Depending on your goal, “jUML” typically refers to one of two tools: a VS Code extension for converting Java code into Unified Modeling Language (UML) class graphs, or JuML, an HPC High-Performance Computing Machine Learning library.
Below are the step-by-step instructions for building your first graph using both interpretations.
Option 1: Creating a UML Class Graph via the jUML VS Code Extension
If you want to auto-generate a relationship diagram (class graph) from your Java source code, you can use the jUML Visual Studio Marketplace extension.
Install Prerequisites: Ensure you have Java 17 or higher installed on your system.
Install the Extension: Search for jUML in the VS Code Extensions marketplace and click install.
Open Your Project: Open the workspace containing your Java source files. Trigger the Conversion:
Open the VS Code Command Palette by pressing Ctrl+Shift+P (Windows/Linux) or Cmd+Shift+P (macOS). Type and select juml : Convert Source to Diagram.
Select Your Source: Choose the specific directory or file containing your Java code.
View Your Graph: The extension automatically analyzes your code structures and maps out an interactive juml.drawio graph file in the same directory. You can view and rearrange the nodes using any standard Draw.io viewer.
Option 2: Building a Computational Network Graph Using JuML (HPC Library)
If you are developing parallel machine learning algorithms (like Neural Network computational graphs) using the FZJ-JSC JuML GitHub Repository, you will build your graph using C++ or Python.
Install the Toolkit: Initialize your environment by installing the package via PyPI: python -m pip install -U juml-toolkit Use code with caution.
Initialize the High-Level API: Import the library into your project script. The high-level API allows you to define processing nodes cleanly without drowning in manual MPI (Message Passing Interface) configurations.
Set Up the Computing Backend: Tell JuML whether your pipeline’s graph will execute on native CPUs or accelerated hardware backends like GPGPUs.
Define Your Layer Nodes: Construct the topology of your computational graph by instantiating data parsing nodes, normalization blocks, and algorithm layers (such as Artificial Neural Networks or K-Means blocks).
Ingest and Stream Data: Feed your high-dimensional structural data into the network graph. JuML will parallelize the data distribution across your high-performance cluster automatically.
Could you clarify if you are looking to build a software architecture diagram from your code or compile a machine learning pipeline graph? I can provide targeted code templates for whichever tool matches your project. juml – Visual Studio Marketplace
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