Do you have a specific use case in mind?
The easiest way would be to store the trained model and load it in another script for inference.
Have a look at the Serialization semantics 437 for more information.
Your inference code might load the data directly from your hard drive / network storage or you could create a web app using e.g. Flask and feed the data to your model.
You can also deploy your model to Caffe2 using ONNX. Here 114 is a nice tutorial.