GPU-accelerated differentiable graph layout engine built on PyTorch

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  • License Expression: MIT
    SPDX License Expression
  • Author: John Mark Taylor
  • Tags graph , layout , visualization , pytorch , dag , graphviz , hierarchical
  • Requires: Python >=3.9
  • Provides-Extra: render , graphviz , dev , test
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Project description

dagua

GPU-accelerated differentiable graph layout engine built on PyTorch.

DAG + agua. Directed acyclic graphs + water. Named after the Dagua River in Colombia — a river flows downhill (like a DAG), never cycles back (acyclic), and finds its own path through the landscape (like gradient descent finding optimal node positions).

Why?

Graphviz has dominated graph visualization for 30 years but has hard scaling limits. No existing Python package provides pip-installable, hierarchical (Sugiyama-style) graph layout. Dagua fills this gap: pip install dagua, pure Python + PyTorch, GPU-accelerated, hierarchical layout with composable constraints.

Status

Pre-alpha. Under active development.

License

MIT

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Avatar for johnmarktaylor91 from gravatar.com johnmarktaylor91

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  • License Expression: MIT
    SPDX License Expression
  • Author: John Mark Taylor
  • Tags graph , layout , visualization , pytorch , dag , graphviz , hierarchical
  • Requires: Python >=3.9
  • Provides-Extra: render , graphviz , dev , test
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0.1.0

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