系统可用内存大于0.85 if total_memory_maybe_used / sys_memory_free > 0.85: + # 计算有效的worker数量，即系统可用内存乘以0.85除以有效的shards数量再除以每个进程的内存 valid_num_worker = math.floor(sys_memory_free * 0.85 / valid_num_shards / process_memory) + # 如果有效的worker数量小于等于0，则将其设置为1 valid_num_worker = 1 if valid_num_worker <= 0 else valid_num_worker + # 构造警告信息，提示用户num_parallel_workers设置过大，可能会导致内存占用过高或OOM，建议将其减小到valid_num_worker或更小 info = "GeneratorDataset's num_parallel_workers: {} is too large which may cause a lot of memory " \ "occupation (>85%) or out of memory(OOM) during multiprocessing. Therefore, it is recommended " \ "to reduce num_parallel_workers to {} or smaller.".format(self.num_parallel_workers, valid_num_worker) + # 打印警告信息 logger.warning(info) @@ -764,37 +820,55 @@ class _NumpySlicesDataset: def __init__(self, data, column_list=None): self.column_list = None # Convert dict data into tuple + # 判断data是否为字典类型 if isinstance(data, dict): + # 如果是字典类型，则调用process_dict方法处理 data = self.process_dict(data) + # 判断data是否为元组类型 if isinstance(data, tuple): + # 如果是元组类型，则将self.data初始化为空元组 self.data = () + # 获取data的长度 data_len = len(data) + # 遍历data中的每个元素 for i in range(data_len): + # 将data中的每个元素转换为numpy数组，并添加到self.data中 self.data = self.data + (np.array(data[i]),) else: + # 如果data不是元组类型，则将data转换为numpy数组，并添加到self.data中 self.data = (np.array(data),) # check whether the data length in each column is equal + # 获取每个data_item的长度 data_len = [len(data_item) for data_item in self.data] + # 如果每个data_item的长度不相等，则抛出ValueError异常 if data_len[1:] != data_len[:-1]: raise ValueError("Data length in each column is not equal.") # Init column_name + # 如果column_list不为空，则将self.column_list赋值为column_list if column_list is not None: self.column_list = column_list + # 如果self.column_list为空，则将self.column_list赋值为空列表 elif self.column_list is None: self.column_list = [] + # 获取data的列数 column_num = len(self.data) + # 遍历列数，将"column_" + str(i)添加到self.column_list中 for i in range(column_num): self.column_list.append("column_" + str(i)) def __getitem__(self, index): + # 获取指定索引的数据行 data_row = [d[index, ...] for d in self.data] + # 将数据行转换为元组 data_res = tuple(data_row) + # 返回数据行 return data_res def __len__(self): + # 返回data的第一个元素的长度 return len(self.data[0]) def process_dict(self, input_data): @@ -802,24 +876,29 @@ class _NumpySlicesDataset: Convert the dict like data into tuple format, when input is a tuple of dicts then compose it into a dict first. """ # Convert pandas like dict(has "values" column) into General dict + # 将pandas样式的字典（有"values"列）转换为通用字典 data_keys = list(input_data.keys()) + # 获取字典的第一个键对应的值 data_col = input_data[data_keys[0]] + # 如果值有values属性，则将其转换为通用字典 if hasattr(data_col, "values"): new_dict = {} for key in data_keys: + # 将字典中的键对应的值转换为列表 item1 = input_data.pop(key) new_dict[key] = item1.values + # 将转换后的字典赋值给input_data input_data = new_dict # Convert the data in dict into tuple - data = () - keys = list(input_data.keys()) - self.column_list = keys - for key in keys: - value = input_data[key] - data = data + (list(value),) + data = () # 初始化一个空元组 + keys = list(input_data.keys()) # 将输入数据的键转换为列表 + self.column_list = keys # 将键列表赋值给实例变量column_list + for key in keys: # 遍历键列表 + value = input_data[key] # 获取键对应的值 + data = data + (list(value),) # 将值转换为列表，并添加到元组中 - return data + return data # 返回元组 class NumpySlicesDataset(GeneratorDataset): @@ -909,7 +988,9 @@ class NumpySlicesDataset(GeneratorDataset): @check_numpyslicesdataset def __init__(self, data, column_names=None, num_samples=None, num_parallel_workers=1, shuffle=None, sampler=None, num_shards=None, shard_id=None): + # 创建一个_NumpySlicesDataset对象，传入data和column_names参数 dataset = _NumpySlicesDataset(data, column_names) + # 调用父类的__init__方法，传入dataset、column_names、num_samples、num_parallel_workers、shuffle、sampler、num_shards和shard_id参数 super().__init__(dataset, column_names=dataset.column_list, num_samples=num_samples, num_parallel_workers=num_parallel_workers, shuffle=shuffle, sampler=sampler, num_shards=num_shards, shard_id=shard_id) diff --git a/src/mindspore2022/mindspore/python/mindspore/train/dataset_helper.py b/src/mindspore2022/mindspore/python/mindspore/train/dataset_helper.py index 7dc22fe6..c026669c 100644 --- a/src/mindspore2022/mindspore/python/mindspore/train/dataset_helper.py +++ b/src/mindspore2022/mindspore/python/mindspore/train/dataset_helper.py @@ -256,36 +256,53 @@ class DatasetHelper: """ def __init__(self, dataset, dataset_sink_mode=True, sink_size=-1, epoch_num=1): + # 检查dataset_sink_mode是否为布尔值 dataset_sink_mode = Validator.check_bool(dataset_sink_mode) + # 检查sink_size是否为整数 Validator.check_is_int(sink_size) + # 如果sink_size小于-1或者等于0，抛出异常 if sink_size < -1 or sink_size == 0: raise ValueError("The 'sink_size' must be -1 or positive, but got sink_size {}.".format(sink_size)) + # 如果sink_size等于-1，则将其设置为dataset的dataset_size if sink_size == -1: sink_size = dataset.get_dataset_size() + # 如果dataset_sink_mode为True，则根据不同的设备类型选择不同的迭代器 if dataset_sink_mode: + # 如果启用了GE，则使用GE的迭代器 if context.get_context("enable_ge"): iterclass = _DatasetIterGE else: + # 如果当前模式为GRAPH_MODE，则根据角色选择不同的迭代器 if context.get_context("mode") == context.GRAPH_MODE: + # 如果当前角色为调度器或者参数服务器，则使用参数服务器的迭代器 if _is_role_sched() or _is_role_pserver(): iterclass = _DatasetIterPSServer + # 如果当前角色为工作节点并且是参数服务器模式，则使用参数服务器工作节点的迭代器 elif _is_role_worker() and _is_ps_mode(): iterclass = _DatasetIterPSWork + # 如果当前设备类型为Ascend或者GPU，则使用多线程循环的迭代器 elif (context.get_context("device_target") == "Ascend") or \ (context.get_context("device_target") == "GPU"): iterclass = _DatasetIterMSLoopSink + # 如果当前设备类型为CPU，则抛出异常，因为CPU不支持数据集下沉模式 elif context.get_context("device_target") == "CPU": raise RuntimeError("Currently dataset sink mode is not supported when the device " "target is CPU, please set dataset sink mode to False.") + # 如果当前模式不是GRAPH_MODE，则使用PyNative的迭代器 else: iterclass = _DatasetIterPyNative + # 创建迭代器 self.iter = iterclass(dataset, sink_size, epoch_num) + # 如果dataset_sink_mode为False，则使用普通的迭代器 else: + # 如果不是分布式训练，则使用_DatasetIterNormal类 iterclass = _DatasetIterNormal + # 初始化迭代器 self.iter = iterclass(dataset, epoch_num=epoch_num) def __iter__(self): + # 返回self.iter的迭代器 return self.iter.__iter__() # A temp solution for loop sink. Delete later @@ -301,6 +318,7 @@ class DatasetHelper: >>> >>> types, shapes = dataset_helper.types_shapes() """ + # 从当前配置的dataset中获取类型和形状 return self.iter.types_shapes() def sink_size(self): @@ -316,18 +334,22 @@ class DatasetHelper: >>> # if sink_size==-1, then will return the full size of source dataset. >>> sink_size = dataset_helper.sink_size() """ + # 返回迭代器的接收缓冲区大小 return self.iter.get_sink_size() def stop_send(self): """Stop send data about data sink.""" + # 停止发送关于数据接收器的数据 self.iter.stop_send() def release(self): """Free up resources about data sink.""" + # 释放数据接收器的资源 self.iter.release() def continue_send(self): """Continue to send data to device at the beginning of epoch.""" + # 在每个epoch的开始处继续向设备发送数据 self.iter.continue_send() def _reset(self, step): @@ -339,6 +361,7 @@ class DatasetHelper: In sink mode, it returns the types and shapes of the current data. Generally, it works in dynamic shape scenarios. """ + # 返回迭代器的数据信息 return self.iter.get_data_info() def dynamic_min_max_shapes(self): @@ -355,6 +378,7 @@ class DatasetHelper: >>> >>> min_shapes, max_shapes = dataset_helper.dynamic_min_max_shapes() """ + # 返回self.iter的dynamic_min_max_shapes方法 return self.iter.dynamic_min_max_shapes() @@ -362,20 +386,27 @@ class _DatasetIter: """Base iter for dataset helper""" def __init__(self, dataset, sink_size, epoch_num): + # 初始化函数，传入数据集、sink大小和epoch数量 self.dataset = dataset self.sink_size = sink_size self.sink_count = self.get_sink_count(dataset) + # 如果数据集没有__transfer_dataset__属性 if not hasattr(dataset, '__transfer_dataset__'): + # 如果数据集有__loop_size__属性 if hasattr(dataset, '__loop_size__'): # PS mode does not support loop sink and need get the real sink size. + # 如果不是worker角色或者不是ps模式，则设置sink_size为dataset的循环大小 if not (_is_role_worker() and _is_ps_mode()): self.sink_size = dataset.__loop_size__ + # 如果sink_size为1，sink_count为1，dataset的大小不为1，并且设备目标为Ascend，则创建数据信息队列 create_data_info_queue = (sink_size == 1 and self.sink_count == 1 and dataset.get_dataset_size() != 1 and context.get_context("device_target") == "Ascend") + # 执行数据图，并将sink_size和create_data_info_queue作为参数传入 dataset.__transfer_dataset__ = _exec_datagraph(dataset, self.sink_size, create_data_info_queue=create_data_info_queue) + # 如果dataset没有__no_send__属性，则发送数据 if not hasattr(dataset, '__no_send__'): _send_data(dataset, epoch_num) else: @@ -384,33 +415,48 @@ class _DatasetIter: _cell_graph_executor.set_queue_name(dataset.__transfer_dataset__.queue_name) _send_data_no_flag(dataset, epoch_num) + # 获取dataset的stop_send方法 self.stop_send = dataset.__transfer_dataset__.stop_send + # 获取dataset的release方法 self.release = dataset.__transfer_dataset__.release + # 获取dataset的continue_send方法 self.continue_send = dataset.__transfer_dataset__.continue_send + # 获取dataset的get_data_info方法 self.get_data_info = dataset.__transfer_dataset__.get_data_info + # 获取dataset的dynamic_min_max_shapes属性 self.dynamic_min_max_shapes = dataset.dynamic_min_max_shapes + # 获取dataset的数据类型和数据形状 self.dataset_types, self.dataset_shapes = _get_types_and_shapes(dataset) + # 如果dataset的__transfer_dataset__属性中有_reset方法，则获取该_reset方法 if hasattr(dataset.__transfer_dataset__, "_reset"): self._reset = dataset.__transfer_dataset__._reset # pylint: disable=W0212 def __iter__(self): + # 初始化索引为0 self.index = 0 + # 返回self return self - + # 迭代器的下一项 def __next__(self): + # 如果索引大于等于sink_count，抛出StopIteration异常 if self.index >= self.sink_count: raise StopIteration() + # 索引加1 self.index += 1 + # 返回op()的返回值 return self.op() def types_shapes(self): """ - Return the types and shapes of the dataset. The type and shape of each data in the dataset - should be consistent. + 返回数据集的类型和形状。数据集中每个数据的类型和形状应该是一致的。 """ return self.dataset_types, self.dataset_shapes - def get_sink_count(self, dataset): + """ + 获取数据集的sink次数 + :param dataset: 数据集对象 + :return: sink次数 + """ sink_count = 1 if hasattr(dataset, '__loop_size__'): loop_size = dataset.__loop_size__ @@ -421,7 +467,10 @@ class _DatasetIter: return sink_count def get_sink_size(self): - """get sink_size to device""" + """ + 获取设备的sink大小 + :return: sink大小 + """ sink_size = 1 if hasattr(self.dataset, '__loop_size__'): sink_size = self.dataset.__loop_size__ diff --git a/src/mindspore2022/setup.py b/src/mindspore2022/setup.py index 092a4f28..26b61f63 100644 --- a/src/mindspore2022/setup.py +++ b/src/mindspore2022/setup.py @@ -75,6 +75,7 @@ def _write_device_target(file): def build_dependencies(): """generate python file""" + # 生成version.py文件 version_file = os.path.join(pkg_dir, 'mindspore', 'version.py') with open(version_file, 'w') as f: _write_version(f) @@ -83,6 +84,7 @@ def build_dependencies(): with open(version_file, 'w') as f: _write_version(f) + # 生成default_config.py文件 config_file = os.path.join(pkg_dir, 'mindspore', 'default_config.py') with open(config_file, 'w') as f: _write_config(f) @@ -91,6 +93,7 @@ def build_dependencies(): with open(config_file, 'w') as f: _write_config(f) + # 向default_config.py文件中追加device_target target = os.path.join(pkg_dir, 'mindspore', 'default_config.py') with open(target, 'a') as f: _write_device_target(f) @@ -99,6 +102,7 @@ def build_dependencies(): with open(target, 'a') as f: _write_device_target(f) + # 向default_config.py文件中追加package_name package_info = os.path.join(pkg_dir, 'mindspore', 'default_config.py') with open(package_info, 'a') as f: _write_package_name(f) @@ -107,6 +111,7 @@ def build_dependencies(): with open(package_info, 'a') as f: _write_package_name(f) + # 生成.commit_id文件 commit_file = os.path.join(pkg_dir, 'mindspore', '.commit_id') with open(commit_file, 'w') as f: _write_commit_file(f) @@ -156,16 +161,24 @@ def update_permissions(path): Args: path (str): Target directory path. """ + # 判断操作系统是否为Windows if platform.system() == "Windows": return + # 遍历目标目录下的所有文件和文件夹 for dirpath, dirnames, filenames in os.walk(path): + # 遍历文件夹 for dirname in dirnames: + # 获取文件夹的完整路径 dir_fullpath = os.path.join(dirpath, dirname) + # 更新文件夹的权限 os.chmod(dir_fullpath, stat.S_IREAD | stat.S_IWRITE | stat.S_IEXEC | stat.S_IRGRP | stat.S_IXGRP) + # 遍历文件 for filename in filenames: + # 获取文件的完整路径 file_fullpath = os.path.join(dirpath, filename) + # 更新文件的权限 os.chmod(file_fullpath, stat.S_IREAD) @@ -174,7 +187,9 @@ class EggInfo(egg_info): def run(self): super().run() + # 获取egg-info目录的路径 egg_info_dir = os.path.join(pkg_dir, 'mindspore.egg-info') + # 更新egg-info目录的权限 update_permissions(egg_info_dir) @@ -183,41 +198,64 @@ class BuildPy(build_py): def run(self): super().run() + # 获取build目录下的lib/mindspore目录的路径 mindspore_dir = os.path.join(pkg_dir, 'build', 'lib', 'mindspore') + # 更新lib/mindspore目录的权限 update_permissions(mindspore_dir) + # 获取build目录下的lib/mindspore/_akg目录的路径 mindspore_dir = os.path.join(pkg_dir, 'build', 'lib', 'mindspore', '_akg') + # 更新lib/mindspore/_akg目录的权限 update_permissions(mindspore_dir) +# 设置包的名称 setup( name=package_name, + # 设置包的版本 version=version, + # 设置包的作者 author='The MindSpore Authors', + # 设置包的作者邮箱 author_email='contact@mindspore.cn', + # 设置包的网址 url='https://www.mindspore.cn', + # 设置包的下载网址 download_url='https://github.com/mindspore-ai/mindspore/tags', + # 设置包的源代码网址 project_urls={ 'Sources': 'https://github.com/mindspore-ai/mindspore', + # 设置包的问题追踪网址 'Issue Tracker': 'https://github.com/mindspore-ai/mindspore/issues', }, + # 设置包的描述 description='MindSpore is a new open source deep learning training/inference ' 'framework that could be used for mobile, edge and cloud scenarios.', + # 读取readme文件作为包的详细描述 long_description=readme, + # 设置详细描述的格式 long_description_content_type="text/markdown", + # 查找包中的所有模块 packages=find_packages(), + # 设置包的数据 package_data=package_data, + # 包含包中的所有数据 include_package_data=True, + # 设置自定义的命令类 cmdclass={ 'egg_info': EggInfo, 'build_py': BuildPy, }, + # 设置包的入口点 entry_points={ 'console_scripts': [ 'cache_admin=mindspore.dataset.engine.cache_admin:main', ], }, + # 设置包的Python版本要求 python_requires='>=3.7', + # 设置包的依赖 install_requires=required_package, + # 设置包的分类器 classifiers=[ 'Development Status :: 4 - Beta', 'Environment :: Console', @@ -234,6 +272,8 @@ setup( 'Topic :: Software Development :: Libraries', 'Topic :: Software Development :: Libraries :: Python Modules', ], + # 设置包的许可证 license='Apache 2.0', + # 设置包的关键词 keywords='mindspore machine learning', )