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PyTorch, TensorFlow, FastAPI, LangChain, Hugging Face深度学习框架

news 2025/10/5 5:58:16

PyTorch：研发阶段的灵活性和创新速度

TensorFlow：生产环境的大规模部署稳定性

FastAPI：高性能API服务和系统集成

LangChain：LLM应用的快速开发和编排

Hugging Face：预训练模型的标准化使用和优化

PyTorch - 深度学习研发与部署

企业应用案例：智能客服对话系统

项目背景：金融企业需要构建能理解用户意图、生成自然回复的客服机器人。

解决问题：

复杂的序列到序列建模任务
实时推理性能要求
模型快速迭代和实验管理

import torch
import torch.nn as nnclass CustomerServiceModel(nn.Module):def __init__(self,vocab_size,hidden_size):super().__init__()self.encoder = nn.LSTM(vocab_size,hidden_size,batch_first=True)self.decoder = nn.LSTM(hidden_size,hidder_size,batch_first=True)self.classifier = nnlLinear(hidden_size,vocab_size)def forward(self,input_ids,target_ids=None):encoder_outputs,(hidden,cell) = self.encoder(input_ids)#解码器逻辑outputs,_ = self.decoder(encoder_outputs,(hidden,cell))return self.classifier(outputs)#模型部署优化
model = CustomerServiceModel(10000,512)
traced_model = torch.jit.trace(model,example_inputs=torch.randint(0, 1000, (1, 50)))
traced_model.save("customer_service_model.pt")

TensorFlow - 大规模生产环境部署

企业应用案例：电商推荐系统

项目背景：为电商平台构建个性化商品推荐系统，处理亿级用户和商品数据。

解决的问题：

超大规模稀疏特征处理
分布式训练和推理
生产环境模型服务稳定性

import tensorflow as tf
from tensorflow.keras.layers import Dense,Embedding,Concatenatedef build_recommendation_model(user_vocab_size,item_vocab_size):user_input = tf.keras.Input(shape=(1,),name="user_id")item_input = tf.keras.Input(shape=(1,),name="item_id")user_embedding = Embedding(user_vocab_size,64)(user_input)item_embedding = Embedding(item_vocab_size,64)(item_input)user_vec = tf.squeeze(user_embedding,axis=1)item_vec = tf.squeeze(item_embedding,axis=1)concat = Concatenate()([user_vec,item_vec])output = Dense(1,activation='sigmoid')(concat)model = tf.keras.Model(inputs=[user_input,item_input],outputs=output)return model#使用TFX进行生产流水线
from tfx.components import Trainertrainer = Trainer(module_file = os.path.abspath("recommendation_model.py")examples = transform.outputs['transformed_examples']schema = schema_gen.outputs['schema'],train_args = trainer_pb2.TrainArgs(num_steps=10000),eval_args = trainer_pb2.EvalArgs(num_steps=5000)
)

FastAPI - 高性能模型服务API

企业应用案例：实时欺诈检测系统

项目背景：金融机构需要实时检测交易欺诈行为，要求低延迟、高并发。

解决的问题：

微服务架构下的API标准化
高并发请求处理
自动API文档生成
请求验证和序列化

from fastapi import FastAPI,HTTPException
from pydantic import BaseModel
import numpy as np
import joblibapp = FastAPI(title="欺诈检测API",version="1.0.0")class TransactionRequest(BaseModel):transaction_amount:floatuser_age:inthistorical_fraud_rate:floattime_since_last_transaction:floatclass FraudResponse(BaseModel):is_fraud:boolconfidence:floatrisk_level:str@app.post("/predict",response_model=FraudResponse)
async def predict_fraud(transaction:TransactionRequest):try:#加载预训练模型model = joblib.load("fraud_detection_model.pkl")features = np.array([[transaction.transaction_amount,transaction.user_age,transaction.historical_fraud_rate,transaction.time_since_last_transaction]])prediction = model.predict(features)[0]probability = model.predict_proba(features)[0][1]risk_level = "high" if probability > 0.8 else "medium" if probability > 0.5 else "low"return FraudResponse(is_fraud = bool(prediction),confidence=float(probability),risk_level=risk_level)except Exception as e:raise HTTPException(status_code=500,detail=str(e))# 启动命令：uvicorn main:app --host 0.0.0.0 --port 8000 --workers 4

LangChain - 大语言模型应用开发

企业应用案例：智能知识库问答系统

项目背景：企业内部知识分散，需要统一的智能问答入口。

解决的问题：

多源数据集成（文档、数据库、API）
上下文感知的对话管理
检索增强生成(RAG)
工具调用和外部系统集成

from langchain.chains import RetrievalQA
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.vectorstors import Chroma
from langchain.document_loaders import DirectoryLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.llms import OpenAI
from langchain.agents import initialize_agent,Tool
from langchain.memory import ConversationBufferMemoryclass KnowledgeBaseQA:def __init__(self,model_name="gpt-3.5-turbo"):#文档加载和预处理loader = DirectoryLoader('./knowledge_docs', glob="**/*.pdf")documents = loader.load()text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000,chunk_overlap=200)texts = text_splitter.split_documents(documents)#向量数据库embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")self.vecotrstore = Chroma.from_documents(texts,embeddings)#检索链self.qa_chain = RetrievalQA.from_chain_type(llm = OpenAI(model_name=model_name,temperature=0),chain_type="stuff",retriever=self.vectorstore.as_retriever(),return_source_documents=True)#对话记忆self.memory = ConversationBufferMemory(memory_key="chat_history")def query(self,question:str) -> dict:return self.qa_chain({"query":question})#企业级使用
kb_qa = KnowledgeBaseQA()
response = kb_qa.query("我们公司的请假政策是什么？")
print(response['result'])

Hugging Face - 预训练模型与应用

企业应用案例：多语言情感分析平台

项目背景：跨国企业需要监控全球社交媒体上的品牌声誉。

解决的问题：

多语言文本理解
零样本学习和小样本适应
模型微调和优化
标准化模型部署

from transformers import(AutoTokenizer,AutoModelForSequenceClassification,pipeline,TrainingArgemtns,Trainer
)
from datasets import Dataset
import pandas as pdclass MultilingualSentimentAnlayzer:def __init__(self):self.model_name = "cardiffnlp/twitter-xlm-roberta-base-sentiment"self.tokenizer = AutoTokenizer.from_pretrained(self.model_name)self.classifier = pipeline("sentiment-analysis",model=self.model,tokenizer = self.tokenizer)def analyze_batch(self,texts:list,languages:list=None):"""批量情感分析"""results = self.classifier(texts)return [{"text":text,"sentiment":result['label'],"score":result['score'],"language":lang if languages else "auto"}for text,result,lang in zip(texts,results,lanauages or [])]def fine_tune(self,train_data:pd.DataFrame,output:dir:str):"""微调模型适应特定领域"""dataset = Dataset.from_pandas(train_data)def tokenize_function(examples):return self.tokenizer(examples["text"],padding="max_length",truncation=True,max_length=128)tokenized_dataset = dataset.map(tokenize_function, batched=True)training_args = TrainingArguments(output_dir=output_dir,num_train_epochs=3,per_device_train_batch_size=16,warmup_steps=500,weight_decay=0.01,logging_dir='./logs',)trainer = Trainer(model=self.model,args=training_args,train_dataset=tokenized_dataset,)trainer.train()trainer.save_model()#企业应用
analyzer = MultilingualSentimentAnalyzer()
texts = ["I love this product!", "产品质量太差了", "C'est incroyable!"]
results = analyzer.analyze_batch(texts)
for result in results:print(f"Text: {result['text']} | Sentiment: {result['sentiment']}")

框架组合实战：智能客服增强系统

# 架构整合示例
from fastapi import FastAPI
from langchain.chains import LLMChain
from transformers import pipeline
import torch
import tensorflow as tfapp = FastAPI()class EnhancedCustomerService:def __init__(self):# 意图分类（TensorFlow）self.intent_classifier = tf.keras.models.load_model('intent_model.h5')# 情感分析（Hugging Face）self.sentiment_analyzer = pipeline("sentiment-analysis")# 对话生成（PyTorch + LangChain）self.llm_chain = LLMChain(llm=OpenAI(), prompt=prompt_template)async def process_query(self, user_input: str):# 多模型协同工作intent = self.intent_classifier.predict([user_input])sentiment = self.sentiment_analyzer(user_input)[0]if intent == "complaint" and sentiment['label'] == "NEGATIVE":# 紧急情况处理return await self.handle_urgent_case(user_input)else:# 普通问答return self.llm_chain.run(user_input)