Creating TensorFlow Custom Ops, Bazel, and ABI compatibility

Custom ops are a way for extending the TensorFlow framework by adding operations that are not natively available in the framework. Adding a new operation is a relatively simple thing especially if you work in the officially supported environment (Ubuntu16, CUDA 10). However, if you built TensorFlow from scratch to support your target environment (e.g. Archlinux, CUDA 11) the official TensorFlow support for creating a custom op - that relies upon a Docker image - becomes useless.

Deploy and Train TensorFlow models in Go: Human Activity Recognition case study

Every Machine Learning (ML) product should reach its final stage: the deployment to production. Unfortunately, there isn't a plethora of examples containing information on how to deploy a model to production and how to design the model environment for the production. In this article, I'm going to cover these points using TensorFlow 2 as the framework of choice and Go as the target language for the deployment and training.

GitLab CI/CD for cross-platform Unreal Engine 4 projects

Continuous Integration (CI) is an essential step in the development pipeline of well-designed software infrastructure. Unreal Engine 4, on its side, does not provide an handy way to design CI/CD easily, since the project itself is huge and the system administration challenges to make it scale are big. This article will guide you through the development of a CI/CD pipeline (using GitLab) for cross-platform Unreal Engine 4 projects

FaceCTRL: control your media player with your face

After being interrupted dozens of times a day while coding with my headphones on, I decided to find a solution that eliminates the stress of pausing and re-playing the song I was listening to. The solution is machine learning / computer vision application developed with TensorFlow 2, OpenCV, and Playerctl. This article will guide you trough the step required to develop such an application.