5.3 torch_utils.py try:importthop# for FLOPs computationexceptImportError:thop=NoneLOGGER=logging.getLogger(__name__)@contextmanagerdeftorch_distributed_zero_first(local_rank:int):"""Decorator to make all processes in distributed training wait for each local_master to do something."""iflocal_rank...
local_rank:进程内 GPU 编号,非显式参数,由 torch.distributed.launch 内部指定。比方说, rank=3,local_rank=0 表示第 3 个进程内的第 1 块 GPU。 注意初始化rank和world_size 你需要确保, 不同机器的rank值不同, 但是主机的rank必须为0, 而且使用init_method的ip一定是rank为0的主机, 其次world_size是...
ERROR:torch.distributed.elastic.multiprocessing.api:failed (exitcode: 1) local_rank: 0 (pid: 2145) of binary: /home/mapengsen/anaconda3/envs/unimol37/bin/python return launch_agent(self._config, self._entrypoint, list(args)) File "/home/mapengsen/anaconda3/envs/unimol37/lib/python3.7/...
import pandas as pd # 创建一个示例数据表 data = {'Name': ['Alice', 'Bob', 'Charlie', 'David'], 'Score': [85, 92, 78, 90]} df = pd.DataFrame(data) # 对Score列进行排名 df['Rank'] = df['Score'].rank() print(df) ...
``` # Python script for text summarization using NLP techniques # Your code here to read the text data and preprocess it (e.g., removing stop words) # Your code here to generate the summary using techniques like TF-IDF, TextRank, or BERT``` 说明: 文本摘要自动执行为冗长的文本文档创建...
``` # Python script for text summarization using NLP techniques # Your code here to read the text data and preprocess it (e.g., removing stop words) # Your code here to generate the summary using techniques like TF-IDF, TextRank, or BERT``` 说明: 文本摘要自动执行为冗长的文本文档创建...
Card(rank='4', suit='spades') ... 我们还可以反向迭代牌组: >>>forcardinreversed(deck):# doctest: +ELLIPSIS...print(card) Card(rank='A', suit='hearts') Card(rank='K', suit='hearts') Card(rank='Q', suit='hearts') ... ...
<rank updated="yes">69</rank> <year>2011</year> <gdppc>13600</gdppc> <neighbor name="Costa Rica" direction="W"/> <neighbor name="Colombia" direction="E"/> </country> </data> xml数据 new_xml=ET.Element('namelist') #Element初始化一个namelist节点,获取一个对象new_xml ...
其中,首先从数据库中读取数据,将其中的文本内容进行合并,并作为输入传入LocalTextRank类中。在该类中,通过分词、构建节点以及构建矩阵等操作,最终使用TextRank算法计算出每个词的权重值,并将其存储在字典中。最后,将每个词及其相应的权重值输出到控制台,并将结果存储在Excel文件中。核心代码如图5.3所示:...
format(ADDR, PORT), rank=RANK, world_size=SIZE, ) local_rank = dist.get_rank() % torch.cuda.device_count() torch.cuda.set_device(local_rank) device = torch.device('cuda:%d' % local_rank) test_allreduce(dist, device) print('training done!') train() if __name__ == "__main_...