3 years ago

Category: Docker, gitlab

As a quick background, we almost always work on deep learning projects with a wide range of different customers from the industrial sector, spanning work on large machine data to optical recognition and quality assurance. As such, our work always involves a tight interaction with our customers: To understand the data, the challenge, and the desired value that should be achieved through the use of Machine Learning.

Thus, we decided to create a structural basis for all our upcoming projects and in the form of a template.

Also, having clearly structured projects makes communication with our customers much easier.Furthermore, we decided to use a single script called run.py as our central interface for all projects: It has to be identical in all repositories and manage things such as GPU usage and running either training or model inference on the specified data.

We started small but we always strive to improve on our daily doing and make it more efficient as well as improving our communication with our customers.