有没有免费网站制作seo关键词优化举例

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前言

当我们需要对大规模的数据向量化以存到向量数据库中时，且服务器上有多个GPU可以支配，我们希望同时利用所有的GPU来并行这一过程，加速向量化。

代码

就几行代码，不废话了

from sentence_transformers import SentenceTransformer#Important, you need to shield your code with if __name__. Otherwise, CUDA runs into issues when spawning new processes.
if __name__ == '__main__':#Create a large list of 100k sentencessentences = ["This is sentence {}".format(i) for i in range(100000)]#Define the modelmodel = SentenceTransformer('all-MiniLM-L6-v2')#Start the multi-process pool on all available CUDA devicespool = model.start_multi_process_pool()#Compute the embeddings using the multi-process poolemb = model.encode_multi_process(sentences, pool)print("Embeddings computed. Shape:", emb.shape)#Optional: Stop the proccesses in the poolmodel.stop_multi_process_pool(pool)

注意：一定要加if __name__ == '__main__':这一句，不然报如下错：

RuntimeError: An attempt has been made to start a new process before thecurrent process has finished its bootstrapping phase.This probably means that you are not using fork to start yourchild processes and you have forgotten to use the proper idiomin the main module:if __name__ == '__main__':freeze_support()...The "freeze_support()" line can be omitted if the programis not going to be frozen to produce an executable.

其实官方已经给出代码啦，我只不过复制粘贴了一下，代码位置：computing_embeddings_multi_gpu.py

官方还给出了流式encode的例子，也是多GPU并行的，如下：

from sentence_transformers import SentenceTransformer, LoggingHandler
import logging
from datasets import load_dataset
from torch.utils.data import DataLoader
from tqdm import tqdmlogging.basicConfig(format='%(asctime)s - %(message)s',datefmt='%Y-%m-%d %H:%M:%S',level=logging.INFO,handlers=[LoggingHandler()])#Important, you need to shield your code with if __name__. Otherwise, CUDA runs into issues when spawning new processes.
if __name__ == '__main__':#Set paramsdata_stream_size = 16384  #Size of the data that is loaded into memory at oncechunk_size = 1024  #Size of the chunks that are sent to each processencode_batch_size = 128  #Batch size of the model#Load a large dataset in streaming mode. more info: https://huggingface.co/docs/datasets/streamdataset = load_dataset('yahoo_answers_topics', split='train', streaming=True)dataloader = DataLoader(dataset.with_format("torch"), batch_size=data_stream_size)#Define the modelmodel = SentenceTransformer('all-MiniLM-L6-v2')#Start the multi-process pool on all available CUDA devicespool = model.start_multi_process_pool()for i, batch in enumerate(tqdm(dataloader)):#Compute the embeddings using the multi-process poolsentences = batch['best_answer']batch_emb = model.encode_multi_process(sentences, pool, chunk_size=chunk_size, batch_size=encode_batch_size)print("Embeddings computed for 1 batch. Shape:", batch_emb.shape)#Optional: Stop the proccesses in the poolmodel.stop_multi_process_pool(pool)

官方案例：computing_embeddings_streaming.py

+-----------------------------------------------------------------------------+
| NVIDIA-SMI 515.105.01   Driver Version: 515.105.01   CUDA Version: 11.7     |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|                               |                      |               MIG M. |
|===============================+======================+======================|
|   0  NVIDIA A800-SXM...  On   | 00000000:23:00.0 Off |                    0 |
| N/A   58C    P0   297W / 400W |  75340MiB / 81920MiB |    100%      Default |
|                               |                      |             Disabled |
+-------------------------------+----------------------+----------------------+
|   1  NVIDIA A800-SXM...  On   | 00000000:29:00.0 Off |                    0 |
| N/A   71C    P0   352W / 400W |  80672MiB / 81920MiB |    100%      Default |
|                               |                      |             Disabled |
+-------------------------------+----------------------+----------------------+
|   2  NVIDIA A800-SXM...  On   | 00000000:52:00.0 Off |                    0 |
| N/A   68C    P0   398W / 400W |  75756MiB / 81920MiB |    100%      Default |
|                               |                      |             Disabled |
+-------------------------------+----------------------+----------------------+
|   3  NVIDIA A800-SXM...  On   | 00000000:57:00.0 Off |                    0 |
| N/A   58C    P0   341W / 400W |  75994MiB / 81920MiB |    100%      Default |
|                               |                      |             Disabled |
+-------------------------------+----------------------+----------------------+
|   4  NVIDIA A800-SXM...  On   | 00000000:8D:00.0 Off |                    0 |
| N/A   56C    P0   319W / 400W |  70084MiB / 81920MiB |    100%      Default |
|                               |                      |             Disabled |
+-------------------------------+----------------------+----------------------+
|   5  NVIDIA A800-SXM...  On   | 00000000:92:00.0 Off |                    0 |
| N/A   70C    P0   354W / 400W |  76314MiB / 81920MiB |    100%      Default |
|                               |                      |             Disabled |
+-------------------------------+----------------------+----------------------+
|   6  NVIDIA A800-SXM...  On   | 00000000:BF:00.0 Off |                    0 |
| N/A   73C    P0   360W / 400W |  75876MiB / 81920MiB |    100%      Default |
|                               |                      |             Disabled |
+-------------------------------+----------------------+----------------------+
|   7  NVIDIA A800-SXM...  On   | 00000000:C5:00.0 Off |                    0 |
| N/A   57C    P0   364W / 400W |  80404MiB / 81920MiB |    100%      Default |
|                               |                      |             Disabled |
+-------------------------------+----------------------+----------------------+

嘎嘎快啊