\u5982\u679c\u4f60\u6b63\u5728\u5bfb\u627e\u4e00\u4e2a\u80fd\u7cbe\u51c6\u7406\u89e3\u4e2d\u6587\u53e5\u5b50\u542b\u4e49，\u5e76\u80fd\u5feb\u901f\u8ba1\u7b97\u5b83\u4eec\u4e4b\u95f4\u76f8\u4f3c\u5ea6\u7684\u5de5\u5177，\u90a3\u4e48StructBERT\u4e2d\u6587\u53e5\u5411\u91cf\u5de5\u5177\u7edd\u5bf9\u503c\u5f97\u4f60\u6df1\u5165\u4e86\u89e3\u3002\u8fd9\u4e2a\u5de5\u5177\u57fa\u4e8e\u963f\u91cc\u8fbe\u6469\u9662\u5f00\u6e90\u7684\u5f3a\u5927\u6a21\u578b，\u80fd\u591f\u5c06\u4efb\u4f55\u4e2d\u6587\u53e5\u5b50\u8f6c\u6362\u6210\u4e00\u4e2a\u9ad8\u7ef4\u5ea6\u7684\u201c\u6570\u5b57\u6307\u7eb9\u201d（\u6211\u4eec\u79f0\u4e4b\u4e3a\u5411\u91cf），\u7136\u540e\u901a\u8fc7\u8ba1\u7b97\u8fd9\u4e9b\u6307\u7eb9\u7684\u76f8\u4f3c\u5ea6，\u6765\u5224\u65ad\u4e24\u4e2a\u53e5\u5b50\u5728\u610f\u601d\u4e0a\u6709\u591a\u63a5\u8fd1\u3002<\/p>\n
\u60f3\u8c61\u4e00\u4e0b，\u4f60\u9700\u8981\u4ece\u6d77\u91cf\u6587\u6863\u4e2d\u627e\u51fa\u610f\u601d\u76f8\u8fd1\u7684\u53e5\u5b50，\u6216\u8005\u4e3a\u667a\u80fd\u5ba2\u670d\u7cfb\u7edf\u5339\u914d\u6700\u5408\u9002\u7684\u7b54\u6848，\u53c8\u6216\u8005\u53ea\u662f\u60f3\u68c0\u67e5\u4e24\u6bb5\u6587\u672c\u662f\u5426\u5728\u8bf4\u540c\u4e00\u4ef6\u4e8b\u3002\u624b\u52a8\u53bb\u505a\u8fd9\u4e9b\u5de5\u4f5c\u4e0d\u4ec5\u8017\u65f6，\u800c\u4e14\u5bb9\u6613\u51fa\u9519\u3002StructBERT\u5de5\u5177\u5c31\u662f\u4e3a\u4e86\u89e3\u51b3\u8fd9\u4e9b\u95ee\u9898\u800c\u751f\u7684，\u5b83\u80fd\u81ea\u52a8\u5316\u3001\u9ad8\u7cbe\u5ea6\u5730\u5b8c\u6210\u8bed\u4e49\u5339\u914d\u4efb\u52a1\u3002<\/p>\n
\u672c\u6559\u7a0b\u5c06\u5e26\u4f60\u8d70\u4e00\u6761\u66f4\u4e13\u4e1a\u3001\u66f4\u9ad8\u6548\u7684\u90e8\u7f72\u4e4b\u8def：\u5c06StructBERT\u6a21\u578b\u90e8\u7f72\u5230NVIDIA Triton\u63a8\u7406\u670d\u52a1\u5668\u4e0a\u3002\u4e0e\u76f4\u63a5\u8fd0\u884cPython\u811a\u672c\u76f8\u6bd4，Triton\u670d\u52a1\u5668\u80fd\u63d0\u4f9b\u66f4\u7a33\u5b9a\u7684\u670d\u52a1\u3001\u66f4\u9ad8\u7684\u5e76\u53d1\u5904\u7406\u80fd\u529b，\u4ee5\u53ca\u66f4\u4fbf\u6377\u7684\u6a21\u578b\u7248\u672c\u7ba1\u7406\u3002\u65e0\u8bba\u4f60\u662f\u60f3\u642d\u5efa\u4e00\u4e2a\u4f9b\u56e2\u961f\u5185\u90e8\u4f7f\u7528\u7684\u8bed\u4e49\u641c\u7d22\u670d\u52a1，\u8fd8\u662f\u60f3\u5c06\u5176\u96c6\u6210\u5230\u66f4\u590d\u6742\u7684\u5e94\u7528\u6d41\u6c34\u7ebf\u4e2d，\u8fd9\u5957\u65b9\u6848\u90fd\u80fd\u6ee1\u8db3\u4f60\u7684\u9700\u6c42\u3002\u63a5\u4e0b\u6765，\u6211\u4eec\u5c31\u4ece\u96f6\u5f00\u59cb，\u4e00\u6b65\u6b65\u5b8c\u6210\u90e8\u7f72\u3002<\/p>\n
\u5728\u5f00\u59cb\u90e8\u7f72\u4e4b\u524d，\u6211\u4eec\u9700\u8981\u51c6\u5907\u597d\u8fd0\u884c\u73af\u5883，\u5e76\u83b7\u53d6\u5230\u6838\u5fc3\u7684\u6a21\u578b\u6587\u4ef6\u3002\u8fd9\u4e2a\u8fc7\u7a0b\u5c31\u50cf\u76d6\u623f\u5b50\u524d\u8981\u6253\u597d\u5730\u57fa\u548c\u51c6\u5907\u597d\u7816\u74e6\u3002<\/p>\n
\u9996\u5148，\u786e\u4fdd\u4f60\u7684\u670d\u52a1\u5668\u6216\u672c\u5730\u5f00\u53d1\u73af\u5883\u6ee1\u8db3\u4ee5\u4e0b\u57fa\u672c\u8981\u6c42：<\/p>\n
\u4f60\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u547d\u4ee4\u5feb\u901f\u5b89\u88c5Docker（Ubuntu\u7cfb\u7edf）：<\/p>\n
sudo apt-get update
\nsudo apt-get install docker.io
\nsudo systemctl start docker
\nsudo systemctl enable docker
\n# \u5c06\u5f53\u524d\u7528\u6237\u52a0\u5165docker\u7ec4，\u907f\u514d\u6bcf\u6b21\u4f7f\u7528sudo
\nsudo usermod -aG docker $USER
\n# \u9700\u8981\u91cd\u65b0\u767b\u5f55\u6216\u91cd\u542f\u4f7f\u66f4\u6539\u751f\u6548<\/p>\n
StructBERT\u6a21\u578b\u672c\u8eab\u53ef\u4ee5\u4eceHugging Face Model Hub\u83b7\u53d6\u3002\u4f46\u4e3a\u4e86\u65b9\u4fbf\u540e\u7eedTriton\u90e8\u7f72，\u6211\u4eec\u9700\u8981\u5c06\u5176\u8f6c\u6362\u4e3a\u7279\u5b9a\u7684\u683c\u5f0f\u3002\u8fd9\u91cc\u6709\u4e24\u79cd\u65b9\u5f0f：<\/p>\n
\u65b9\u5f0f\u4e00：\u4ece\u539f\u59cb\u4ed3\u5e93\u8f6c\u6362（\u63a8\u8350）<\/p>\n
\u65b9\u5f0f\u4e8c：\u4f7f\u7528\u9884\u8f6c\u6362\u7684\u6a21\u578b（\u5982\u679c\u53ef\u7528） \u6709\u65f6\u793e\u533a\u6216\u6a21\u578b\u63d0\u4f9b\u8005\u4f1a\u76f4\u63a5\u63d0\u4f9b\u9002\u7528\u4e8eTriton\u7684\u6a21\u578b\u5305\u3002\u4f60\u53ef\u4ee5\u641c\u7d22 nlp_structbert_sentence-similarity_chinese-large triton model repository \u6765\u5bfb\u627e\u3002\u5982\u679c\u627e\u5230，\u53ef\u4ee5\u8df3\u8fc7\u8f6c\u6362\u6b65\u9aa4\u3002<\/p>\n
\u4e3a\u4e86\u6559\u7a0b\u7684\u5b8c\u6574\u6027，\u6211\u4eec\u7b80\u8981\u4ecb\u7ecd\u8f6c\u6362\u7684\u6838\u5fc3\u601d\u8def\u3002\u4f60\u9700\u8981\u7f16\u5199\u4e00\u4e2aPython\u811a\u672c，\u7c7b\u4f3c\u4e0b\u9762\u8fd9\u6837：<\/p>\n
from transformers import AutoModel, AutoTokenizer
\nimport torch<\/p>\n
model_name = "iic\/nlp_structbert_sentence-similarity_chinese-large"
\nmodel = AutoModel.from_pretrained(model_name)
\ntokenizer = AutoTokenizer.from_pretrained(model_name)<\/p>\n
# \u4fdd\u5b58\u6a21\u578b\u6743\u91cd（\u4ec5state_dict）
\ntorch.save(model.state_dict(), "structbert_large.pth")<\/p>\n
# \u4fdd\u5b58\u5206\u8bcd\u5668\u548c\u914d\u7f6e
\ntokenizer.save_pretrained(".\/triton_model_tokenizer")
\nmodel.config.save_pretrained(".\/triton_model_config")<\/p>\n
\u8fd9\u4e2a\u811a\u672c\u4f1a\u5c06\u6a21\u578b\u6743\u91cd\u548c\u914d\u7f6e\u6587\u4ef6\u5206\u5f00\u4fdd\u5b58，\u8fd9\u662f\u4e3a\u540e\u7eed\u6b65\u9aa4\u505a\u51c6\u5907\u3002<\/p>\n
NVIDIA Triton\u63a8\u7406\u670d\u52a1\u5668\u901a\u8fc7\u4e00\u4e2a\u6e05\u6670\u7684\u76ee\u5f55\u7ed3\u6784\u6765\u7ba1\u7406\u6a21\u578b，\u8fd9\u4e2a\u7ed3\u6784\u53eb\u505a\u201c\u6a21\u578b\u4ed3\u5e93\u201d\u3002\u6211\u4eec\u7684\u76ee\u6807\u5c31\u662f\u6309\u7167Triton\u7684\u89c4\u5219，\u628aStructBERT\u6a21\u578b\u201c\u5305\u88c5\u201d\u597d，\u653e\u8fdb\u53bb\u3002<\/p>\n
\u4e00\u4e2a\u5178\u578b\u7684Triton\u6a21\u578b\u4ed3\u5e93\u76ee\u5f55\u7ed3\u6784\u5982\u4e0b：<\/p>\n
model_repository\/
\n\u251c\u2500\u2500 structbert_similarity\/ # \u4f60\u7684\u6a21\u578b\u540d\u79f0
\n\u2502 \u251c\u2500\u2500 1\/ # \u7248\u672c\u53f7，Triton\u53ef\u4ee5\u7ba1\u7406\u591a\u4e2a\u7248\u672c
\n\u2502 \u2502 \u2514\u2500\u2500 model.pt # \u6a21\u578b\u6743\u91cd\u6587\u4ef6（PyTorch\u683c\u5f0f）
\n\u2502 \u251c\u2500\u2500 config.pbtxt # \u6a21\u578b\u914d\u7f6e\u6587\u4ef6（\u6700\u91cd\u8981！）
\n\u2502 \u2514\u2500\u2500 tokenizer\/ # \u5206\u8bcd\u5668\u76f8\u5173\u6587\u4ef6（\u53ef\u9009，\u53ef\u5185\u5d4c\u5728\u4ee3\u7801\u4e2d）
\n\u2502 \u251c\u2500\u2500 tokenizer.json
\n\u2502 \u251c\u2500\u2500 vocab.txt
\n\u2502 \u2514\u2500\u2500 config.json<\/p>\n
config.pbtxt \u6587\u4ef6\u662f\u6838\u5fc3\u3002\u5bf9\u4e8e\u6211\u4eec\u7684StructBERT\u53e5\u5411\u91cf\u6a21\u578b，\u4e00\u4e2a\u7b80\u5316\u7684\u914d\u7f6e\u53ef\u80fd\u5982\u4e0b\u6240\u793a\u3002\u6211\u4eec\u9700\u8981\u4f7f\u7528Python\u540e\u7aef，\u56e0\u4e3a\u6d89\u53ca\u5206\u8bcd\u548c\u6c60\u5316\u7b49\u590d\u6742\u64cd\u4f5c\u3002<\/p>\n
name: "structbert_similarity"
\nbackend: "python"
\nmax_batch_size: 8 # \u6839\u636e\u4f60\u7684GPU\u663e\u5b58\u8c03\u6574，\u8868\u793a\u4e00\u6b21\u6700\u591a\u5904\u74068\u4e2a\u53e5\u5b50\u5bf9<\/p>\n
input [
\n {
\n name: "TEXT"
\n data_type: TYPE_STRING
\n dims: [ -1 ] # -1 \u8868\u793a\u53ef\u53d8\u957f\u5ea6
\n }
\n]<\/p>\n
output [
\n {
\n name: "EMBEDDING"
\n data_type: TYPE_FP32
\n dims: [ 768 ] # StructBERT-large \u9690\u85cf\u5c42\u5927\u5c0f\u662f768
\n }
\n]<\/p>\n
instance_group [{ kind: KIND_GPU }] # \u6307\u5b9a\u5728GPU\u4e0a\u8fd0\u884c<\/p>\n
parameters: {
\n key: "EXECUTION_ENV_PATH",
\n value: {string_value: "$$TRITON_MODEL_DIRECTORY\/py_env.tar.gz"}
\n}<\/p>\n
\u8fd9\u4e2a\u914d\u7f6e\u544a\u8bc9Triton：\u6a21\u578b\u540d\u53eb structbert_similarity，\u4f7f\u7528Python\u540e\u7aef；\u5b83\u63a5\u53d7\u4e00\u4e2a\u53ef\u53d8\u957f\u5ea6\u7684\u5b57\u7b26\u4e32\u8f93\u5165（TEXT），\u5e76\u8f93\u51fa\u4e00\u4e2a768\u7ef4\u7684\u6d6e\u70b9\u6570\u5411\u91cf（EMBEDDING）；\u5728GPU\u4e0a\u8fd0\u884c\u3002<\/p>\n
\u63a5\u4e0b\u6765，\u6211\u4eec\u9700\u8981\u7f16\u5199\u4e00\u4e2aPython\u6587\u4ef6（\u4f8b\u5982 model.py），\u653e\u5728\u7248\u672c\u76ee\u5f55（1\/）\u4e0b\u3002\u8fd9\u4e2a\u6587\u4ef6\u5fc5\u987b\u5305\u542b\u4e00\u4e2a TritonPythonModel \u7c7b，Triton\u4f1a\u5728\u52a0\u8f7d\u6a21\u578b\u65f6\u8c03\u7528\u5b83\u3002<\/p>\n
\u8fd9\u4e2a\u811a\u672c\u7684\u4e3b\u8981\u4efb\u52a1\u662f：<\/p>\n
\u5173\u952e\u90e8\u5206 execute \u51fd\u6570\u7684\u903b\u8f91\u5982\u4e0b：<\/p>\n
import torch
\nimport numpy as np
\nimport triton_python_backend_utils as pb_utils
\nfrom transformers import AutoTokenizer, AutoModel<\/p>\n
class TritonPythonModel:
\n def initialize(self, args):
\n # \u52a0\u8f7d\u5206\u8bcd\u5668\u548c\u6a21\u578b
\n model_dir = args['model_repository'] + '\/' + args['model_version']
\n self.tokenizer = AutoTokenizer.from_pretrained(f"{model_dir}\/tokenizer")
\n self.model = AutoModel.from_pretrained(f"{model_dir}\/config")
\n # \u52a0\u8f7d\u6211\u4eec\u4e4b\u524d\u4fdd\u5b58\u7684\u6743\u91cd
\n state_dict = torch.load(f"{model_dir}\/model.pt")
\n self.model.load_state_dict(state_dict)
\n self.model.to('cuda').eval()
\n print("Model loaded successfully.")<\/p>\n
def execute(self, requests):
\n responses = []
\n for request in requests:
\n # 1. \u83b7\u53d6\u8f93\u5165
\n in_text = pb_utils.get_input_tensor_by_name(request, "TEXT")
\n sentences = in_text.as_numpy().astype(str).tolist() # \u5047\u8bbe\u6bcf\u4e2a\u8bf7\u6c42\u4e00\u4e2a\u53e5\u5b50<\/p>\n
# 2. \u5206\u8bcd\u4e0e\u7f16\u7801
\n encoded_input = self.tokenizer(
\n sentences,
\n padding=True,
\n truncation=True,
\n max_length=512,
\n return_tensors='pt'
\n ).to('cuda')<\/p>\n
# 3. \u6a21\u578b\u63a8\u7406
\n with torch.no_grad():
\n model_output = self.model(**encoded_input)
\n # 4. \u5747\u503c\u6c60\u5316 (Mean Pooling)
\n token_embeddings = model_output.last_hidden_state
\n attention_mask = encoded_input['attention_mask']
\n input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
\n sentence_embeddings = torch.sum(token_embeddings * input_mask_expanded, 1) \/ torch.clamp(input_mask_expanded.sum(1), min=1e-9)
\n sentence_embeddings = sentence_embeddings.cpu().numpy()<\/p>\n
# 5. \u6784\u9020\u8f93\u51fa
\n out_tensor = pb_utils.Tensor("EMBEDDING", sentence_embeddings.astype(np.float32))
\n inference_response = pb_utils.InferenceResponse(output_tensors=[out_tensor])
\n responses.append(inference_response)
\n return responses<\/p>\n
def finalize(self):
\n self.model = None
\n torch.cuda.empty_cache()<\/p>\n
\u8fd9\u4e2a\u811a\u672c\u5c06\u5b8c\u6574\u7684\u53e5\u5b50\u5230\u5411\u91cf\u7684\u6d41\u7a0b\u5c01\u88c5\u4e86\u8d77\u6765\u3002\u4f60\u9700\u8981\u786e\u4fdd tokenizer \u76ee\u5f55\u548c config \u76ee\u5f55（\u5305\u542bconfig.json）\u4ee5\u53ca model.pt \u6587\u4ef6\u90fd\u653e\u5728\u6a21\u578b\u7684\u7248\u672c\u76ee\u5f55\u4e0b\u3002<\/p>\n
\u6a21\u578b\u4ed3\u5e93\u51c6\u5907\u597d\u540e，\u6211\u4eec\u5c31\u53ef\u4ee5\u542f\u52a8Triton\u670d\u52a1\u5668\u4e86\u3002<\/p>\n
\u8fd9\u662f\u6700\u7b80\u5355\u7684\u65b9\u5f0f\u3002\u786e\u4fdd\u4f60\u7684\u6a21\u578b\u4ed3\u5e93\u8def\u5f84（\u4f8b\u5982 \/home\/user\/model_repository）\u5df2\u7ecf\u5305\u542b\u5b8c\u6574\u7684 structbert_similarity \u6a21\u578b\u3002<\/p>\n
\u8fd0\u884c\u4ee5\u4e0b\u547d\u4ee4：<\/p>\n
docker run –gpus=all –rm -p 8000:8000 -p 8001:8001 -p 8002:8002 -v \/home\/user\/model_repository:\/models nvcr.io\/nvidia\/tritonserver:23.10-py3 tritonserver –model-repository=\/models<\/p>\n
\u547d\u4ee4\u89e3\u91ca：<\/p>\n
\u5982\u679c\u4e00\u5207\u987a\u5229，\u4f60\u4f1a\u5728\u65e5\u5fd7\u7684\u6700\u540e\u770b\u5230\u7c7b\u4f3c\u8fd9\u6837\u7684\u8f93\u51fa：<\/p>\n
+———————-+———+——–+
\n| Model | Version | Status |
\n+———————-+———+——–+
\n| structbert_similarity | 1 | READY |
\n+———————-+———+——–+<\/p>\n
\u8fd9\u8868\u660e\u4f60\u7684StructBERT\u6a21\u578b\u5df2\u7ecf\u6210\u529f\u52a0\u8f7d\u5e76\u5904\u4e8e\u5c31\u7eea\u72b6\u6001\u3002<\/p>\n
\u670d\u52a1\u5668\u8dd1\u8d77\u6765\u4e86，\u6211\u4eec\u600e\u4e48\u7528\u5462？\u9700\u8981\u5199\u4e00\u4e2a\u5ba2\u6237\u7aef\u7a0b\u5e8f\u6765\u8c03\u7528\u5b83\u3002\u8fd9\u91cc\u63d0\u4f9b\u4e00\u4e2a\u4f7f\u7528Python tritonclient \u5e93\u7684\u7b80\u5355\u793a\u4f8b\u3002<\/p>\n
\u9996\u5148\u5b89\u88c5\u5ba2\u6237\u7aef\u5e93：<\/p>\n
pip install tritonclient[all]<\/p>\n
\u7136\u540e\u7f16\u5199\u6d4b\u8bd5\u811a\u672c test_client.py：<\/p>\n
import tritonclient.http as httpclient
\nimport numpy as np<\/p>\n
# \u8fde\u63a5\u5230Triton\u670d\u52a1\u5668
\nclient = httpclient.InferenceServerClient(url='localhost:8000')<\/p>\n
# \u51c6\u5907\u8f93\u5165\u6570\u636e
\nsentences = ["\u4eca\u5929\u5929\u6c14\u771f\u597d", "\u9633\u5149\u660e\u5a9a\u7684\u4e00\u5929"]
\n# Triton\u671f\u671b\u7684\u8f93\u5165\u683c\u5f0f
\ninput_data = np.array(sentences, dtype=object).reshape((-1, 1))<\/p>\n
# \u8bbe\u7f6e\u8f93\u5165
\ninputs = [httpclient.InferInput("TEXT", input_data.shape, "BYTES")]
\ninputs[0].set_data_from_numpy(input_data)<\/p>\n
# \u8bbe\u7f6e\u8f93\u51fa
\noutputs = [httpclient.InferRequestedOutput("EMBEDDING")]<\/p>\n
# \u53d1\u9001\u8bf7\u6c42
\nresponse = client.infer(model_name="structbert_similarity", inputs=inputs, outputs=outputs)<\/p>\n
# \u83b7\u53d6\u7ed3\u679c
\nresult = response.as_numpy("EMBEDDING")
\nprint("\u751f\u6210\u7684\u53e5\u5411\u91cf\u5f62\u72b6：", result.shape) # \u5e94\u8be5\u662f (2, 768)
\nprint("\u7b2c\u4e00\u4e2a\u53e5\u5b50\u7684\u5411\u91cf（\u524d10\u7ef4）：", result[0][:10])<\/p>\n
# \u8ba1\u7b97\u4e24\u4e2a\u53e5\u5b50\u7684\u4f59\u5f26\u76f8\u4f3c\u5ea6
\nfrom numpy.linalg import norm
\nvec1, vec2 = result[0], result[1]
\ncosine_sim = np.dot(vec1, vec2) \/ (norm(vec1) * norm(vec2))
\nprint(f"\u53e5\u5b501\u4e0e\u53e5\u5b502\u7684\u4f59\u5f26\u76f8\u4f3c\u5ea6：{cosine_sim:.4f}")<\/p>\n
\u8fd0\u884c\u8fd9\u4e2a\u811a\u672c，\u5982\u679c\u6210\u529f，\u4f60\u4f1a\u770b\u5230\u8f93\u51fa\u7684\u5411\u91cf\u548c\u8ba1\u7b97\u51fa\u7684\u76f8\u4f3c\u5ea6\u5206\u6570\u3002\u8fd9\u8bc1\u660e\u6574\u4e2aTriton\u63a8\u7406\u670d\u52a1\u94fe\u8def\u5df2\u7ecf\u5b8c\u5168\u6253\u901a\u3002<\/p>\n
\u901a\u8fc7\u672c\u6559\u7a0b，\u6211\u4eec\u5b8c\u6210\u4e86\u4e00\u4ef6\u5f88\u6709\u4ef7\u503c\u7684\u4e8b\u60c5：\u5c06\u5f3a\u5927\u7684StructBERT\u4e2d\u6587\u53e5\u5411\u91cf\u6a21\u578b，\u4ece\u4e00\u4e2a\u666e\u901a\u7684Python\u811a\u672c，\u90e8\u7f72\u6210\u4e86\u4e00\u4e2a\u4e13\u4e1a\u7684\u3001\u53ef\u901a\u8fc7\u7f51\u7edc\u8c03\u7528\u7684\u63a8\u7406\u670d\u52a1\u3002\u8ba9\u6211\u4eec\u56de\u987e\u4e00\u4e0b\u5173\u952e\u6b65\u9aa4\u548c\u5b83\u7684\u4ef7\u503c：<\/p>\n
\u90e8\u7f72\u6d41\u7a0b\u56de\u987e<\/p>\n
\u65b9\u6848\u6838\u5fc3\u4f18\u52bf<\/p>\n
\u4f60\u73b0\u5728\u62e5\u6709\u7684\u662f\u4e00\u4e2a\u4f01\u4e1a\u7ea7\u7684\u8bed\u4e49\u76f8\u4f3c\u5ea6\u8ba1\u7b97\u670d\u52a1\u3002\u4f60\u53ef\u4ee5\u8f7b\u677e\u5730\u5c06\u5176\u96c6\u6210\u5230\u4f60\u7684\u641c\u7d22\u7cfb\u7edf\u3001\u5185\u5bb9\u53bb\u91cd\u6a21\u5757\u6216\u667a\u80fd\u5bf9\u8bdd\u5e94\u7528\u4e2d，\u4eab\u53d7\u7a33\u5b9a\u3001\u9ad8\u6548\u4e14\u4e13\u4e1a\u7684AI\u80fd\u529b\u3002\u4e0b\u4e00\u6b65，\u4f60\u53ef\u4ee5\u63a2\u7d22\u5982\u4f55\u7ed3\u5408Faiss\u6216Milvus\u7b49\u5411\u91cf\u6570\u636e\u5e93，\u6784\u5efa\u4e00\u4e2a\u5b8c\u6574\u7684\u8bed\u4e49\u68c0\u7d22\u7cfb\u7edf，\u8ba9\u5e94\u7528\u53d8\u5f97\u66f4\u52a0\u667a\u80fd\u3002<\/p>\n
\u83b7\u53d6\u66f4\u591aAI\u955c\u50cf<\/p>\n
\u60f3\u63a2\u7d22\u66f4\u591aAI\u955c\u50cf\u548c\u5e94\u7528\u573a\u666f？\u8bbf\u95ee CSDN\u661f\u56fe\u955c\u50cf\u5e7f\u573a，\u63d0\u4f9b\u4e30\u5bcc\u7684\u9884\u7f6e\u955c\u50cf，\u8986\u76d6\u5927\u6a21\u578b\u63a8\u7406\u3001\u56fe\u50cf\u751f\u6210\u3001\u89c6\u9891\u751f\u6210\u3001\u6a21\u578b\u5fae\u8c03\u7b49\u591a\u4e2a\u9886\u57df，\u652f\u6301\u4e00\u952e\u90e8\u7f72\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"
StructBERT\u4e2d\u6587\u53e5\u5411\u91cf\u5de5\u5177\u90e8\u7f72\u6559\u7a0b：NVIDIA Triton\u63a8\u7406\u670d\u52a1\u5668\u96c6\u6210\u65b9\u6848
\n1. \u5f15\u8a00
\n\u5982\u679c\u4f60\u6b63\u5728\u5bfb\u627e\u4e00\u4e2a\u80fd\u7cbe\u51c6\u7406\u89e3\u4e2d\u6587\u53e5\u5b50\u542b\u4e49，\u5e76\u80fd\u5feb\u901f\u8ba1\u7b97\u5b83\u4eec\u4e4b\u95f4\u76f8\u4f3c\u5ea6\u7684\u5de5\u5177，\u90a3\u4e48StructBERT\u4e2d\u6587\u53e5\u5411\u91cf\u5de5\u5177\u7edd\u5bf9\u503c\u5f97\u4f60\u6df1\u5165\u4e86\u89e3\u3002\u8fd9\u4e2a\u5de5\u5177\u57fa\u4e8e\u963f\u91cc\u8fbe\u6469\u9662\u5f00\u6e90\u7684\u5f3a\u5927\u6a21\u578b，\u80fd\u591f\u5c06\u4efb\u4f55\u4e2d\u6587\u53e5\u5b50\u8f6c\u6362\u6210\u4e00\u4e2a\u9ad8\u7ef4\u5ea6\u7684\u201c\u6570\u5b57\u6307\u7eb9\u201d（\u6211\u4eec\u79f0\u4e4b\u4e3a\u5411\u91cf），\u7136\u540e\u901a\u8fc7\u8ba1\u7b97\u8fd9\u4e9b\u6307\u7eb9\u7684\u76f8\u4f3c\u5ea6<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[9113,9114,2068,2112],"topic":[],"class_list":["post-81434","post","type-post","status-publish","format-standard","hentry","category-server","tag-9113","tag-9114","tag-nlp","tag-triton"],"yoast_head":"\n