How it works

A no code platform for developing AI projects

How Deepyt works

Software Application

deepyt software funcionalities
How Deepyt works

Software Functionalities

How Deepyt works

Key features

1. Software Core

Deepyt Core

  • Python programmed software (3.8.10)
  • Multiplatform (Windows 10/11, MacOS, Unix, edge devices (?) )
  • Full compatibility with Tensorflow, Pytorch, Sklearn and other opensource artificial intelligence libraries.
  • Implementation, execution, saving of acyclic direct graphs (DAG). A graph is represented by an undefined sequence of operations encoded in Python code.
  • Optimised graph execution: only the necessary parts are executed
  • Modular postprocessing: possibility of developing many calculation and visualisation solutions with dedicated tools. Possibility of customising the dashboard.
  • Project-based structure: the user works within a path, containing some mandatory project files and his working documents.
  • The software has a node-and-edge based structure, typical of dataflow programming
  • A node can represent any programming object, from simple classes or functions to complex operations, or another node realised with Deepyt.
  • A graph can be realised via the graphical interface or via the Python library
  • For the execution of a graph, the DeepytCore library is sufficient.
  • It is possible to join several graphs together, create concatenated graphs, nested graphs, or execute only parts of them, as required.
  • The module (node) based structure allows the programming flow to be managed from very low-level operations, moving on to higher level operations. Each correctly programmed module will be reusable.
  • The structure of the graph provides a series of inputs (placeholders), operations and Outputs. Only the specified outputs are calculated.



2. Wrapping Python Code

Built-ins, libraries, custom code

  • At the lowest level of use, Deepyt can be used to create work chains in Python code.
  • It is possible to use all built-ins of the Python version that are compatible with the released version of the software.
  • Deeplabs provides several ready-touse libraries, wrapped in graphical versions (numpy, Tensorflow, PyTorch…), with a potentially growing catalogue.
  • A user can create an indefinite number of libraries (in the form of custom nodes) which he can choose to import or not into the project folder.
  • Libraries can be downloaded or uninstalled at any time, you just need an internet connection and a valid licence.
  • The user can use automatic node wrap to transform his classes and functions (or entire python modules) into graphical nodes that can be used directly in Deepyt.


3. Artificial Intelligence

Model development

  • Develop AI models without a line of Code, with a node-based structure
  • Mixed No-Code / Code approach for advanced models’ implementation
  • Development, training, evaluation, testing and results visualization


4. Examples

Growing Gallery of Complete Projects

  • Gallery of ready-to-go examples for AI and Engineering applications
  • Easily apply examples to your case study changing few parameters


5. Jupiter Notebooks

Handling servers

  • Run and handle multiple Jupiter servers
  • Develop with your Notebooks and Python code, using all the pre-implemented libraries and DeepytCore

6. Modular Dashboards and Postprocessing

Dashboards Builder

  • Create custom Dashboards with a growing gallery of available tools for data visualization, analysis and processing
  • Every tool works as a standalone Qt Based software: it’s possible to cover a wide range of applications


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