How to deploy and infer models in PyTorch
Deploying and performing inference on models in PyTorch typically involve the following steps:
- Load a pre-trained model.
import torch
import torch.nn as nn
# 加载已经训练好的模型
model = torch.load('model.pth')
- Set the model to evaluation mode.
model.eval()
- Prepare to input data and make inferences.
# 准备输入数据
input_data = torch.tensor([[1.0, 2.0, 3.0]])
# 进行推理
with torch.no_grad():
output = model(input_data)
print(output)
- Process model output:
# 可以根据需要对模型输出进行进一步处理
The process above outlines a simple deployment and inference workflow for a PyTorch model, which may need further adjustments and optimizations based on specific real-world scenarios.