边缘部署：让AI无需联网即可使用

Jetson设备部署LLM

# 1. 环境准备
sudo apt update && sudo apt upgrade
sudo apt install python3-pip nvidia-jetpack

# 2. 安装推理框架
pip3 install torch torchvision torchaudio
pip3 install transformers accelerate

# 3. 模型优化部署
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

class EdgeLLMDeployment:
    def __init__(self, model_path, optimization_config):
        # 加载量化模型
        self.model = AutoModelForCausalLM.from_pretrained(
            model_path,
            torch_dtype=torch.float16,
            device_map="auto",
            load_in_8bit=True  # INT8量化
        )
        
        # TensorRT优化
        if optimization_config.get('tensorrt', False):
            self.model = self.optimize_with_tensorrt(self.model)
        
        # 编译优化
        self.model = torch.compile(self.model, mode="reduce-overhead")
        
    def optimize_with_tensorrt(self, model):
        """TensorRT优化"""
        import torch_tensorrt
        
        trt_model = torch_tensorrt.compile(
            model,
            inputs=[
                torch_tensorrt.Input(
                    shape=[1, 512],  # batch_size, seq_len
                    dtype=torch.int32
                )
            ],
            enabled_precisions={torch.float16},
            workspace_size=1 << 30,  # 1GB
            truncate_long_and_double=True
        )
        
        return trt_model
    
    def deploy_server(self, port=8080):
        """部署推理服务"""
        from fastapi import FastAPI
        import uvicorn
        
        app = FastAPI()
        
        @app.post("/inference")
        async def inference(text: str, max_length: int = 100):
            # 推理逻辑
            inputs = self.tokenizer(text, return_tensors="pt")
            with torch.no_grad():
                outputs = self.model.generate(
                    **inputs,
                    max_length=max_length,
                    temperature=0.7,
                    do_sample=True
                )
            
            response = self.tokenizer.decode(outputs[0])
            
            # 性能监控
            metrics = {
                "latency": self.measure_latency(),
                "memory_usage": self.get_memory_usage(),
                "throughput": self.calculate_throughput()
            }
            
            return {"response": response, "metrics": metrics}
        
        uvicorn.run(app, host="0.0.0.0", port=port)