Introducing the AI Blueprint Engine: A Code Generator for Deep Learning

Simple example model from our previous article.

The Graphical User Interface

Overview on basic building blocks and concepts of the graphical user interface.
The set of layers differs for the 2D data (left) and 3D data (right). Also, every option is well annotated with additional information (left).

The Code Generator

Overview of content generated by the AI Blueprint Engine
  • Python source code of the graphically designed neural architecture, data preprocessing pipelines, training and inference functions plus command-line interfaces with several exposed configuration options such as hyper-parameters or paths to data sources
The generated command-line interface of the training script.
  • Python requirements files with package dependencies of the project for CPU and GPU-accelerated execution
  • Dockerfiles for building CPU and GPU-enabled Docker images with installed package dependencies and the generated project code
  • a README file with comprehensive project documentation as well as setup and runtime instructions
Two excerpts of the generated file showing the setup instructions (left) and the model overview (right) in GitLab.
The main function that is part of the that has been generated by the code generator.


  • a GUI for building highly customized and flexible neural network architectures including data preprocessing workflows, suitable for (multi-task) supervised and unsupervised learning
  • a guided graphical design process for building neural architectures by exposing only context-related options
  • extensive UI component annotation including tooltips and references to third-party resources supporting the design process
  • generation of modular, fully-documented, and style-compliant Python/TensorFlow source code for training and inference including command-line interfaces that expose several configuration options such as hyper-parameters or paths to data sources, making the generated code compatible with third-party tools for managing experiments
  • generation of auxiliary files including a README file, package dependency reference files, and Dockerfiles for containerized CPU and GPU-accelerated execution

What’s next?



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Bridging the gap between ease of use and flexibility in artificial intelligence development —