Boost Deep Learning Deployment on Windows with LabVIEW + Python

Why Deploy Deep Learning Models

For a company, a deep‑learning engineer who only knows how to train models in Python but cannot deploy them as a product that delivers value is considered unqualified.

Recommended Development Stack

On the Windows platform, the fastest way to develop deep‑learning applications is to use Python for model training and LabVIEW for model deployment.

Why Python

Python is the best choice for developing and debugging deep‑learning algorithms and for writing inference functions.

Why LabVIEW

LabVIEW is a graphical development platform especially suitable for scientists who are not computer‑science majors. It offers a short learning curve and rapid application development. If you have a CS background, languages like C# or Java are also viable options.

LabVIEW Setup

Download LabVIEW 2018 or later (the version that introduced the Python Node) from the official NI site: https://www.ni.com/zh-cn/shop/labview/download.html.

Using the LabVIEW Python Node

The LabVIEW Python Node allows you to call Python functions directly. The following images illustrate its usage:

After training a deep‑learning model in Python, you can invoke its inference function from LabVIEW, enabling fast UI development and a complete Windows‑based commercial application.

Summary of Roles

Python handles deep‑learning algorithm development, debugging, training, and provides the inference function.

LabVIEW builds the user interface and application framework, and calls the Python inference function to perform predictions.

Passing Image Data from LabVIEW to Python

LabVIEW can capture images, convert them to a two‑dimensional array, and pass the array to Python for processing. The following images show the LabVIEW code and the resulting Python call: