模型部署测试环境搭建完整指南
环境准备
首先搭建基础测试环境，推荐使用NVIDIA GPU服务器：
# 安装CUDA 11.8和cuDNN 8.9
wget https://developer.download.nvidia.com/compute/cuda/11.8.0/local_installers/cuda_11.8.0_520.61.05_linux.run
sudo sh cuda_11.8.0_520.61.05_linux.run
部署工具安装
# 安装TensorRT 8.6
pip install tensorrt==8.6.1.6
# 安装ONNX Runtime
pip install onnxruntime-gpu==1.15.1
# 安装PyTorch 2.0
pip install torch==2.0.1+cu118 torchvision==0.15.2+cu118 --extra-index-url https://download.pytorch.org/whl/cu118
模型量化测试示例
import torch
import torch.nn as nn
import torch.quantization
# 构建测试模型
model = nn.Sequential(
nn.Linear(768, 512),
nn.ReLU(),
nn.Linear(512, 10)
)
# 启用量化
model.qconfig = torch.quantization.get_default_qconfig('fbgemm')
quantized_model = torch.quantization.prepare(model, inplace=True)
quantized_model = torch.quantization.convert(quantized_model)
# 性能测试
import time
inputs = torch.randn(1, 768)
start = time.time()
for _ in range(100):
output = quantized_model(inputs)
end = time.time()
print(f"平均推理时间: {(end-start)/100*1000:.2f}ms")
剪枝优化实践
import torch.nn.utils.prune as prune
# 对模型进行剪枝
prune.l1_unstructured(model[0], name='weight', amount=0.3)
prune.l1_unstructured(model[2], name='weight', amount=0.5)
# 重新评估模型精度
accuracy = evaluate_model(model, test_loader)
print(f"剪枝后准确率: {accuracy:.4f}")
性能基准测试
使用以下脚本进行完整的部署性能测试：
python benchmark.py --model-path model.onnx --batch-size 32 --device cuda
讨论