How it works
A no code platform for developing AI projects
How Deepyt works
How Deepyt works
How Deepyt works
1. Software 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
- 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
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
- 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
- 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